Enhanced Task Scheduling Algorithm Using Multi-objective Function for Cloud Computing Framework

Narwal, Abhikriti; Dhingra, Sunita

doi:10.1007/978-981-10-8657-1_9

Cited by 7 publications

(9 citation statements)

References 7 publications

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“…The Experimental results of the proposed technique has been taken using various scenario created in cloudsim by varying the quantity of cloudlets and VMs. The results are compared with the existing techniques EMOSA [14] and CBSA According to the Credit Based Scheduling Algorithm with load Balancing (CBSA_LB), the simulations results will be carried out by using CloudSim-3.0 simulator and parameter setting on the CloudSim-3.0 is shown in Table 1 Table 1…”

Section: Resultsmentioning

confidence: 99%

“…The goal of task scheduling in cloud computing is to offer scheduling of tasks which is optimal for users, and offer the QoS and throughput of entire system of cloud at the same time. In the existing approach, cloudlets were being scheduled according to multi objective task scheduling [14], considering the sorting of tasks using nondominating sorting algorithm, thereby mapping the task having highest rank according non-dominating i.e. the task dominates all other tasks is mapped to highest rank virtual machines, according to their arrival times.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Figure 2 shows the Processing cost comparison of algorithms. Here, five different cases have been evaluated by varying number of tasks, the processing cost of the proposed algorithm(CBSA_LB) is 11211.73 in case of 500 tasks, which is 82273.50 in Credit based scheduling algorithm and for EMOSA [14,15] the processing cost is 93320.75. The figure above clearly shows that the planned algorithm performs better on the basis of Processing Cost metric.…”

Section: Performance Based On Processing Costmentioning

confidence: 99%

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Credit Based Scheduling with Load Balancing in Cloud Environment

Narwal¹

2020

IJATCSE

Self Cite

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Cloud computing is the recent advancement in the technology of distributed computing that works on the basis of pay as you use model. It comprises of virtual machines that provides both storage and computational facility. The main aim of cloud computing is to offer access to remotely distributed resources to its users. Scheduling of tasks plays a vital role in the efficient working of cloud computing. Sometimes situation comes where multiple tasks with same priority arrives so a good scheduler is one which handles the situation appropriately with proper load balancing. There are various scheduling algorithms proposed in the past. Most of the scheduling algorithms neglect the concept of load balancing. Load balancing in cloud computing is also as much important as task scheduling. As cloud environment consists of virtual machine, it should take care that no virtual machine remains idle and also no virtual machine is under heavy load of the tasks. So, it is important to balance the load equally among the virtual machines to solve the issue of under loading and over loading of virtual machines. In this paper, a credit based scheduling algorithm with load balancing (CBSA_LB) is proposed that balances the load along with the scheduling of tasks. The results are evaluated on the basis of six parameters: processing time, processing cost, response time, makespan time, Throughput and Execution Time. Experimental results show that the proposed technique outperforms the existing techniques (EMOSA and CBSA). .

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Performance Based On Processing Costmentioning

confidence: 99%

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Credit Based Scheduling with Load Balancing in Cloud Environment

Narwal¹

2020

IJATCSE

Self Cite

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“…Lakra et al [19] proposed a Two-objective tasks scheduling algorithm for mapping tasks to a VMs improving the throughput of the datacenter and reducing the cost without compromising the service level agreement in cloud environment. The proposed scheduling algorithm in [20] is involving Non-dominated sorting which targets the twoobjective issues considering completion time and cost minimizations. The exposed results by the authors outperform the preceding algorithms and this method represents better convergence performance and resource scheduling capability in the different number of resources.…”

Section: Related Workmentioning

confidence: 99%

Improved Virus Optimization algorithm for two-objective tasks Scheduling in Cloud Environment

Bey

Bouznad

Benhammadi

et al. 2019

Communication Papers of the 2019 Federated Conference on Computer Science and Information Systems

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Cloud computing is increasingly recognized as a new way to use on-demand, computing, storage and network services in a transparent and efficient way. The development of applications in cloud environments is faced with the need to efficiently schedule a large number of tasks and resources. However, in the most of the time, the resources in cloud are not efficiently utilized due to inadequate scheduling task algorithm in virtual machines. Therefore, task scheduling is one of the most challenging issues in cloud computing. In this paper, we propose two-objective virus optimization algorithm of the makespan and the cost, for mapping tasks to virtual machines in order to meet the needs of cloud service quality and proper assignment of resources. Thus, based on genetic algorithm some parameters of Virus optimization algorithm are redefined to strengthen sorting ability between virus infection strategies. Our combined methods aims to improve the performance of scheduling algorithms. It outperforms some existing approaches for task scheduling in Cloud computing.

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“…Narwal and Dhingra [12] had proposed another algorithm of multi-objective task scheduling considering various attributes in the environment of the cloud. This algorithm has three different parameters which are the total cost of processing, the total time of processing and the average waiting time.…”

Section: Related Workmentioning

confidence: 99%

An Energy and Deadline Aware Scheduling Using Greedy Algorithm for Cloud Computing

Venuthurumilli¹,

Mandapati²

2019

ISI

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Cloud computing has been providing various services to different users by means of an aid of large and scalable virtualized resources on the internet. Owing to all the recent and inventive developments that are found in the field, there are several scheduling algorithms which were developed in a cloud computing environment with the intention of decreasing the services given in cloud computing. For a very enormous gauge, assorted and the multiuser atmosphere in the cloud scheme where maximization of profit for that of the Cloud Service Provider (CSP) has been the primary objective. For the purpose of this work, the inclusive optimization problem in the operation of the cloud system by means of lowering the cost of procedure and by maximizing the efficiency of energy. At the same time, it satisfies the deadlines that are definite in Service Level Agreements (SLA) that has been addressed from a CSP perspective. The work proposes a Greedy algorithm for the environment of the cloud and this is compared to the scheduling of a First Come First Served (FCFS) and the Min-Min scheduling procedure. This system exploits the tasks and their heterogeneity and also the resources using a scheduler unit that schedules and allocates the tasks which are deadline-constrained which is delimited to the nodes that are energy conscious. After this, the CSP capitalizes on the parallelisms of data for every user workload and also effectively manages all collective user requests and also apply the custom optimization that creates a cost of global energy and a cloud platform which is deadline aware. The results of the experiment prove that this proposed Greedy algorithm which achieves a performance which is better (a guarantee ratio, utilization of resources and energy saving) compared to the FCFS and the Min-Min scheduling algorithm.

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Enhanced Task Scheduling Algorithm Using Multi-objective Function for Cloud Computing Framework

Cited by 7 publications

References 7 publications

Credit Based Scheduling with Load Balancing in Cloud Environment

Credit Based Scheduling with Load Balancing in Cloud Environment

Improved Virus Optimization algorithm for two-objective tasks Scheduling in Cloud Environment

An Energy and Deadline Aware Scheduling Using Greedy Algorithm for Cloud Computing

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