A two-phase virtual machine placement policy for data-intensive applications in cloud

Sadegh, Samaneh; Zamanifar, Kamran; Kasprzak, Piotr; Yahyapour, Ramin

doi:10.1016/j.jnca.2021.103025

Cited by 9 publications

(4 citation statements)

References 14 publications

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“…Lastly, they present holistic evaluation experiments to validate the feasibility and evaluate the performance of the proposed methods. By representing the VM placement challenge as the minimal weight K-vertex-connected induced subgraph, the authors of [4] examine the issue. They present a unique two-phase technique for setting up virtual machines on hosts and demonstrate the NP-Hardness of the problem.…”

Section: Related Workmentioning

confidence: 99%

A Study on Graph-Based Affinity Aware VM Colocation Problems

Sensarma

2024

Int. J. Res. Publ. Rev.

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The rapid growth of cloud computing has led to an unprecedented demand for efficient virtual machine (VM) allocation strategies in data centers. One critical challenge is the Affinity-Aware VM Colocation Problem, which aims to optimise the placement of virtual machines while considering the affinity relationships between co-located VMs. In this paper, a study on some existing approaches to cope with affinity-aware VM Colocation problems based on graph theory is given.

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Section: Related Workmentioning

confidence: 99%

A Study on Graph-Based Affinity Aware VM Colocation Problems

Sensarma

2024

Int. J. Res. Publ. Rev.

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“…In placement, many methods with different objectives have been proposed from the container/VNF placement point of view [12]. Some typical examples are the minimization of operational cost and service latency [13,12], optimizing the host utilization and the communication cost while considering load balancing [14], jointly optimizing cloud data center energy usage and resource utilization in [15], balancing the access, switching and communication delay via access network selection and service placement in [16]. Hosting all VNFs on the same host can minimize the cost [13] while latency minimization can be achieved by replicating VNF instances through load balancing [12].…”

Section: Related Workmentioning

confidence: 99%

A Multi-criteria Decision Making Approach for Scaling and Placement of Virtual Network Functions

et al. 2022

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This paper investigates the joint scaling and placement problem of network services made up of Virtual Network Functions (VNFs) that can be provided inside a cluster managing multiple Points of Presence (PoPs). Aiming at increasing the VNF service satisfaction rates and minimizing the deployment cost, we use both transport and cloud-aware VNF scaling as well as multi-attribute decision making (MADM) algorithms for VNF placement inside the cluster. The original joint scaling and placement problem is known to be NP-hard and hence the problem is solved by separating scaling and placement problems and solving them individually. The experiments are done using a dataset containing the information of a deployed digital-twin network service. These experiments show that considering transport and cloud parameters during scaling and placement algorithms perform more efficiently than the only cloud based or transport based scaling followed by placement algorithms. One of the MADM algorithms, Total Order Preference By Similarity to the Ideal Solution

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“…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].…”

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%

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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A two-phase virtual machine placement policy for data-intensive applications in cloud

Cited by 9 publications

References 14 publications

A Study on Graph-Based Affinity Aware VM Colocation Problems

A Study on Graph-Based Affinity Aware VM Colocation Problems

A Multi-criteria Decision Making Approach for Scaling and Placement of Virtual Network Functions

Virtual Machine Placement Optimization for Big Data Applications in Cloud Computing

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