Reliability aware scheduling of bag of real time tasks in cloud environment

Swain, Chinmaya Kumar; Saini, Neha; Sahu, Aryabartta

doi:10.1007/s00607-019-00749-w

Cited by 20 publications

(5 citation statements)

References 20 publications

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“…The algorithm positively captured the boundless resource model; however, in the workflow computational directly increases with the types of VMs offered by the supplier and the number of tasks. Swain, Saini & Sahu (2020) Extend the existing approach with if tasks are having other properties and if more than one VMs required for a task.…”

Section: Et Al (2016)mentioning

confidence: 99%

“…Failures are the reason for the absence of services, which interrupt the reliability of the cloud environment. Swain, Saini & Sahu (2020) analyze, design, and provide an explanation of two special problems which are tasks with corresponding execution time and with usual deadline with the same failure ratio on the machines and present in the cloud the reliability aware scheduling with deadlines. Authors propose two-phase heuristic techniques, task orders and task mapping to machines.…”

Section: Resource Scheduling Techniquesmentioning

confidence: 99%

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Efficiency aware scheduling techniques in cloud computing: a descriptive literature review

Sana¹,

Li²

2021

PeerJ Computer Science

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In the last decade, cloud computing becomes the most demanding platform to resolve issues and manage requests across the Internet. Cloud computing takes along terrific opportunities to run cost-effective scientific workflows without the requirement of possessing any set-up for customers. It makes available virtually unlimited resources that can be attained, organized, and used as required. Resource scheduling plays a fundamental role in the well-organized allocation of resources to every task in the cloud environment. However along with these gains many challenges are required to be considered to propose an efficient scheduling algorithm. An efficient Scheduling algorithm must enhance the implementation of goals like scheduling cost, load balancing, makespan time, security awareness, energy consumption, reliability, service level agreement maintenance, etc. To achieve the aforementioned goals many state-of-the-art scheduling techniques have been proposed based upon hybrid, heuristic, and meta-heuristic approaches. This work reviewed existing algorithms from the perspective of the scheduling objective and strategies. We conduct a comparative analysis of existing strategies along with the outcomes they provide. We highlight the drawbacks for insight into further research and open challenges. The findings aid researchers by providing a roadmap to propose efficient scheduling algorithms.

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Section: Et Al (2016)mentioning

confidence: 99%

Section: Resource Scheduling Techniquesmentioning

confidence: 99%

Efficiency aware scheduling techniques in cloud computing: a descriptive literature review

Sana¹,

Li²

2021

PeerJ Computer Science

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“…These above works didn't concern task deadlines, which may lead to deadline violations of some tasks. Swain et al [38] studied on optimizing deadline misses in unreliable computing servers, e.g., clouds, and four scheduling method with different mapping strategies between long/short tasks and reliable/unreliable machines. This work assumed servers (virtual/physical machines) are homogeneous in performance, which narrowed its application scope.…”

Section: Related Workmentioning

confidence: 99%

A Study on Heuristic Task Scheduling Optimizing Task Deadline Violations in Heterogeneous Computational Environments

et al. 2020

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In this paper, we focus on the problem of optimizing deadline violations for executing tasks in various heterogeneous computational environments. To address the problem, we formulated it as a binary nonlinear programming (BNP) model, which maximize the number of completed tasks and optimize the resource utilization of servers. To solve the BNP model in a polynomial complexity, we propose a heuristic task scheduling method, which iteratively schedules a task to the first core such that the accumulated slack time of all scheduled tasks is minimum, until the core cannot finish any task, and executes tasks with the earliest deadline first in each core to execute as many task as possible in a core. Experiment results based on a real world trace show that our method has upto 100% less task violations, and has the best performance in resource efficiency optimization in overall, compared with eight classical and state-of-the-art heuristic methods.

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“…Recently, the issue of task scheduling [7] has received enormous attention from the research community and has become the main issue because of its direct effect on the energy efficiency, resource utilisation and performance of the cloud data centre. It is usually viewed as a multi-dimensional bin-packing problem, which is NP hard [8][9][10]. The challenge in the task scheduling problem is how to optimally schedule different tasks to fewer Virtual Machines (VMs) in order to maximise the cloud provider's revenue and satisfy the user's needs.…”

Section: Introductionmentioning

confidence: 99%

MOTS‐ACO: An improved ant colony optimiser for multi‐objective task scheduling optimisation problem in cloud data centres

Elsedimy

Algarni

2022

IET Networks

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Task scheduling in cloud data centres is an optimisation problem that aims to minimise power consumption and task makespan as well as ensures the quality of service delivered to cloud consumers. Although there are several existing task scheduling approaches, these methods mainly focus on optimising makespans of tasks while ignoring critical issues. This paper presents a comprehensive multi-objective task scheduling model based on an improved Ant Colony Optimisation (ACO) algorithm, referred to as MOTS-ACO. In order to promote the diversity of the Pareto set and accelerate the convergence speed, adaptive distribution probability is incorporated into the proposed algorithm, specifically in the process of updating the global rule. The performance of MOTS-ACO is compared with several existing multi-objectives task scheduling algorithms based on the makespan time, turnaround time, power efficiency and load balancing parameters. The results show the superiority of MOTS-ACO in terms of the makespan time, turnaround time, power efficiency and load balancing. Moreover, the proposed MOTS-ACO algorithm introduces more diversity in the search and accelerates the convergence speed towards the Pareto optimal solution. K E Y W O R D S ACO, cloud computing, load balancing, makespan, power efficiency, task scheduling, turnaround time This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Reliability aware scheduling of bag of real time tasks in cloud environment

Cited by 20 publications

References 20 publications

Efficiency aware scheduling techniques in cloud computing: a descriptive literature review

Efficiency aware scheduling techniques in cloud computing: a descriptive literature review

A Study on Heuristic Task Scheduling Optimizing Task Deadline Violations in Heterogeneous Computational Environments

MOTS‐ACO: An improved ant colony optimiser for multi‐objective task scheduling optimisation problem in cloud data centres

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