A Hybrid Approach of Genetic Algorithm and Multi Objective PSO Task Scheduling in Cloud Computing

Kumari, Komal; Sengottuvelan, P.; Shanthini, J.

doi:10.5958/2249-7315.2017.00241.6

Cited by 5 publications

(9 citation statements)

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“…Scale assessment statistics are detailed in Table 1. Proposal execution is measured by comparing to existing method known as "multi objective PSO task scheduling (MO-PSO)" [27]. In the experimental study, the statistics noticed for the performance metrics in the form of the overall performance is presented in Table 1.…”

Section: Resultsmentioning

confidence: 99%

“…Better solutions are detected by Hyper-heuristic algorithms (HHA) for CC scheduling and also for further enhancing such outcomes of scheduling leads for makespan. The work [27] proposed other new "multi objective PSO task scheduling (MO-PSO)" and GA on the basis of HHA aimed at resource scheduling that was hybridalgorithm. Here, algorithm performance is assessed utilizing Sim-toolkit of cloud.…”

Section: Related Workmentioning

confidence: 99%

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Calibration Factors based intrusion detection (CFID) in Cloud Computing

Sowjanya¹,

Sunitha²

2019

IJEAT

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The distributed computing is the buzz in recent past, cloud computing stands first in this category. This is since, the users can adapt anything related to data storage, magnificent computing facilities on a system with less infrastructure from anywhere at any time. On other dimension such public and private cloud computing strategies would also attracts the foul players to perform intrusion practices. This is since, the comfortability that the cloud platform providing to end users intends them to adapt these services in regard to save or compute the sensitive data. The scope of vulnerability to breach the data or services over cloud computing is more frequent and easier, which is since, these services relies on internet protocol. In this regard, the research in intrusion detection defense mechanisms is having prominent scope. This manuscript, projecting a novel intrusion detection mechanism called "calibration factors-based intrusion detection (CFID)" for cloud computing networks. The experimental study portrayed the significant scope of the proposal CFID to detect the intrusion activities listed as remoteto-Local, Port Scanning, and Virtual-Machine-Trapping.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Calibration Factors based intrusion detection (CFID) in Cloud Computing

Sowjanya¹,

Sunitha²

2019

IJEAT

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“…The resource powers are diverse in the execution frameworks. The attained results are compared with the PSO [17], PSO-HC by involving the proposed algorithm. The CloudSim environment is utilized to test the efficiency of the proposed methodology.…”

Section: Resultsmentioning

confidence: 99%

A Hybrid AFSS-SHC Optimization Methodology for Balancing the Load Efficiently in Cloud Environment

Aswini¹,

Malarvizhi²,

Anitha³

et al. 2018

IJET

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Cloud computing helps to share data and provide many resources to users. Users pay only for those resources as much they used. Rapid increase in load to these cloud framework cannot be predicted. Load balancing is one of the issues in cloud computing that distributes the workload to the nodes in such a way no node is overloaded or under - loaded. Load balancing is a main challenge in cloud environment. In this work, scheduling algorithm is applied for load balancing by considering the cost of task execution and make span. This scheduling algorithm efficiently maps task to available nodes in cloud and it is beneficial to user and service provider. Load balancing segregates assignments of tasks among all available virtual machines from datacenters. Assignment of tasks to virtual machines can be done with minimum delay. To enhance the make span, resource utilization, our proposed framework utilizes AFSS-SHC load balancing strategy. A metaheuristics swarm intelligence algorithm which is NP-hard have been suggested to balance load across devices. The algorithms taken into account are-HEFT,PSO and PSO-HC. The proposed methodology AFSS-SHC optimized the task scheduling. Random tasks have been taken for this purpose and simulated to show that the proposed methodology works efficiently to reduce the make span of tasks to reduce the cost.

