Cheddar Release 3.x user's guide

Lab-STICC technical report

Updated in December, 2018

Frank Singhoff, Christian Fotsing

Cheddar is a free real time scheduling framework. Cheddar is designed for checking task temporal constraints of a real time application/system. It can also help you for quick prototyping of real time schedulers. Finally, it can be used for educational purpose. Cheddar is a free software, and you are welcome to redistribute it under certain conditions; See the GNU General Public License for details. The Cheddar project was started in 2002 by Frank Singhof, Lab-STICC Team, University of Brest. Since 2008, Ellidiss technologies also contributes to the development of Cheddar and provides industrial support.

WARNING : this user's guide supposes that you have a minimum background on real-time scheduling. If it's not your case, take a look on this link which includes some very basic articles or book references. This link also provides a description of the analytical methods implemented into Cheddar and gives some publications that show how to use Cheddar.

To completed this user guide, you also have in this technical report , a precise description of all entities used in Cheddar.

I. Basic scheduling simulation features and feasibility tests for independent tasks

In this chapter, you find a description of the most important scheduling and feasibility services provided by Cheddar in the case of independent tasks.

I.1 First step : a simple scheduling simulation

This section shows you how to call the simpliest features of Cheddar.

Cheddar provides tools to check temporal constraints of real time tasks. These tools are based on classical results from real time scheduling theory. Before calling such tools, you have to define a system which is mainly composed of several processors and tasks.

To define a processor, you should first define one or multiple cores. For that choose the "Edit/Hardware/Core" submenu. The window below is then displayed :

Figure 1.1 Adding a core

A core is defined by the following fields (see Figure 1.1) :

  1. The name of the core. A core name can be any combination of literal characters including underscore. Space is forbidden. Each core must have a unique name.
  2. The scheduler hosted by the core. Basically, you can choose from a various set of schedulers such as (to get a detailed description on these schedulers, see your preferred real time books of the publications provided with this program) :
    • "Earliest Deadline First (or EDF)". Tasks can be periodic or not and are scheduled according to their deadline.
    • "Least Laxity First (or LLF)". Tasks can be periodic or not and are scheduled according to their laxity.
    • "Rate Monotonic (or RM, or RMA, or RMS)". Tasks have to be periodic, and deadline must be equal to period. Tasks are scheduled according to their period. You have to be aware that the value of the priority field of the tasks is ignored here.
    • "Deadline Monotonic (or DM)". Tasks have to be periodic and are scheduled according to their deadline. You have to be aware that the value of the priority field of the tasks is ignored here.
    • "POSIX 1003.1b scheduler ". Tasks can be periodic or not. Tasks are scheduled according to the priority and the policy of the tasks. (Rate Monotonic and Deadline Monotonic use the same scheduler engine except that priorities are automatically computed from task period or deadline). POSIX 1003.1b scheduler supports SCHED_RR, SCHED_FIFO and SCHED_OTHERS queueing policies. SCHED_OTHERS is a time sharing policy. SCHED_RR and SCHED_FIFO tasks must have priorities ranging between 255 and 1. Priority level 0 is reserved for SCHED_OTHERS tasks. The highiest priority level is 255.
    • "Time sharing based on waiting time (which is a Linux-like scheduler)" and "Time sharing based on cpu usage". These two schedulers provide a way to share the processor as on a time sharing operatong system. With the first scheduler, the more a ready task waits for the processor and the more its priority increases. With the second scheduler, the more a ready task uses the processor and the more its priority decreases.
    • Round robin (with quantum). The processor is regulary shared between all the tasks. A quantum (which is a bound on the time a task keeps the processor) can be given.
    • "Maximum Urgency First based on laxity" and "Maximum Urgency First based on deadline". Such schedulers are based on an hybrid priority assignment : a task priority is made of a fixed part and a dynamic part (see ).
    • "D-Over/Stable EDF". This scheduler is an EDF like but which is work fine when the processor is over-loaded. When the processor is over-loaded, D-Over is always able to predict which tasks will miss its deadline (in contrary to EDF).
    • User-defined schedulers ("Pipeline user-defined scheduler", "Automata user-defined scheduler" or "Compiled user-defined scheduler"). These schedulers allow users to define their own scheduler into Cheddar (see section VI for details).
    • ...
  3. If the scheduler is preemptive or not. By default, the scheduler is set to be preemptive.
  4. The quantum value associated with the scheduler. This information is useful if a scheduler has to manage several tasks with the same dynamic or static priority : in this case, the simulator has to choose how to share the processor between these tasks. The quantum is a bound on the delay a task can hold the processor (if the quantum is equal to zero, there is no bound on the processor holding time). At the time we're speaking, the quantum value can be used with the POSIX 1003.1b scheduler (only with SCHED_RR tasks) and the round robin scheduler. With POSIX 1003.1b, two SCHED_RR tasks with the same priority level should share the processor with a POSIX round-robin policy. In this case, the quantum value is the time slot of this round-robin scheduler. Finally, the quantum value could also be used for user-defined scheduler (see part VI for details).
  5. Automaton type ....
  6. Capacity ....
  7. Period ....
  8. Priority ....
  9. User Defined Scheduler Source ....
  10. The file name : it's the name of a file which contains the source code of a user-defined scheduler (see section VI for details).
  11. Start time ....
  12. Speed ....
  13. L1 Cache system name ....

Warning : with Cheddar, to add a core (or any object), you have to push the Add button before pushing the Close button. That allows you to define several objects quickly whithout closing the window (you should then push Add for each defined object).

Then you can define a processor. For that choose the "Edit/Entities/Hardware/Processor" submenu. The window below is then displayed :

Figure 1.2 Adding a processor

A processor is defined by the following fields (see Figure 1.2) :

  1. The name of the processor. A processor name can be any combination of literal characters including underscore. Space is forbidden. Each processor must have a unique name.
  2. At the time we're speaking, the network field is not used (planned to be used in order to simulate message scheduling).
  3. Processor type ....
    • Monocore type ....
    • Identical multicores type ....
    • Uniform multicores type ....
    • Unrelated multicores type ....
  4. Migration type ....
    • No migration type ....
    • Job level migration type ....
    • Time unit migration type ....
  5. Cores table which contain the list of cores initially defined. The user should select one core in the monocore processor case, and almost one core in other case.

