An innovative approach of Ant Colony optimization for load balancing in peer to peer grid environment

Jain, Anamika; Singh, Ravinder

doi:10.1109/icicict.2014.6781242

Cited by 10 publications

(7 citation statements)

References 5 publications

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“…ACO: Ant colony is heuristic notion for resolving difficult optimization method. If the power of pheromone value is great ants survey that track, else no optimal resolution [4]. ACO routines this spectacle and smears it to unravel factual life optimization complications [6].…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…Recently population based heuristics methods are widely used to resolve this problem. Anamika Jain et al (2014) [4]: Grid computation is collection of different sources that are used as effective resource to a user also provide good network environment. Load matching is an important factor to match the overall load of the nodes or jobs.…”

Section: Genetic Algorithmmentioning

confidence: 99%

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Efficient Hierarchical Scheduling Algorithms in Grid Computing

Kaur¹,

Singh²,

Singh³

2016

IJERT

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Section: Genetic Algorithmmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

Efficient Hierarchical Scheduling Algorithms in Grid Computing

Kaur¹,

Singh²,

Singh³

2016

IJERT

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show abstract

“…Uncertainty the control for value stands great survey that track, else not any ideal resolution [11]. ACO routines this spectacle and smears it to unravel factual life optimization complications [17].…”

Section: Flowchart 6: Block Diagram For Acomentioning

confidence: 99%

Comparative Study of Energy Saving Grid-based heuristic approaches in dispersed computational environment

Kaur¹

2017

ijecs

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Completing for huge parallel jobs in dispersed computational environments requires co-distribution for significant resources mutually shared with their possessors. The scheduling plus resource selection complications in Grid remain NPhard because for their combinatorial environment. Various algorithms, depending on heuristic solutions, or their groupings have been implemented for similar jobs with dependences in disseminated environments. Task schedulers target to improve the whole performance for a segment, e.g., decreasing the average work response time and increasing the number for tasks accomplished in certain point for time. A 'Network' stands an arrangement aimed at reserve allocation. This stands cast off in huge scale files dispensation, numerous for solicitations presence logical ones. Grid forecast is an important constituent Network structure.

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“…A. Jain and R. Singh [44] described grid computing for classification of non-identical resources that are cast off as virtual resource to a user and impart superior grid domain. Now-a-days, large amount of resource management in peer-to-peer grid environment is used.…”

mentioning

confidence: 99%

Dynamic Task Scheduling using Balanced VM Allocation Policy for Fog Computing Platforms

Singh

Nayyar

Kaur

et al. 2019

SCPE

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The fog computing models are getting popular as the demand and capacity of data processing is rising for the various applications every year. The fog computing models incorporate the various task scheduling algorithms for the resource selection among the given list of virtual machines (VMs). The task scheduling models are designed around the various task metrics, which include the task length (time), energy, processing cost etc. for the various purposes. The cost oriented scheduling models are primarily built for the customer's perspectives, and saves them a handful amount of money by efficiently assigning the resources for the tasks. In this paper, we have worked upon the multiple task scheduling models based upon the Local Regression (LR), Inter Quartile Range (IQR), Local Regression Robust (LRR), Non-Power Aware (NPA), Median Absolute Deviation (MAD), Dynamic Voltage and Frequency Scheduling (DVFS) and The Static Threshold (THR) methods using the ifogsim simulation designed with the 50 nodes and 50 virtual machines, i.e. 1 virtual machine per node. All of the models have been implemented using the standard input simulation parameters for the purpose of performance assessment in the various domains, specifically in the time domain and effective consumption of energy. The results obtained from the experiments have shown the overall time of 86,400 seconds during the simulation, where the DVFS has been recorded with the 52.98 kWh consumption of energy, which shows the efficient processing in comparison to the 150.68 kWh of energy consumption in the NPA model. Also, there are no SLA violations recorded during both of the simulation, because no VM migration model has been utilized among both of the implemented models, which clearly shows that the VM migrations are the major cause of SLA violation cases. The LRR (2520 VMs) has been observed as best contender on the basis of mean of number of VM migrations in comparison with LR (2555 VMs), THR (4769 VMs), MAD (5138 VMs) and IQR (5352 VMs).

show abstract

An innovative approach of Ant Colony optimization for load balancing in peer to peer grid environment

Cited by 10 publications

References 5 publications

Efficient Hierarchical Scheduling Algorithms in Grid Computing

Efficient Hierarchical Scheduling Algorithms in Grid Computing

Comparative Study of Energy Saving Grid-based heuristic approaches in dispersed computational environment

Dynamic Task Scheduling using Balanced VM Allocation Policy for Fog Computing Platforms

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