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Scheduling of Soft Real-time systems for context-aware Applications

This presentation discusses the scheduling of soft real-time systems for context-aware applications in domains such as databases, multimedia, mobile applications, sensor networks, and more. It covers topics like statistical distribution functions, task dependencies, benchmarks, and the task model. The use of statistical techniques and potential-based scheduling is explored.

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Scheduling of Soft Real-time systems for context-aware Applications

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  1. Scheduling ofSoft Real-time systemsfor context-aware Applications Presented by, Hemanth Amara CSE 666 - Real Time Computer Systems

  2. Introduction: Soft real-time systems has been mainly motivated by applications from two domains : Databases and Multimedia. Context-sensitive applications Mobile applications, Sensor networks, Pervasive and Ubiquitous Computing, Internet browsing, Video Games and virtual reality. (Context can be defined as the set of environmental states and settings that either determine an application’s behavior or in which an application event occurs and is interesting to the user ) CSE 666 - Real Time Computer Systems

  3. Statistical Distribution Function: A taskis a single program which can be invoked and executed. E.g. single execution of jpeg encoder, audio player. The statistical distribution function (SDF) for execution time provides information about the statistical likelihood of specific runtimes for a task. Dependency: Results of one task needed by another task. Weighted Likelihood relationship reflects the user’s likelihood to request the execution of a task after the completion of another task. CSE 666 - Real Time Computer Systems

  4. Benchmarks: e.g. SDFs of Location Discovery, g721encode tasks. CSE 666 - Real Time Computer Systems

  5. Statistical SRTS: Statistical SRTS: Non-parametric statistical techniques are used to model the runtime of each task. The resulting output is the statistical distribution function (SDF) of execution times for each task. Analysis of each of the tasks on different simulation platforms is performed in order to gather statistical runtime data for each of the tasks. CSE 666 - Real Time Computer Systems

  6. Global Flow for Scheduling SSRTS: CSE 666 - Real Time Computer Systems

  7. Statistical SSRTS: Once the user profile and task profile have been established the user can begin to use the system. When the user invokes a task, one of two situations may occur: 1)The scheduler had pre-fetched the invoked task and the data is available. 2)If the scheduler had not pre-fetched the task, the task may still be finished prior to the deadline placed according to user's profile. User is benefited in either case. CSE 666 - Real Time Computer Systems

  8. Task Model: The purpose of the task model is to provide a compact and accurate representation of soft real-time tasks with respect to their execution time. Consists of three hierarchical layers: Window of consideration, Intertask and Intratask. CSE 666 - Real Time Computer Systems

  9. Algorithm: CSE 666 - Real Time Computer Systems

  10. Conclusions: Uncertainties are addressed using statistical techniques. The use of the SDF in the task model is more effective than task model with SDF replaced with average run-time or worst case run-time. Non parametrical statistical techniques can be used for modeling the runtime of tasks. Cumulative potential based scheduling can be used to maximize utilization. CSE 666 - Real Time Computer Systems

  11. Questions ? “It is important that students bring a certain ragamuffin barefoot irreverence to their studies, they are here not to worship the known but to question it”   Jacob Bronowski CSE 666 - Real Time Computer Systems

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