Figure 1.3 Adding an address space

The next step in order to run a simulation, is to define an address space. Choose the "Edit/Entities/Software/Address space" submenu. An address space models a piece of memory which contain tasks, buffers or shared resources. The Figure 1.3 shows the widget used to define such a feature. At the time we are speaking, the information you have to provide is :

Figure 1.4 Adding a task

Let see now, how to define a task, the last feature required to perform the most simpliest performance analysis. Choose the "Edit/Entities/Software/Task" submenu. The window of Figure 1.4 is then displayed. This window is composed of 3 sub-parts : the "main part", the "offset part" and the "user's defined parameters part". The main part contains the following informations :

  1. At least, a task is defined by a name (the task name should be unique), a capacity (bound on its execution time) and a place to run it (a processor name and an address space name). The other parameters are optional but can be required for a particular scheduler
  2. A type of task . It describes the way the task is activated. An aperiodic task is only activated once. A periodic task is activated many times and the delay between two activations is a fixed one. A poisson process task is activated many times and the delay between two activations is a random delay : the random law used to generated these delays is an exponential one (poisson process). a sporadic task is a task which is activated many times with a minimal delay between two succesive activations. If the task type is "user-defined", the task activation law is defined by the user (see section VI.2 of this user's guide).
  3. The period. It is the time between two task activations. The period is a constant delay for a periodic task. It's an average delay for a poisson process task. If you have selected a processor that owns a Rate Monotonic or a Deadline Monotonic scheduler, you have to give a period for each of its tasks.
  4. A start time. It is the time when the task arrives in the system (its first activation time).
  5. A deadline. The task must end its activation before its deadline. A deadline is a relative information : to get the absolute date at which a task must end an activation, you should add the time when the task was awoken/activated to the task deadline. Warning : the deadline must be equal to the period if you define a Rate Monotonic scheduler.
  6. A priority and a policy. These parameters are dedicated to the POSIX 1003.1b/Highest Priority First scheduler. Priority is the fixed priority of a task. Policy can be SCHED RR, SCHED FIFO or SCHED OTHERS and describes how the scheduler chooses a task when several tasks have the same priority level. Warning : the priority and the policy are ignored by a Rate Monotonic and a Deadline Monotonic scheduler.
  7. A jitter. The jitter is a maximum lateness on the task wake up time. This information can be used to express task precedencies and to applied method such as the Holistic task response time method.
  8. A blocking time. It's a bound on shared resource waiting time. This delay could be set by the user but could also be computed by Cheddar if you described how shared resources are accessed.
  9. An activation rule. The name of the rule which defines the way the task should be activated. Only used with user-defined task. (see section VI.2 for details).
  10. A criticality level . The field indicates how the task is critical. Currently used by the MUF scheduler or any user-defined schedulers.
  11. A seed . If you define a poisson process task or a user-defined task, you can set here how random activation delay should be generated (in a deterministic way or not). The "Seed" button proposes you a randomly generated seed value but of course, you can give any seed value. This seed value is used only if the Predictable option is selected. If the Unpredictable option is selected, the seed is initialized at simulation time with "gettimeofday".
  12. The text memory size and stack memory size. The fields related to task memory size will be used in the next Cheddar's release in order to perform memory requirement analysis.
The second and the third parts store task information which are less used by users.

The offsets part is a table. Each entry of the table stores two informations : an activation number and a value. The offset part allows the user to change the wake up time of a task on a given activation number. For each activation number stored in the "Activations:" fields, the task wake up time will be delayed by the amount of time given in the "Values" fields.

Finally, the third part (the "User's defined parameters" part) contains task parameters (similar to the deadline, the period, the capacity ...) used by user-defined schedulers. With this part, a user can define new task parameters. A user-defined task parameter has a value, a name and a type. The types currently available to defined user-defined task parameters are : string, integer boolean and double.

Warning : when you create tasks, in most of cases, Cheddar does not check if your task parameters are erronous according to the scheduler you previously selected : these checks are done at task analysis/scheduling. Of course, you can always change task and processor parameters with "Edit menus.

When tasks and processors are defined, we can start the task analysis. Cheddar provides two kind of analysis tools :

  1. Feasibility analysis tools : these tools compute much information without scheduling the set of tasks. Equation references used to compute this feasibility information are always provided with the results. Feasibility services are provided for tasks and buffers.
  2. Simulation analysis tools : With these tools, scheduling has to be computed first. When the scheduling is computed (of course, this step can be long to proceed ...), the resulting scheduling is drawn in the top part of the window and information is computed and displayed in the bottom part of the window. Information retrieved here is only valid in the computed scheduling.The simpliest tools provided by Cheddar check if a set of tasks meet their temporal constraints. Simulation services are also provided for other resources (for buffers for instance).

All these tools can be called from the "Tools" Menu and from some toolbar Buttons :

Figure 1.5 The Cheddar's main window

In the top part of this window, each resource, buffer, message and task is shown by a time line :

  1. For a task time line :
    • Each vertical red line means that the task is activated (woken up) at this time.
    • Each black rectangle means that the task is running at this time.
  2. For a resource time line :
    • Each vertical blue line means that the resource is allocated by a task at this time.
    • Each black rectangle means that the resource is used by the task which is running at this time.
  3. For a message time line : each black rectangle means that the message is beeing transmitted by the network.
  4. For a buffer time line :
    • Each write rectangle means that a task writes data into a buffer.
    • Each red rectangle means that a task reads data from a buffer.

The scheduling result can also be saved in XML file. This is particulary usefull if you do not want to use the Cheddar Machine-Man-Interface. For example, the computed scheduling as an event table. The event table is the data structure used by the simulator engine to perform analysis on scheduling. The event produced by the simulator are : Start_Of_Task_Capacity, End_Of_Task_Capacity, Write_To_Buffer, Read_From_Buffer, Running_Task, Task_Activation, Send_Message, Receive_Message, Allocate_Resource, Release_Resource Address_Space_Activation and Wait_For_Resource. In a XML event table file, each event is exported with the time the event occurs. For each event, some extra data related to the event can also be exported :

You can take a look on the following event tables to have an idea of the data layout of such XML file :

To get a summary of the tools provided by Cheddar, see section VI .

I.2 Other Available schedulers and task arrival patterns

In Cheddar, you will find several schedulers. Some of them are directly implemented into the framework ; others can be defined by the user. The list below describes the currently built-in schedulers you may find in the 1.3p15 release : The current Cheddar's release also provides some User-defined/parametric schedulers stored in some .sc files (see project_examples sub-directory and section VI). These schedulers are :

In the same way, Cheddar provides a set of built-in task arrival patterns. The built-in task arrival patterns are :

Some examples of User-defined/Parametric task provided with this Cheddar release can be found in the files :

I.3 Scheduling options.

Figure 1.6 Scheduling options windows

The submenu "Tools/Scheduling/Options" allows you to tune the way all next scheduling simulations will be done (see Figure 1.6) :

Figure 1.7 Scheduling options windows (both feasibility and simulation)

The submenu "Tools/Scheduling/Scheduling simulation" allows you to tune the way the next scheduling simulation and the next feasibility test will be done (see the Figure 1.7).

Options related to which information the engine has to compute when the scheduling sequence is built are :

Options related to which information the feasibility tests will compute are :

II. About Cheddar project files (XML and AADL files).

Information stored during a simulation can be saved into project files. A project file is a XML file defined by this DTD. By the way, you do not need a deep understanding of the layout of cheddar project files except if you want to edit project files by hand. If so, you should check if your project files are correctly structured by the tool xml2xml (xml2xml just reads, parses and displays the content of a XML Cheddar project file on the screen ).

