Multi-objective Virtual Machine Placement for Load Balancing

Feng, Fang; Qu, Binbin

doi:10.1051/itmconf/20171101011

Cited by 8 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MBBO/DE algorithm offers better results than the ACO algorithm and the genetic algorithm [33]. As can be seen in Table 1, the MOACO algorithm provides better results than ACO, first fit (FF) and greedy scheduling (GS) algorithms [34]. From the comparison shown in Table 2, it can be concluded that the LBA-HB algorithm is more efficient than round robin, modified throttled, ACO, artificial bee colony (ABC) and honey bee [32].…”

Section: Comparison and Analysis Of Optimization Algorithmsmentioning

confidence: 98%

See 1 more Smart Citation

Load Balancing Based on Optimization Algorithms: An Overview

Mbarek¹,

Mosorov²

2019

JTIT

View full text Add to dashboard Cite

Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other significant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to efficiently handle load balancing issues.

show abstract

Section: Comparison and Analysis Of Optimization Algorithmsmentioning

confidence: 98%

“…• For load balancing, MBBO/DE algorithm provides same results as ACO algorithm in scenario 1, • Migration time is longer than in other algorithms simulated in scenario 2. MOACO algorithm Multi-objective virtual machine placement for load balancing [34].…”

Section: Moo Algorithmmentioning

confidence: 99%

Load Balancing Based on Optimization Algorithms: An Overview

Mbarek¹,

Mosorov²

2019

JTIT

View full text Add to dashboard Cite

show abstract

“…Modern cloud systems have numerous open research challenges including resource management, load balancing, security, privacy, and multi‐clouds 16–22. In this section, the recent works on load balancing are summarized as this study concentrates on multi‐objective load balancing.…”

Section: Related Workmentioning

confidence: 99%

“…VM placement approaches can be classified based on the number of objectives being optimized and the optimization scheme being used. Based on the number of objectives, these algorithms can be classified into single‐objective and multi‐objective solutions 16,22,24. Furthermore, VM placement schemes can be classified into mono‐objective, multi‐objective as mono‐objective (MAM), and true multi‐objective approaches based on the optimization scheme employed 25.…”

Section: Related Workmentioning

confidence: 99%

Resource‐efficient load‐balancing framework for cloud data center networks

2020

View full text Add to dashboard Cite

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource‐management approach. In this paper, we present a novel load‐balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics‐based VM placement approaches.

show abstract

“…Here, u c ¯ , u m ¯ , and u b ¯ represent the average utilization rates of CPU, memory, and bandwidth of all PMs, respectively. In a similar work, Fang and Qu 14 propose a metric called outer load balancing degree (OBD) to reflect the degree of load balancing among the PMs. Gao et al 15 propose a resource wastage model to minimize the total resource wastage and fully utilize multidimensional resources.…”

Section: Modeling and Formulationmentioning

confidence: 99%

An efficient load balancing algorithm for virtual machine allocation based on ant colony optimization

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

With the rapid development of information technologies and the popularization of Internet applications, more and more companies and developers pay great attention to the cloud computing. As one of the most significant problems in cloud computing, virtual machine allocation has attracted significant attention. However, early studies usually ignore the load balance issue of the resources. In this article, we aim at multidimensional resource load balancing of all the physical machines in the cloud computing platform to maximize the utilization of resources. To achieve this goal, we leverage the ant colony optimization to design an efficient virtual machine allocation algorithm based on the NP-hard feature of this problem. Specifically, we customize the ant colony optimization in the context of virtual machine allocation and introduce an improved physical machine selection strategy to the basic ant colony optimization in order to prevent the premature convergence or falling into the local optima. Through extensive simulations, we demonstrate that our proposed algorithm can effectively achieve load balancing in virtual machine allocation and improve resource utilization for the cloud computing platform.

show abstract

Multi-objective Virtual Machine Placement for Load Balancing

Cited by 8 publications

References 19 publications

Load Balancing Based on Optimization Algorithms: An Overview

Load Balancing Based on Optimization Algorithms: An Overview

Resource‐efficient load‐balancing framework for cloud data center networks

An efficient load balancing algorithm for virtual machine allocation based on ant colony optimization

Contact Info

Product

Resources

About