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 Process that run independently. Speedup Factor S= F(Algorithm,System,Schedule)

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

Example

Example

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.

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.

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.

Real Time Scheduling Rate Monotonic Deadline 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 It is harder to analyze as no formula based on the load that guarantee feasible schedule.

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

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.

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

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