Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling

Abstract

Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.

Cost effective hybrid genetic algorithm for workflow scheduling in cloud

“…In research [10], authors focused on balancing the load among virtual machines to increase performance. To achieve this, a greedy seeding strategy was applied with the genetic algorithm, but there was no efficient heuristic approach to reduce the makespan and cost.…”

“…Step 2. During the population initialization, we employed a greedy technique [10] to balance the load among several virtual machines, as illustrated through flowchart in this paper. This strategy assigns the new task to those VMi which have minimum load at that time.…”

“…That's why we used them as baseline algorithms as they contributed to solving the same problem addressed here. Except these we used CEGA [20] and CLGA [10] as baseline algorithms.…”

“…Due to its task-independent character, the meta-heuristic method delivers a global optimal solution. The researcher's ultimate objective is to maximize cloud resource usage while lowering costs for cloud's end users [9], [10].…”

“…This represents the novelty of our model. To keep the load balanced among the virtual machines, we employed a greedy method [10] in our proposed model "cost-effective hybrid genetic algorithm" (CHGA).…”