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Cloud computing is a collection of distributed computers that allow users to access resources and services over the Internet. The optimal use of resources for various applications depends on how tasks are scheduled in the cloud environment, which has a significant impact on the efficiency of cloud service providers. Task scheduling is a key process in the cloud computing environment, which aims to execute user requests on resources in an efficient manner, taking into account other features of the cloud environment. The main goal of task scheduling is minimizing the makespan time and maximizing throughput as well as minimizing the cost. Since task scheduling is considered as an NP-complete problem, the necessity of using nondeterministic and meta-heuristic algorithms to optimize task scheduling is evident at a logical time. The major part of this problem is to design an efficient intelligent searching pattern to schedule the tasks in available virtual machines. In this paper, we propose a meta-heuristic algorithm called hybrid chaotic grey wolf optimizer (HCGWO) to tackle the problem of task scheduling in various heterogeneous virtual machines. This paper focuses on minimizing overall makespan and cost by modeling the swarm intelligence. The proposed algorithm prevents the local convergence and explores the global intelligent searching in finding the best optimized virtual machine for the user task among the set of virtual machines. We have made the simulation and performance evaluation using Cloudsim toolkit and compared the results with other swarm intelligent based algorithms such as Genetic Algorithm, Particle Swarm Optimization, and Cuckoo Search. The evaluation results show that there is a major improvement in minimizing the makespan and cost.
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