Abstract
In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.
Highlights
Scientific computing is a promising field of study that is usually associated with large-scale computer modeling and simulation and most often requires a massive amount of computing resources [1]
Analyzing and disseminating these datasets among researchers/scientists located over a wide geographic area requires high power of computing that goes beyond the capabilities of a single machine. erefore, given the evergrowing data produced by scientific applications and the complexities of the applications themselves, it becomes prohibitively slow to deploy and execute such applications on traditional computing paradigms
To cope with the complexities and ever-increasing computational demand of those large-scale scientific applications, the concept of cloud computing is introduced. It provides elastic and flexible resources of computing (e.g., CPU, storage, memory, and networks) which can be rapidly provisioned and released with minimal management effort or service provider interaction [2]. ese cloud services can be automatically as well as scaled up or down and delivered to the end customers based on a pay-per-use payment model. e major services offered by cloud providers can be classified as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS)
Summary
Scientific computing is a promising field of study that is usually associated with large-scale computer modeling and simulation and most often requires a massive amount of computing resources [1]. Some of the most popular metaheuristic techniques that have been introduced to solve the cloud job scheduling problem include genetic algorithm (GA) [8], particle swarm optimization (PSO) [9], ant colony optimization (ACO) [10], tabu search [11], BAT algorithm [12], simulated annealing (SA) algorithm [13], symbiotic organisms search (SOS) [14], and cuckoo search (CS) algorithm [15] Some of these algorithms have shown promising improvements in finding the global optimal solution for the job scheduling problem in the cloud, they are all suffering from premature convergence and difficulty to overcome local minima especially when faced with a large solution space [16].
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