Self regulatory graph based model for managing VM migration in cloud data centers

Kumar, Narander; Agarwal, Shalini

doi:10.1109/iadcc.2014.6779414

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…An arbitrary diagram model is of the system of servers in a server farm. Starting arbitrary strolls and utilizing the heuristics Maximum Correlation Coefficient and Migration Opportunity, relocating set of VMs just as the objective server separately [17]. Energy efficiency for overload host selection and VM assignment from an overload host are important to enhance the energy effectiveness and SLA infringement of a cloud server subsequent to all VM migrations from under loaded have go to host, which change to energy sparing mode is likewise effective [18].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

Kumar¹,

Kumar²

2019

IJEAT

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Including the expanding fame of the cloud model and quick multiplication of cloud frameworks there are expanding concerns about energy utilization and subsequent effect of cloud as a supporter of worldwide CO2 discharges. Until now, little is thought about how to fuse energy utilization and CO2 worries into cloud application. Energy consumption has become an important cost factor for computing resources. In this research article, we proposed an algorithm to VM Allocation and VM migrations in the context of power utilisation in the data centers. This mechanism is to minimize the energy utilization in the cloud computing environment. We validate our results with the help of prediction based faster energy efficient VMs approach and modified Best Fit approach which shows the faster assignments and increase the performance when consumption of the energy is optimised. As well as we also simulate our results in the cloudsim in the multiple numbers of host and virtual machine to reduce the energy consumptions.

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Section: Related Workmentioning

confidence: 99%

Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

Kumar¹,

Kumar²

2019

IJEAT

Self Cite

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“…These migration techniques improves system level performance and overload network performance [24] [25]. In [2], authors designed a new approach for even distribution of VMs using VM migration technique. In this approach the cloud data center was modeled such that a random walk initiated from overloaded to migrated VMs.…”

Section: Related Workmentioning

confidence: 99%

“…In this proposed algorithm, we utilize the basic features of ACO for scheduling the tasks [2,27]. We can design a new task scheduling algorithm experiences of previous task algorithm because they are very helpful in developing a new algorithm.…”

Section: Proposed Algorithm and Architecturementioning

confidence: 99%

“…A process which is running on a remotely located computer system instead of a user personal computer is known as Cloud computing. Cloud computing is an environment which provides infrastructure as a services (IaaS) for sharing infrastructure, platform as a service (PaaS) for sharing platform and software as a service (SaaS) for sharing software among multiple users [1][2][3]. The data stored on the cloud environment is cloud data, services provided by remote machines is cloud services, etc.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

Kushwaha*¹,

Amjad²,

Kumar³

2019

IJITEE

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Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime & anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO

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Load monitoring and system-traffic-aware live VM migration-based load balancing in cloud data center using graph theoretic solutions

2018

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Self regulatory graph based model for managing VM migration in cloud data centers

Cited by 4 publications

References 8 publications

Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

Dynamic Consolidation of VM Allocation and VM Migration to Optimize Energy Consumption of Cloud Data Centers

Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

Load monitoring and system-traffic-aware live VM migration-based load balancing in cloud data center using graph theoretic solutions

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