Optimal Power and Performance Management for Heterogeneous and Arbitrary Cloud Servers

We investigate optimal power and performance management for heterogeneous and arbitrary cloud servers in a data center. In particular, we study the problems of power-constrained performance optimization and performance-constrained power optimization in a data center with multiple heterogeneous and arbitrary servers. These problems are essential to find optimal server speeds, such that: 1) the average task response time is minimized, and that the total power consumption does not exceed certain power constraint or 2) the total power consumption is minimized, and that the average task response time does not exceed certain performance constraint. Each server is treated as a G/G/1 queuing system, whose task interarrival times and task execution requirements can have arbitrary probability distributions. Furthermore, these servers are entirely heterogeneous in terms of task interarrival time, task execution requirement, and power consumption models. The main contributions of this paper are summarized as follows: 1) we formulate the average task response time as well as the total power consumption in a data center with multiple heterogeneous and arbitrary servers as the functions of server speeds; 2) we define our optimization problems by finding optimal server speeds, since the server speeds determine both the average task response time and total power consumption; 3) we develop algorithms to find the optimal solutions and demonstrate numerical data; and 4) we also develop several closed-form heuristic solutions and compare their quality with that of the optimal solution. Our approach provides an analytical way of studying the power-performance tradeoff at the data center level.