Energy Conservation in Datacenters through Cluster Memory Management and Barely-Alive Memory Servers.pdf

Energy Conservation in Datacenters through Cluster Memory Management and Barely-Alive Memory Servers Vlasia Anagnostopoulou ? , Susmit Biswas ? , Alan Savage ? , Ricardo Bianchini ? , Tao Yang ? , Frederic T. Chong ? ? Department of Computer Science, University of California, Santa Barbara ? Department of Computer Science, Rutgers University ABSTRACT As a result of current resource provisioning schemes in In- ternet services, servers end up less than 50% utilized almost all the time. At this level of utilization, the servers’ en- ergy efficiency is less than half their efficiency at peak uti- lization. A solution to this problem could be consolidating workloads into fewer servers and turning others off. How- ever, services typically resist doing so. A major reason is the fear of slow response times during re-activation in handling traffic spikes. Another reason is that services want to maxi- mize the amount of main memory available for data caching across the server cluster. In this paper, we propose an approach that does not com- pletely shutdown idle servers and allows free memory space to be used for cooperative data caching. Specifically, we make two key contributions. First, we propose to send servers to a new “barely-alive” power state, instead of turn- ing them off after consolidation. Our barely-alive servers allow remote accesses to their main memories even when all processing cores have been turned off. Second, we design a distributed middleware that accommodates barely-alive servers and is capable of dynamically re-sizing the amount of cache space across the cluster to the minimum required to respect the service’s service-level agreement (SLA). Any memory that is not in use by the middleware can be used by applications. Our trace-driven simulations of a server clus- ter using our middleware and barely-alive servers show very encouraging results. 1. INTRODUCTION Energy represents a large fraction of the operational cost of Internet services. As a result, p