Exploiting process lifetime distributions for dynamic load balancing
ACM Transactions on Computer Systems (TOCS)
A parallel workload model and its implications for processor allocation
HPDC '97 Proceedings of the 6th IEEE International Symposium on High Performance Distributed Computing
Provisioning servers in the application tier for e-commerce systems
ACM Transactions on Internet Technology (TOIT)
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We explore the performance of an M/GI/1 queue under various scheduling policies from the perspective of a new metric: the it slowdown experienced by largest jobs. We consider scheduling policies that bias against large jobs, towards large jobs, and those that are fair, e.g., Processor-Sharing. We prove that as job size increases to infinity, all work conserving policies converge almost surely with respect to this metric to no more than 1/(1-ρ), where ρ denotes load. We also find that the expected slowdown under any work conserving policy can be made arbitrarily close to that under Processor-Sharing, for all job sizes that are sufficiently large.