An Implementation of Modified Blowfish Technique with Honey Bee Behavior Optimization for Load Balancing in Cloud System Environment

Table 1

A comparative analysis of various optimization techniques.

Algorithm	Key objective	Issue	Compare with
HBB-LB [42]	Optimize the load distribution across VMs to maximize the throughput.	Only applied to those virtual machines located in the same data center	HBB-LB, First-in-First-out (FIFO), dynamic load; Balancing (DLB), Weighted Round Robin (WRR)
-Medoid Clustering, Blowfish encryption, and Dragonfly Algorithm [43]	In addition to enhancing accuracy, Dragonfly’s algorithm is optimized		Blowfish, Rivest, Shamir, Adleman (RSA), and Advanced Encryption Standard (AES)
Heuristic-based algorithms [44]	Energy saving, throughput maximization, makespan minimization	Improve resource utilization, good performance	MCT, Min-Max, MET, Max-Min, and Min-Min load balancing technique
Enhanced ABC-based load balancing technique [30]	Minimizes makespan and reduces the number of VM migrations	Load balancing is made more efficient and effective by altering the bee colony algorithm for cloud environments	Bee colony algorithm
Load balancing-based hyperheuristic technique; [45]	To determine which low-level heuristics are necessary to search for improved candidate solutions, HBB-LB with the improvement detection operators are used	Analyze the performance of the proposed load balancing technique	The heuristic technique, including ACO, PSO, and GA
Spider Monkey Optimization [46]	Increasing performance by balancing workload among VMs	They are reducing the average response time and makespan on tasks	Round Robin and Throttled methods
Multiresource Load Balancing Algorithm (MrLBA) [47]	It aims to achieve a makespan, cost, and load-balanced system	Performance metrics, execution time, and costs are reduced, and existing resources are efficiently utilized	ACO, GA-PSO, and hybrid approach
Dynamic load balancing Max-Min and Max DLB3M [48]	Minimum execution time, efficient utilization of resources, and given optimum solution of load	Provide the better synchronization of jobs such that jobs never dead	LB3M (load balancing Max-Min and Max)
Binary Bird Swarm Optimization [32]	This algorithm reduces the response time of the system and maintains balance throughout it, thus increasing the overall performance	Resources are better utilized, and tasks are completed faster.	MAX-MIN, improved PSO, RASA, Come-First-Served (FCFS), SJF, and RR