Proteus: Power Proportional Memory Cache Cluster in Data Centers

Abstract

In this paper, we describe the design, implementation and evaluation of Proteus, a power-proportional cache cluster which eliminates the delay penalty during server provisioning dynamics. To speed up data center services, a cache cluster is used in front of the database tier, providing fast in-cache data access. Since the number of cache servers is large, building power-proportional cache clusters can lead to considerable monetary savings. Dynamic server provisioning, one common methodology for realizing power proportionality in data centers, calls for agile load balancing schemes and smart in-cache data migration algorithms when applied to cache clusters. Otherwise, it induces unacceptable delay spikes due to data re-allocation among cache servers. Proteus addresses both challenges by using a specifically designed virtual nodes placement algorithm and an amortized data migration policy. We implement Proteus, and evaluate it on a 40-server cluster using real Wikipedia data and workload traces. The results show that, with Proteus, the load distribution is much more evenly balanced compared to the case of applying unmodified consistent hashing. At the same time, Proteus induces almost no extra delay during provisioning transitions, which is a significant advantage over other state-of-the-art solutions.

An in-memory object caching framework with adaptive load balancing

“…Proteus [34] is a dynamic server provisioning framework for memory cache cluster, which provides deterministic memory load balancing under provisioning dynamics. Similarly, Hwang et al [27] proposed an adaptive hash space partitioning approach that allows hash space boundary shifts between unbalanced cache nodes without further dividing the hash space.…”

Self-awareness of Cloud Applications

“…It requires algorithms that minimize unnecessary data movement, while performing autoscaling. These algorithms must amortize cost of data movement over time [82]. The latter may require a prediction of future data access patterns [68].…”