MCTE: Minimizes Task Completion Time and Execution Cost to Optimize Scheduling Performance for Smart Grid Cloud

Zhang, Hualu; Shi, Jie; Deng, Boya; Jia, Gangyong; Han, Guangjie; Shu, Lei

doi:10.1109/access.2019.2942067

Cited by 25 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The execution cost, ‘ c ’ of each task, is the cost the resource node is subject to after all functions have been completed. The price for each node will vary depending on the runtime, and the cost estimation is calculated using Equation ().

c = \sum_{j = 1}^{n} {italicTime}_{j} * p_{j}

Resource node j must pay the resource fee p j per unit time (Zhang et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Optimizing time delay in cloud using enhanced multi‐hold inherited maximization algorithm to reduce cost and improve bandwidth

Kumar

Sriramya

2022

Expert Systems

View full text Add to dashboard Cite

Optimizing the time delay in the cloud is one of the essential parameters to be considered for providing an efficient environment for the user to work. Virtual machine (VM) allocation is performed on various heuristic and genetic algorithms (GAs) to schedule the VM to the users in lesser time. However, the scheduling mechanisms do not provide the best serving VM among different data centres. This paper proposes an Enhanced multi‐hold inherited maximization (Enhanced MHIM) algorithm to provide an optimal allocation of VMs across multiple geographically distributed data centres. Based on load information shared between the various data centres, the algorithm computes the best performing machine among the data centres and shares the template for allocating the new device to the user. As a result, the workload is completed quicker by assigning to each user optimally. Heuristic scheduling algorithms, including Min‐Min, Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and GA, are compared with the proposed algorithm. Experimental results indicate that the proposed algorithm is efficient by maximum reduction of 12.63% of the makespan time, 13.06% of execution time, 3.89% of computational cost, and producing the 3× times best serving VM.

show abstract

c = \sum_{j = 1}^{n} {italicTime}_{j} * p_{j}

Resource node j must pay the resource fee p j per unit time (Zhang et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Optimizing time delay in cloud using enhanced multi‐hold inherited maximization algorithm to reduce cost and improve bandwidth

Kumar

Sriramya

2022

Expert Systems

View full text Add to dashboard Cite

show abstract

“…A task scheduling approach in the grid cloud to diminish execution time and cost was proposed by Zhang [23]. The strategy also uses computational models to solve the challenge of grid-cloud work scheduling.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Faceted Job Scheduling Optimization Using Q-learning With ABC In Cloud Environment

Sharma,

Kumar Pandey

2024

IJCDS

View full text Add to dashboard Cite

Resource allocation is the utmost challenging and common problem, particularly in the cloud service model in Infrastructure as a Service (IaaS). The issue of load balancing is so harmful that irregular load balancing may result in a structure smash. Adopting a suitable access plan and allowing the system to spread work among all existing resources leads to utilizing Virtual Machines (VMs) appropriately. To get enhanced results from the Artificial Bee Colony Algorithm (ABC), a reinforcement learning technique Q-learning is combined using multifaceted job scheduling optimization based on ABC(QMFOABC) has been proposed. The proposed approach improves resource utilization and scheduling created on resource, cost, and makespan. The efficiency of the suggested strategy was evaluated in Datasets Synthetic Workload, Google Cloud Jobs (GoCJ), and Random by using CloudSim to the remaining scheduling strategies for load matching like Max-min, Multifaceted Cuckoo Search (MFCS), Multifaceted Particle Swarm Optimization (MFPSO), Q-learning, Heuristic job scheduling with Artificial Bee Colony approach with Largest Job First algorithm (HABC LJF), First come first serve (FCFS). According to the findings of the experiments, the algorithm that employed the QMFOABC method has better results in resource utilization, throughput, cost, and makespan. Compared to Max-Min (82.31%), MOPSO (35.62%), HABC LJF (21.65%), Q-Learning (11.72%), VTO-QABC FCFS(5.87%), and VTO-ABC LJF (5.86%) shorter time than MOCS, a considerable improvement is found.

show abstract

“…Zhang et al [25] studied the application of heuristic algorithms in cloud scheduling problems. They proposed a method for scheduling tasks to minimize the task completion time and execution cost (MCTE) in a smart grid-cloud system.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning

2022

View full text Add to dashboard Cite

Workload balancing in cloud computing is still challenging problem, especially in Infrastructure as a Service (IaaS) in the cloud model. A problem that should not occur during cloud access is a host or server being overloaded or underloaded, which may affect the processing time or may result in a system crash. Therefore, to prevent these problems, an appropriate schedule of access should be considered so that the system can distribute tasks across all available resources, which is called load balancing. The load balancing technique should ensure that all Virtual Machines (VMs) are used appropriately. In this paper, an independent task scheduling approach in cloud computing is proposed using a Multi-objective task scheduling optimization based on the Artificial Bee Colony Algorithm (ABC) with a Q-learning algorithm, which is a reinforcement learning technique that helps the ABC algorithm work faster, called the MOABCQ method. The proposed method aims to optimize scheduling and resource utilization, maximize VM throughput, and create load balancing between VMs based on makespan, cost, and resource utilization, which are limitations of concurrent considerations. Performance analysis of the proposed method was compared using CloudSim with the existing load balancing and scheduling algorithms: Max-Min, FCFS, HABC_LJF, Q-learning, MOPSO, and MOCS algorithms in three datasets: Random, Google Cloud Jobs (GoCJ), and Synthetic workload. The experimental results indicated that the algorithms used MOABCQ approach outperformed the other algorithms in terms of reducing makespan, reducing cost, reducing degree of imbalance, increasing throughput and average resource utilization.INDEX TERMS Cloud computing, hybrid artificial bee colony algorithm, multi-objective task, Q-learning, task scheduling.

show abstract

MCTE: Minimizes Task Completion Time and Execution Cost to Optimize Scheduling Performance for Smart Grid Cloud

Cited by 25 publications

References 21 publications

Optimizing time delay in cloud using enhanced multi‐hold inherited maximization algorithm to reduce cost and improve bandwidth

Optimizing time delay in cloud using enhanced multi‐hold inherited maximization algorithm to reduce cost and improve bandwidth

Multi-Faceted Job Scheduling Optimization Using Q-learning With ABC In Cloud Environment

Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning

Contact Info

Product

Resources

About