All Cheddar XML files can be displayed with an Internet Browser if you put the following XSLT file and the following.CSS file in the directory hosting your XML Cheddar files. To do so, you should use a recent release of Internet Explorer (version 6.0 or later), Netscape (version 7.0 or later) or Mozilla (version 1.0 or later).

From Cheddar, there are two ways to load a project file :

Saving a project can be done with the same "File" menu.

Cheddar can also import AADL specification [SAE 04]. This service can be accessed through the submenu "File/AADL/Import AADL". In the same way, an XML project can be exported towards an AADL specification (see the "File/AADL/Export AADL" sub-menu).

WARNING : the AADL parser included in Cheddar is only compliant with AADL V1 models. It is clearly deprecated and only kept for legacy reasons. If you plan to use Cheddar with AADL, please see AADLInspector or the Cheddar OSATE 2 plugin.

As with XML files, you can launch Cheddar with an AADL file given from the command line. To launch Cheddar and automatically read the foo.aadl AADL specification file, do :

my_shell$cheddar -a foo.aadl

Finally, XML or AADL files can be loaded from any directory and a project can be saved in several project files. For example, to load a project saved in two AADL files called bar1.aadl and bar2.aadl, which are stored in the directory /home/foo, you must use the following command-line :

my_shell$cheddar -I/home/foo -a bar1.aadl bar2.aadl

By default, Cheddar automatically loads the standards AADL files AADL_Project.aadl, AADL_Properties.aadl, Cheddar_Properties.aadl and User_Defined_Cheddar_Properties.aadl. The -I option can also be used to give the directory storing these standard AADL files. Otherwise, these files ares supposed to be in the current directory. A copy of them can be generated from the File/AADL/Export property sets used by Cheddar and File/AADL/Export standard AADL property set submenus.

III. Summary of the Cheddar command line.

The basic command line of cheddar is

my_shell$cheddar [switches] foo1 foo2 ...

where foo1 foo2 can be an unique XML file or one or several AADL files.

Switches can be :

IV. Scheduling with task dependencies

This chapter describes services provided by Cheddar when the system you want to study has task dependencies. By task dependencies, we mean resources shared by several tasks (ex : semaphores) or precedency relationships between several tasks (due to buffer access or message exchange or also constraints between the end of a task and the start of another one).

IV.1 Shared resources analysis tools.

With Cheddar, you can define shared resources. Shared resources can be seen as semaphores. They can be accessed by several tasks. Tasks that require access to an already allocated semaphore are blocked (and then, unscheduled). To define a shared resource in a Cheddar project, call the submenu "Edit/Entities/Softwares/Resource". The window below is then displayed :

Figure 4.1 Add a new shared resource

Before adding a shared resource, at least one processor and one task must already exist in your project. A resource is defined by the following information :

  1. An unique name.
  2. An initial value/state (simular to a semaphore initial value). During a scheduling simulation, at a given time, if a resource value is equal or less than zero, the requesting tasks are blocked until the semaphore/shared resource is released. An initial value equal to 1 allows you to design a shared resource that is initially free and that can be used by only one task at a given time.
  3. A protocol. Currently, you can choose between PCP (for Priority Ceiling Protocol), PIP (for Priority Inheritance Protocol) or "No protocol". With PCP or PIP, accessing shared resources may change task priorities [SHA 90]. The "No protocol" just means that no task prioriy will be changed at accessing the shared resource.
  4. A processor name. Each shared resource has to be hosted by a given processor.
  5. A priority. ...
  6. A priority assignment. ...:
    • automatic assignment ...
    • manual assignment ...
  7. Finally, we must give information on tasks that need the resource. Tasks hold resources in critical section. Each critical section has to be defined by :
    • The task name requiring the shared resource.
    • The start time of the critical section.
    • The end time of the critical section.
    Of course, you can define several critical sections for a given task of a given shared resource.

By default, shared resources analysis tools are not included in the scheduling simulation engine of Cheddar. See "Tools/Scheduling/Options" if you want to take care of shared resources during scheduling simulation and if you want to display shared resources time line. Blocking time on shared resources can be computed from scheduling simulation analysis if scheduling simulation is invoked from the sub-menu "Tools/Scheduling/Scheduling Simulation".

Finally, from the "Tools/Resources/Bound on Blocking time" sub-menu, you will find services to compute bounds on blocking time of each tasks. These bounds are computed without assumption on the scheduling actually generated for the analyzed system. To compute blocking time bound, shared resources have to used PCP or PIP protocols.

IV.2 Task precedencies : the dependencies.

With Cheddar, dependencies are links between at least two tasks. There are three different types of dependencies : precedencies, message and buffer dependencies. Precendencies express order constraints between end or beginning of task execution. Message dependencies express relationships between a sender and a receiver task of a given message. Buffer dependencies express relationships between producer and consumer of data in a given buffer.

To create a dependency, choose "Edit/Entities/Softwares/Dependencies". The window of figure 4.2 is then displayed :

Figure 4.1 Add a new dependency

A dependency is characterized by:
  1. The type of dependency. We distinguish:

IV.3 Buffer analysis tools.

Cheddar allows you to define buffers shared by tasks. If you want to define a buffer, a processor, an address space and a least one task have to be defined before. A buffer can be added to a Cheddar project with the submenu "Edit/Entities/Softwares/Buffer". The window below is then displayed :

Figure 4.2 Add a new buffer

  • A buffer has a unique name, size, initial data size and is hosted by a processor and an address space.
  • A queueing system model is assigned to each buffer. This queueing system model describes the way buffer read and write operations will be done at simulation time. This information is also used to apply buffer feasibility tests.
  • A list of tasks which access to the buffer (read or write operations). Two type of tasks can access a buffer : producers and consumers. We suppose that a producer/consumer writes/reads a fixed size of information in the buffer. For each producer or consumer, the size of the information produced or consummed have to be defined. The time of the read/write operation is also given : this time is relative to the task capacity.
    • Buffer Underflow : Underflow event occurs when a task reads from a buffer and the read data size is greater than the current data size in the buffer. When it happens, a task does not read the buffer and current data in a buffer is not consumed.
    • Buffer Overflow : Overflow event occurs when a task writes to a buffer and the write data size plus the current data size in the buffer is greater than Buffer Size. When it happens, a task does not write any data to the buffer.
Like tasks, two kinds of tools can be invoked by the user from a buffer : simulation and feasibility tools. At first, the simulation of the task scheduling can help the user to see how the buffer is filled or not with messages (see "Tools/Buffer/Buffer simulation" submenu). In this case, a scheduling simulation must be previously run. The result is then displayed in a window as below :
Buffer Feasibility mainly consists of computing buffer bounds. Bounds computed here suppose that each task that is defined as "producer", produces one message per periodic activation. In the same manner, each "consumer" extracts one message during each of its periodic activation.

Figure 4.3 Display buffer utilization factor computed from scheduling simulation

The picture contains the buffer utilization level for each time.
Second, the feasibility tool provides a way to compute bounds on buffer utilization level. At the time we write this User's guide, bounds do not depend on the type of the scheduler. Bounds can be computed from the "Tools/Buffer/Buffer feasibility" submenu.

