## 1 Introduction  
The evolution of cloud computing paves the way for realizing the long-held dream of the deliverance of computing resources as a utility to users. The applications, development environments and infrastructure services are delivered to the users through large and small datacenters [1]. However, the datacenters persistently suffer from their ever-increasing energy consumption and CO\({}_{2}\) emissions. Besides this, the money spent on energy consumption by a datacenter plays a crucial role in deciding the cloud provider's service cost [2]. According to the natural resources defense council (NRDC) report [3], U.S. datacenters' energy consumption is expected to reach 140 billion kilowatt-hours of electricity in the current year of 2020, which is equivalent to 50 large coal-based power plants emitting nearly 150 million tons of carbon dioxide (CO\({}_{2}\)). Further, the report emphasizes that the best energy efficiency practices and policies could reduce electricity use by 40%. Due to this, energy consumption reduction has become a crucial design parameter in modern datacenters [4]. Interestingly, the primary source of energy consumption in a datacenter is its enterprise servers, which consume more than 60% of the overall energy [5]. At the granular level, the power consumption of enterprise servers can be categorized into static and dynamic: static power consumption is due to powered-on servers with no circuit activity, whereas dynamic power consumption is due to the switching of transistors during workload execution. On average, 30% of the existing servers are comatose, meaning that they are idle and contributing to static power consumption [6]. Further, more than 80% of the information technology (IT) budget is spent to keep them running as an essential measure to ensure availability, which is an overestimated resource requirement. To meet the service level agreement, it is essential to keep all servers running. An idle server consumes approximately two-thirds energy of its 100% utilization at full load [7; 8; 9]. In addition, it is noteworthy that idle power and dynamic power consumption on a utilization level varies based on the different power models of physical servers. The energy reduction caused by shrinking the number of existing resources by virtual machine (VM)-consolidation may spin-off lower-level resource availability resulting in jeopardizing the goodwill and credibility of the provider. Utilization of the server at a high voltage results in a high temperatures and shorter lifetime. Optimal server utilization provides an optimized power consumption of central processing unit (CPU) resources resulting in the increased lifetime of the resource with a power consumption that is limited to its specification. The computation capacities of the servers have to be realized and optimum utilization of resources should be achieved [10] to reduce idle and active servers' power consumption. By considering the above-mentioned advantage, in this research work an adaptive harmony search algorithm (AHSA) and power award (PA) algorithm is proposed to achieve minimum power consumption of the servers.  
The rest of the paper is organized as follows: Firstly, general facts about the datacenter power consumption are outlined in Section 1. Secondly, several closely associated research works are given in Section 2. Following that, the research problem formulation is given in Section 3. Subsequently, Section 4 elaborates on the algorithms that are considered in this research work. Afterwards, the virtual machine distribution algorithm is explained in Section 5. Then, the experimental set-up for evaluation, results and discussions about the significance of load balancing are discussed in Section 6. Finally, Section 7 concludes the findings of this research work.