Big Data Aware Virtual Machine Placement in Cloud Data Centers

Hall, Logan; Harris, Bryan T.; Tomes, Erica; Altiparmak, Nihat

doi:10.1145/3148055.3148057

Cited by 5 publications

(6 citation statements)

References 31 publications

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Section: Related Workmentioning

confidence: 99%

“…VM placement or consolidation is a challenging issue in cloud computing that has been extensively investigated in recent years [5], [8][9][10], [14][15][16], [28][29][30][31], [33]. So far, different analyses have been performed, and various VM placement solutions have been proposed [28]. Since the placement problem is proven to be NP-hard in the general case [14][15], [28][29], [34][35], alternative algorithms using different techniques are presented to solve it.…”

Section: Related Workmentioning

confidence: 99%

“…So far, different analyses have been performed, and various VM placement solutions have been proposed [28]. Since the placement problem is proven to be NP-hard in the general case [14][15], [28][29], [34][35], alternative algorithms using different techniques are presented to solve it. Ant colony optimization (ACO) [14], [36], genetic algorithm (GA) [15][16], [30], greedy algorithm [4], [28], [33], game theory [32], and biogeography-based optimization (BBO) [37] are some of the heuristic or metaheuristic methods.…”

Section: Related Workmentioning

confidence: 99%

“…Since the placement problem is proven to be NP-hard in the general case [14][15], [28][29], [34][35], alternative algorithms using different techniques are presented to solve it. Ant colony optimization (ACO) [14], [36], genetic algorithm (GA) [15][16], [30], greedy algorithm [4], [28], [33], game theory [32], and biogeography-based optimization (BBO) [37] are some of the heuristic or metaheuristic methods. Furthermore, some other models like graph [5], [8], [29], [33], approximation algorithms [38], and linear programming [9-10], [33] have been widely used to solve the problem.…”

Section: Related Workmentioning

confidence: 99%

“…Table 2 shows the used notations in the AGAFF. The Knapsack is an NP-hard problem [14][15], [28][29], [34][35], including a set of items, each with a weight and a value. Our proposed method is to maximize the total value regarding the weight limitation.…”

Section: Problem Formulationmentioning

confidence: 99%

See 4 more Smart Citations

Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing

Seyyedsalehi

Khansari

2022

IEEE Access

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Big data and cloud computing are two advanced technologies that have overcome many computing and analytical challenges in recent years. With the rise in the applications of these technologies, the necessity of efficiency and optimization in the utilization of related resources has made sense. The procedure of locating virtual machines (VM) in physical machines (PM) affects the performance, speed, and costs of cloud computing services. VM placement in cloud computing is an NP-hard problem. Indeed, the problem is more complicated in big data tasks due to the need for transferring high volumes of traffic between VMs. This paper proposes a new approach for VM placement in a multi data center (DC) cloud environment. The aware genetic algorithm first fit (AGAFF) is a context-aware algorithm that distinguishes big data tasks with an input tag and uses a structure to minimize the traffic between MapReduce nodes. This multi-objective algorithm is based on the genetic algorithm, which is incorporated with the first fit methodology. The algorithm minimizes energy usage by minimizing the number of used servers, intra-DC traffic of big data tasks, and VMs' live migration while maximizing relevant usage of CPU and RAM in every server. Furthermore, it improves job execution time, especially in big data processing, and reduces service level agreement (SLA) violations. A comparison between the results of AGAFF and four other algorithms shows by about 61% energy consumption reduction on average on different scales and approves a decrease in the number of needed PMs, intra-DC traffic of big data processing, and the number of live migrations.

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