MAHA: Migration-based Adaptive Heuristic Algorithm for Large-scale Network Simulations

Ibrahim, Muhammad; Iqbal, Muhammad Azhar; Aleem, Muhammad; Islam, Muhammad Arshad; Vo, Nguyen-Son

doi:10.1007/s10586-019-02991-5

Cited by 5 publications

(6 citation statements)

References 26 publications

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“…The Cloud services are provided on a pay-as-go basis [ 4 ] to the users. The Cloud service model consists of Cloud service provider [ 5 , 6 , 7 ], Cloud user, and datacenter. A Cloud service provider acquires Cloud resources and provides these resources to the Cloud users according to their requirements.…”

Section: Introductionmentioning

confidence: 99%

AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing

Nabi

Ahmad

Ibrahim

et al. 2022

Sensors

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Cloud computing has emerged as the most favorable computing platform for researchers and industry. The load balanced task scheduling has emerged as an important and challenging research problem in the Cloud computing. Swarm intelligence-based meta-heuristic algorithms are considered more suitable for Cloud scheduling and load balancing. The optimization procedure of swarm intelligence-based meta-heuristics consists of two major components that are the local and global search. These algorithms find the best position through the local and global search. To achieve an optimized mapping strategy for tasks to the resources, a balance between local and global search plays an effective role. The inertia weight is an important control attribute to effectively adjust the local and global search process. There are many inertia weight strategies; however, the existing approaches still require fine-tuning to achieve optimum scheduling. The selection of a suitable inertia weight strategy is also an important factor. This paper contributed an adaptive Particle Swarm Optimisation (PSO) based task scheduling approach that reduces the task execution time, and increases throughput and Average Resource Utilization Ratio (ARUR). Moreover, an adaptive inertia weight strategy namely Linearly Descending and Adaptive Inertia Weight (LDAIW) is introduced. The proposed scheduling approach provides a better balance between local and global search leading to an optimized task scheduling. The performance of the proposed approach has been evaluated and compared against five renown PSO based inertia weight strategies concerning makespan and throughput. The experiments are then extended and compared the proposed approach against the other four renowned meta-heuristic scheduling approaches. Analysis of the simulated experimentation reveals that the proposed approach attained up to 10%, 12% and 60% improvement for makespan, throughput and ARUR respectively.

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Section: Introductionmentioning

confidence: 99%

AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing

Nabi

Ahmad

Ibrahim

et al. 2022

Sensors

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“…The fundamental challenges in end-user applications incorporate their availability, capability requirements, functionality, performance and quality of service (QoS). The Infrastructure as a Service delivery model of cloud computing includes hardware (server, network and storage) that delivers a large amount of computational power to use for remote users efficiently (Sang et al , 2008; Ibrahim et al , 2020; Iturriaga et al , 2017; Zafar et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, service availability is when a piece of equipment or system is committed, operational and available at a given time and frequency, as the system requires high availability. The cloud services are used in arts, entertainment, business, education, data storage, management, archiving services, social media and so on (Sang et al , 2008; Ibrahim et al , 2020; Iturriaga et al , 2017; Zafar et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

A load-balanced hybrid heuristic for allocation of batch of tasks in cloud computing environment

Shiekh

Shahid

Sambare

et al. 2022

IJPCC

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Purpose Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment. Design/methodology/approach In this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results. Findings The acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study. Originality/value The outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.

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“…Cloud computing [1], [2], [3], [4] has become an attractive and popular platform for individuals and organizations that provides the solution for execution and storage of large applications. Cloud computing work on a pay-as-you-use basis which satisfying the users' task scheduling requests in a scalable way [15] with the lowest cost than maintaining their computing infrastructure for their ad-hoc computing needs [5].…”

Section: Introductionmentioning

confidence: 99%

DRALBA: Dynamic and Resource Aware Load Balanced Scheduling Approach for Cloud Computing

2021

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For the last few years, Cloud computing has been considered an attractive high-performance computing platform for individuals as well as organizations. The Cloud service providers (CSPs) are setting up data centers with high performance computing resources to accommodate the needs of Cloud users. The users are mainly interested in the response time, whereas the Cloud service providers are more concerned about the revenue generation. Concerning these requirements, the task scheduling for the users' applications in Cloud computing attained focus from the research community. Various task scheduling heuristics have been proposed that are available in the literature. However, the task scheduling problem is NP-hard in nature and thus finding optimal scheduling is always challenging. In this research, a resource-aware dynamic task scheduling approach is proposed and implemented. The simulation experiments have been performed on the Cloudsim simulation tool considering three renowned datasets, namely HCSP, GoCJ, and Synthetic workload. The obtained results of the proposed approach are then compared against RALBA, Dynamic MaxMin, DLBA, and PSSELB scheduling approach concerning average resource utilization (ARUR), Makespan, Throughput, and average response time (ART). The DRALBA approach has revealed significant improvements in terms of attained ARUR, throughput, and Makespan.This fact is endorsed by the average resource utilization results (i.e., 98 % for HCSP dataset, 75 % for Synthetic workload (improve ARUR by 72.00 %, 77.33 %, 78.67 %, and 13.33 % as compared to RALBA, Dynamic MaxMin, DLBA and PSSELB respectively), and 77 % for GoCJ (i.e., the second best attained ARUR)). INDEX TERMS Cloud ComputingResource scheduling Dynamic Task scheduling scheduling heuristics tasks to vm mapping load imbalance

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MAHA: Migration-based Adaptive Heuristic Algorithm for Large-scale Network Simulations

Cited by 5 publications

References 26 publications

AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing

AdPSO: Adaptive PSO-Based Task Scheduling Approach for Cloud Computing

A load-balanced hybrid heuristic for allocation of batch of tasks in cloud computing environment

DRALBA: Dynamic and Resource Aware Load Balanced Scheduling Approach for Cloud Computing

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