Mathematical Problems in Engineering

Volume 2015, Article ID 380926, 13 pages

http://dx.doi.org/10.1155/2015/380926

## Dynamic Energy Storage Control for Reducing Electricity Cost in Data Centers

School of Information Science and Technology, University of Science and Technology of China, Hefei 230027, China

Received 5 June 2014; Accepted 20 October 2014

Academic Editor: Jonathan N. Blakely

Copyright © 2015 Shuben Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

As the scale of the data centers increases, electricity cost is becoming the fastest-growing element in their operation costs. In this paper, we investigate the electricity cost reduction opportunities utilizing energy storage facilities in data centers used as uninterrupted power supply units (UPS). Its basic idea is to combine the temporal diversity of electricity price and the energy storage to conceive a strategy for reducing the electricity cost. The electricity cost minimization is formulated in the framework of finite state-action discounted cost Markov decision process (MDP). We apply -Learning algorithm to solve the MDP optimization problem and derive a dynamic energy storage control strategy, which does not require any priori information on the Markov process. In order to address the slow-convergence problem of the -Learning based algorithm, we introduce a Speedy -Learning algorithm. We further discuss the offline optimization problem and obtain the optimal offline solution as the lower bound on the performance of the online and learning theoretic problem. Finally, we evaluate the performance of the proposed scheme by using real workload traces and electricity price data sets. The experimental results show the effectiveness of the proposed scheme.

#### 1. Introduction

Cloud computing is an emerging Internet-based computing paradigm which offers on-demand computing services to cloud consumers. To meet the increasing demands of computing and storage resources in cloud computing, there is an increasing trend toward large-scale data centers. As more data centers are deployed and their scale increases, energy consumption cost is becoming the fastest-growing element in their operation costs, including the computing energy cost, cooling energy cost, and other energy overheads. It has been estimated that energy consumption cost may amount to 30%–50% percentage of operation cost of large-scale data centers built by companies such as Google, Microsoft, and Facebook [1]. In fact, data centers consumed approximately 1.5% of all electricity consumption worldwide in 2010, which was about 56% higher than the preceding five years [2, 3]. In the near future, the energy consumption cost problem of data centers is likely to worsen and be more challenging since the technology infrastructures emerge and upgrade from a recessionary period. Hence, efficiently controlling the electricity cost of data centers has attracted an intensive concern of broader research community participating from both academia and industry in the recent years.

As we know, electricity cost generation depends not only on the total amount of energy consumed by the data centers, but also on the electricity price. Therefore, the electricity price is also an important factor in the electricity cost of data centers. With the development of smart grid technology which is a technology for the next generation power grid, more and more electricity markets are undergoing deregulation where the electricity market operators offer dynamic electricity rates to large industrial and commercial customers instead of traditional flat rates at the retail level. Thus, there is an opportunity for us to achieve the electricity consumption cost saving in data centers by observing and utilizing the time-varying electricity price in the deregulated electricity markets.

Normally, the UPS units may be deployed in data centers, and provide emergency energy to power them up using stored energy before the backup diesel generators (DG) can start up and operate as a secondary power source when the main power system experiences an outage. Usually, the transition from the main power system to the secondary power source takes 10–20 seconds. As an improvement of the rechargeable battery, the UPS units have enough energy storage capacity for keeping a data center working 5–30 minutes at its maximum power demand [4]. Hence, the excessive energy storage capacity gives us a good opportunity for electricity cost saving utilizing the UPS units to dynamically control energy storage.

Based on the above two facts, the basic principle for achieving the electricity cost saving is recharging the UPS units residing in the data center when the outside electricity price is low and discharging for powering the data center when the outside electricity price is high. Hence, this paper focuses on a dynamic energy storage control strategy for reducing the electricity cost of the data centers. Dynamic energy storage control is expected to adapt the fluctuation of the electricity price and the workload by dynamically making recharge/discharge decisions for the UPS units. It aims for achieving substantial electricity cost saving without performance degradation.

In this paper, we formulate the electricity cost reduction problem utilizing energy storage facilities as the discounted cost Markov decision process. Since the statistical information about the workload arrival and the electricity price is not available, we propose an online algorithm based on -Learning and Speedy -Learning approaches to solve the optimization problem. Particularly, the main contributions of this paper are summarized as follows.(i)The problem of electricity cost minimization in data centers with energy storage facilities for time-varying electricity prices under deregulated electricity markets is modeled by a discounted cost Markov decision process, which achieves the cost saving by making decisions to recharge/discharge the battery.(ii)In order to solve the optimization problem, we propose a dynamic energy storage control strategy based on the -Learning algorithm, which avoids the reliance on any prior knowledge of the workload and the electricity prices. Furthermore, we introduce a Speedy -Learning algorithm to accelerate convergence of the standard -Learning.(iii)We formulate an offline optimization problem of electricity cost minimization for obtaining the optimal offline solution as the lower bound on the performance of the online and learning theoretic problem. The offline optimization problem is solved by mapping it into a tractable mixed integer linear programming instead of nonlinear programming.(iv)Finally, the experiments are carried out based on real workload traces and electricity price data sets to show the performance of the proposed scheme. By using the real traces that may not provably follow the Markovian assumption, the result also shows that the proposed scheme generally performs well.

