Journal of Computer Networks and Communications

Volume 2019, Article ID 4390917, 11 pages

https://doi.org/10.1155/2019/4390917

## A Stochastic Approach to Energy Cost Minimization in Smart-Grid-Enabled Data Center Network

^{1}Department of Information Technology, ICT Research Institute (ITRC), Tehran, Iran^{2}Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada

Correspondence should be addressed to Pejman Goudarzi; ri.ca.crti@izraduogp

Received 11 August 2018; Revised 18 December 2018; Accepted 21 January 2019; Published 20 March 2019

Guest Editor: Md. Kafiul Islam

Copyright © 2019 Abolfazl Ghassemi 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

We propose a Lyapunov drift-plus-penalty- (LDPP-) based algorithm to optimize the average power cost for a data center network. In particular, we develop an algorithm to minimize the operational cost using real-time electricity pricing with the integration of green energy resources from the smart grid. The LDPP technique can achieve significant energy cost savings under quality of service (QoS) constraints. Numerical results are presented to evaluate and validate our solution. These results illustrate significant operational/energy cost reductions for a data center network over the conventional approach which optimizes the predicted values of stochastic parameters under a fixed QoS constraint.

#### 1. Introduction

Data centers are primary information and communications technology (ICT) energy consumers. For example, data centers in the USA consume 100 billion kWh per year which costs approximately 7.4 billion dollars [1]. Recently, the next-generation power grid, known as the smart grid, has been introduced to facilitate the participation of power consumers and the integration of renewable generation to balance power supply and demand in real-time [2]. This provides consumers with the ability to dynamically track electricity price variations and efficiently manage their power consumption. In this case, we can improve system efficiency by shaping and balancing the power loads (e.g., servers). The smart grid also promotes the use of sustainable and green energy resources such as wind and solar panels (SPs).

The number of demand applications, particularly video streaming traffic, e.g., video on demand (VoD), web browsing, online gaming, and IPTV, has substantial impact on the energy consumption of data centers and can dynamically change according to the number of user demands. Thus, as emerging services grow as well as the number of connected smart devices rises, the energy consumption of data centers is anticipated to increase significantly.

In the current work, we propose and investigate the performance of a Lyapunov optimization method considering actual real-time prices. Experimental data and computer simulation are used to assess the performance of the LDPP-based energy cost reduction strategy. The results obtained show that this approach can significantly outperform the expected value algorithm (e.g., linear programming or LP (the LP method is a well-known benchmark method for solving linear constrained optimization problems)) in terms of energy cost savings. In this case, we also derived the probability of violating the QoS (connection delay) and processing (connection handling capacity of the data center) constraints. It is also shown that the proposed LDPP algorithm has a lower probability of violating the QoS and processing constraints compared to the conventional linear programming (LP) approach.

In summary, the main contributions of the present paper are as follows.(i)We consider energy and cost savings for a data center network in the context of the smart grid. A real-time energy management algorithm is proposed to reduce the cost of server cluster operation according to real-time pricing, energy demand, and power supply estimation. We also incorporate green power generators such as energy storage devices and solar panels as a power supply for the server clusters.(ii)We develop an algorithm based on optimizing the total energy consumption cost in data center networks. This allows us to intelligently route requests among data centers according to the type of user demand application (web/VoD) and energy efficiency considerations.(iii)LP and LDPP methods are investigated to solve the optimization problem. This leads to a tradeoff between operational cost saving and computational complexity.(iv)We examine the number of batteries required to support the average daily streaming traffic for a data center network.(v)Finally, a probabilistic model is given to model the complementary cumulative distribution function (CCDF) of violating the QoS and processing constraints for data center network users, and the performance with LP and LDPP is compared.

The remainder of this paper is organized as follows. Section 2 provides a review of the related work including system models and some assumptions used in the paper. Section 3 presents the formulation of a optimization problem for real-time cost reduction for a green data center. Section 4 describes the proposed LDPP-based algorithm. The performance evaluation results are presented in Section 5, and finally some conclusions are given in Section 6.

