Novel algorithms and equivalence optimisation for resource allocation in cloud computing

Lin, Weiwei; Zhu, Chaoyue; Li, Jin; Liu, Bo; Lian, Huiqiong

doi:10.1504/ijwgs.2015.068899

Cited by 23 publications

(13 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, power models are often used to evaluate the effectiveness of resource scheduling algorithms (e.g. Peng et al 2015;Lin et al 2015b) because of its feasibility. However, currently available power models proposed by previous researches have defects listed below:…”

Section: Introductionmentioning

confidence: 99%

An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment

Lin

Peng

2016

Soft Comput

Self Cite

View full text Add to dashboard Cite

Cloud computing has gained enormous popularity by providing high availability and scalability as well as on-demand services. However, with the continuous rise of energy consumption cost, the virtualized environment of cloud data centers poses a challenge to today's power monitoring system. Software-based power monitoring is gaining prevalence since power models can work precisely by exploiting soft computing methodologies like genetic programming and swarm intelligence for model optimization. However, traditional power models barely consider virtualization and have drawbacks like high error rate, low feasibility as well as insufficient scalability. In this paper, we first analyze the power signatures of virtual machines in different configurations through experiments. Then we propose a virtual machine (VM) power model, named CAM, which is able to adapt to the reconfiguration of VMs and provide accurate power estimating under CPU-intensive workload. We also propose two training methodologies corresponding to two typical situations for model training. CAM can estimate the power of a single VM as well as a physical server hosting several heterogeneous VMs. We exploited public Linux benchmarks to evaluate CAM .The experimental results show that CAM produced very small errors in power estimating for both VMs (4.26 % on average) and the host server (0.88 % on average).

show abstract

Section: Introductionmentioning

confidence: 99%

An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment

Lin

Peng

2016

Soft Comput

Self Cite

View full text Add to dashboard Cite

show abstract

“…There have already been vast amount of research work in the area of cloud service discovery and composition, along with techniques and tools that are powerful enough for cloud service consumers to rely on. However, the attention was paid to the Infrastructure-as-a-Service (IaaS) layer and virtualisation types [17][18][19][20], as opposed to energy efficient service searching, allocation, and provision.…”

Section: Problem Statementmentioning

confidence: 99%

Cloud-SEnergy: A bin-packing based multi-cloud service broker for energy efficient composition and execution of data-intensive applications

Baker

Aldawsari

Asim

et al. 2018

Sustainable Computing: Informatics and Systems

View full text Add to dashboard Cite

The over-reliance of today's world on Information and Communication Technologies (ICT) has led to an exponential increase in data production, network traffic, and energy consumption. To mitigate the ecological impact of this increase on the environment, a major challenge that this paper tackles is how to best select the most energy efficient services from cross-continental competing cloud-based datacenters. This selection is addressed by our Cloud-SEnergy, a system that uses a bin-packing technique to generate the most efficient service composition plans. Experiments were conducted to compare Cloud-SEnergy's efficiency with 5 established techniques in multi-cloud environments (All clouds, Base cloud, Smart cloud, COM2, and DC-Cloud). The results gained from the experiments demonstrate a superior performance of Cloud-SEnergy which ranged from an average energy consumption reduction of 4.3% when compared to Based Cloud technique, to an average reduction of 43.3% when compared to All Clouds technique. Furthermore, the percentage reduction in the number of examined services achieved by Cloud-SEnergy ranged from 50% when compared to Smart Cloud and average of 82.4% when compared to Base Cloud. In term of run-time, Cloud-SEnergy resulted in average reduction which ranged from 8.5% when compared to DC-Cloud, to 28.2% run-time reduction when compared to All Clouds.

show abstract

“…This is one among several publications dealing with the carbon emissions or renewables in relation to DCs. In particular, the literature is rich with new algorithms for optimizing workload distribution among data centres [21][22][23][24][25][26][27][28][29] and server electricity demand optimization [30,31]. Some publications have also highlighted the role of geographic location or siting in carbon emissions and energy use optimization [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

The Water Footprint of Data Centers

2015

View full text Add to dashboard Cite

Abstract:The internet and associated Information and Communications Technologies (ICT) are diffusing at an astounding pace. As data centers (DCs) proliferate to accommodate this rising demand, their environmental impacts grow too. While the energy efficiency of DCs has been researched extensively, their water footprint (WF) has so far received little to no attention. This article conducts a preliminary WF accounting for cooling and energy consumption in DCs. The WF of DCs is estimated to be between 1047 and 151,061 m 3 /TJ. Outbound DC data traffic generates a WF of 1-205 liters per gigabyte (roughly equal to the WF of 1 kg of tomatos at the higher end). It is found that, typically, energy consumption constitues by far the greatest share of DC WF, but the level of uncertainty associated with the WF of different energy sources used by DCs makes a comprehensive assessment of DCs' water use efficiency very challenging. Much better understanding of DC WF is urgently needed if a meaningful evaluation of this rapidly spreading service technology is to be gleaned and response measures are to be put into effect.

show abstract

Novel algorithms and equivalence optimisation for resource allocation in cloud computing

Cited by 23 publications

References 31 publications

An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment

An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment

Cloud-SEnergy: A bin-packing based multi-cloud service broker for energy efficient composition and execution of data-intensive applications

The Water Footprint of Data Centers

Contact Info

Product

Resources

About