IV.4 Message scheduling services.

V. Multiprocessor scheduling services.

Multiprocessor system is good for heavy computing demands. It is sometimes the only way to provide sufficient processing power to meet critical real-time deadlines. In general multiprocessor systems are also more reliable than uni-processor systems. Scheduling of multiprocessor systems is proven to be a NP-hard (Non-deterministic Polynomial-time) problem [LEU 82]. The complexity class NP is the set of decision problems that can be solved by a non-deterministic machine in polynomial time. The complexity theory is part of the theory of computation dealing with the resources required during computation to solve a given problem. The most common resources are time (how many steps does it take to solve a problem) and space (how much memory does it take to solve a problem). Other resources can also be considered, such as how many parallel processors are needed to solve a problem in parallel. There are many scheduling heuristics to solve this. Rate-monotonic scheduling is good for numerous reasons: Rate-monotonic algorithm is optimal for fixed priority assignment of periodic tasks on a processor so it’s easy to design predictable real-time system. Also it is easy to implement and it takes minimal scheduling overhead.

Cheddar has four algorithms: RMNF, RMFF, RMBF, RMST and RMGT. Each of these are off-line schemes, so the entire task set must be known before starting task assignment. Bounds for functions are calculated using .

Rate-Monotonic-Next-Fit [SON 93]

Upper bound for this algorithm is 2.67. Tasks are sorted in non-decreasing order of periods. Then tasks are placed on processors, according to the IP Condition (Increasing Period). The first task is placed on the first processor. Then the second task is placed on the first processor, if it meets the IP Condition. Otherwise it is placed on a new processor. This continues until all tasks are scheduled.

Rate-Monotonic-First-Fit [SON 93]

The upper bound for this algorithm is 2.33 [SON 93] (original study by Liu and Dhall had a wrong bound of 2.23). Tasks are sorted in non-decreasing order of periods. The IP Condition is used to verify teh schedulability of tasks on processors. The first task is placed on the first processor. Then the second task is placed on the first processor, if it meets the IP Condition. Otherwise it is placed on a new processor. The third task is tried to be placed on the first processor according to the IP Condition. If it does not meet the condition, the task is tried to be placed on the second processor. Otherwise a new processor is selected for the third task…

Rate-Monotonic-Best-Fit [SON 93]

The upper bound for this algorithm is 2.33. Tasks are sorted in non-decreasing order of periods. The first task is placed on the first processor. For the second task, the function checks all processors, whether they meet the IP Condition. For processors that satisfy the condition, the algorithm checks the number kj of tasks already assigned to each processor j, and computes Uj, the total utilization of the kj tasks. And the task is assigned to the processor that has the smallest value. If the condition is not met, a new processor is selected for the task.

Rate-Monotonic Small-Tasks [BUR 94]

The upper bound for RMST is , a = max Ui, i = 1,…,K and U is utilization of all tasks. Tasks are sorted in increasing Si. Si = log2(Ti). The main idea of RMST is to minimize the value of b for each processor. ß = max Si – min Si, 1= i =K.

Rate-Monotonic General-Tasks [BUR 94]

The upper bound for RMGT is 1.75. RMGT uses the RMST algorithm for task s = 1/3 and First-Fit heuristics for the rest of the tasks.

Example of use :
  1. First, Define cores for processors. They have to be of Rate Monotonic type.

  2. Second, Define processors for tasks.

  3. Third, Define Address space used by tasks.

  4. Then, Define tasks (host tasks on any processor and address space)

  5. Finally, compute partitioning, with the submenu "Tool/Scheduling/Partition/With Small Task". :

VI. User-defined simulation code : how to run simulations of specific systems.

Usual feasibility tests are limited to only few task models (mainly periodic tasks) and to only few schedulers. When an application built with a particular task activation pattern or scheduled with a particular scheduler has to be checked, feasibility tests are not necessarily available. In this case, the only solution consists in analyzing the scheduling simulation. Cheddar allows the user to design and easily build framework extensions to do simulation of user-defined schedulers or task activation patterns. By easy, we mean quickly write and test framework extensions without a deep understanding of the framework design and of the Ada language. We propose the use of a simple language to describe framework extensions. Framework extensions are interpreted at simulation time. As a consequence, they can be changed and tested without recompiling the framework itself.

Figure 5.1 How a user-defined code is run by the scheduling engine

Figure 5.1 gives an idea on the way the simulation engine is implemented in the framework. Running a simulation with Cheddar is a three-step process.

The first step consists of computing the scheduling : we have to decide which events occur for each unit of time. Events can be allocating/releasing shared resources, writing/reading buffers, sending/receiving messages and of course running a task at a given time. At the end of this step, a table is built which stores all the generated events. The event table is built according to the XML description file of the studied application and according to a set of task activation patterns and schedulers. Usual task activation patterns and schedulers are predefined in the Cheddar framework but users can add their own schedulers and task activation patterns.

In the second step, the analysis of the event table is performed. The table is scanned by "event analyzers" to find properties on the studied system. At this step, some standard information can be extracted by predefined event analyzers (worst/best/average blocking time, missed deadlines ..) but users can also define their own event analyzers to look for ad-hoc properties (ex : synchronization constraints between two tasks, shared resources access order, ...). The results produced during this step are XML formatted and can be exported towards other programs.

Finally, the last step consists of displaying XML results in the Cheddar main window (see Figure 1.4).

VI.1 Defining new schedulers or task activation patterns.

Now, let's see how user-defined schedulers or task activation patterns can be added into the framework. Basically, all tasks are stored in a set of arrays. Each array stores a given information for all tasks (ex : deadline, capacity, start time, ...). The job of a scheduler is to find a task to run from a set of ready tasks. To achieve this job, Cheddar models a scheduler with a 3 stages pipe-line which is similar to the POSIX 1003.1b scheduler (see [GAL 95]). These 3 stages are :

  1. The priority stage. For each ready task, a priority is computed.
  2. The queueing stage. Ready tasks are inserted into different queues. There is one queue per priority level. Each queue contains all the ready tasks with the same priority value. Queues are managed like POSIX scheduling queues : if a quantum is associated with the scheduler, queues work like the SCHED_RR scheduling queueing policy. Otherwise, the SCHED_FIFO queueing policy is applied.
  3. The election stage. The scheduler looks for the non empty queue with the highest priority level and allocates the processor to the task at the head of this queue. The elected task keeps the processor during one unit of time if the designed scheduler is preemptive or during all its capacity if the scheduler is not preemptive.

Defining a new scheduler is simply giving piece of code for some of the pipe-line stages we described above. Each of these stages can be defined by a user without the need to have a deep knowledge of the way the scheduling simulator works. User-defined schedulers are stored in text files. These files are organized in several sections :

In the sequel, we first give you some simple examples of user-defined schedulers. Then, we explain how to use this kind of scheduler to do scheduling simulation with Cheddar. The list of statements and the list of predefined variables is given at the end of this section.

VI.1 Examples of user-defined schedulers.

