Analysis of Restart Mechanisms in Software Systems
IEEE Transactions on Software Engineering
Adaptivity metric and performance for restart strategies in web services reliable messaging
WOSP '08 Proceedings of the 7th international workshop on Software and performance
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Evaluating the adaptivity of computing systems
Performance Evaluation
Restart strategies in optimization: parallel and serial cases
Parallel Computing
On-line adaptive algorithms in autonomic restart control
ATC'10 Proceedings of the 7th international conference on Autonomic and trusted computing
Impact of censored sampling on the performance of restart strategies
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Experimental analysis of the correlation of HTTP GET invocations
EPEW'06 Proceedings of the Third European conference on Formal Methods and Stochastic Models for Performance Evaluation
A measurement study of the interplay between application level restart and transport protocol
ISAS'04 Proceedings of the First international conference on Service Availability
Self-Management of systems through automatic restart
Self-star Properties in Complex Information Systems
Self-Aware software – will it become a reality?
Self-star Properties in Complex Information Systems
Electronic Notes in Theoretical Computer Science (ENTCS)
Multiple class G-networks with restart
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
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We analyse and optimise the completion time for a class of jobs whose conditional completion time is not always decreasing with the time invested in the job. For such jobs, restarts may speed up the completion. Examples of such jobs include download of web pages, randomised algorithms, distributed queries and jobs subject to network or other failures. This paper derives computationally attractive expressions for the moments of the completion time of jobs under restarts and provides algorithms that optimise the restart policy. We also identify characteristics of optimal restart times as well as of probability distributions amenable to restarts.