Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment

Kumar, Mohit; Sharma, S. C.

doi:10.1016/j.compeleceng.2017.11.018

Cited by 71 publications

(53 citation statements)

References 10 publications

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“…A dynamic load balancing algorithm [11] is proposed, that balances the workload among the VM using elastic provisioning and de-provisioning depending upon the last optimal k-interval. This algorithm aims at reducing the makespan and increasing the cloud resource utilization ratio.…”

Section: Literature Reviewmentioning

confidence: 99%

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Design o f a Fault Tolerant Strategy f or Resource Scheduling i n Cloud Environment

P*,

et al. 2019

IJEAT

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Cloud computing supports the technological need of the industry supporting many other technologies. Also, the demand for computing power and storage by recent technologies is reasonably growing in a drastic way. Cloud computing, serving for these technologies are to be developed with advancements that lead to performance improvement both in support to the technologies like block-chain and big data. The allocation of cloud resources is an important strategy to be followed in a wiser manner to incorporate the needs of extra ordinary computing power. In this paper, an efficient resource allocation strategy (FTVMA) is introduced that involves the creation of effective virtual machines (VMs) and performs VM allocation in an efficient manner by considering the failure rates, previous history of failure of VM, execution efficiency as a part of effective scheduling. There exist many reasons for cloudlet failure in VMs. Some of them are overloading of VMs and non-availability of VMs. The introduced FTVMA algorithm considers the failure rate of the physical machine, load of virtual machines and the cost priority of the tasks in order to achieve Quality of Service (QoS) and Quality of Experience (QoE) of the user. The FTVMA methodology proposed in this paper works better for computation intensive VMs and is tested usingCloudSim environment. The QoS metrics used to measure the performance of the proposed algorithm are Makespan and VM Utilization. The metric to measure QoE are Priority Miss Rate and Failure Rate. The proposed algorithm shows its improvement in terms of the QoS and QoE metrics. The results obtained are compared with the existing resource scheduling algorithms and it is inferred that the proposed algorithm performs better in terms of QoS and QoE.

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Section: Literature Reviewmentioning

confidence: 99%

“…The utilization of each VM is calculated with its load assigned to it by the scheduler and is given in the equation (11). (11) and the Average VM Utilization is given by the equation (12).…”

Section: Average Vm Utilizationmentioning

confidence: 99%

Design o f a Fault Tolerant Strategy f or Resource Scheduling i n Cloud Environment

P*,

et al. 2019

IJEAT

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“…But the framework failed to improve the throughput. A dynamic scheduling technique was presented in [10] for balancing the workload among all the virtual machines using task migration. Though the technique reduces the makespan, the throughput was not improved.…”

Section: Related Workmentioning

confidence: 99%

Support Vector Regression based Mapreduce Throttled Load Balancer for Data Centers

Devi *,

Chezian

2019

IJITEE

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The process of analyzing big data and other valuable information is a significant process in the cloud. Since big data processing utilizes a large number of resources for completing certain tasks. Therefore, the incoming tasks are allocated with better utilization of resources to minimize the workload across the server in the cloud. The conventional load balancing technique failed to balance the load effectively among data centers and dynamic QoS requirements of big data application. In order to improve the load balancing with maximum throughput and minimum makespan, Support Vector Regression based MapReduce Throttled Load Balancing (SVR-MTLB) technique is introduced. Initially, a large number of cloud user requests (data/file) are sent to the cloud server from different locations. After collecting the cloud user request, the SVR-MTLB technique balances the workload of the virtual machine with the help of support vector regression. The load balancer uses the index table for maintaining the virtual machines. Then, map function performs the regression analysis using optimal hyperplane and provides three resource status of the virtual machine namely overloaded, less loaded and balanced load. After finding the less loaded VM, the load balancer sends the ID of the virtual machine to the data center controller. The controller performs migration of the task from an overloaded VM to a less loaded VM at run time. This in turn assists to minimize the response time. Experimental evaluation is carried out on the factors such as throughput, makespan, migration time and response time with respect to a number of tasks. The experimental results reported that the proposed SVR-MTLB technique obtains high throughput with minimum response time, makespan as well as migration time than the state -of -the -art methods.

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“…Load balancing is an important requirement in uses of cloud computing. Load balancing approach should consider following: (1) makespan time (2) performance (3) scalability (4) resource utilization (5) execution time (6) user satisfaction [3]. A good balancing approach focuses on increasing resource utilization and decreasing makespan time of tasks.…”

Section: A Load Balancingmentioning

confidence: 99%

“…This algorithm is suited for static environment. A dynamic load balanced algorithm is proposed in [3] with constraints like elasticity and deadline in cloud. Aim of this dynamic algorithm is to minimize makespan time and improve the number of task meet the deadline specified by client.…”

Section: Related Workmentioning

confidence: 99%

Load Balancing in Cloud Computing: A Review

soni

Sangwan

2018

ijarcs

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Cloud computing is rapidly growing due to the enormous benefits it offers over the traditional approach. Earlier, lot of things like buying server, managing traffic and maintenance needs to be managed individually leading to increase in cost and overhead for users. Cloud offers a less expensive and easy way of managing things. With increased number of applications and users, resources are not utilized efficiently This calls for efficient techniques to balance load on cloud. A good load balancing approach is required to distribute load among virtual machines and to provide maximum utilization of resource. A discussion and comparative analysis of some important approaches for balancing load in cloud is presented in this paper.

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Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment

Cited by 71 publications

References 10 publications

Design o f a Fault Tolerant Strategy f or Resource Scheduling i n Cloud Environment

Design o f a Fault Tolerant Strategy f or Resource Scheduling i n Cloud Environment

Support Vector Regression based Mapreduce Throttled Load Balancer for Data Centers

Load Balancing in Cloud Computing: A Review

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