In this section, we give some user-defined scheduler examples. We first show that a user-defined scheduler can be built with two kinds of statements : high-level and low-level statements. Second, we present how to add new task parameters with User's defined task parameters.

VI.1.1 Low-level statements versus High-level statements

Now let's see some very simple user-defined schedulers. The most simple user-defined scheduler can be defined like below :

return min_to_index(tasks.period);
end section;
Figure 5.2 a simple Rate Monotonic scheduler

This first example shows you how to give the processor to the task with the smallest period. This scheduler is equivalent to the Rate monotonic implemented into Cheddar. tasks.period is a predefined variable initialized at task definition time by the user. To implement a Rate Monotonic scheduler, no dynamic priorities are computed and no variable is necessary. Then, the scheduler designer does not have to redefine the start and priority sections. The only section which is defined is the election one. The election section contains an unique return statement to inform the scheduling simulator engine which task should be run for the next unit of time. The return statement uses the high level min_to_index operator. This operator scans the task array to find the ready task with the minimum value for the variable tasks.period. In Cheddar, the scheduler designer can use two kinds of statements : high-level and low-level statements. High level statements like min_to_index, hides the data type organization of the scheduling simulator engine. For example, the scheduler designer do not need to give statement into its user-defined scheduler to scan manually the task array. Writing a scheduler with high-level statements is then easy work. On the contrary, low-level statements assume that the user has a deeper idea of the design of the scheduling engine simulator. By the way, these statements are sometimes necessary when the scheduler designer wants to code a too much specific scheduler.

Now let's see how to define an EDF like scheduler :

1 start_section:
2       dynamic_priority : array (tasks_range) of integer;
3 end section;
5 priority_section:
6       dynamic_priority := tasks.start_time + tasks.deadline
7          + ((tasks.activation_number-1)*tasks.period);
8 end section;
10 election_section:
11      return min_to_index(dynamic_priority);
12 end section;
Figure 5.3 an EDF like scheduler using vectorial operators

EDF is a dynamic scheduler which computes a dynamic priority for each task. This dynamic priority is in fact a deadline. EDF just gives the processor to the task with the shortest deadline. In our example, this deadline is stored in a variable called dynamic_deadline. Since we need one value per task, the type of this variable is integer array. With this example the priority_section is not empty any more and contains (lines 5 to 7) the necessary code to compute EDF dynamic priorities. You should notice that the code in line 6/7 is in fact a vectorial operation : the arithmetic operation to compute the deadline is done for each item of the table dynamic_priority ranging from 1 to nb_tasks (nb_tasks is a static predefined variable initialized by the number of tasks in the current processor). To compute the dynamic priorities of our example, we used many predefined variables : You can find in VI.5 a list of all predefined variables and all available statements you can used to build your user-defined scheduler.

The example of the Figure 5.3 is built with vectorial operators : each arithmetic operation is done for all the tasks of the system. The scheduler designer does not need to take care of the task array and just gives rules to computed the EDF dynamic deadline. As max_to_index/min_to_index, these statements are High-level ones because they do not required to directly access the data type organization of the scheduling engine of Cheddar (mainly the task arrays).

Now, let's see a third example:

to_run : integer;
   current_priority : integer;

  for  i in tasks_range  loop
        if (tasks.ready(i) = true) and (tasks.priority(i)>current_priority)
                  then to_run:=i;
       end if;
   end loop;
end section;

return to_run;
end section;
Figure 5.4 Building a user-defined with low-level statement

This scheduler looks for the highest priority ready task of a processor and is fully equivalent to the scheduler described by :

      return max_to_index(tasks.priority);
end section;
Figure 5.5 a HPF scheduler built with hight-level statements

but, in the example of Figure 5.4, the code scans itself the task array to find a ready task to run. To achieve this, the example of Figure 5.4 is built with low-level instructions : a for loop and an if statement. The priority_section is then composed of a loop that tests each task entry. This loop is made with a for statement, a loop that runs the inner statement for each task defined in the task array. Contrary to a high-level implementation, a scheduler made of low-level statements has to carry out more tests. For instance, the example of the Figure 5.4 checks with the ready dynamic variable if tasks are ready at the time the scheduler is called. Low-level scheduler are then more complicated and more difficult to test. The reader will find some tips to help test complicated user-defined schedulers in section VI.3 .

VI.1.2 User-defined scheduler built with User's defined Task Parameters

In the previous examples, the data used to built user-defined schedulers were either static variables initialized at task definition time, either dynamic variables predefined or declared in the start section. A last type of data exists in Cheddar : User's defined task parameters. This kind of data are static ones and are defined at task definition time. User's defined task parameters allow the user to extend the set of static variables. Since they describe new task parameters, User's defined task parameters are table type. User's defined task parameters can be boolean, integer, double or string table type. To define User's defined task parameters, you have to update the third part of the entity task. Use the submenu "Edit/Entities/Software/Task" :

Figure 5.6 Adding an Users's Defined Task Parameter

The example above shows you a system composed of 3 tasks (T1, T2 and T3) where a criticity level is defined. Like usual task parameters, you should give a value to a User's defined task parameter (ex : the criticity level for task T1 is 1) but you also have to set a type to the parameter (integer in our example). When tasks are created, as usually, you can call the scheduling simulation services of Cheddar. The next window is a snapshoot of the resulting scheduling of our example composed of 3 tasks scheduled according to their criticity level. (T2 is the most critical task and T1 the less critical).

Figure 5.7 Scheduling according to a criticity level.

To conclude this chapter, let's have a look to a more complex example of user-defined scheduler which summarises all the features presented before. This example is an ARINC 653 scheduler (see [ARI 97]). An ARINC 653 system is composed of several partitions. A partition is a unit of software and is itself composed of processes and memory spaces. A processor can host several partitions so that two levels of scheduling exist in an ARINC653 system : partition scheduling and process scheduling.
  1. Process scheduling. In one partition, process are scheduled according to their fixed priority. The scheduler is preemptive and always gives the processor to the highiest fixed priority task of the partition which is ready to run. When several tasks of a partition have the same priority level, the oldest one is elected.
  2. Partition scheduling. Partitions share the processor in a predefined way. On each processor partitions are activated according to an activation table. This table is built at design time and defines a cycle of partition scheduling. The table describes for each partition when it has to be activated and how much time it has to run for each of its activation.

Figure 5.8 An example of ARINC 653 scheduling

The Figure 5.8 displays an example of ARINC 653 scheduling (see the XML project file project_examples/arinc653.xml). The studied system is made of 3 tasks hosted by one processor. The processor owns 2 partitions : partition number P0 and partition number P1. The task T1 runs in partition P0 and the two others run in partition P1. Each task has a fixed priority level : the T1 priority is 1, the T2 priority is 5 and the T3 priority is 4. The cyclic partition scheduling should be done so that P0 runs before P1. In each cycle, P0 should be run during 2 units of time and P1 should run during 4 units of time. The user-defined scheduler source code used to compute the scheduling displayed in Figure 5.8 is given below :

    partition_duration :  array (tasks_range) of integer;
    dynamic_priority :  array (tasks_range) of integer;
    number_of_partition : integer :=2;
    current_partition : integer :=0;
    time_partition : integer :=0;
    i : integer;

end section;

    if time_partition=0
        then  current_partition:=(current_partition+1)
           mod number_of_partition;
    end if;

    for i in tasks_range loop
        if tasks.task_partition(i)=current_partition
                  then dynamic_priority(i]:=priority(i);              

                else  dynamic_priority(i):=0; tasks.ready(i):=false;
        end if;
     end loop;
end section;

        return max_to_index(dynamic_priority);
end section;
Figure 5.9 Processes and partitions scheduling into an ARINC 653 system

In this code, tasks.task_partition is a User's defined task parameter. tasks.task_partition stores the partition number hosting the associated task. The variable partition_duration stores the partition cyclic activation table.

