SLA Aware Task-Scheduling Algorithm in Cloud Computing Using Whale Optimization Algorithm

Table 1

Summary of task-scheduling algorithms using various metaheuristic approaches.

References	Methodology	Objectives of resource scheduling algorithms modeled by different nature-inspiredalgorithms
[7]	Adaptive PSO	Makespan, throughput, resource utilization
[9]	MCFCM-PSO	Load balancing, makespan
[10]	PSO-RDAL	Makespan, response time, penalty cost, total execution cost
[11]	CR-PSO	Makespan, execution time, execution cost, energy consumption
[5]	GA-ACO	Response time, task completion time, throughput
[12]	PSO-ACO	Makespan, resource utilization, total computation cost
[13]	CCSA	Makespan, overall cost
[14]	MOTS-ACO	Makespan, turnaround time, power consumption
[15]	EDA-GA	Task completion time, load balance of tasks
[16]	EPETS	Energy consumption
[17]	MVO-GA	Task transfer time
[18]	GACCRATS	Makespan, customer satisfaction
[19]	MGGS	Makespan, response time, QoS
[20]	OCSA	Makespan, cost
[24]	CGA	Task completion time, total execution cost
[25]	IWC	Task scheduling time, scheduling cost
[26]	SLNO	Energy, power consumption, resource utilization
[27]	GCWOAS2	Task completion time, load balance of virtual resources
[28]	GAGELS	Makespan, resource utilization
[29]	DILS	Makespan, learning rate