Autoscaling Tiered Cloud Storage in Anna Chenggang Wu, Vikram Sreekanti, Joseph M. Hellerstein UC Berkeley fcgwu, vikrams,
[email protected] ABSTRACT deal with a non-uniform distribution of performance require- In this paper, we describe how we extended a distributed ments. For example, many applications generate a skewed key-value store called Anna into an autoscaling, multi-tier access distribution, in which some data is \hot" while other service for the cloud. In its extended form, Anna is designed data is \cold". This is why traditional storage is assembled to overcome the narrow cost-performance limitations typi- hierarchically: hot data is kept in fast, expensive cache while cal of current cloud storage systems. We describe three key cold data is kept in slow, cheap storage. These access dis- aspects of Anna's new design: multi-master selective repli- tributions have become more complex in modern settings, cation of hot keys, a vertical tiering of storage layers with because they can change dramatically over time. Realistic different cost-performance tradeoffs, and horizontal elastic- workloads spike by orders of magnitude, and hot sets shift ity of each tier to add and remove nodes in response to and resize. These large-scale variations in workload moti- load dynamics. Anna's policy engine uses these mechanisms vate an autoscaling service design, but most cloud storage to balance service-level objectives around cost, latency and services today are unable to respond to these dynamics. fault tolerance. Experimental results explore the behavior The narrow performance goals of cloud storage services of Anna's mechanisms and policy, exhibiting orders of mag- result in poor cost-performance tradeoffs for applications.