Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation

Archives of Computational Methods in Engineering https://doi.org/10.1007/s11831-022-09852-2 REVIEW ARTICLE Adaptive Computational Solutions to Energy Efficiency in Cloud Computing Environment Using VM Consolidation Bhagyalakshmi Magotra1 · Deepti Malhotra1 · Amit Kr. Dogra1 Received: 11 August 2022 / Accepted: 5 November 2022 © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022 Abstract Cloud Computing has emerged as a computing paradigm where services are provided through the internet in recent years. Offering on-demand services has transformed the IT companies' working environment, leading to a linearly increasing trend of its usage. The provisioning of the Computing infrastructure is achieved with the help of virtual machines. A great figure of physical devices is required to satisfy the users' resource requirements. To meet the requirements of the submitted workloads that are usually dynamic, the cloud data centers cause the over-provisioning of cloud resources. The result of this over-provisioning is the resource wastage with an increase in the levels of energy consumption, causing a raised operational cost. High CO2 emissions result from this huge energy consumption by data centers, posing a threat to environmental stability. The environmental concern demands for the controlled energy consumption, which can be attained by optimal usage of resources to achieve in the server load, by minimizing the number of active nodes, and by minimizing the frequency of switching between active and de-active server mode in the data center. Motivated by these actualities, we discuss numerous statistical, deterministic, probabilistic, machine learning and optimization based computational solutions for the cloud computing environment. A comparative analysis of the computational methods, on the basis of architecture, consolidation step involved, objectives achieved, simulators involved and resources utilized, has also been presented. A taxonomy for virtual machine (VM) consolidation has also been derived in this research article followed by emerging challenges and research gaps in the field of VM consolidation in cloud computing environment. 1 Introduction A distributed system is a set of independent elements that work together to accomplish a common goal. As stated by [1], Cloud Computing (CC) can be referred to as a form that originated from distributed computing and has revolutionized the industry of Information and Communication Technology (ICT) by introducing the concept of ondemand availability of computing resources. Because of the significant development in the terms of capabilities of the technology, computational resources have become easily available. This advancement in technology has led to the emergence of cloud computing wherein the resources are provided to multiple users on an on-demand and sharing basis. Cloud Computing is considered one of the most important computing paradigms in IT sector. Based upon * Bhagyalakshmi Magotra 1 MIET: Model Institute of Engineering and Technology, Jammu, India the internet technology, the emergence of CC has provided services to the applications that are compute-intensive. To provide the compute resources on-demand over the internet located at a remote data center, the cloud providers share a pool of resources that can be accessed from any location in the world. Applications and data are stored in a data center. The services are provided by the cloud service providers to the users via cloud data centers. The workload of the data centers is heterogeneous and thus, proper provisioning of the resources is required to provide good quality of service to the users. Cloud computing is majorly dependent on its functioning on a technology named virtualization. Underutilization of mainframe computers led to the development of Virtualization technology by IBM in 1960 to make the most out of hardware resources. With the help of virtualization, multiple virtual machines can run over a single host machine. The concept of Virtualization enables the endusers and the service providers to have efficient utilization of cloud resources with optimum usage and least cost [2]. This technique is responsible for effectively handling the increasing need of the users in terms of needed resources in Cloud 13 Vol.:(0123456789) B. Magotra et al. Data Centers (CDCs). Various objectives like balancing of load, energy management, the sharing of resources among multiple users, making the system fault-free, can be achieved with the help of virtualization [3]. With this advancement in technology, there is a great pace escalation in the number of users requesting resources in CDCs. A great pace escalation in the number of users requesting resources in CDCs has exponentially raised the power consumption making the network operation costly. This ever-increasing process to resource ratio results in degradation of network performance and increased energy consumption. Data centers are one of the major contributors to worlds power and energy consumption. Further, due to the outbreak triggered by the novel coronavirus, the organizations have suspended office work and suggested that employees work from home. This has led to an enormous increase in the use of cloud computing services. It is predicted that the information technology (IT) sector will consume up to 13% of global electricity by 2030, which is currently 7%. This percentage of energy consumption is increasing at the rate of 12% every year. According to the analysis done in 2020, 60% of the total data traffic was consumed by online shopping, gaming, and streaming, which is also forecasted to raise to 80% in the next five years [4]. The increased energy consumption is not only due to the electronic devices involved in the cloud system but also because of inefficient utilization of resources. Improvement in cloud resource utilization can lead to the minimization of this energy consumption. Here, the dynamic VM consolidation comes into play to efficiently reduce the amount of energy consumed and the carbon footprints [5] of the data center. Figure 1 shows the major reasons for energy inefficiency in the data centers. Server utilization is represented in terms of the ratio of the number of resources used to the total resource capacity of the server. For example, the current utilization of the CPU of a machine divided by its maximum capacity of the CPU gives an estimate of the utilization of that particular machine. Over the last decade, the demand for computing resources has increased exponentially. This ever-increasing process to resource ratio has led to various performance issues [6]. The key problem with this growing demand for various cloud resources is the overutilization of resources which results in degradation of network performance. Reasons for low Energy Efficiency Also, if the servers remain underutilized, they result in an exceptio (...truncated)