Load-Balancing Strategy: Employing a Capsule Algorithm for Cutting Down Energy Consumption in Cloud Data Centers for Next Generation Wireless Systems

Table 1

Study of energy efficiency methods and algorithms.

Energy efficiency algorithm	Approaches	Weakness	Tools used
Novel resource allocation algorithm for energy efficiency [24]	Autoregressive linear prediction	Energy consumption is not efficient	Power edge
Energy-based scheduling and accounting of VM [25]	Supervised learning methods	High in energy consumption	Simulator (self-designed)
Energy-efficient VM allocation technique using interior search algorithm [26]	Self-adaptive differential evolution algorithm	Maximum the power consumed by data centers	Open-source cloud middleware Eucalyptus
Exact allocation and migration algorithm [27]	Adaptive selector neural network	Power consumed by data centers has not reduced and low reduction in the rate of task rejection	Cloud hypervisor xen
Energy-saving VM migration [28]	Linear regression	No support to the heterogeneous environment and unstable QoS	Scheduler is implemented
Energy-aware resource allocation algorithm [29]	Neural network	SLA nonviolation, no control wastage, and given no scalability	Cloud sim
Energy-efficient dynamic resource management [30]	Gradient boosting tree	Reduction in control utilization with the nonviolation of SLA	Cloud sim