10th December, 2013

Lab Meeting

Papers Reviewed:

Fault tolerant workflow scheduling

• Primary Issue :– Scheduling workflows in a cloud environment, with

deadlines, is a complex problem– Issues of shared resources, time to release, resource

availability, access control during execution, failures, etc.– The main goal is to schedule workflows and execute these

workflows within the deadline in-spite of many failures that occur in the environment.

• Solution:– Use of replication and resubmission of tasks based on

priority of task.– Need to avoid resource wastages– Heuristic metric finds trade-off between replication and

resubmission factors without the need for history data

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1. Fault tolerant workflow scheduling based on replication and resubmission of tasks in Cloud Computing. Jayadivya et. al.

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1. Fault tolerant workflow scheduling based on replication and resubmission of tasks in Cloud Computing. Jayadivya et. al.

Results

Ratio of success rate and resource usage

Failure Probability

My Observations

• Difficult to understand the relevance of the deadline to the success or failure curves

• Failure probabilities for ‘with replication’ would be expected to be much better

• Surprising result is Performance Comparison are similar – this is probably due to heuristic metric – which is not discussed in detail.

• The mean number of replications necessary to achieve results is not specified

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1. Fault tolerant workflow scheduling based on replication and resubmission of tasks in Cloud Computing. Jayadivya et. al.

Prediction of Remaining Service Execution Time

• Primary Benefits:– Dynamic Process Tracking requires predicted remaining

duration– It also assists scheduling of resources– It provides feedback to the client

• Present State– Present methods update predictions on event arrival and

subtract elapsed time• New Approach:

– Also consider expected events that have not occurred– Prediction approach based on PN formalism so that

concurrency can be modelled

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2. Prediction of remaining service execution time using stochastic PN with arbitrary firing delays. Andreas Rogge-Solti et. al.

Prediction of Remaining Service Execution Time

Prediction of Remaining Service Execution Time

My Observations

• Empirical Models are not use• Requires immediate notification of event • Simulations assume a stable state• In a real-time system this may not be

feasible

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QUESTIONSThank you….