Resource Allocation Strategy with Lease Policy and Dynamic Load Balancing

Kshirsagar, Pooja S.; Pujar, Anita Mukund

doi:10.5815/ijmecs.2017.02.03

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…A hybrid lease model [12] for improving dynamic load balancing and resource utilization followed a lease policy…”

Section: Related Workmentioning

confidence: 99%

Energy Saving VM Placement in Cloud

Acharya¹,

D’Mello²

2018

IJMECS

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The tremendous gain owing to the ubiquitous acceptance of the cloud services across the globe results in more complexity for the cloud providers by way of resource maintenance. This has a direct effect on the cost economy for them if the resources are not efficiently utilized. Most of the allocation strategies follow mechanisms involving direct allotment of VMs onto the servers based on their capabilities. This paper presents a VM allocation strategy that looks at VM placement by allowing server capacity to be partitioned into different classes. The classes are mainly based on the RAM and processing abilities which would be matched with VMs need. When the match is found the servers from this category are provisioned for the task executions. Based on the experimentation for various datacenter scenarios, it has been found that the proposed mechanism results in significant energy savings with reduced response time compared to the traditional VM allocation policies.

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“…A hybrid lease model [12] for improving dynamic load balancing and resource utilization followed a lease policy…”

Section: Related Workmentioning

confidence: 99%

Energy Saving VM Placement in Cloud

Acharya¹,

D’Mello²

2018

IJMECS

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“…Many researchers have proposed different strategies to schedule the tasks and balance the workload of the system considering different areas of application . Some application areas are load balancing in parallel computing [4], fog computing [5], cloud computing [6,7] and real time environment [8][9][10][19][20][21][22][23][24]. An efficient load balancing real time algorithm is one in which the tasks are schedulable, their deadlines are met and the load is balanced after task assignment such that the resources are effectively used with no overload or under load constraints.…”

Section: Related Workmentioning

confidence: 99%

“…The imbalance created between 1 and each core selected from remaining M-1 core is computed as (7) where m= 2 to M:…”

Section: Heuristics Based Load Balancingmentioning

confidence: 99%

Load Balancing in Multicore Systems using Heuristics Based Approach

Jadon¹,

Yadav²

2018

IJISA

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Multicore processing is advantageous over single core processors in the present highly advanced time critical applications. The tasks in real time applications need to be completed within the prescribed deadlines. Based on this philosophy, the proposed paper discusses the concept of load balancing algorithms in such a way that the work load is equally distributed amongst all cores in the processor. The equal distribution of work load amongst all the cores will result in enhanced utilization and increase in computing speed of application with all the deadlines met. In the heuristic based load balanced algorithm (HBLB), the best task from the set of tasks is selected using the feasibility check window and is assigned to the core. The application of HBLB reduces imbalance among the cores and results in lesser migration leading to low migration overhead. By utilizing all the cores of the multicore system, the computing speed of the application increases tremendously which results in the increase in efficiency of the system. The present paper also discusses the improved version of HBLB, known as Improved_Heuristic Based Load Balancing (Improved_HBLB), which focuses on further reducing the imbalance and the number of backtracks as compared to HBLB algorithm. It was observed that Improved_HBLB gives approximately 10% better results over the HBLB algorithm.

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“…Thus, to balance the level of supply and demanding of resources, Resource Allocation Strategy must be able to handle the following issues regarding the resources: Contention, Fragmentation, Over-provisioning and underprovisioning. [15]. Those issues have been investigated in the literature.…”

Section: Related Workmentioning

confidence: 99%

Phoenix: A Framework to Support Transient Overloads on Cloud Computing Environments

Bernardo¹,

Pinheiro²,

Pinto³

2018

IJITCS

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Abstract-This paper aims to present a computational framework capable of withstanding the effects produced by transient overloads on physical and virtual servers hosted on cloud computing environment. The proposed framework aims at automating management of virtual machines that are hosted in this environment, combining a proactive strategy, which performs load balancing when there is not overload of physical and/or virtual machines with a reactive strategy, which is triggered in the event of overload in these machines. On both strategies, it is observed the service level agreement (SLA) established for each hosted service according to the infrastructure as a service (IaaS) model. The main contribution of this paper is the implementation of a computational framework called Phoenix, capable of handling momentary overloads, considering the CPU, memory and network resources of physical and virtual machines and guaranteeing SLAs. The results demonstrate that Phoenix framework is effective, and it has outstanding performance in handling overloads virtual machine network, which has achieved the isolation of momentary overload on the physical machine preventing the propagation of their effects on the cloud.

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Resource Allocation Strategy with Lease Policy and Dynamic Load Balancing

Cited by 5 publications

References 12 publications

Energy Saving VM Placement in Cloud

Energy Saving VM Placement in Cloud

Load Balancing in Multicore Systems using Heuristics Based Approach

Phoenix: A Framework to Support Transient Overloads on Cloud Computing Environments

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