VI.2 Scheduling with specific task models.

In the same way you can define specific schedulers, you can also define specific task activation patterns. By default, 3 kinds of task activation pattern are defined in Cheddar : If the application you want to study can not be modeled with this 3 kinds of activation rules above, a possible solution is to explain your own task activation pattern with a user-defined scheduler. The description of task activation pattern is done in .sc files in a particulary section which is called task_activation_section. In this section, you can define named activation rules with set statements. The set statement just link a name/identifier (the left part ot the set statement) and an expression (the right part of the set statement). The expression explains the amount of time the scheduling simulator engine has to wait between two activations of a given task.

gen1 : random;
gen2 : random;
exponential(gen1, 200);
uniform(gen2, 0, 100);
end section;

return max_to_index(tasks.priority);
end section;

set activation_rule1 10;
set activation_rule2 2*tasks.capacity;
set activation_rule3 gen1*20;
set activation_rule4 gen2;
end section;
Figure 5.10 Defining new task activation patterns : how to run simulation with specific task models

The example of the Figure 5.10 describes a Highest Priority First scheduler which hosts tasks activated with different patterns. Each pattern is described by a set statement : When task activation rules are defined, task activation names (ex : activation_rule1) have to be associated with "real" task. The picture below shows you an "Edit/Entities/Software/Task" window :

Figure 5.11 Assigning activation rules to tasks

In this example, the task activation rule activation_rule1 is associated with task T1. The task activation rule activation_rule2 is associated with task T2. The task activation rule activation_rule3 is associated with tasks T3.

VI.3 Running a simulation with a user-defined scheduler.

Let's see how to run a simulation with one or several user-defined schedulers. First, you have to add a scheduler by selecting the submenu "Edit/Entities/Hardware/Core". The following window is then launched :

Figure 5.13 Define a core with a user-defined scheduler

To add a user-defined scheduler into a Cheddar project, select the right item of the Combo Box and give a name to your scheduler. You should then provide the code of your user-defined scheduler. This operation can be done by pushing the "Read" button.
In this case, the following window is spawned and you should give a file name containing the code of your user-defined scheduler :

Figure 5.14 Selecting the .sc file which contains the user-defined scheduler

By convention, files that contain user-defined scheduler code should be prefixed by .sc. For example, the file in our example should almost contain an election section and of course, can also contain a start and a priority sections. When a processor is defined, you have to add tasks on it. To do so, select the submenu "Edit/Entities/Software/Task" like in section I. Just place the task on the previously defined processor. Finally, you can run scheduling simulations as the usual case.

Since a user-defined scheduler is also a piece of code, you sometimes need to debug it. To do so, you can use the following tips :

VI.4 Looking for user-defined properties during a scheduling simulation.

i : integer;
nb_T2 : integer;
nb_T1 : integer;
bound_on_jitter : integer;
max_delay : integer;
min_delay : integer;
tmp : integer;
T1_end_time : array (time_units_range) of integer;
T2_end_time : array (time_units_range) of integer;
nb_T1:=0; nb_T2:=0;
end section;

if (events.type = "end_of_task_capacity")
if (events.task_name = "T1")
end if;
if (events.task_name = "T2")
end if;
end if;
end section;

while (i < nb_T1) and (i < nb_T2) loop
min_delay:=min(tmp, min_delay);
max_delay:=max(tmp, max_delay);
end loop;

end section;

Figure 5.16 Example of user-defined event analyzer : computing task termination jitter bound

In the same way that users can define new schedulers, Cheddar makes it possible to create user-defined event analyzers. These event analyzers are also writen with an Ada-like language and interpreted at simulation time.

The event table produced by the simulator records events related to task execution and related to objects that tasks access. Event examples stored in this table can be :

Each of these events is stored with the time it occurs and with information related to the event itself (eg. name of the resource, of the buffer, of the message, of the task ...). The event table is scanned sequentially by event analyzers. User-defined event analyzers are composed of several sections : a start section, a data gathering section and an analyze and display section. Figure 5.16 gives an example of user-defined event analyzer. From an ARINC 653 scheduling this event analyzer computes the minimum, the maximum and the jitter on the delay between end times of two tasks owned by different partitions (tasks T1_P0 and T2_P1 ; see Figure 5.9).

VI.5 List of predefined variables and available statements.

The tables below list all predefined variables that are available when you write a user-defined code:

