Cost Aware Adaptive Load Sharing
We consider load sharing in distributed systems where a stream of service requests arrives at a collection of n identical servers. The goal is to provide the service with the lowest possible average waiting time. This problem has been extensively studied before, but most previous models have not incorporated the monitoring costs explicitly. This paper focuses on a rigorous study of maximizing the utility of monitoring.
We extend the Supermarket Model for dynamic load sharing by explicitly incorporating monitoring costs. These costs stem from the fact that the servers have to answer load queries, a task which consumes both CPU and communication resources. This Extended Supermarket Model (ESM) allows us to formally study the tradeoff between the usefulness of monitoring information and the cost of obtaining it. In particular, we prove that for each service request rate, there exists an optimal number of servers that should be monitored to obtain minimal average waiting time.
Based on this theoretical analysis, we develop an autonomous load sharing scheme that adapts the number of monitored servers to the current load. We evaluate the performance of this scheme using extensive simulations. It turns out that in realistic scenarios, where monitoring costs are not negligible, the self-adaptive load balancing scheme is clearly superior to any load-oblivious load sharing mechanisms.
KeywordsService Time Load Balance Queue Length Service Request Monitoring Cost
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