Towards green data centers: A comparison of x86 and ARM architectures power efficiency

Abstract

Servers and clusters are fundamental building blocks of high performance computing systems and the IT infrastructure of many companies and institutions. This paper analyzes the feasibility of building servers based on low power computers through an experimental comparison of server applications running on x86 and ARM computer architectures. The comparison executed on web and database servers includes power usage, CPU load, temperature, request latencies and the number of requests handled by each tested system. Floating point performance and power usage are also evaluated. The use of ARM based systems has shown to be a good choice when power efficiency is needed without losing performance.

Introduction

Much has been said about green computing and energy efficient data centers recently, but most solutions are focused in enhancing current server architectures or using virtualization [20] and other techniques to consume less power in data centers. This article discusses if processors typically used in mobile devices are suitable for server and cluster applications. Considering such possibilities, we designed a series of experiments in order to compare x86 and ARM systems in the context of two classical network services applications. Several hardware platforms are evaluated, measuring their power usage, temperature and application performance. We also analyze floating point performance and its power usage using Linpack [6].

Low power consumption is historically related to embedded systems running with batteries, but over time more data centers and high performance systems are concerned with power efficiency [19], [2]. One characteristic of data centers is their huge electric power consumption [21], which is a serious problem [16]. According to Mahadevan et al., the biggest consumers of energy are servers and cooling systems [21]. Reducing power is not only interesting for ecological reasons, but also to reduce electrical bill costs. Svanfeldt-Winter et al. relate that 57% of monthly data-center costs are spent on servers’ electricity bills [36].

As designers work towards faster systems there is an increasing concern about the expected power consumption of future systems. For exascale computing performance to be achieved in the years to come, energy efficiency will be the most important constraining factor [9], [11]. This is why power consumption dominates every discussion of exascale [41], and in fact, there is still no clear roadmap to power-efficient exascale computing [19].

The technical report “The landscape of parallel computing: A view from Berkeley” [2], analyzes both extremes of computing: servers and embedded systems, and concludes that these extremes are “colliding and merging worlds”. One example is power consumption: until some years ago no one was concerned with power consumption in data centers, but today, most data centers are looking for energy efficient systems. Embedded systems, on the other hand, have always been concerned with battery life, requiring minimal power consumption. In fact, according to Jensen and Rodrigues, high performance systems must borrow concepts from embedded systems to achieve exascale performance [19].

In this work, we could verify that the use of low power servers in parallel applications is suitable. In our experiments, while power consumption goes down, performance stays almost the same for x86 based architectures typically used to build clusters. We find out that processing clusters based on low power systems such as ARM processors are feasible and that this is a nice way to decrease power usage of several server applications.

Grounded in experimental analysis, our main contributions are:

• A low cost and reliable testing setup to analyze servers’ power efficiency;

• A systematic and quantitative evaluation of several server architectures running web server, database server and Linpack benchmarks.

This paper is structured as follows. Section 2 presents several differences of the analyzed systems and a review of the state of the art. Section 3 describes the equipment used in the measurements and our experimental setup, while Section 4 depicts the experimental results. Section 5 discusses the results addressing our main contributions and Section 6 features our final remarks.