Power Consumption-Aware Virtual Machine Allocation in Cloud Data Center

Portaluri, Giuseppe; Adami, Davide; Gabbrielli, Andrea; Giordano, Stefano; Pagano, Michele

doi:10.1109/glocomw.2016.7849005

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…This paper is an extension of our previous work [4] with two new contributions. First of all, we added a comparison between two versions of our allocators:…”

Section: Introductionmentioning

confidence: 86%

“…• the disjoint allocator, which performs a two step allocation, first choosing the server where to place the VM and then looking for the proper network path; • the joint allocator that considers at the same time both computational and network requirements. Moreover, in [4] we evaluated the behavior of the joint allocators when we saturate our DC by allocating as many VMs as possible; now, instead, we consider the behavior of all our allocators during the transient phase, showing some "snapshots" of the DC state and comparing the DC performance at different loads.…”

Section: Introductionmentioning

confidence: 99%

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Power Consumption-Aware Virtual Machine Placement in Cloud Data Center

Portaluri

Adami

Gabbrielli

et al. 2017

IEEE Trans. on Green Commun. Netw.

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In this paper, we present a set of power-aware dynamic allocators for virtual machines (VMs) in cloud data centers (DCs) taking advantage of the software defined networking paradigm. Each VM request is characterized by four parameters: 1) CPU; 2) RAM; 3) disk; and 4) bandwidth. We design the allocators in order to accept as many VM requests as possible, taking into account the power consumption of the network devices. In this paper, we introduce ten different allocation strategies, and compare them with a baseline that consists of using the first available server (first fit). The allocators differ in terms of allocation policy (best fit/worst fit), allocation strategy (single/multi objective optimization), and joint/disjoint selection of IT and network resources. For both joint and disjoint approaches, we evaluate the behavior of all possible pairs policy-strategy, varying the load of the DC and the number of VMs to be allocated. Moreover, the experimental results highlight that joint approaches outperform disjoint ones

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“…This paper is an extension of our previous work [4] with two new contributions. First of all, we added a comparison between two versions of our allocators:…”

Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Power Consumption-Aware Virtual Machine Placement in Cloud Data Center

Portaluri

Adami

Gabbrielli

et al. 2017

IEEE Trans. on Green Commun. Netw.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Em Portaluri et al [30] os autores realizam a implementac ¸ão de diferentes algoritmos de alocac ¸ão baseados em otimizac ¸ão com abordagem da FL para único ou multi-objetivo, duas variantes de uma heurística muiti-objetiva e uma abordagem analítica. O objetivo central do trabalho é o foco no encaminhamento de pacotes visando a eficiência energética, além de agregar ao trabalho o objetivo da pesquisa anterior em Adami et al [36].…”

Section: Trabalhos Relacionadosunclassified

Int-FLBCC: Model for Load Balancing in Cloud Computing using Fuzzy Logic Type-2 and Admissible Orders.

Schneider

Moura

Yamin

et al. 2020

RITA

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Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing, directly affecting Quality of Service aspects. This paper presents Int-FLBCC, a new proposal with exploring a Type-2 Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. Validation results in a simulated cloud computing environment showed improvements in energy efficiency of 8.83% with IQR_XY and 22.43% with MAD_XY. For fulfillment of Service Level Agreements (SLA), the best values achieved were 9.06% with MAD_XY and 25% of THR_Lex1.

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“…(2) Portaluri et al [17] implement different allocation algorithms based on a Fuzzy Logic approach for single or multi-objective, two variants of a multi-objective heuristic, and an analytic approach. The primary objective of the work is the focus on the routing of packages aiming at energy efficiency.…”

Section: Related Workmentioning

confidence: 99%

Type-2 Fuzzy Logic Approach for Overloaded Hosts in Consolidation of Virtual Machines in Cloud Computing

Moura¹,

Schneider²,

Yamin³

et al. 2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing. Determining when it is best to relocate VMs from an overloaded host is one aspect of dynamic VM consolidation that directly influences the resource utilization and Quality of Service (QoS) offered by the system. This paper presents a new proposal with a Type-2 Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. Results in a simulated cloud computing environment show improvements in energy efficiency of 15.64% in the Median Absolute Deviation (MAD), 15.43% in the Inter Quartile Range (IQR), and 9.19% in the Local Regression (LR).The following research questions stand out: (i) do load balancers consider the uncertainties associated with factors such as CP, CC, and RAM? (ii) are the load balancers equipped with consistent techniques for the treatment of the uncertainties related to cloud computing environments? and (iii) do the users have the necessary knowledge about the computational and communication demands of their applications?This proposal extends the approaches in [16,15], modeling the uncertain variables generating CP, CC, and RAM in Load Balancing (LB) considering Type-2 Fuzzy Logic (T2FL) which can preserve the balance between energy efficiency and manage resources while ensuring QoS through SLAs for such cloud-based services.The paper is structured as follows. Related works are presented in section 2. Section 3 introduces basic concepts of Type-2 Fuzzy Logic. In section 4, details of the Int-FLBCC component and its conception are discussed, including database, fuzzification, rule base, inference, and defuzzification. Section 5 describes the experimental evaluation. Finally, section 6 presents conclusions and future work.

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Power Consumption-Aware Virtual Machine Allocation in Cloud Data Center

Cited by 6 publications

References 21 publications

Power Consumption-Aware Virtual Machine Placement in Cloud Data Center

Power Consumption-Aware Virtual Machine Placement in Cloud Data Center

Int-FLBCC: Model for Load Balancing in Cloud Computing using Fuzzy Logic Type-2 and Admissible Orders.

Type-2 Fuzzy Logic Approach for Overloaded Hosts in Consolidation of Virtual Machines in Cloud Computing

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