Many-objective virtual machine placement for dynamic environments

Ihara, Diego; Lopez-Pires, Fabio; Barán, Benjamín

doi:10.1109/ucc.2015.22

Cited by 17 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering that six selection strategies were evaluated, these strategies are compared through two methods [23] in order to analyse which selection strategy could be considered as the best optionin terms of the values of the objective functions:…”

Section: Selection Strategy Analysismentioning

confidence: 99%

A Multi-Objective Approach for Multi-Cloud Infrastructure Brokering in Dynamic Markets

Lopez-Pires¹,

Chamorro²,

Barán³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Cloud Service Brokers (CSBs) facilitate complex resource allocation decisions, efficiently mapping dynamic tenant demands onto dynamic provider offers, where several objectives should ideally be considered. This work proposes for the first time a pure multi-objective formulation of a broker-oriented Virtual Machine Placement (VMP) problem for dynamic environments, simultaneously optimizing the following objective functions: (i) Total Infrastructure CPU (TICPU), (ii) Total Infrastructure Memory (TIMEM) and (iii) Total Infrastructure Price (TIP) while considering load balancing across providers. To solve the formulated multi-objective problem, a Multi-Objective Evolutionary Algorithm (MOEA) is proposed. Considering that each time a demand (or offer) change occurs, a set of non-dominated solutions is found by Pareto-based algorithms as the one proposed, different selection strategies were evaluated in order to automatically select a convenient solution. Additionally, the proposed algorithm, including the considered selection strategies, was compared against mono-objective state-of-the-art alternatives in different scenarios with real data from providers in actual markets. Experimental results demonstrate that a pure multi-objective optimization approach considering the preferred solution selection strategy (S3) outperformed other mono-objective evaluated alternatives.

show abstract

Section: Selection Strategy Analysismentioning

confidence: 99%

A Multi-Objective Approach for Multi-Cloud Infrastructure Brokering in Dynamic Markets

Lopez-Pires¹,

Chamorro²,

Barán³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Objectives [1,10,20,33,52,69,87] • The extent to which an objective is prioritized over other objectives.…”

Section: Sharability [54 78]mentioning

confidence: 99%

“…• Place a VM in a specific PM [48,71]. [20], Memetic Algorithm [33,52] Ant-colony [50,51], heuristics [54] Hybrid algorithm [69] Matching…”

Section: Sharability [54 78]mentioning

confidence: 99%

“…Indeed, one of the most well-suited roles of preferences in EA is to reduce the final Pareto set. Thus, the work of Ihara et al [33], Blagodurov et al [10] and López-Pires and Barán [52] considered administrator preferences to reduces final solutions of the VM problem. A decentralized datacenter over different local departments have been considered in Saber et al [69] where it assumes every department administrator has a (possibly conflicting) preference.…”

Section: Sharability [54 78]mentioning

confidence: 99%

“…Reducing Search Space Multiobjective problems typically obtain multiple Pareto solutions, and the number of these solutions grows significantly as the number of objectives grows. Thus, preferences have been utilized to help mitigate this burden [2,20,33,51,52,69]. The same can be said about other algorithms e.g., alternative based algorithms.…”

Section: Potential Benefitsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on the Use of Preferences for Virtual Machine Placement in Cloud Data Centers

Alashaikh¹,

Alanazi²,

Al-Fuqaha³

2019

Preprint

View full text Add to dashboard Cite

With the rapid developments of virtualization techniques, cloud data centers have had enabled cost effective, flexible, and customizable deployments of applications in virtualized infrastructure. Virtual machine placement problem is a problem of paramount importance to the design of cloud data centers. It aims to assign each virtual machine to a server in the cloud environment. Typically, the problem involves complex relations and multiple design factors as well as local policies that govern the assignment decisions. It also involves different parties including cloud administrators and customers that we need to consider their preferences while opting for a solution. Thus, it is significant to not only return an optimized solution to the underlying placement problem but also a solution that reflects the given preferences of these parties. In this paper, we provide a detailed review on the role of preferences in the current literature of the virtual machine placement problem. We further discuss some challenges and identify possible research opportunities to better incorporate preferences within the problem.

show abstract

Resource Allocation for Cloud Infrastructures: Taxonomies and Research Challenges

Barán

Lopez-Pires

2017

Research Advances in Cloud Computing

View full text Add to dashboard Cite

Many-objective virtual machine placement for dynamic environments

Cited by 17 publications

References 14 publications

A Multi-Objective Approach for Multi-Cloud Infrastructure Brokering in Dynamic Markets

A Multi-Objective Approach for Multi-Cloud Infrastructure Brokering in Dynamic Markets

A Survey on the Use of Preferences for Virtual Machine Placement in Cloud Data Centers

Resource Allocation for Cloud Infrastructures: Taxonomies and Research Challenges

Contact Info

Product

Resources

About