GSAGA: A hybrid algorithm for task scheduling in cloud infrastructure

Pirozmand, Poria; Javadpour, Amir; Nazarian, Hamideh; Pinto, Pedro; Mirkamali, Seyed Saeid; Ja’fari, Forough

doi:10.1007/s11227-022-04539-8

Cited by 27 publications

(11 citation statements)

References 40 publications

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“…It was compared against several baseline approaches and it was finally identified that it outperforms the baseline approaches while minimizing the makespan and energy consumption. For achieving a high performance in the cloud paradigm, the authors in [ 20 ] designed a task scheduling mechanism which is a hybridized approach in which the GSA is used as a local search and the GA is used as the global search. It was implemented on Cloudsim and the workload captured in this approach is a real time workflow benchmark dataset of a heterogenous nature.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

Mangalampalli

Karri

Elngar

2023

Sensors

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Task scheduling in the cloud computing paradigm poses a challenge for researchers as the workloads that come onto cloud platforms are dynamic and heterogeneous. Therefore, scheduling these heterogeneous tasks to the appropriate virtual resources is a huge challenge. The inappropriate assignment of tasks to virtual resources leads to the degradation of the quality of services and thereby leads to a violation of the SLA metrics, ultimately leading to the degradation of trust in the cloud provider by the cloud user. Therefore, to preserve trust in the cloud provider and to improve the scheduling process in the cloud paradigm, we propose an efficient task scheduling algorithm that considers the priorities of tasks as well as virtual machines, thereby scheduling tasks accurately to appropriate VMs. This scheduling algorithm is modeled using firefly optimization. The workload for this approach is considered by using fabricated datasets with different distributions and the real-time worklogs of HPC2N and NASA were considered. This algorithm was implemented by using a Cloudsim simulation environment and, finally, our proposed approach is compared over the baseline approaches of ACO, PSO, and the GA. The simulation results revealed that our proposed approach has shown a significant impact over the baseline approaches by minimizing the makespan, availability, success rate, and turnaround efficiency.

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Section: Related Workmentioning

confidence: 99%

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

Mangalampalli

Karri

Elngar

2023

Sensors

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“…The model was compared with baseline algorithms; hybrid lion–GA improves load balancing compared to GA and lion optimization approaches. In [ 20 ], the authors proposed a task scheduler to improve the efficiency of the task-scheduling process. The model was constructed by the GSAGA algorithm where GSA was used in local search exploration and GA was used in global search exploration.…”

Section: Related Workmentioning

confidence: 99%

“…Datacenter processing time is one of the crucial aspects in a cloud model as it impacts QoS and operational costs of a cloud provider. In [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ], the authors again discussed load balancing, resource utilization, makespan, and cost, but the references mentioned previously, which tackle makespan and cost, were based on single-nature-inspired approaches, whereas the approaches mentioned in [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ] are all hybridized approaches.…”

Section: Related Workmentioning

confidence: 99%

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing

Mangalampalli,

Karri,

Gupta

et al. 2023

Sensors

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Cloud computing is a distributed computing model which renders services for cloud users around the world. These services need to be rendered to customers with high availability and fault tolerance, but there are still chances of having single-point failures in the cloud paradigm, and one challenge to cloud providers is effectively scheduling tasks to avoid failures and acquire the trust of their cloud services by users. This research proposes a fault-tolerant trust-based task scheduling algorithm in which we carefully schedule tasks within precise virtual machines by calculating priorities for tasks and VMs. Harris hawks optimization was used as a methodology to design our scheduler. We used Cloudsim as a simulating tool for our entire experiment. For the entire simulation, we used synthetic fabricated data with different distributions and real-time supercomputer worklogs. Finally, we evaluated the proposed approach (FTTATS) with state-of-the-art approaches, i.e., ACO, PSO, and GA. From the simulation results, our proposed FTTATS greatly minimizes the makespan for ACO, PSO and GA algorithms by 24.3%, 33.31%, and 29.03%, respectively. The rate of failures for ACO, PSO, and GA were minimized by 65.31%, 65.4%, and 60.44%, respectively. Trust-based SLA parameters improved, i.e., availability improved for ACO, PSO, and GA by 33.38%, 35.71%, and 28.24%, respectively. The success rate improved for ACO, PSO, and GA by 52.69%, 39.41%, and 38.45%, respectively. Turnaround efficiency was minimized for ACO, PSO, and GA by 51.8%, 47.2%, and 33.6%, respectively.

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“…Proper task scheduling will establish energy-efficient and, finally, green computing in the cloud network. In the task scheduling problem, we are looking for a network that establishes load balancing and handles tasks well so that we can have low makespan, overhead, response time, and execution time [2,9,10]. It is also very important to handle high-performance computing data, for example, for cloud service providers such as Google and Amazon or DCs such as websites where different users from all over the world send tasks to the virtual machines (VMs) on the websites.…”

Section: Introductionmentioning

confidence: 99%

Efficient task scheduling in cloud networks using ANN for green computing

Zavieh,

Javadpour,

Sangaiah

2024

Int J Communication

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SummaryRecently, there has been a growing emphasis on reducingenergy consumption in cloud networks and achieving green computing practices toaddress environmental concerns and optimize resource utilization. In thiscontext, efficient task scheduling minimizes energy usage and enhances overallsystem performance. To tackle the challenge ofenergy‐efficient task allocation, we propose a novel approach that harnessesthe power of Artificial Neural Networks (ANN). Our Artificial neural network Dynamic Balancing (ANNDB) method is designed toachieve green computing in cloud environments. ANNDB leverages the feed‐forwardnetwork architecture and a multi‐layer perceptron, effectively allocatingrequests to higher‐power and higher‐quality virtual machines, resulting inoptimized energy utilization. Through extensive simulations, wedemonstrate the superiority of ANNDB over existing methods, including WPEG,IRMBBC, and BEMEC, in terms of energy and power efficiency. Specifically, ourproposed ANNDB method exhibits substantial improvements of 13.81%, 8.62%, and9.74% in the Energy criterion compared to WPEG, IRMBBC, and BEMEC,respectively. Additionally, in the Power criterion, the method achievesperformance enhancements of 3.93%, 4.84%, and 4.19% over the mentioned methods.The findings from this research hold significant promise for organizations seekingto optimize their cloud computing environments while reducing energyconsumption and promoting sustainable computing practices. By adopting theANNDB approach for efficient task scheduling, businesses and institutions cancontribute to green computing efforts, reduce operational costs, and make moreenvironmentally friendly choices without compromising task allocationperformance.

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GSAGA: A hybrid algorithm for task scheduling in cloud infrastructure

Cited by 27 publications

References 40 publications

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

An Efficient Trust-Aware Task Scheduling Algorithm in Cloud Computing Using Firefly Optimization

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing

Efficient task scheduling in cloud networks using ANN for green computing

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