## 2 Related Works
The attention and expansion of solar energy have increased due to a decrease in its installation cost. Despite its cost, there is a need to overcome the regulatory, technical barriers to achieving its growth [33]. The most efficient server first greedy task scheduling algorithm considers the energy profile of the servers and allocates the task to the servers with a minimum increment in overall power consumption. The energy reduction is achieved by two factors: (i) by reducing the number of active servers, (ii) by choosing an appropriate server for task placement [34]. The most urgent, CPU-intensive, Bag-of-Task scheduling is performed by introducing a dynamic voltage frequency scaling intelligence in the scheduling algorithm to achieve power reduction [35]. Energy reduction is achieved by executing the processors in minimum frequency level and the quality of service is maintained by executing the tasks within the deadline. The unbalanced resource usage, high energy usage and the number of migrations are handled by task categorization and a resource utilization square model [36]. The weight function is applied to each task based on its resource requirement for task categorization. The individual resource is ranked based on its utilization via the resource utilization square model and the resource famine is reduced by efficient VM consolidation by selecting energy-efficient target servers. The sufficient utilization of green energy, maintaining the required resource demand within the datacenter is achieved by combining the heuristics and statistical approaches. The optimization problem for cost reduction is formulated considering the server and cooling device's power consumption, aiming at overall net revenue.VM migration is based on the joint optimal planning on two parameters: maximum profit and maximum green energy utilization [37]. Scheduling of workflow considering data file assignment reduces the execution time of the task with reduced overall makespan. The distribution of the tasks and data are considered as a single dependent problem, rather than two independent problems. A hybrid evolutionary algorithm-based task scheduling and data assignment approach outperforms the standard approaches, Min-Min and heterogeneous earliest finish time [38]. VM placement and data placement are considered as a single problem to reduce cross-network traffic and bandwidth usage. Ant colony optimization was used for the selection of physical machine's (PMs) for the placement of data adjacent to VMs. Data, placed in close proximity to the VM, with the decrease in VM communication distance, resulted in a reduction in job completion time [39]. A queuing structure to handle a large set of VMs and crow-search-based multi-objective optimization was used to reduce resource wastage and power consumption in datacenters [40]. The results were compared against the genetic algorithm and the first fit decreasing approach. The live migration was handled in serial, parallel and improved serial methods. A hybrid krill herd optimization algorithm with an eagle strategy was used for VM expansion during heavy load to maintain the quality of service agreement. The problem of eccentrics and congestion was handled by a new proposed change and response and agreement protocol by monitoring the packet delay, latency and throughput. The experimental results of the hybrid krill herd optimization approach were compared against particle swarm optimization, ant colony optimization, genetic algorithm and simulated annealing [41]. In this article, AHSA, a population-based method suitable for solving continuous and discrete optimization problems, is proposed to reduce the power consumption of the servers. AHSA holds its strength in obtaining a new solution by considering both the probabilistic parameters of all the individual values of the initial harmony and independent variable tuning (stochastic operators). The additional credit is that the rate of convergence and quality of the solution does not depend on initial values, such as a gradient-search.