Many-Objective Virtual Machine Placement

Lopez-Pires, Fabio; Barán, Benjamín

doi:10.1007/s10723-017-9399-x

Cited by 41 publications

(17 citation statements)

References 22 publications

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“…Genetic algorithms (GAs) [23] are a subset of evolutionary algorithms [24], that have emerged as flexible and efficient metaheuristic methods for solving optimization problems and achieving a high level of problem-solving efficacy in most research domains, e.g., aircraft design [25], battery systems [26], resource allocation [27], job-shop scheduling [28], virtual machine placement [29], cloud task scheduling [30], quadratic assignment [31], and vehicle…”

Section: Genetic Algorithmsmentioning

confidence: 99%

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

Wang

Chen

2020

IEEE Access

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Most traditional recommender systems focus specifically on increasing consumer satisfaction by providing a list of relevant content to consumers. However, the perspectives of other multisided marketplace stakeholders are also equally important, i.e., the exposure for suppliers or providers and profit for the platform. The suppliers want their products to be presented to users, and the objective of the platform is to maximize their profit. Nevertheless, because consumers' preferences and the objectives of providers as well as the platform may conflict with each other, it degrades the utility of the recommendation methods by only considering users' views. Therefore, in this work, we use a many-objective optimization method to maintain a tradeoff among five objectives for three stakeholders and obtain multiple Pareto front solutions in a single run. We first combine customer lifetime value and user purchase preference to create a new similarity model (Sim_RFMP) to increase the recommendation accuracy of the recommendation list. Furthermore, we propose a many-objective model (NBHXMAOEA) for multistakeholder recommendation. In NBHXMAOEA, we present a novel N-block heuristic crossover operator (NBHX) that recombines blocks of chromosomes based on heuristics. Through extensive experiments, the results demonstrate that our proposed NBHXMAOEA achieves superior performance in terms of average accuracy, diversity, novelty, provider coverage, and platform profit to its competing methods. INDEX TERMS Many-objective, recommender systems, similarity model, stakeholders.

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Section: Genetic Algorithmsmentioning

confidence: 99%

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

Wang

Chen

2020

IEEE Access

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“…where f Energy represents the total power consumption of the PMs, while PM [62,63] defines the minimum power consumption of PM i . U CPU i (g) represents the utilization ratio of resource utilized by PM i at instant t, while Y i (g) ∈ [0, 1] is equal to 1 if the PM i is turned on; otherwise, Y i (g) = 0.…”

Section: Objective Functionsmentioning

confidence: 99%

Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Alresheedi

Elaziz

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOS-SASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the

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“…The scenario filtering is based on the solution's dominance concept, used in previous studies . However, the original definition compares the objectives assessments separately, which is not suitable to environments with many objectives (four or more) . Instead, we compare the summary of the placements' evaluations, to prevent stagnation due to quantity of objectives, a combination of all qualifiers assessments and their weights.…”

Section: Related Workmentioning

confidence: 99%

“…4,17,25 However, the original definition compares the objectives assessments separately, which is not suitable to environments with many objectives (four or more). 3,25 Instead, we compare the summary of the placements' evaluations, to prevent stagnation due to quantity of objectives, a combination of all qualifiers assessments and their weights. As a final complement, we also filter out scenarios with costs beyond the budget, ie, greater than the max cost function.…”

Section: Discussing the Basesmentioning

confidence: 99%

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Order@Cloud: An agnostic meta‐heuristic for VM provisioning, adaptation, and organisation

2019

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Summary We propose a flexible meta‐heuristic framework for virtual machine (VM) organisation, provisioning, and adaptation in the cloud domain, based on migration costs and environment constraints. Order@Cloud improves VM placements according to multiple objectives represented by rules, qualifiers, and improvement cost, which can be easily modified and extended. Order@Cloud theoretically guarantees the adoption of a better set of placements, after considering their costs and benefits, by prioritising the worst VM placements. While existing solutions address only specific objectives, our framework is objective‐agnostic and extensible, which enables the adoption and implementation of new policies and priorities. We conduct experiments using a real cloud environment data and discuss the framework's performance, flexibility, and optimality and provide insights on the challenges and benefits of deploying this framework.

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Many-Objective Virtual Machine Placement

Cited by 41 publications

References 22 publications

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Order@Cloud: An agnostic meta‐heuristic for VM provisioning, adaptation, and organisation

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