A systematic review on effective energy utilization management strategies in cloud data centers

Journal of Cloud Computing: Advances, Systems and Applications (2022) 11:95 Panwar et al. Journal of Cloud Computing https://doi.org/10.1186/s13677-022-00368-5 Open Access REVIEW A systematic review on effective energy utilization management strategies in cloud data centers Suraj Singh Panwar*, M. M. S. Rauthan and Varun Barthwal Abstract Data centers are becoming considerably more significant and energy-intensive due to the exponential growth of cloud computing. Cloud computing allows people to access computer resources on demand. It provides amenities on the pay-as-you-go basis across the data center locations spread over the world. Consequently, cloud data centers consume a lot of electricity and leave a proportional carbon impact on the environment. There is a need to investigate efficient energy-saving approaches to reduce the massive energy usage in cloud servers. This review paper focuses on identifying the research done in the field of energy consumption (EC) using different techniques of machine learning, heuristics, metaheuristics, and statistical methods. Host CPU utilization prediction, underload/overload detection, virtual machine selection, migration, and placement have been performed to manage the resources and achieve efficient energy utilization. In this review, energy savings achieved by different techniques are compared. Many researchers have tried various methods to reduce energy usage and service level agreement violations (SLAV) in cloud data centers. By using the heuristic approach, researchers have saved 5.4% to 90% of energy with their proposed methods compared with the existing methods. Similarly, the metaheuristic approaches reduce energy consumption from 7.68% to 97%, the machine learning methods from 1.6% to 88.5%, and the statistical methods from 5.4% to 84% when compared to the benchmark approaches for a variety of settings and parameters. So, making energy use more efficient could cut down the air pollution, greenhouse gas (GHG) emissions, and even the amount of water needed to make power. The overall outcome of this review work is to understand different methods used by researchers to save energy in cloud data centers. Keywords: Cloud computing, Resources, Data center, Virtual machine, Energy consumption, SLAV Introduction Cloud Computing has become a flexible, resourceful, efficient, and prevalent computational technology that offers users reliable, customized, and dynamic computing environments. Cloud applications are hosted on high-capacity systems and storage devices in multiple locations around the world. Rapid demand for cloud-based facilities essentially requires the development of massive data centers that consume excessive amounts of electricity. *Correspondence: Hemvati Nandan Bahuguna Garhwal University, Srinagar Garhwal, Uttarakhand, India Optimization of energy can be proficient by uniting resources based on current utilization, well-organized network, and the thermal position of nodes and computing equipment. Because maximizing the utilization of physical servers is essential in lowering a data center’s (DC) energy demand, virtual machines (VMs) have been effectively introduced in DCs to increase server resource utilization. A method for cost-effective VM migration based on fluctuating electricity prices cuts the energy costs of running a cloud service by a large amount. Cloud computing is an extension of parallel computing, utility computing, cluster computing, and grid computing. It is distributed in nature, so a group of independent © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Panwar et al. Journal of Cloud Computing (2022) 11:95 resources are spread in remote locations. Cloud computing is defined by NIST as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., storage, networks, servers, services, and applications) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [1, 2]. The service models of cloud computing are Software as a Service (SaaS), Platform as a Platform (PaaS), and Infrastructure as a Service (IaaS). In SaaS, the client has access to cloud services via a web browser to maintain user interaction and data in the cloud. PaaS is a service that allows customers to use the platform and tools instead of purchasing and paying for software licences for platforms such as operating systems, databases, and intermediary applications. IaaS means the necessary environment to facilitate cloud services. It contains the pool of hardware resources related to computing, storage, networking, etc. Based on the model of deployment, clouds are categorized into four types. The term “public cloud” refers to an infrastructure that allows the general public to store and access data from any location using a client device with an internet connection. Private Cloud: A private cloud or enterprise cloud is one where the facilities and infrastructure are available for the organization or partner’s use only. A Hybrid Cloud: When a private cloud is combined with public cloud computing. Community Cloud: Resources are shared by multiple organizations that serve a particular community with common concerns [3, 4]. Today, research community’s top priorities are energy conservation and effectiveness. The issue of excessive energy utilization arises as a result of unexpected and rapid changes in the environment around the globe [5]. The levels of carbon footprint and Green House Gases (GHG) in the environment have rapidly increased. The information and communication technology (ICT) industry has been identified as the primary emitter [6]. The rise of sophisticated and diverse data-intensive services and applications has exacerbated energy challenges. The intensity and constant growth of ICT energy demand have necessitated not only meeting energy requirements but also developing and implementing efficient energy-savings methods. According to a 2016 survey, the total global energy consumption and C O2 emissions are expected to rise by 48% and 34%, respectively, between 2010 and 2040 [7]. Als (...truncated)