GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud‐Whale Optimization in Cloud Computing

Abstract

An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users’ requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.

Energy-Aware Bag-of-Tasks Scheduling in the Cloud Computing System Using Hybrid Oppositional Differential Evolution-Enabled Whale Optimization Algorithm

“…A number of WOA-based scheduling solutions have already been suggested which are inspired by the hunting strategy of humpback whales, for scheduling BoT applications to obtain near-optimal results. These include solutions using standard, modified, and hybrid WOA approaches [8,22,23]. A recent cloud scheduling solution named GCWOAS2 [22] combines a Gaussian model, standard WOA, and OBL methods to generate efficient task-resource pairs.…”

“…These include solutions using standard, modified, and hybrid WOA approaches [8,22,23]. A recent cloud scheduling solution named GCWOAS2 [22] combines a Gaussian model, standard WOA, and OBL methods to generate efficient task-resource pairs. In another recent research work [8], a hybrid metaheuristic solution called OWPSO is proposed to remove the WOA deficiencies by combining OBL and PSO algorithms with the original WOA.…”

A Heuristic Projection Pursuit Method Based on a Connection Cloud Model and Set Pair Analysis for Evaluation of Slope Stability

“…Namely, the digital characteristic parameters of CCM, Ex, and En are adopted to express the randomness and fuzziness of the optimal food source location and search range in the smell search stage, and the correlation between random and fuzzy characteristics and smell concentration parameters of the individual search. At the same time, based on the rule of identitydiscrepancy-contrary (IDC) of set pair analysis [16,28], the candidate mechanism of the best projection direction is enhanced by the greedy strategy, and an adaptive entropy accelerates the local convergence speed with the specific number of iterations. Then next, the mutation search radius near the location of the primary optimal drosophila is produced by the chaos theory to increase the diversity and ambiguity of individuals to prevent the algorithm from falling into the local optimum and ensure the obtaining of the solution of the optimal projection direction.…”

“…Some evolutionary algorithms and intelligent swarm methods were introduced to handle this problem and have achieved specific results, but they rarely consider the multiple uncertainties of evaluation indicators. The main techniques include the differential evolution method [10], particle swarm optimization method [11], shuffled frog leaping algorithm [12], moth-flame optimization method [13], grey wolf optimization method [14], real coding based accelerating genetic algorithm (RAGA) [15] and a whale optimization strategy based on the Gaussian cloud model [16]. Nevertheless, the fruit fly optimization algorithm (FOA) [17] raised recently provides a new way to find the best PDV for the PP evaluation method, since it can conduct global information exchange and local deep search.…”

“…Although Shao and Xin [27] introduced the PP method coupled with the normal cloud model to evaluate the safety of the earth-rock dam, it does not apply a normal cloud model to optimize the PDV. Nevertheless, the normal cloud model is helpful for expressing fuzzy and random characteristics in the infinite interval [16]. It is difficult to accurately describe the fuzziness of the index values on the bounds and the interval-valued measured data.…”