Optimized Energy Cost and Carbon Emission-Aware Virtual Machine Allocation in Sustainable Data Centers
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sustainability Article Optimized Energy Cost and Carbon Emission-Aware Virtual Machine Allocation in Sustainable Data Centers T. Renugadevi 1,*, K. Geetha 1, K. Muthukumar 2 and Zong Woo Geem 3,* 1 School of Computing, SASTRA Deemed University, Thanjavur 613401, India; [email protected] 2 School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur 613401, India; [email protected] 3 Department of Energy IT, Gachon University, Seongnam 13120, Korea * Correspondence: [email protected] (T.R.); [email protected] (Z.W.G.); Tel.: +91-975-0887-871 (T.R.); +82-317-505-586 (Z.W.G.) Received: 29 May 2020; Accepted: 4 August 2020; Published: 7 August 2020 Abstract: Cloud data center’s total operating cost is conquered by electricity cost and carbon tax incurred due to energy consumption from the grid and its associated carbon emission. In this work, we consider geo-distributed sustainable datacenter’s with varying on-site green energy generation, electricity prices, carbon intensity and carbon tax. The objective function is devised to reduce the operating cost including electricity cost and carbon cost incurred on the power consumption of servers and cooling devices. We propose renewable-aware algorithms to schedule the workload to the data centers with an aim to maximize the green energy usage. Due to the uncertainty and time variant nature of renewable energy availability, an investigation is performed to identify the impact of carbon footprint, carbon tax and electricity cost in data center selection on total operating cost reduction. In addition, on-demand dynamic optimal frequency-based load distribution within the cluster nodes is performed to eliminate hot spots due to high processor utilization. The work suggests optimal virtual machine placement decision to maximize green energy usage with reduced operating cost and carbon emission. Keywords: cloud computing; virtual machine placement; sustainable data center; energy efficiency; renewable energy; carbon footprint 1. Introduction Large data centers are nowadays an integral part of the information technology (IT) industry. Cloud-based services are of high preference to organizations and individuals. Organizations consolidate multiple clusters to large data centers. Power consumption has been a significant economic and environmental issue in data centers due to growing demand. The growth of the data center’s energy consumption is approximately 10–12% per year [1]. The geo-distributed data centers enable providers to establish different renewable energy sources based on the environment. The energy cost associated with data centers is approximately 42% of the overall operating cost of the data centers [2]. The service providers are compelled to improve the infrastructure related to server power consumption, cooling provisioning and heat dissipation while maintaining service level agreement (SLA). Data centers contribute to 2% of the world’s total carbon dioxide (CO2) emission due to high energy consumption. The cost involved with cooling infrastructure can be 50% or more in a poorly designed data center [3]. Due to increasing power densityheat and thermal management are crucial for data centers to increase the lifetime of the servers and to reduce economic loss in the form of electricity bill. The two possible ways to overcome the problem of CO2 emission are (1) grid power source to be replaced with renewable Sustainability 2020, 12, 6383; doi:10.3390/su12166383 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 6383 2 of 27 energy sources; (2) Improve the Power Usage Effectiveness (PUE) of the data centers. The Green Grid consortium [4] defines the PUE metric as the ratio between the total power consumed by the data center (IT power + overhead power) and energy consumed by servers executing IT load (IT power). The overhead power includes the power consumed by data center infrastructure other than server power. The overhead power is mainly dominated by the power consumed by Computer Room Air Conditioning (CRAC) devices. The increase in temperature inside the data center is due to two factors: (1) Utilization of CPU in higher frequencies; (2) Increase in outside temperature. Thermal management of CRAC units is performed based on rack-level IT loads [5,6]. Two temperature-aware algorithms were proposed to prevent hot spots and to minimize the rise of operating temperature [7]. A game-based thermal-aware resource allocation was proposed in [8]. It uses a cooperative Nash-bargaining solution to reduce the thermal imbalance in data centers. Threshold-based thermal management was introduced in [9] to handle hot spots effectively but failed to treat the thermal imbalance. Thermal management is proposed to distribute the load at the rack level to handle temperature drop effectively but fails to handle hotspots [10]. The lower PUE indicates a more efficient data center showing less overhead power and more IT power. The cloud provider’s PUE ranges from 1.1 to 1.2 [11,12]. Collocated small data centers still provide PUE up to 2 [13]. Mixed-integer linear programming was used to minimize operating cost, energy cost and reliability cost by minimizing active PMs in data centers [14]. Stochastic search based on a genetic algorithm was used to reduce IT power consumption and migration cost by considering energy-aware vitual machine migration [15]. Facebook, Amazon, Microsoft, Apple and Google have built their suitable clean energy sources based on its location [16–18]. Since clean energy is not consistent, it carries more challenges in its efficient usage. Data centers provide a way in for off-site grid energy to power the infrastructure to balance the inconsistent nature of renewable energy. The nature of variable workloads in data centers and prediction algorithms contribute to power and resource management to use clean energy more effectively in data centers. The two popular on-site energy sources considered are solar and wind. Solar energy follows a pattern; it increases gradually from the morning, reaches its peak at noon, and progressively slows down. Wind energy does not have a pattern of generation. Renewable energy availability varies based on the location of the data center. It paves a way to target the load to the data center with the maximum renewable source to use clean energy effectively. In the current state of the art, the works are carried out in different perspectives considering traditional energy management techniques to act on energy reduction within data centers. This work highlights the factors, namely, server energy consumption reduction and service providers’ operating cost and carbon emission reduction. For server energy consumption reduction, it considers the variation of the core parameters of DVFS (Dynamic Voltage Frequency Scaling), namely, frequency, utilization and power consumption. Concerning workload, the on-demand dynamic optimal frequency for the nodes in the cluster is identified and load balancing is performed to eliminate hot spots due to high processor utilization. Secondly, as many providers own geo-distributed data centers powered by a mixed supply of both grid and renewable sources, this work aims to efficiently utilize the renewable source to reduce the total operating cost and carbon emission. The impact of electricity price, carbon footprint, carbon cost on server and cooling device power consumption are taken into consideration while formulating the proposed objective function. In our previous work [19], VM placement considering dynamic optimal frequency-based allocation and standard power efficient algorithm (C-PE) were compared. This work is the extension of our previous work with both brown and green energy sources and related energy cost parameters towards the realization of the proposed objective. In this work, we provoked the following questions: (1) When the renewable energy source is not in a stable condition, how to maximize its usage? (2) How to reduce the power consumed by CRAC devices and IT devices to reduce the total electricity cost? (3) How to reduce the carbon emission? In this work, energy source and DVFS-aware VM placement algorithm is proposed to minimize total cost, carbon footprint and cooling device power consumption for geo-distributed data centers with Sustainability 2020, 12, 6383 3 of 27 a mixed supply of grid and clean energy. Container technology along with virtualization is used to provide the necessary environment and isolation for task execution [20]. To achieve the above said objective, the following measures are carried out in this work as key contributions. Optimal DVFS-based VM scheduling is performed to distribute the load among the servers to • minimize the operating temperature. Formulation of an objective function for data center selection with the consideration of varying • carbon tax, electricity cost and carbon intensity. Investigation on the effect of renewable energy source-based data center selection on total cost, • carbon cost and CO2 emission. The efficient utilization of VMs is carried out by appropriate VM sizing and mapping of containers • to available VM types. K-medoids algorithm is used to identify container types. • Examined the upshot of workload-based tuning of cooling load on total power consumption. • The remaining sections of the paper are structured as follows: In Section1, data centers’ power consumption information is delineated. In Section2, existing research works in the literature related to virtual machine placement and containers are discussed.