#### 2. Background and System Models

##### 2.1. Related Work

There has been significant research on reducing data center power costs and emissions in the smart grid context [3–12]. Previous approaches have focused on the design of real-time energy management systems which optimize the data center network power costs according to system uncertainties while considering a quality of service (QoS) constraint such as connection delay. These uncertainties are related to stochastic parameters including real-time pricing, power supplies from renewable sources, and data center workloads. To characterize these uncertainties, the expected values of the parameters during specific time periods have been employed under QoS constraints. Thus, the problem formulations have been deterministic and solved using techniques such as linear programming (LP) to minimize the power costs. However, this does not necessarily maximize the power cost savings.

Previous methods for saving data center power costs can be categorized as minimizing electricity costs [3–7], decreasing energy consumption [8, 9], or using renewable energy resources [10, 12]. Methods in the first category typically use the temporal and spacial variations in electricity prices from the smart grid to exchange connections among data centers to reduce operational costs. The second class of techniques aims at lowering the energy consumption of a data center and/or a data center network by considering cloud computing to reduce the power consumption of end users. The impact of real-time pricing on the operational cost and energy efficiency of the cloud transport network infrastructure was examined in [10]. The third class of techniques employs renewable energy from local sources to reduce emissions and operational costs. For example, the green star network (GSN) testbed [10] was used to examine the practicality of powering a network of data centers with solar and wind power.

In [13], a stochastic optimization problem was formulated to tackle the stochastic renewable generation and workload arrival processes. Then, an online control algorithm based on Lyapunov optimization was proposed to solve it. In [14], Mao et al. proposed a low-complexity online algorithm to minimize the long-term average network service cost, namely, the Lyapunov optimization-based base station assignment and power control (LBAPC) algorithm. The main advantage of this algorithm is that the decisions depend only on the instantaneous side information without requiring distribution information of channels and energy harvesting processes. In [15], a gamebased traffic exchange mechanism for green data center networks was proposed. Unlike in [15], the current work, a fast online optimization method based on the Lyapunov drift is employed which can reduce the system complexity more efficiently.

Different from the approaches in [13], we integrate smart grid concepts into the optimization formulation and use a QoS metric for user satisfaction in the proposed constraints. In [14], a similar Lyapunov optimization approach was used in a different context which is base station assignment and power control.

Current results in the literature such as in [3–12] focus on reducing the power cost of data center networks according to dynamic electricity pricing, integration of renewable energy resources, and considering fixed QoS constraints. Rather than predicting the values of stochastic parameters as in [3–12], we consider an algorithm based on *Lyapunov optimization* [16]. This method minimizes the long-term average power cost under QoS and processing constraints using the Lyapunov drift-plus-penalty (LDPP) algorithm. This approach can reduce the optimization to a greedy minimization problem which provides a significant cost reduction.

In this paper, an optimization model is developed for a data center network based on stochastic parameters extracted from green data centers, and a *drift-plus-penalty* framework is used to optimize the time-averaged power cost reduction. This model includes the dynamic and static power consumption of the servers, electricity prices, the availability of renewable generation, QoS and processing constraints, and service payments as well as penalties.

##### 2.2. Green Data Centers

The data center model is composed of servers, users, power management control units, the electrical grid, renewable power sources, power storage, and batch schedulers, as illustrated in Figure 1. The total power consumption within a data center typically consists of the consumption by the facility infrastructure and the ICT infrastructure, as shown in Figure 1. In the facility infrastructure, a cooling system is the main source of energy consumption, while in the ICT infrastructure, computing resources (servers) and switches are the major consumers. The facility infrastructure also includes additional power loads such as uninterrupted power supplies (UPSs), batteries, lighting, and switchgear. The sensor network in Figure 1 is one of the smart grid components for measuring energy usage.