AutoScale: Dynamic, Robust Capacity Management for Multi-Tier Data Centers
ACM Transactions on Computer Systems (TOCS)
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Multi-tier data centers have become a norm for hosting modern Internet applications because they provide a flexible, modular, scalable and high performance environment. However, these benefits come at a price of the economic dent incurred in powering and cooling these large hosting centers. Thus, energy efficiency has become a critical consideration in designing Internet data centers. In this paper, we propose a multifaceted approach, Hybrid, consisting of dynamic provisioning, frequency scaling and dynamic power management (DPM) schemes to reduce the energy consumption of multi-tier data centers, while meeting the Service Level Agreements (SLAs). We formulate a mathematical model of the energy and performance/SLA optimization problem followed by a queueing theory based approach to develop two heuristics for solving the optimization problem. The first heuristic dynamically provisions the optimal number of servers required in each tier. The second heuristic proactively decides the CPU speed and the duration of sleep states of a server to achieve further energy savings. We evaluate our heuristics using a simulator that was validated with real measurements on a prototype three-tier data center consisting of 25 servers with two multi-tier application benchmarks. Our experimental results indicate that the proposed scheme, Hybrid, can reduce the energy consumption by 50% relative to static provisioning without CPU frequency scaling and DPM. We demonstrate that Hybrid satisfies the SLAs for dynamically varying workloads. In addition, the proposed multifaceted approach is more energy efficient than the other methods such as dynamic provisioning with exploiting deep sleep states.