Continental Division of Load and Balanced Ant Family(Baf) Algorithm for Load Balancing on Public Cloud

Nadaph, Anisaara; Maral, Vikas

doi:10.5121/ijci.2014.3504

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…That score helps to manage the less execution time and reduces the failure rate in application of workflow and using simulation that has been done on the cloudsim toolkit is given by [10]. To manage the load in a cloud computing environment on this work enhancement of the load balancing algorithm with ant colony algorithm has to be done [11]. Financial plans and timeliness parameters are considered to achieve the fault tolerant load balanced scheduling algorithm in computational grids for the user satisfaction that working in heterogeneous dynamic cloud environment discussed in [7,[12][13][14].…”

Section: Related Workmentioning

confidence: 99%

Resource Management Using ANN-PSO Techniques in Cloud Environment

Kumar¹,

Patel

2016

Advances in Intelligent Systems and Computing

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In the cloud environment, multiple requests are coming from the client on the datacenters. We have to assign the resources to all the requests. In this paper, the main objective is to find out suitable mapping between requests and resources. To do this we are using the artificial neural network (ANN) with the PSO algorithm. In this algorithm input layer (client) sends request with some requirement. According to requirements we calculate the resources cost on the behalf of the three clusters namely high, medium, and low. Since ANN supports the parallel processing, so we can process all the requests whether they belong to high, medium, and low, hence we optimize the processing time and cost also. PSO algorithm works on the hidden layer as a scheduler. Since particle swarm ptimization (PSO) algorithm supports fast convergence and time constraints, etc. Therefore both techniques minimize the cost and increase the availability and the reliability as well as results show improved performance.

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Section: Related Workmentioning

confidence: 99%

Resource Management Using ANN-PSO Techniques in Cloud Environment

Kumar¹,

Patel

2016

Advances in Intelligent Systems and Computing

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Least Afflicted Load Balancing Algorithm (LALBA) for Performance Improvement of Multi-Scale Applications in Cloud Environment

Kumbhar¹

2021

TURCOMAT

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Cloud computing is new image of Information Technology and the way applications are available to end-users. Also, the applications available are gaining popularity as being able to demonstrate the flexibility of use of resources as in Multi-Scale Cloud-Based Applications. These applications on the cloud are hosted on remote virtual machines with multi-tenancy architecture. Multi-tenant cloud computing architecture helps cloud users and customers to share computing resources and still the individual tenant's data is stored separately so as to make it is invisible to other tenants. With this multi-tenancy architecture, the application is being used by multiple people at the same time. Also, the application resides in various copies across various servers situated at different geographical locations. Virtual Machines that are housed in multiple Data Centers are coupled by way of load balancers. These load balancers are responsible for the optimal distribution of load/demand/tenant requests and the size of the data held within the request. So in short, two areas need to be focused on when architecting such systems. Firstly, the load must be balanced optimally across data centers and virtual machines for seamless operation at the back end. Secondly, at the front end, it is imperative that every tenant enjoys optimal service speeds without any degradation of service request speed. In case the back-end virtual machines are unbalanced in their load then there is bound to be degradation of services. For this very purpose, various researchers/academicians, and practitioners in the cloud space have been working tirelessly towards developing optimal algorithms to get the best load balancing architecture to ensure that the objective of seamless back end and front end quality of service in terms of average response times. Multi-Scale applications consist of variable payload for time and often unpredictable i.e. load can be increased/decreased suddenly or gradually. Hence to balance these loads equally at any time, a new algorithm is proposed. This algorithm considers the characteristics of multiscale applications in terms of data size and runtime characteristics of VM in terms of capacity and availability. Finally, the performance of this method is analyzed qualitatively as well as quantitatively by performing simulation experiments of multiscale applications.

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Continental Division of Load and Balanced Ant Family(Baf) Algorithm for Load Balancing on Public Cloud

Abstract: ABSTRACT

Cited by 2 publications

References 8 publications

Resource Management Using ANN-PSO Techniques in Cloud Environment

Resource Management Using ANN-PSO Techniques in Cloud Environment

Least Afflicted Load Balancing Algorithm (LALBA) for Performance Improvement of Multi-Scale Applications in Cloud Environment

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