The rest of the paper is organized as follows: in Section 2 some related works in this area are presented and discussed. Section 3 describes a system model for energy management system using energy storage facilities in date centers. Section 4 formulates the problem of electricity cost consumption in the data centers with energy storage facilities as a discounted cost Markov decision process. Section 5 is devoted to designing a dynamic energy storage control strategy of battery based on -Learning and Speedy -Learning algorithms to solve the optimization problem. The optimal offline solution is discussed in Section 6. In Section 7, we provide the numerical evaluation results and performance comparisons. Finally, conclusions are drawn in Section 8.

#### 2. Related Work

The severe energy consumption problem in data centers has motivated many works on reducing their electricity cost. These works may be roughly categorized into two basic types of mechanisms: (1) reduce the energy consumption or improve the energy efficiency of the data centers; and (2) exploit the temporal and geographical variation of electricity prices to achieve the electricity cost saving.

Regarding the first mechanism, new hardware designs and engineering techniques such as energy-efficient chips, multicore servers [5], DC power supplies [6], advanced cooling systems [7, 8], and virtualization [9, 10] have been developed in order to improve the power utilization efficiency (PUE) of data centers. From the perspective of algorithm design, the energy consumption saving can operate at two different levels: the server level and the data center level [4]. At the server level, dynamic voltage-frequency scaling (DVFS) [11] offers a way to reduce power consumption by adapting both voltage and frequency of CPU with respect to changing workloads. However, DVFS can be applicable only for components (like CPU) that support multiple speed and voltage levels. DVFS based power saving policies can be found in [12, 13]. Dynamic power management (DPM) is another energy conservation approach, which turns off the power or switches the system to a low-power state when inactive. It can be employed for any system component with multiple power states. In [14], DPM is applied to achieve energy-efficient computation by selectively turning off (or reducing the performance of) system components when they are idle (or partially unexploited).

At the data center level, dynamic cluster reconfiguration (DCR) [15], VM migration and consolidation for load balancing and power management [16], and so forth, approaches are widely discussed for reducing energy consumption in the data centers. DCR in [15] develops an online measurement based algorithm to decide the number of servers to power on/off to achieve energy saving while keeping the overload probability below a desired threshold, which makes a decision without any prior knowledge of the workload statistics. VM migration and consolidation [16] achieve energy saving by continuous consolidation of VMs according to current resource utilization, virtual network topologies connecting VMs, and thermal state of computing nodes. These methods mentioned above mainly focus toward reducing energy consumption to save electricity cost. They can operate as a complementary way to assist the method proposed in this paper to further reduce the electricity cost.

The second mechanism for reducing electricity cost relies on the fact of the notable temporal and geographical variations in electricity prices. In [1], Qureshi et al. develop and analyze a new method for reducing the electricity costs when running large Internet-scale systems. The key idea of the method is to distribute more traffic to data centers with low electricity price. In [17], Rao et al. utilize both the location diversity and the time diversity of electricity prices in the multiple electricity markets environment to minimize the total electricity cost while guaranteeing the quality of service (QoS). Luo et al. [18] propose a novel spatiotemporal load balancing approach to leverage both geographic and temporal variations of electricity price to minimize energy cost for distributed internet data centers (IDC). However, those works mentioned above do not utilize energy storage facilities residing in data centers, which may be used to achieve further electricity cost saving. Compared with existing techniques for electricity cost reduction, the methods of energy storage have no performance degradation of the data center. In this paper, our work focuses on the problem of electricity cost minimization in data centers with energy storage facilities under deregulated electricity markets where the electricity prices exhibit temporal variation, which is mainly motivated by [19]. In [19], an online control algorithm using Lyapunov optimization theory is proposed for reducing the time average electric utility bill in a data center, and the solution has the threshold structure. Although simple, the technique of Lyapunov optimization is unable to learn the system dynamics, which may not lead to an optimal control of energy storage. Alternatively, by exploiting a Markov decision process approach and reinforcement learning tool, the proposed algorithms learn the system dynamics and adapt the control decision accordingly for saving more electricity cost. Generally, the optimal control policies for Markov decision process suffer from the “curse of dimensionality.” In our work, we consider the total energy consumption of all components in the data center as the energy consumption state instead of each component’s individually. Furthermore, there are only three actions on the battery, that is, recharging, discharging, and doing neither. Thus, all of those considerations may effectively alleviate the problem of “curse of dimensionality.”

#### 3. System Model

In this section, we describe system architecture model for energy management in data center, present the models for battery, energy consumption, and electricity cost, as well as formulating the problem of dynamic energy storage control to minimize the expected total electricity cost.

##### 3.1. System Architecture

A general system architecture model for data center with energy storage facilities, depicted in Figure 1, is composed of an energy management system (EMS) and a data center facility. EMS acts as the heart of the energy management framework and manages the energy provision in data center, while the data center facility provides computation and storage resources for executing the submitted tasks. In EMS, the key components include information collector (IC) and energy storage management unit (ESMU). IC is to collect the information of the electricity prices, energy storage, and the energy demand generated by the data center periodically, while ESMU is to make the optimal decision on whether recharging or discharging the energy storage facilities for electricity cost minimization according to the information collected by IC. The energy storage unit (ESU), that is, UPS, has the capability of storing energy drawn from the power grid and discharging the stored energy to power the data center. Below, we use the terms UPS and battery interchangeably. The main work of this paper is to propose a dynamic energy storage control strategy for ESMU.