Virtual Machines Migration based on Greedy Algorithm in Cloud Computing

SoltanBaghshahi, Somayeh; Jabbehdari, Sam; Adabi, Sahar

doi:10.5120/16849-6709

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…classified into 6 regions [27][28][29][30][31][32][33][34][35][36]. In this research, we consider region 0, which is North America, because the percentage of users in this region is 80 million [31].…”

Section: Firefly Optimization Approachmentioning

confidence: 99%

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

Wang

2022

Mathematical Problems in Engineering

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As the cloud data centers size increases, the number of virtual machines (VMs) grows speedily. Application requests are served by VMs be located in the physical machine (PM). The rapid growth of Internet services has created an imbalance of network resources. Some hosts have high bandwidth usage and can cause network congestion. Network congestion affects overall network performance. Cloud computing load balancing is an important feature that needs to be optimized. Therefore, this research proposes a 3-tier architecture, which consists of Cloud layer, Fog layer, and Consumer layer. The Cloud serves the world, and Fog analyzes the services at the local edge of network. Fog stores data temporarily, and the data is transmitted to the cloud. The world is classified into 6 regions on the basis of 6 continents in consumer layer. Consider Area 0 as North America, for which two fogs and two cluster buildings are considered. Microgrids (MG) are used to supply energy to consumers. In this research, a real-time VM migration algorithm for balancing fog load has been proposed. Load balancing algorithms focus on effective resource utilization, maximum throughput, and optimal response time. Compared to the closest data center (CDC), the real-time VM migration algorithm achieves 18% better cost results and optimized response time (ORT). Realtime VM migration and ORT increase response time by 11% compared to dynamic reconFigure with load (DRL) with load. Realtime VM migration always seeks the best solution to minimize cost and increase processing time.

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Section: Firefly Optimization Approachmentioning

confidence: 99%

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

Wang

2022

Mathematical Problems in Engineering

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“…VMs within a cluster are set with priority which lessens the migration time. Then it is simulated and results point out that the link bandwidth is significant parameter in the migration [23].…”

Section: Erlang Concurrent Trafficmentioning

confidence: 99%

Cloud computing based load balancing algorithm for erlang concurrent traffic

Jittawiriyanukoon

2020

IJEECS

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<p>The distribution of scheduler from user inquiries in the clouds is complex. In keeping up with the cloud computing environment and the inquirers, the clouds meet with some problematic load balancing complications as an improving load balancing tool induces the rigorous efficiency of the cloud based website’s user access. Overloaded or underloaded conditions originate processing catastrophe regarding the prolonged execution time, bandwidth hog, malfunction, and etc. Besides, to manipulate Erlang concurrent tasks is another skyward situation. Hence, the load balancing is obliged to exhaust all mentioned conditions. The proposed load balancing algorithm for Erlang concurrent tasks (those are and could also be autonomous and unstable.) on VMware workstations is introduced. There are several load patterns within the clouds corresponding to CPU’s load (utilization), memory load (queue size), link capacity load (bandwidth), and so on. The proposed load balancing is to spot underloaded and overloaded conditions then stabilizes the weight amidst computing nodes. There are countless load balancing approaches in the cloud environment to examine performance parameters. A short outline of corresponding performance metrics in the review and their findings are presented. To investigate the fit efficiency of the proposed algorithm, the simulation is applied then results based on the proposed method are compared to the existing ones. The outcomes settle the weight balancing, outperform others when executing Erlang traffic, and are catered in the context.</p>

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“…In the case of the Greedy Algorithm, every time the data items were taken in a sequence which was deemed best in accordance with certain criterion. The GA will arrive at a new solution by making some choices that look the best [23]. This algorithm will begin with an empty set that adds items to a set in a sequence until such time a set represents a new solution to the problem.…”

Section: B Data Replication Using Greedy Search Algorithmmentioning

confidence: 99%

A Cuckoo Search Based Heuristic for Replicating IoT Data in Cloud Edge System

2020

IJRTE

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The rapid development in information technology has rendered an increase in the data volume at a speed which is surprising. In recent times, cloud computing and the Internet of Things (IoT) have become the hottest among the topics in the industry of information technology. There are many advantages to Cloud computing such as scalability, low price, and large scale and the primary technique of the IoTs like the Radio-Frequency Identification (RFID) have been applied to a large scale. In the recent times, the users of cloud storage have been increasing to a great extent and the reason behind this was the cloud storage system bringing down the issues in maintenance and also has a low amount of storage when compared to other methods. This system provides a high degree of reliability and availability where redundancy is introduced to the systems. In the replicated systems, objects get to be copied many times and every copy resides in a different location found in distributed computing. So, replication of data has been posing some threat to the cloud storage for users and also for the providers since it has been a major challenge providing efficient storage of data. So, the work has been analysing different strategies of replication of data and have pointed out several issues that are affected by this. For the purpose of this work, replication of data has been presented by employing the Cuckoo Search (CS) and the Greedy Search. The research is proceeding in a direction to reduce the replications without any adverse effect on the reliability and the availability of data.

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Virtual Machines Migration based on Greedy Algorithm in Cloud Computing

Cited by 9 publications

References 7 publications

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

Efficient Smart Grid Load Balancing via Fog and Cloud Computing

Cloud computing based load balancing algorithm for erlang concurrent traffic

A Cuckoo Search Based Heuristic for Replicating IoT Data in Cloud Edge System

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