A pair-based task scheduling algorithm for cloud computing environment

Panda, Sanjaya Kumar; Nanda, Shradha Surachita; Bhoi, Sourav Kumar

doi:10.1016/j.jksuci.2018.10.001

Cited by 41 publications

(15 citation statements)

References 29 publications

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“…Haizea is a well-known open-source resource lease manager developed in Python (11) . It is used to provision computing resources in the form of VMs.…”

Section: Methodsmentioning

confidence: 99%

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EBTASIC: An Entropy-Based TOPSIS Algorithm for Task Scheduling in IaaS Clouds

Shrivastava¹,

Shaikh²

2022

IJST

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Objectives:To propose an algorithm to balance resource utilization and revenue generation in the cloud environment. Methods: This study proposes the Entropy-Based TOPSIS algorithm for task scheduling in IaaS Clouds (EBTASIC) to balance resource utilization and revenue generation using the objective-based Entropy Weighting Method (EWM) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Findings/Novelty: Various performance evaluation factors are calculated using EBTASIC and compared with baseline algorithms First Come First Serve (FCFS) and Earliest Deadline First (EDF) algorithms over 12750 lease requests with hard deadlines. The actual response time of EBTASIC is 37.61 percent faster than FCFS and 47.95 percent faster than EDF. Total time spent on lease execution by EBTASIC is reduced by 3.43 percent when compared to FCFS and 3.99 percent when compared to EDF. Turnaround time of EBTASIC is lowered by 21.14 percent when compared to FCFS and 28.81 percent when compared to EDF. EBTASIC throughput is enhanced by 40.80 percent over FCFS and 54.3 percent over EDF. The average response time of EBTASIC is shortened by 9.38 percent compared to FCFS and 14.81 percent compared to EDF. Resource utilization of the proposed algorithm EBTASIC is enhanced by 25.54 percent over FCFS and 6.79 percent over the EDF algorithm.

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“…Haizea is a well-known open-source resource lease manager developed in Python (11) . It is used to provision computing resources in the form of VMs.…”

Section: Methodsmentioning

confidence: 99%

“…Various types of leasing policies are adapted by CSUs. Literature on leasing policies reveals that there are mainly three leasing policies namely Advance Reservation (AR), Best Effort (BE) and Immediate leasing policies (11,12) .…”

Section: Lease Requests Typesmentioning

confidence: 99%

EBTASIC: An Entropy-Based TOPSIS Algorithm for Task Scheduling in IaaS Clouds

Shrivastava¹,

Shaikh²

2022

IJST

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“…The task scheduling problem was overcome using the Dynamic Task Scheduling model named Dynamic Dispatch Queue and simulated annealing methodologies [16] in the cloud environment by cloud service providers to improve customers' satisfaction efficiently with particle swarm optimization. A pair-based task scheduling model for the cloud has been using the optimization algorithm [17] with an unequal number of clouds and tasks based on first-come, first-serve, and Hungarian algorithms. Solving NP-hard in a heterogeneous environment and quality of service to schedule tasks in the cloud environment through cloud service providers are made by a parallel multi-objective genetic model [18].…”

Section: Makespanmentioning

confidence: 99%

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

Rajakumari¹,

Kumar²,

Verma³

et al. 2022

Computer Systems Science and Engineering

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Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.

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“…The proposed algorithm considered an unequal number of tasks and clouds and pairs the tasks to make the scheduling decision. Nayak et al 18 have presented a scheduling mechanism for deadline sensitive task or lease. It is very impelling and challenging to allocate on‐demand resources within the deadline rather than rejecting the lease.…”

Section: Related Workmentioning

confidence: 99%

Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm

Ramathilagam¹,

Vijayalakshmi²

2021

Int J Communication

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Summary Workflow scheduling is the most focused research issue in the on‐demand clouds where the user satisfaction like cost and bandwidth is more difficult. Several research works have been conducted earlier towards performing reliable workflow scheduling with the aim of reducing cost or execution time. However, those works lack to produce better result by compromising any attributes for attaining the goal. The existing work lacks from the security where the tasks might get corrupted during execution. To resolve this problem, an Enhanced Artificial Fish Swarm Algorithm (EAFSA)‐based IaaS Cloud Partial Critical Path (IC‐PCP) Replication and Hyper Elliptic Curve Cryptography (EAFSAIPR with HECC) is proposed. The main goal is to perform better workflow scheduling which can complete the task execution before deadline given by the users. This is done by predicting the early start time and latest finish time using EAFSA algorithm, so that task replication can be made to meet the soft deadline constraint. The task authentication is done efficiently using HEEC algorithm, so that corruption from malicious users can be avoided. The task replication is done securely using the cryptographic algorithm. The proposed EAFSAIPR with HECC algorithm uses idle time of provisioned resources to replicate workflow tasks optimally. The proposed EAFSAIPR algorithm scheduler focused to ensure the lowest cost while serving a deadline set by the user. The experimental results show that the scheduler can find good schedules of deadlines being met and reduces the total execution time of applications as the budget available for replication increases.

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A pair-based task scheduling algorithm for cloud computing environment

Cited by 41 publications

References 29 publications

EBTASIC: An Entropy-Based TOPSIS Algorithm for Task Scheduling in IaaS Clouds

EBTASIC: An Entropy-Based TOPSIS Algorithm for Task Scheduling in IaaS Clouds

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

Workflow scheduling in cloud environment using a novel metaheuristic optimization algorithm

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