Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks. Index Terms-Cloud computing; task scheduling; whale optimization algorithm; metaheuristics; multi-objective optimization I. INTRODUCTION W Ith the ubiquitous growth of Internet access and big data, cloud computing becomes more and more popular in today's business world [1]. Compared to other distributed computing techniques (e.g., cluster and grid computing), cloud computing has provided an elastic and scalable way on delivering services to consumers. Namely, consumers do not need to possess the underlying technology and they can make use of computing resources and platforms in a pay-per-use fashion [2], [3]. The basic mechanism of cloud computing is to dispatch computing tasks to a resource pooling constituting of a

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