QoS Driven Task Scheduling in Cloud Computing

Dubey, Sonal; Agrawal, Sanjay

doi:10.7753/ijcatr0205.1013

Cited by 12 publications

(12 citation statements)

References 16 publications

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“…Sundareswaran et al (2012) focuses on multi-cloud architecture, responsible for the selection and ranking cloud service providers based on end-users request. The author in Dubey and Agrawal (2013), Jayadivya et al (2012) and Bansal et al (2011) focuses on task scheduling algorithm on QoS constraints for improving various QoS parameters such as cost, reliability, etc. Moreover, many of the research paper highlights priority-based service-scheduling algorithm (Ghanbari and Othman, 2012; Patel and Bhoi, 2013; Yang et al , 2012) for efficient and effective execution of service workflow during run-time.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…Traditional Web service composition (Bo et al , 2008; Chang et al , 2007; Zeng et al , 2009) and scheduling (Dubey and Agrawal, 2013; Jayadivya et al , 2012; Bansal et al , 2011) methodology is hard to support dynamic and flexible enterprise service composition workflow because it does not have rapid monitoring strategies for error handling and dynamic reconfiguration. Thus, in the paper (Kuzu and Cicekli, 2012; Gutierrez-Garcia and Sim, 2010a; Nai-zhong, 2013) the author focus on agent-based service composition approach that facilitating the challenges of SCP dynamically.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic service composition in enterprise cloud bus architecture

Khan¹,

Sengupta²,

Sarkar

2019

IJWIS

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Purpose Service composition phenomenon based on non-scenario aspects are become the latest issues in enterprise software applications of the multi-cloud environment due to the phenomenal increase in a number of Web services. The traditional service composition patterns are hard to support the dynamic, flexible and autonomous service composition in the inter-cloud platform. To address this problem, this paper aims to describe a dynamic service composition framework (SCF) that is enriched with various structural and functional aspects of composition patterns in a cloud computing environment. The proposed methodology helps to integrate various heterogeneous cloud services dynamically to acquire an optimal and novel enterprise solution for delivering the service to the end-users automatically. Design/methodology/approach SCF and different composition patterns have been used to compose the services present in the inter-cloud architecture of the multi-agent-based system. Further, the proposed dynamic service composition algorithm is illustrated using a hybrid approach, where service are chosen according to various needs of quality of service parameters. Besides, a priority-based service scheduling algorithm is proposed that facilitates the automation of delivering cloud service optimally. Findings The proposed framework is capable of composing the heterogeneous service and facilitate the structural and functional aspects of service composition process in enterprise cloud-based applications in terms of flexibility, scalability, integrity and dynamicity of the cloud bus. The advantage of the proposed algorithm is that it helps to minimize the execution cost, processing time and get better success rate in delivering the service as per customer’s need. Originality/value The novelty of the proposed architecture coordinates cloud participants, automate service discovery pattern, reconfigure scheduled services and focus on aggregating a composite services in inter-cloud environments. Besides, the proposed framework supported several non-functional characteristics such as robustness, flexibility, dynamicity, scalability and reliability of the system.

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Section: Review Of Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic service composition in enterprise cloud bus architecture

Khan¹,

Sengupta²,

Sarkar

2019

IJWIS

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“…It is used to decide which of the outstanding requests is to be allocated resources. A task scheduling is defined as a set of rules that decide the tasks to be executed at a particular time [2].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment

Nzanywayingoma¹,

Yang²

2017

ijacsa

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Abstract-Since the beginning of cloud computing technology, task scheduling problem has never been an easy work. Because of its NP-complete problem nature, a large number of task scheduling techniques have been suggested by different researchers to solve this complicated optimization problem. It is found worth to employ heuristics methods to get optimal or to arrive at near-optimal solutions. In this work, a combination of two heuristics algorithms was proposed: particle swarm optimization (PSO) and genetic algorithm (GA). Firstly, we list pros and cons of each algorithm and express its best interest to maximize the resource utilization. Secondly, we conduct a performance comparison approach based on two most critical objective functions of task scheduling problems which are execution time and computation cost of tasks in cloud computing. Thirdly, we compare our results with other existing heuristics algorithms from the literatures. The experimental results was examined with benchmark functions and results showed that the particle swarm optimization (PSO) performs better than genetic algorithm (GA) but they both present a similarity because of their population based search methods. The results also showed that the proposed hybrid models outperform the standard PSO and reduces dramatically the execution time and lower the processing cost on the computing resources.

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“…Thus, the task of job scheduling is challenging, since it is necessary to arrange and manage this access to the pool of resources with satisfactory Quality of Service (QoS), reasonable waiting time, good throughput, effective CPU utilization, optimal CPU speed, and optimized scheduling algorithm. In a cloud environment, the case is quite different; the demand for resources varies frequently, according to the dynamic request of resources [8].…”

mentioning

confidence: 99%

“…Scheduling algorithms deployed in distributed systems aim to schedule received jobs and map them to the available resources in a reduced time and compute the optimal scheduling of upcoming jobs to be executed under logic constraints. One of the key advantages of scheduling approaches is accomplishing the optimal system throughput with high-performance computing [8].…”

mentioning

confidence: 99%

Subvention Scheme for the Cloud Computing Scheduling Algorithm

Moustafa¹

2019

JCP

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Resource management and task scheduling are necessary for satisfying proper job (task) scheduling. Scheduling defines the process or methodology for data flows which are implemented to insure resource high utilization. One of the most used algorithms is shortest job first (SJF) in which deciding jobs order is queued to be processed, with cloud computing more than one virtual machine could be used. Distributed scheduling schemes need to be efficient for better performance. In this research, we proposed a new, efficient approach for allocating and scheduling users' tasks in the cloud environment. The main goal is to enhance the performance metrics such as: 1-reducing the waiting time and 2-increasing the throughput of a given task set to have an efficient mapping of tasks using the same shortest job first (SJF) scheduling algorithm. The proposed policy is tested to conduct performance evaluation.

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QoS Driven Task Scheduling in Cloud Computing

Cited by 12 publications

References 16 publications

Dynamic service composition in enterprise cloud bus architecture

Dynamic service composition in enterprise cloud bus architecture

Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment

Subvention Scheme for the Cloud Computing Scheduling Algorithm

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