Resource Allocation for Cloud Infrastructures: Taxonomies and Research Challenges

Barán, Benjamín; Lopez-Pires, Fabio

doi:10.1007/978-981-10-5026-8_11

Cited by 2 publications

(9 citation statements)

References 62 publications

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“…The above mentioned surveys and research articles focused into specific issues related to the VMP problem. Consequently, López-Pires and Barán proposed in [8,9,10] a general and extensive study of a large part of the VMP literature including 84 studied articles [24], presenting a wide analysis of the existing approaches for the formulation and resolution of the VMP as an optimization problem. Additionally, a novel taxonomy was proposed in [8] for the classification of the studied articles by the following criteria: (1) optimization approach, (2) objective function and (3) solution technique.…”

Section: Vmp Taxonomies For Cloud Computingmentioning

confidence: 99%

“…The taxonomy presented in [8] was extended in later works [9,10], including novel taxonomies and presenting a detailed view of the existing approaches as well as several possible research opportunities to further advance in this research area. The taxonomies presented in [9,10] could guide interested readers to: (1) understand different possible environments where a VMP problem could be studied, considering both provider and broker perspectives in different deployment architectures, (2) identify existing approaches for the formulation and resolution of the VMP as an optimization problem and (3) present a detailed view of the VMP problem, identifying research opportunities to further advance in this research area.…”

Section: Single-cloudmentioning

confidence: 99%

“…Additionally, both provider-oriented and brokeroriented VMP problems, in any of the possible deployment architectures, could be studied considering two different types of formulation: offline or online formulations. Interested readers could refer to [9,10] for details on the VMP Environment Taxonomy presented in Figure 2.1.…”

Section: Vmp Environment Taxonomymentioning

confidence: 99%

“…These criteria can even change from one period of time to another, which implies a variety of possible environments, formulations and objectives to be considered for optimization of the VMP problem. As part of the summarised research, nearly 60 different objective functions were identified in the VMP literature [8,9,10]. Taking into account the large number of existing objective functions, providers of cloud computing datacenters must be able to formulate the VMP problem as a Pure Multi-Objective Optimization Problem (PMO), optimizing more than just one objective function at a time.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, determining which solution to keep and which to discard in order to converge toward the Pareto set is still a relevant issue to be addressed [12], making more difficult to solve MaOPs. Clearly, existing difficulties in solving MaOPs explain why Many-Objective Optimization was considered an unexplored domain in resource management of cloud computing datacenters before this research work [14] and no many-objective formulation was proposed for the VMP problem [8,9,10].…”

Section: Introductionmentioning

confidence: 99%

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Many-Objective Resource Allocation for Elastic Infrastructures in Overbooked Cloud Computing Datacenters Under Uncertainty

Lopez-Pires

2021

CLEIej

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In cloud computing resource allocation, Virtual Machine Placement (VMP) is one of the most studied problems with several possible formulations and different optimization criteria. The present work summarizes main advances focused on studying Many-Objective Virtual Machine Placement (MaVMP) problems. As first contributions, novel taxonomies were proposed for VMP problems in cloud computing environments, in order to gain a systematic understanding of the existing approaches. Additionally, first formulations of MaVMP problems were proposed in: (1) static MaVMP for initial placement, (2) semi-dynamic MaVMP with reconfiguration of VMs and (3) dynamic two-phase MaVMP for complex cloud computing environments under uncertainty. Considering the novelty of the proposed formulations, several methods and algorithms were also proposed to address main identified issues on solving each particular MaVMP problem. Experimental results prove the correctness, effectiveness and scalability of the proposed methods and algorithms in different experimental scenarios when comparing to state-of-the-art and industry alternatives. Open challenges for further advance of the area are discussed.

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Section: Vmp Taxonomies For Cloud Computingmentioning

confidence: 99%

Section: Single-cloudmentioning

confidence: 99%

Section: Vmp Environment Taxonomymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Many-Objective Resource Allocation for Elastic Infrastructures in Overbooked Cloud Computing Datacenters Under Uncertainty

Lopez-Pires

2021

CLEIej

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Rationalizing Resource Utilization in Cloud Computing Using Coalition Formation Strategy

Noureldin¹,

Fadel²

2021

Journal of Computer Science

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Even though the contribution of cloud computing towards the Sustainable Development (SD) of communities is still under research investigation, cloud computing has become an integral part of many ICT solutions that shape our daily lives. Thus, some researchers recommend taking considerable actions to point cloud computing development towards supporting SD. In this research, an approach to designing energy efficient cloud architecture as a way of supporting SD is proposed. Resource allocation is a challenging process in cloud management, the goal is to allocate the exact amount of resources needed throughout the service duration; tight enough to avoid unnecessarily wasting resources and loose enough to prevent any degradation in Quality of Service (QoS) that may lead to the violation of the Service Level Agreement (SLA) between the service provider and the cloud user. This study aims to achieve the desired balance by benefiting from the history of the user's behaviour and from sharing resourcesmore specifically Virtual Machines (VM)among a coalition of users. Coalition formation strategy is used to build groups of cloud users based on their cloud behaviour history. Users are grouped in a way that their usage patterns complement each other, either to avoid the loss stemming from VM excess reserved space or from idle times. A type of architecture that fulfils this improvement process is proposed and implemented on Google Compute Engine (GCE). The contribution of this research is that it applies the Coalition formation strategy in cloud computing resource management in a novel way and experiments show that there are scenarios where the efficiency of resource management has improved. Evaluation of the performance of the proposed architecture is done by comparing resource utilization for both the cloud following this architecture and the cloud that runs the basic GCE strategy. In conclusion, it is observed that improvements depend on accuracy of the prediction of usage pattern of the user. Results show that in certain scenarios, improvements can be made to up to 24% of VM usage and, in other scenarios, it can minimize the number of required VMs, thus contributing to green computing.

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Resource Allocation for Cloud Infrastructures: Taxonomies and Research Challenges

Cited by 2 publications

References 62 publications

Many-Objective Resource Allocation for Elastic Infrastructures in Overbooked Cloud Computing Datacenters Under Uncertainty

Many-Objective Resource Allocation for Elastic Infrastructures in Overbooked Cloud Computing Datacenters Under Uncertainty

Rationalizing Resource Utilization in Cloud Computing Using Coalition Formation Strategy

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