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“…This algorithm is simulated with different inertia weights and out of those LDIW has outperformed in terms of makespan. Kumari et.al, [19] proposed a scheduling algorithm which uses the combination of PSO and GA by switching the tasks based on the priority to foreground and background. The experiments are simulated in the cloudsim and compared with the BATS and CMCBF algorithms and the proposed algorithm is outperformed in terms of resource utilization.…”

Section: Ms Sudheer Drm Vamsi Krishna mentioning

confidence: 99%

“…This was simulated on the cloudsim and the proposed algorithm achieved a good results for the makespan, standard deviation and degree of imbalance when compared with the Honey bee and ant colony algorithms. PSO Algorithm Workflow Scheduling Mathematical Model --- [5] SA and PSO algorithms Task Scheduling Simulation Cloudsim [6] PSO algorithm Task Scheduling Simulation Cloudsim [7] PSO Algorithm Task Scheduling Simulation Cloudsim [8] PSO Algorithm Task Scheduling Simulation Cloudsim [9] Modified PSOAlgorithm Task Scheduling Simulation Cloudsim [10] MDAPSO algorithm Task Scheduling Simulation Cloudsim [11] LBMPSO algorithm Task Scheduling Simulation Cloudsim [12] PSO Algorithm Task Scheduing Mathematical Model ---- [13] Enhanced PSO algorithm, Task Scheduling Mathematical Model --- [14] BPSO Algorithm Workflow Scheduling Mathematical Model --- [15] GA-PSO Algorithm Workflow Scheduling Simulation Workflowsim [16] ACO and PSO Algorithms Workflow Scheduling Simulation Workflowsim [17] Modified PSO algorithm Workflow Scheduling Simulation Workflowsim [18] LDIW model Task Scheduling Simulation Cloudsim [19] PSO and GA Algorithms Task Scheduling Simulation Cloudsim [20] Symbiotic Organism Search Task Scheduling Simulation Cloudsim [21] Extended PSO Task Scheduling Simulation Cloudsim [22] modified PSO Task Scheduling Simulation Cloudsim [23] PSO Algorithm Task Scheduling Simulation Cloudsim [24] modified PSO Task Scheduling Real time implementation Amazon EC2 and Google cloud [25] GA and PSO Task Scheduling Mathematical Model --- [26] PSO and Hill Climbing Algorithms Task Scheduling Mathematical Model --- [27] Modified PSO Task Scheduling Mathematical Model --- [28] Modified PSO Task Scheduling Simulation Cloudsim [29] PSO Algorithm Task Scheduling Simulation Cloudsim [30] IPSO Algorithm Task Scheduling Simulation Cloudsim…”

Section: Ms Sudheer Drm Vamsi Krishna mentioning

confidence: 99%

Dynamic PSO for Task Scheduling Optimization in Cloud Computing

2019

IJRTE

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Task scheduling is still a challenge in cloud computing as no existing scheduling algorithms are not effectively provisioning and scheduling the resources in the cloud. Existing authors considered only metrics like makespan, execution time and turnaround time etc. and the previous authors concentrated only to optimize the above mentioned metrics. But no existing authors were considered about the effective provisioning of the resources in the cloud i.e, compute, storage and network capacities and still many resources in the cloud were underutilized. In this paper, we want to propose an algorithm which can effectively utilize the resources in the cloud by extending Particle Swarm Optimization by addressing the metrics Bandwidth utilization and Memory utilization particularly. We have simulated this algorithm by using cloudsim and compared the modified Dynamic PSO with the PSO algorithm and it outperforms in terms of Bandwidth and Memory utilization and the makespan is also optimized.

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A Hybrid Approach of Genetic Algorithm and Multi Objective PSO Task Scheduling in Cloud Computing

Cited by 5 publications

References 0 publications

Calibration Factors based intrusion detection (CFID) in Cloud Computing

Calibration Factors based intrusion detection (CFID) in Cloud Computing

A Hybrid AFSS-SHC Optimization Methodology for Balancing the Load Efficiently in Cloud Environment

Dynamic PSO for Task Scheduling Optimization in Cloud Computing

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