Name Type Is updated by the simulator engine Can be changed by user code Meaning
Variables related to processors
Gives the number of processors of the current analyzed system.
Variables related to tasks
array (tasks_range) of integer
Stores the value of the parameter given at task definition time. For the meaning of this variable, see section I.
array (tasks_range) of string
Name of the task
array (tasks_range) of string
Type of the task (periodic, aperiodic, sporadic, poisson_process or userd_defined)
array (tasks_range) of string
Stores the processor name of the cpu hosting the corresponding task.
array (tasks_range) of integer
Stores the sum of the bounded times the task has to wait on shared resource accesses.
array (tasks_range) of integer
Stores the value of the parameter given at task definition time. For the meaning of this variable, see section I.
array (tasks_range) of integer
Stores the value of the parameter given at task definition time. For the meaning of this variable, see section I.
array (tasks_range) of integer
Stores the value of the parameter given at task definition time. For the meaning of this variable, see section I.
array (tasks_range) of integer
Stores the amount of processor time wasted by the associated task.
array (tasks_range) of integer
Stores the activation number of the associated task. Of course, using this variable is meaningless for aperiodic tasks.
array (tasks_range) of integer
Stores the value of the parameter given at task definition time. For the meaning of this variable, see section I.
array (tasks_range) of integer
Stores the value of the parameter given at task definition time. For the meaning of this variable, see section I.
array (tasks_range) of integer
This variable stores the umount of time unit the task had consumed since its last activation. When tasks.used_capacity reaches tasks.capacity, the task stops to run and waits its next activation
array (tasks_range) of integer
For each task activation, this variable is initialized to the task capacity each time the task starts a new activation. If rest_of_capacity is equal to zero, the task has over its its current activation and then task is blocked upto its next activation.
array (tasks_range) of integer
This variable can be used by scheduler programmers to block a task : remove a task from schedulable tasks.
Gives the number of tasks of the current analyzed system.
array (tasks_range) of boolean
Stores the state of the task : this boolean is true if the task is ready ; it means the task has a capacity to run, does not wait for a shared resource, does not wait for a delay, does not wait for a offset constraint and does not wait for a precedency constraint.
Variables related to messages
Gives the number of messages of the current analyzed system.
array (messages_range) of string
Gives the names of each message.
array (messages_range) of integer
Jitter on the time the periodic message becomes ready to be sent.
array (messages_range) of integer
Gives the sending period if the message is a periodic one.
array (messages_range) of integer
time needed by a message to go from the sendrer to the receiver node.
array (messages_range) of integer
Stores the deadline if the message has to meet one.
array (messages_range) of integer
Stores the size of the message.
array (messages_range) of integer
Stores the time when the task should send or receive the message.
array (messages_range) of string
Stores the task name that sends/receives the message.
array (messages_range) of string
Stores sender if the corresponding task sends the message or stores receiver if the task receives it.
Variables related to buffers
Gives the number of buffers of the current analyzed system.
array (buffers_range) of integer
The maximum size of a given buffer.
array (buffers_range) of string
Gives the processor name that owns the buffer.
array (buffers_range) of string
Unique name of the buffer.
array (buffers_range) of integer
Stores the time a given task consumes/produces a message from/into a buffer.
array (buffers_range) of integer
Stores the size of the message produced/consumed into/from a buffer by a given task.
array (buffers_range) of string
Stores the task name that procudes/consumes messages into/from a given buffer.
array (buffers_range) of string
Stores consumer if the corresponding task consumes messages from the buffer or stores producer if the task produces messages.
Variables related to shared resources
Gives the number of shared resources of the current analyzed system.
array (resources_range) of integer
Stores the state of the resource when the simulation is started. If this integer is equal of less than zero, the first allocation request will block the requesting task.
array (resources_range) of integer
Stores the current state of the resource. If this integer is equal of less than zero, the first allocation request will block the requesting task. After an allocation of the resource, this counter is decremented. After the task has released the resource, this counter is incremented.
array (resources_range) of string
Stores the name of the processors hosting the shared resource.
array (resources_range) of string
Contains the protocol name used to manage the resource allocation request. Could be either no_protocol, priority_ceiling_protocol or priority_inheritance_protocol
array (resources_range) of integer
Unique name of the shared resource
array (resources_range) of string
Gives the name of a task that can access the shared resource.
array (resources_range) of integer
Gives the time the task starts accessing the shared resource during its capacity.
array (resources_range) of integer
Gives the time the task ends accessing the shared resource during its capacity.
Variables related to the scheduling simulation
At the time the user-defined scheduler runs,
this variable stores the TCB index of the task elected at the
previous simulation time
Stores the current simulation time .
Variables related to the event table
Type of event on the current index table. Can be task_activation, running_task, write_to_buffer, read_from_buffer, send_message, receive_message, start_of_task_capacity, end_of_task_capacity, allocate_resource, release_resource, wait_for_resource.
The time when the event occurs.
The processor name hosting the task/resource/buffer related to the current event.
The task name related to the current event.
The message name related to the current event.
The buffer name related to the current event.
The resource name related to the current event.

The BNF syntax of a .sc file is given below :

entry := start_rule priority_rule election_rule task_activation_rule gather_event_analyzer display_event_analyzer

declare_rule := "start_section:" statements
priority_rule := "priority_section:" statements
election_rule := "election_section:" statements
task_activation_rule := "task_activation_section" statements
gather_event_analyzer := "gather_event_analyzer_section" statements
display_event_analyzer:= "display_event_analyzer_section" statements

statements := statement {statement}
statement :=
     "put" "(" identifier [, integer] [, integer]")" ";"
     | identifier ":" data_type [ ":=" expression ] ";"
     | identifier ":=" expression ";"
     | "if" expression "then" statements [ "else" statements ] "end" "if" ";"
     | "return" expr ";"
     | "for" identifier "in" ranges "loop" statements "end" "loop" ";"
     | "while" expression "loop" statements "end" "loop" ";"
     | "set" identifier expression ";"
     | "uniform" "(" identifier "," expression "," expression ")" ";"
     | "exponential" "(" identifier "," expression ")" ";"

data_type := scalar_data_type
     | "array" "(" ranges ")" "of" scalar_data_type
ranges := "tasks_range" | "buffers_range" | "messages_range" | "resources_range" | "processors_range" | "time_units_range"
scalar_data_type := "double" | "integer" | "boolean" | "string" | "random"

operator := "and" | "or" | "mod" | "<" | ">" | "<=" | ">=" | "/=" | "=" | "+" | "/" | "-" | "*" | "**"

expression := expression operator expression
     | "(" expression ")"
     | "not" expression
     | "-" expression
     | "max_to_index" "(" expression ")"
     | "min_to_index" "(" expression ")"
     | "max" "(" expression "," expression ")"
     | "min" "(" expression "," expression ")"
     | "lcm" "(" expression "," expression ")"
     | "abs" "(" expression ")"
     | identifier "[" expression "]"
     | identifier
     | integer_value
     | double_value
     | boolean_value

Notes on the BNF of .sc file syntax :

Two kinds of statements exist to build your user-defined scheduler : low-level and high-level statements. high-level statements operate on all task information. low-level statements operate only on one information of a task at a time. all these statements work as follows :

  1. The if statement : works like in Ada or most of programming languages : run the else or the then statement branch according to the value of the if expression.
  2. The while statement : works like in Ada or most of programming languages : run the statements enclosed in the loop/end loop block until the while condition becomes false.
  3. The for statement : it's an Ada loop with a predefined iterator index. With a for statement, the statements enclosed in the loop are run for each task defined in the TCB table. At each iteration, the variable defined in the for statement is incremented. Then, in the case of task loop for instance (use keyword tasks_range in this case), its value ranges from 1 to nb_tasks (nb_tasks is a predefined static variable initiliazed to the number of tasks hosted by the currently analyzed processor).
  4. The return statement. You can use a return statement in two cases :
    1. With any argument in any section except in the election_section. In this case, the return statement just end the code of the section.
    2. With a integer argument and only in the election_section . Then, the return statement give the task number to be run.
  5. The put(p,a,b) statement : displays the value of the variable p on the screen. This statement is useful to debug your user-defined scheduler. If a and b are not equal to zero and if p is an array type, put(p,a,b) displays entries of the table with index between a and b. If a and b are equal to zero and if p is an array, all entries of the array are displayed.
  6. The exponential(a,b) statement : intializes the random generator a to generate exponential random values with an average value of b.
  7. The uniform(a,b,c) statement : intializes the random generator a to generate uniformly random values between b and c.
  8. The set statement : description of new task activation model : assign an expression which shows how to compute task wake up time with an identifier.

