Challenges in Design and Implementation of Middlewares for Real-Time Systems

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Challenges in Design and Implementation of Middlewares for Real-Time Systems

Second, a policy must communicate its decisions to an underlying scheduler, which implements the actual C P U reservations. In response, the policy is responsible for re-evaluating the allocation of C P U resources on the host. Most policies depend on additional data in order to be useful: The broker also provides a policy that partitions tasks into groups, where each group is assigned a maximum fraction of the C P U , and contention with each group is resolved by a secondary policy. The scheduler proxy provides a facade to the actual scheduling services of an underlying R T O S.

In practice, our implemented policies avoid making inadmissible reservation requests. The policy finishes with the scheduler and finally sends information about any changed reservations back to the advocates, which send the data up the advocate chains. An advocate may use this information to inform future requests, to cooperate with other Q O S management frameworks, or to signal its application to adapt to its available C P U resources, for example. To achieve non-invasive integration with middleware-based real-time applications and other Q O S management services, we used the QuO framework.

Finally, to apply and demonstrate our architecture atop a commercial off-the-shelf R T O S , we implemented the broker for TimeSys Linux. TimeSys Linux provides several features that are key to our implementation. First, the kernel implements reservation-based C P U scheduling through an abstraction called a resource set. A resource set may be associated with a periodic C P U reservation; zero or more threads are also associated with the resource set and draw collectively from its reservation.

Second, the TimeSys kernel A P I allows a thread to manipulate resource sets and resource set associations that involve other threads, even threads in other processes. These timers were essential in providing accurate reservations — and allowing high overall C P U utilization — in the real-time task loads we studied. This has practical significance for both usability and performance. The broker can be easily extended with new advocates and policies without modifying existing broker code: When performance is critical, new objects can be located in the same process as other broker objects; high-quality implementations of C O R B A can optimize communication between colocated objects.

A primary goal in designing and implementing the broker was to support applications that are not developed in conjunction with our system: Effective support for this programming style requires that the broker be able to integrate with its managed tasks in a non-invasive manner, i. The broker itself runs as a user-level process that acts as the default container for the advocate, policy, and scheduler objects described previously.

The implementation of the reporting delegate class is generic and reusable, not application-specific. Further, delegates can typically be inserted in ways that are transparent to the application, or localized to just the points where objects are created. In our experience, delegates can often and effectively modularize the monitoring and control of real-time behavior in C O R B A servers. For applications that are not built on middleware, however, a second strategy is required. The process advocate is an adapter between an unmodified application and the broker.


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    • Other data about the child process, such as its period, are specified as command-line options to the process advocate. To demonstrate the framework, in particular for tasks with dynamically changing C P U requirements, we implemented a core set of scheduling advocates and policies. These objects are generic and reusable in combination to construct a variety of adaptive systems, but we have not attempted to implement a complete toolbox.

    Rather, we have implemented example advocates and policies that we have found useful to date. For conditioning the feedback from tasks with dynamically changing demands, the broker provides two advocates called MaxDecay and Glacial. MaxDecay tracks recent feedback from its task: A Glacial advocate, on the other hand, is used to adapt slowly: In general, MaxDecay advocates are useful for applications whose normal behavior includes frequent spikes in demand that should be anticipated, whereas Glacial advocates are useful for tasks whose spikes represent abnormal situations that are not useful in predicting future demands.

    The broker provides three main policy objects. The first, called Strict , allocates reservations to tasks in order of their user-assigned importances. Requests from high-priority tasks are satisfied before those from low-priority tasks: This policy is good for periodic tasks with critical deadlines, because important tasks can be strongly isolated from less important ones. The second policy, Weighted , satisfies requests according to a set of user-assigned task weights. Weighted is often preferred over Strict for aperiodic and C P U -bound tasks. The third policy provided by the broker is Partition , which divides the available C P U resources into two or more parts.

    The resources within each part are controlled by a separate policy object, i. This allows for isolation between task groups and combinations of contention policies, e. The Partition policy keeps track of which tasks belong to which groups, and it allows an administrator to move tasks between groups at run time. By manipulating the subpolicy objects, the administrator can also dynamically change the amount of C P U time available within each subpolicy.

    When this process begins, it creates a bootstrap object that can be contacted by other C O R B A tools in order to configure the broker and implement a desired C P U management strategy. We provide a command-line tool, called cbhey , that allows interactive communication with the broker, its advocates, and its policies. For performance-critical situations, we also provide a mechanism for dynamically loading shared libraries into the main broker process.

    For experiments with the distributed military application, the three hosts were connected via three unshared Mbps Ethernet links. Monitoring and Scheduling Overhead There are two primary overheads in the broker: To measure the total overhead, we built three test applications. Each test application ran a single sleeping thread.

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    The first data column shows the average of the total user and kernel time spent in both the monitor and broker per report: The second column shows the average wall-clock time required for each report as viewed from the monitoring point. This is a measure of per-report latency: We believe that the measured overheads are reasonable and small for the class of real-time systems based on C O T S operating systems and middleware that we are targeting.

    Hourglass is a synthetic and configurable real-time application: The goal is for the reservations over time to be both adequate allowing the task to meet its deadlines and accurate so as not to waste resources. In a typical case like this, a MaxDecay advocate is appropriate for adapting to the task.

    There is no C P U contention in this experiment, so the policy is unimportant: To drive this experiment, we created a test program that uses the core of Hourglass in a simple C O R B A application. We configured our test application to have a period of ms and to run a time-varying workload. The broker is configured to adapt to increased demands immediately, and to reduced demands only after several periods of reduced need. The MaxDecay task advocate used in this experiment can be configured to track the actual demand more or less closely.

    The graph illustrates two points. Second, the MaxDecay advocate operates as intended, predicting future C P U demand based on the greatest recent demand of the task. This prevents the broker from adapting too quickly to periods of reduced demand, which is appropriate for tasks that have occasional but unpredictable periods of low activity. Because the previous experiment concerned adaptation to tasks, we chose to focus this experiment on changes to the policy. The compute times of the tasks were set to 95, , and 40 ms per ms period.


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      • The lines show the compute time reserved for each task. When task importances change, the broker policy updates reservations according to the Strict policy. We then ran the three Hourglass processes and used cbhey to change the importances of the three tasks dynamically. These tasks can use unreserved C P U on a best-effort basis to occasionally meet their deadlines. This experiment highlights three key points. First, the configured broker policy works correctly and allocates resources to the most important tasks.

      As described previously, the broker implements and is open to other policies that arbitrate in different styles. Second, the broker properly and quickly adjusts reservations in response to changes to the policy configuration. In a real system, these kinds of changes could be made by an automated Q O S management system or by a human operator. Third, the total utilization of the system is constantly high.

      In other words, the broker allocates all of the C P U time that it is allowed to assign, and the sum of the three reservations is always equal to that amount. The broker enables a system designer to keep utilization high while choosing how to divide resources in the face of contention.

      This software simulates a system of one or more U A V s that fly over a battlefield in order to find military targets. The U A V s send images to one or more ground stations, which forward the images to endpoints such as automatic target recognition A T R systems. The A T R task is a Java program that receives images at a rate of two frames per second.

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      This change was introduced non-invasively by running the A T R with a modified class path. This kind of situation is not uncommon in software that was not carefully designed for predictable behavior. Instead, we implemented a custom advocate that predicts when the A T R will G C and requests an increased C P U reservation only in those cases — a proactive , rather than a reactive advocate.