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Distributed Process Scheduling : A Summary

Distributed Process Scheduling : A Summary. By Pragati Sahu. System Performance Model. Precedence process Model Applied for concurrent process. Communication process Model Applied for process that coexist and communicate asynchronously. Disjoint Process Model

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Distributed Process Scheduling : A Summary

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  1. Distributed Process Scheduling : A Summary By Pragati Sahu

  2. System Performance Model • Precedence process Model Applied for concurrent process. • Communication process Model Applied for process that coexist and communicate asynchronously. • Disjoint Process Model Process that run independently. Speedup Factor S= F(Algorithm,System,Schedule)

  3. Static Process Scheduling • Mapping of process to processor is determined before the execution process. • Precedence Process Model • Communication Process Model

  4. Example

  5. Example

  6. Static Scheduling Challenges • Prior knowledge of execution time and communication behavior of the process is required. • Once a process is assigned to a processor it remains there until completion of execution.

  7. Dynamic Load Sharing and Balance • Sender initiated Algorithm • Transfer of process require 3 basic decisions. i.e. Transfer Policy, Selection Policy and location policy. • Receiver initiated Algorithm • Receiver pulls process to be executed to its site. • Uses similar transfer policy i.e. activates pull when queue size is below threshold. • More Stable than the sender.

  8. Distributed Process Implementation The three significant application scenario : • Remote Service The message is interpreted as a request for a known service at remote site • Remote Execution The messages contain a program to be executed at the remote site. • Process Migration The messages representing process are migrated to the remote site for continuing execution.

  9. Real Time Scheduling Rate Monotonic • Optimal static-priority scheduling • It assigns priority according to period • A task with a shorter period has a higher priority • Executes a job with the shortest period Deadline Monotonic • Optimal static-priority scheduling • It is harder to analyze as no formula based on the load that guarantee feasible schedule.

  10. Real Time Scheduling Earliest Deadline First • Optimal dynamic priority scheduling • A task with a shorter deadline has a higher priority • Executes a job with the earliest deadline

  11. Recent Research Paper • Liu Dun-nan, Jiang Xin-fan, Hu Bin-qi ,hang Si-yuan, Real-time scheduling feedback fuzzy control system based on area control error and power generation error in :9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD),2012. • Weijing Song,Shasha Yue, Lizhe Wang, Wanfeng Zhang, Dingsheng Liu, Task Scheduling of Massive Spatial Data Processing across Distributed Data Centers: What's New?, in: 17th International Conference on Parallel and Distributed Systems (ICPADS) ,2011.

  12. Future Work • Enhancements in real time scheduling for Cloud and Big Data. • Energy efficient scheduling techniques for vast datacenters i.e. Big Data.

  13. Thank You !!!

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