The predefined operators work as follows :

  1. abs(a) : returns the unsigned value of a.
  2. lcm(a,b) : returns the last common multiplier of a and b.
  3. max(a,b) : returns the maximum value between a and b.
  4. min(a,b) : returns the minimum value between a and b.
  5. max_to_index (v) : firstly finds the task in the TCB with the maximum value of v ,and then returns its position in the TCB table. Only ready tasks are considered by this operator.
  6. min_to_index(v) : firstly finds the task in the TCB with the minimum value of v, and then returns its position in the TCB table Only ready tasks are considered by this operator.
  7. a mod b : computes the modulo of a on b (rest of the integer division).

VII. Using Cheddar within OSATE2

Cheddar can be called from OSATE2 through two OSATE2 plugin. The basic idea is to transform an AADL mode to an Cheddar ADL model (.i.e, a XMLV3 file). To install the Cheddar OSATE plugin, see the installation procédure on the OSATE web-site (? Basically, two plugins are provided:

Once the Cheddar OSATE plugins installed, in order to use them, you must:
  1. Create and populate a new OSATE project, or open any existing AADL project.
  2. Notice that the current Cheddar OSATE 2 plugin is limited to uniprocessor models. Cheddar provides some means to analyze multicore architectures and a new Cheddar plugin version will be developed in the next months for such purpose.
  3. Generate the Cheddar XML model to perform the scheduling analysis. In the windows above, you can see a screenshot of OSATE. The declarative AADL model cannot be used for scheduling analysis. Analysis are run on the instance model only. To create an instance model from a declarative model with OSATE, in the list of components on the right side of the OSATE windows, select a top system component and open the context menu (with the right mouse button), then select the "instanciate" menu item. The instance model appears in the middle of the OSATE windows. From this instance model, we can then generate the Cheddar XML model by selecting the root component of the instance model and pushing the Left Cheddar button. The generated Cheddar XML model is then saved in a location given by the dialog windows opened when the transformation is over.

  4. You can now call Cheddar manually with the generated Cheddar XML file.
  5. The right Cheddar button in OSATE both generates the XML Cheddar model and launches Cheddar with this Cheddar model.

VIII. Summary of Cheddar's editor menus and sub-menus

All Cheddar analysis tools are called from the "Tools" menu. This section gives a short description of them. Some of them compute
tasks parameters, and then are composed of two submenus : "Compute and update tasks set"
and "Compute and display".

Choose "Compute and update tasks set" submenu if you want to save computed parameters into your project tasks set.
Choose "Compute and display" if you only want to display computed parameters on the bottom of the main Cheddar window.

Menus and Sub-menus of the Cheddar's editor :

  1. File Menu :
    1. New sub-menu : creates a new XML project.
    2. Open sub-menu : loads a XML project file into the editor.
    3. Save sub-menu : saves the current XML project into a file with the current XML project file name.
    4. Save as sub-menu : saves the current XML project into a file with a new XML project file name.
    5. AADL sub-menu : provides ane features related to AADL specifications.
      1. AADL import : reads an AADL specification into Cheddar.
      2. AADL export : translates a Cheddar specification towards an AADL specification.
      3. Export property sets used by Cheddar : writes the Cheddar's property sets into files of the current directory.
      4. Export standard AADL property set : writes the standard AADL property set into files of the current directory.
      5. Customize how AADL services work : allows the user to set some options related to the AADL services provided by Cheddar.
    6. Exit sub-menu : Quit the Cheddar's editor.

  2. Edit menu : creates/updates/deletes entities of the current architecture to analyse (entities of the current XML project). Entities can be a processor, a task, a message, a buffer, a network or an event analyzer.

  3. Tools menu :
    1. Clear work space sub-menu : cleans the working area (main window). Does not change anything on the project itself.

    2. Scheduling sub-menu :
      1. Customized scheduling simulation sub-menu : computes and draws scheduling simulation. This sub-menu allows the user to customize the way the scheduling is computed.
      2. Customized scheduling feasibility sub-menu : computes some basics feasibility tests on all processors. The feasibility tests computed there are the utilization factor test and the response time test.
      3. Set priorities according to Rate Monotonic sub-menu : change the task priority according to its period (Tasks with the smallest period become tasks with the highest priority).
      4. Set priorities according to Deadline Monotonic sub-menu : changes the task priority according to its deadline (Tasks with the smallest deadline become tasks with the highest priority).
      5. Partition sub-menu : provides some services to assign tasks on a set of processors.
        1. With Best Fit sub-sub-menu : assigns tasks on the set of processors according to the Best Fit algorithm.
        2. With General Task sub-sub-menu : assigns tasks on the set of processors according to the General Task algorithm.
        3. With Next Fit sub-sub-menu : assigns tasks on the set of processors according to the Next Fit algorithm.
        4. With First Fit sub-sub-menu : assigns tasks on the set of processors according to the First Fit algorithm.
        5. With Small Task sub-sub-menu : assigns tasks on the set of processors according to the Small Task algorithm.
      6. Event table services sub-menu : provides some basic services on event tables.
        1. Compute scheduling and generate event table sub-sub-menu :computes the scheduling and produces the event table.
        2. Draw time line from event table sub-sub-menu : draws time line from the last computed or loaded scheduling/event table.
        3. Run analysis on event table sub-sub-menu : performs analysis on the last computed or loaded scheduling/event table.
        4. Export event table sub-sub-menu : saves the last scheduling/event table into a file with a XML format.
      7. Options sub-menu : describes how the scheduling simulation will be carried out.

    3. Resource sub-menu :
      1. Bound on blocking time sub-sub-menu : computes bound on shared resources blocking time according to PCP and PIP protocols without computing the scheduling
      2. Looking for priority inversion from simulation sub-sub-menu : runs analysis on a previously computed scheduling to look for high priority tasks blocked by lower priority task at shared resource access.
      3. Looking for priority inversion from simulation sub-sub-menu : runs analysis on a previously computed scheduling to look for tasks blocked forever on shared resources.

    4. Buffer sub-menu : this submenu can help you to study buffers shared by tasks.
      1. Buffer simulation sub-sub-menu : computes buffer utilization factor and message waiting time from a given scheduling simulation.
      2. Buffer feasibility tests sub-sub-menu : computes bound on buffer utilization factor and message waiting time without computing scheduling.

    5. Precedency sub-menu : . You will find here some heuristics/algorithms that can schedule or check feasibility of a tasks set with dependencies.
      1. Chetto/Blazewicz modifications on priorities sub-sub-menu : This service creates an independent task set from a dependent task set by modifying task priorities according to precedency constraints.
      2. Chetto/Blazewicz modifications on deadlines sub-sub-menu : This service creates an independent task set from a dependent task set by modifying task deadlines according to precedency constraints.
      3. End to End response time : computes response time from a set of task (which have precendency relationships) with the Holistic method.

    6. Random sub-menu : this submenu should provide necessary tools to carry out simulations with random events.
      1. Compute response time density sub-menu : compute statistic distribution of task response time from a scheduling simulation.

  4. Help Menu :
    1. About Cheddar sub-menu : provides version number of the Cheddar's binaries.
    2. Manual sub-menu : contains the text given in this section.
    3. Scheduling references sub-menu : gives all paper references used to compute feasibility tests and simulation results.

Contact : Frank Singhoff
Last update : January, the 4th, 2019