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WOSE workshop, Edinburgh

WOSE workshop, Edinburgh. Title: Average-Based Workload Allocation Strategy for QoS-Constrained Jobs In A Web Service-Oriented Grid ) Authors: Yash Patel and John Darlington. Previous Work. Recent WOSE related work presented at All Hands Meeting in September Grid Workflow Scheduling in WOSE

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WOSE workshop, Edinburgh

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  1. WOSE workshop, Edinburgh • Title: Average-Based Workload Allocation Strategy for QoS-Constrained Jobs In A Web Service-Oriented Grid) • Authors: Yash Patel and John Darlington

  2. Previous Work • Recent WOSE related work presented at All Hands Meeting in September • Grid Workflow Scheduling in WOSE • Similar work presented at Grid 2006 Conference, Barcelona, Spain • QoS Support for Workflows in a Volatile Grid • Both works focus on satisfying QoS requirements and scheduling individual workflows • And use stochastic programming technique to tackle uncertainty

  3. Previous Work - Drawbacks • Overhead for scheduling workflows one by one • One needs to gather information about Grid services more frequently (leads to monitoring overheads) • May be impractical when workflow arrival rate is high

  4. Extension of previous work • Advantages over previous work • Collectively schedule workflows • Information about states of Grid services need to be obtained only periodically • Use of • Queueing theory • Mathematical programming

  5. Overview • Web services emerging as a powerful mechanism to achieve loosely coupled distributed computing • Grid users can effectively compose web services in the form of workflows and tools such as BPEL engine can execute their workflows

  6. Applications • Financial services industry. E.g. portfolio optimisation, risk analysis • News/weather/stock price etc services are web services • Complex tasks can be interfaced through web services. E.g. GridSAM • Basically any complex piece of code can be interfaced through a web service

  7. Our Approach • Problem: Satisfy QoS requirements of end-users in dynamic environments such as Grid • Motivation: Develop an effective method that doesn’t rely on obtaining real-time information to make scheduling decisions • Solution: Formulate scheduling problem of workflows as a MINLP + model a web service as a G/G/k queue

  8. Our Approach • MINLP: Mixed-Integer Non-linear Program • Objective and constraints may be non-linear and both real (continuous) and integer variables in the optimisation program • G/G/k queue • General distribution of inter-arrival times and general distribution of service times and k processing threads

  9. Why this approach • MINLP: Mixed-Integer Non-linear Program • Embed the non-linear equations arising from G/G/k analysis into the program • G/G/k queue • Provides a general enough model • No need for assuming specific distributions e.g. M/M/k

  10. Scheduling Problem as MINLP • MINLP: • minimise penalty • Subject To: • Deadline Constraint (deadlines allocated to workflow tasks) • Cost Constraint (budget allocated to workflow tasks) • Reliability Constraint (reliability requirements of workflow tasks)

  11. MINLP Penalty Variables penalty Deadline constraint Cost constraint Reliability constraint Task assignments should be less than arrival rate Stable queue requirement

  12. Response Time for G/G/k queue

  13. Calculation of diy and eiy • Calculation of deadline and cost allocations for workflow tasks • diy = (Upper bound of the 95th confidence interval of the workflow task y) * (Remaining workflow Deadline) / (sum of upper bound of the 95th confidence interval of all workflow tasks along workflow path starting with task y) Similarly scaling with respect to remaining cost budget we can calculate eiy

  14. MINLP drawbacks • NP-hard as apart from being non-linear it also falls under combinatorial optimisation • Solution time may increase exponentially with increase in the number of variables / constraints • How to get around the above problems: • Linearise the MINLP model to MILP or LP • Or reduce the number of variables Doing so may not lead to good enough representations of original problems

  15. Experimental Evaluation • We want to compare the ability to satisfy QoS requirements for different scheduling strategies with our developed strategy • Next • Simulation in a nutshell • Scheduling Strategies • Workflows used • Simulation Setup • Experimental Results • Summary of Results

  16. Simulation Summary • Simulation developed in SimJava • Web services, brokering service etc are SimJava objects • Workflows arrive with a general inter-arrival time distribution • Statistics (mean response time, cost, failures, utilisation etc) collected for 1000 jobs following 500 jobs that require system initiation • Workflows have overall deadline and cost requirements apart from individual workflow tasks having reliability requirements

  17. Simulation in a nutshell

  18. Scheduling Strategies • GWA: Global Weighted Allocation • MINLP based workload allocation scheme (FF) • RTLL: Real Time based Least Loaded Scheme • Comparison: Workflow failures (workflows that fail to meet either their deadlines or budget)

  19. Experimental Setup • Next • Workflows Used • Simulation Setup • Summary of results

  20. Workflows used

  21. Simulation Setup

  22. Failures vs Arrival Rate [Low CV]

  23. Failures vs Arrival Rate [High CV]

  24. Results • The workload allocation strategy performs considerably better than the algorithms that do not use these strategies • Workflow and workload nature don't change the performance of the scheme notably • When arrival rates are low, performance is nearly similar to RTLL • Execution time variability does not change the performance of the workload allocation strategy significantly for both low are high arrival rates • Don’t require to schedule individual workflows • Doesn’t require real time information of web services

  25. Future Work • Experiment with workflows having slack periods • Investigate techniques to linearise the optimisation program and/or develop pre-optimisation strategies that help to reduce the number of unknowns in the MINLP • Overhead analysis of RTLL and our approach

  26. Thank You

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