Performance evaluation of task scheduling algorithms in virtual cloud environment to minimize makespan

Kaur, Rajbhupinder; Laxmi, Vijay; Balkrishan,

doi:10.1007/s41870-021-00753-4

Cited by 22 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because more VMs are completing a given number of tasks, the Makespan gets shorter as the number of virtual machines increases. [36] The outcomes of the CyberShake workflow for the jobs (40, 60, 110, and 1500) are shown in Figure 5. The X-axis displays the number of virtual machines, and the Y-axis displays the Makespan in seconds.…”

Section: Cybershake Workflowmentioning

confidence: 99%

An Efficient Task Scheduling for Cloud Computing Platforms Using Energy Management Algorithm: A Comparative Analysis of Workflow Execution Time

Ahmed,

Adnan,

Abdullah

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Cloud computing platform offers numerous applications and resources such as data storage, databases, and network building. However, efficient task scheduling is crucial for maximizing the overall execution time. In this study, workflows are used as datasets to compare scheduling algorithms, including Shortest Job First, First Come, First Served, (DVFS) and Energy Management Algorithms (EMA). To facilitate comparison, the number of virtual machines in the Visual Studio Net framework environment is increased. The experimental findings indicate that increasing the number of virtual machines reduces Makespan. Moreover, the Energy Management Algorithm (EMA) outperforms Shortest Job First by 2.79% for the CyberShake process and surpasses the First Come, First Serve algorithm by 12.28%. Additionally, EMA produces 21.88% better results than both algorithms combined. For the Montage process, EMA performs 4.50% better than Shortest Job First and 25.75% superior to the First Come, First Serve policy. Finally, we ran simulations to determine the performance of the suggested mechanism and contrasted it with the widely used energy-efficient techniques. The simulation results demonstrate that the suggested structural design may successfully reduce the amount of data and give suitable scheduling to the cloud. INDEX TERMS Energy Management Algorithm (EMA); First come First Serve (DVFS); Shortest Job First (RR); Makespan; VMs I. INTRODUCTION

show abstract

Section: Cybershake Workflowmentioning

confidence: 99%

An Efficient Task Scheduling for Cloud Computing Platforms Using Energy Management Algorithm: A Comparative Analysis of Workflow Execution Time

Ahmed,

Adnan,

Abdullah

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…On the one hand, some metrics such as makespan and waiting time are user metrics. On the other hand, metrics such as the cost of the provider and energy consumption are the metrics of the provider [12,13]. The popularity of cloud computing is growing day by day, so with an increasing demand for high-performance computing resources, energy consumption in the cloud data center is greatly increased [14].…”

Section: Task Schedulingmentioning

confidence: 99%

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

2022

IJE

View full text Add to dashboard Cite

Cloud computing provides computing resources like software and hardware as a service by the network for several users. Task scheduling is one of the main problems to attain cost-effective execution. The main purpose of task scheduling is to allocate tasks to resources so that it can optimize one or more criteria. Since the problem of task scheduling is one of the Nondeterministic Polynomial-time (NP)-hard problems, meta-heuristic algorithms have been widely employed for solving task scheduling problems. One of the new bio-inspired meta-algorithms is Seagull Optimization Algorithm (SOA). In this paper, we proposed an energy-aware and cost-efficient SOA-based T ask Scheduling (SOAT S) algorithm. The aims of proposed algorithm to make a trade-off between five objectives (i.e., energy consumption, makespan, cost, waiting time, and load balancing) using a fewer number of iterations. The experiment results by comparing with several meta-heuristic algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA)) prove that the proposed technique performs better in solving task scheduling problem. Moreover, we compared the proposed algorithm with well-known scheduling methods: Cost -based Job Scheduling (CJS), Moth Search Algorithm based Differential Evolution (MSDE), and Fuzzy -GA (FUGE). In the heavily loaded environment, the SOAT S algorithm improved energy consumption and cost saving by 10 and 25%, respectively.

show abstract

“…Task scheduling is a process of ordering the user tasks for efficient utilization of cloud resources in a fashion that leverages the overall performance [4]. Eventually, user applications in the cloud environment are submitted over the internet media online and these applications divided as tasks with various uncertain characteristics that outworths researchers to get deep into the challenges posed by dynamically changing task behaviour [5]. The uncertain nature of tasks is hindering the service provider to complete the tasks at a given time resulting in performance downfall.…”

Section: Introductionmentioning

confidence: 99%

Task Classification and Scheduling Using Enhanced Coot Optimization in Cloud Computing

2023

IJIES

View full text Add to dashboard Cite

Cloud computing benchmarks the dream of rendering computing as a utility, providing high agility and reachability from an existing set of technologies. It facilitates a wider dimension to architect and manage remote resources. Cloud technology with exponential growth is drilling towards issues that tend to lower its explored possibilities. As cloud systems by virtue deal with various virtualized resources, scheduling is opted as an important metric for measuring and leveraging performance. But scheduling efficiency is deteriorated by various parameters that pave scope for our research and projects immense need for improvising the overall makespan of the system. The proposed work aims at projecting a greater drift in the first phase by witnessing a sequence of phases like preprocessing the user tasks for improved accuracy, classifying the tasks with respect to resource demand and execution time using the improved density based clustering method (IDCM). The second phase deals with enhanced coot optimization algorithm for task scheduling (ECOA-TS) that proceeds and proves its novelty by adopting Cauchy mutation overcoming the convergence backdrop for generating an optimal mapping between clustered user tasks and VMs. The overall performance of the proposed work overrides by reduced makespan against existing state-of-the-art optimization algorithms like particle swam optimization (PSO), grey wolf optimization (GWO) and whale optimization algorithm (WOA) by 27.41%, 19.8%, and 15.33% respectively.

show abstract

Performance evaluation of task scheduling algorithms in virtual cloud environment to minimize makespan

Cited by 22 publications

References 17 publications

An Efficient Task Scheduling for Cloud Computing Platforms Using Energy Management Algorithm: A Comparative Analysis of Workflow Execution Time

An Efficient Task Scheduling for Cloud Computing Platforms Using Energy Management Algorithm: A Comparative Analysis of Workflow Execution Time

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

Task Classification and Scheduling Using Enhanced Coot Optimization in Cloud Computing

Contact Info

Product

Resources

About