Efficient task scheduling strategies using symbiotic organisms search algorithm for cloud computing environment

In recent times, the cloud computing model is gaining tremendous migration of users in both private and government organisations. Users are charged based on their resources usage as well as Quality of Service (QoS) desired due to its pay-as-you-go feature. As such, task scheduling approaches play a vital role in specifying and ensuring an adequate set of resources to execute the users’ applications (i.e., tasks). Hence, schedule decisions using task scheduling approaches are according to the users’ outlined QoS requirements. Task scheduling in the cloud environment is an NP-Complete problem, because of its dynamism and huge search space given by problem instances that are large scale. Although many approaches (heuristics and metaheuristics) were proposed, they incur high computational complexity as well as not that efficient in terms of obtaining global optimum solutions. Recently, a nature-inspired metaheuristic known as Symbiotic Organisms Search (SOS) optimisation algorithm was proposed. It imitates the survival relationships (mutualism, commensalism and parasitism) between organisms in an ecosystem. The SOS and its variants Discrete Symbiotic Organisms Search (DSOS) algorithm have been used to solve different optimisation problems including tasks scheduling in cloud computing environment where results obtained are promising in comparison with stateof- the-art metaheuristic algorithms. However, the efficiency of the clouds drops as the size of the search space gets larger, like in the case of most metaheuristic optimisation algorithms. Furthermore, existing tasks scheduling solutions suffer from local optima entrapment due to inadequate diversification of their local search space, a high degree of imbalance because of their static control parameters cannot maintain a balance between local and global search space and scalability issue as a result of static benefit factors. In this study, an enhanced Discrete Symbiotic Organisms Search (eDSOS), a Cuckoobased Symbiotic Organisms Search (CDSOS), and an Adaptive Symbiotic Organisms Search (ADSOS) approaches are proposed to address the issues of local optima entrapment, load balancing as well as scalability due to large scale task scheduling optimisation problem in IaaS cloud computing environment. To solve the issue of local optima entrapment, the concept of diversifying the local search space by enhancing SOS named eDSOS approach to avoid entrapment in local optima for global convergence. Then CDSOS approach further improves the SOS algorithm by hybridising it with Cuckoo search’s levy flight to minimise the degree of imbalance between the local search space and the global search thereby improving the tasks to VM mapping. Finally, to solve the issue of scalability ADSOS approach adaptively turn SOS benefit factors to make SOS more efficient for solving large scale task scheduling problems as well as faster convergence speed. To assess the effectiveness of the proposed approaches (eDSOS, CDSOS, and ADSOS) CloudSim simulator was used, using synthesised workloads (normal, left-half, right-half and uniform distributions). Moreover, a comparison of the proposed approaches was done with DSOS, SASOS and OTB-CSO, respectively. The proposed approaches obtained considerable improvement in terms of the following metrics: makespan, response time, degree of imbalance, execution cost and execution time while meeting the desired QoS requirements. Furthermore, the simulation results showed that the eDSOS task scheduling approach outperformed the benchmark algorithm, produced better makespan time performance of 15.93%, 16.22%, 19.69% and 14.54% whereas the benchmark algorithm produced 18.04%, 19.64%, 16.08% and 14.72%, when implemented on the same dataset. Also, the results of the simulations on normal, left-half, right-half and uniform datasets showed the proposed scheduling approach obtained a better performance on degree of imbalance over the benchmarked OTB-CSO algorithm. Moreover, the results of the simulations also showed the ADSOS produced Performance Improvement Rate (PIR%) of 39.45%, 35.08% and 23.91%, 21.36% compared to the benchmarked algorithm in term of execution cost and execution time respectively. Hence, these gives a superior middle way between the execution of cost and time which makes it be dependable for its implementation in a real cloud computing environment. Consequently, the approaches proposed have abilities to better the QoS delivery. The research made some recommendations such as to implement the proposed SOS-based task scheduling approaches using NASA Ames iPSC/860 and HPC2N workloads as well a real cloud environment to validate their performances.

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