On Energy Management, Load Balancing and Replication

Abstract

Energy consumption is a crucial and rising operational cost for data-intensive computing. In this paper we investigate some opportunities and challenges that arise in energy-aware computing in a cluster of servers running data-intensive workloads. A key insight is that in most data centers, servers are underutilized, which makes it attractive to consider powering down some servers and redistributing their load to others. Of course, powering down servers naively will render data stored only on powered down servers inaccessible. While data replication can be exploited to power down servers without losing access to data, unfortunately, care must be taken in the design of the replication and server power down schemes to avoid creating load imbalances on the remaining live servers. Accordingly, in this paper we study the interaction between energy management, load balancing, and replication strategies for data-intensive cluster computing. In particular, we show that Chained Declustering - a replication strategy proposed more than 20 years ago - can support very flexible energy management schemes.