Energy-Aware Autonomic Resource Allocation in Multitier Virtualized Environments

Ardagna, Danilo; Panicucci, Barbara; Trubian, Marco; Zhang, Li

doi:10.1109/tsc.2010.42

Cited by 166 publications

(139 citation statements)

References 32 publications

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“…[24][25][26], we set c P to $0.08, set c A to $0.00024, and set c MIG to $0.00012. The VM migration delay is set to 5s, and the PM wakeup delay is set to 15s.…”

Section: Parameter Settingsmentioning

confidence: 99%

Green-Aware Virtual Machine Migration Strategy in Sustainable Cloud Computing Environments

Wang¹,

Zhang²,

Yang³

et al. 2017

Cloud Computing - Architecture and Applications

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As cloud computing develops rapidly, the energy consumption of large-scale datacenters becomes unneglectable, and thus renewable energy is considered as the extra supply for building sustainable cloud infrastructures. In this chapter, we present a green-aware virtual machine (VM) migration strategy in such datacenters powered by sustainable energy sources, considering the power consumption of both IT functional devices and cooling devices. We define an overall optimization problem from an energy-aware point of view and try to solve it using statistical searching approaches. The purpose is to utilize green energy sufficiently while guaranteeing the performance of applications hosted by the datacenter. Evaluation experiments are conducted under realistic workload traces and solar energy generation data in order to validate the feasibility. Results show that the green energy utilization increases remarkably, and more overall revenues could be achieved.

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“…[24][25][26], we set c P to $0.08, set c A to $0.00024, and set c MIG to $0.00012. The VM migration delay is set to 5s, and the PM wakeup delay is set to 15s.…”

Section: Parameter Settingsmentioning

confidence: 99%

Green-Aware Virtual Machine Migration Strategy in Sustainable Cloud Computing Environments

Wang¹,

Zhang²,

Yang³

et al. 2017

Cloud Computing - Architecture and Applications

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show abstract

“…Linear functions are a flexible mechanism to express the discontent of final end-users as a function of response times and have been widely used in the literature (see e.g. [12,15,38]). …”

Section: Saas Providersmentioning

confidence: 99%

“…The performance parameters of the applications and infrastructural resources costs have been randomly generated uniformly in the ranges reported in Table 2 as in [8,12,31], considering also real applications [11], according to commercial fees applied by IaaS/PaaS Cloud providers [4,33] and the energy costs available in the literature [28].…”

Section: Equilibria Efficiencymentioning

confidence: 99%

Generalized Nash equilibria for SaaS/PaaS Clouds

Anselmi

Ardagna

Passacantando

2014

European Journal of Operational Research

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Cloud computing is an emerging technology that allows to access computing resources on a pay-per-use basis. The main challenges in this area are the efficient performance management and the energy costs minimization.In this paper we model the service provisioning problem of Cloud Platform-as-aService systems as a Generalized Nash Equilibrium Problem and show that a potential function for the game exists. Moreover, we prove that the social optimum problem is convex and we derive some properties of social optima from the corresponding KarushKuhn-Tucker system. Next, we propose a distributed solution algorithm based on the best response dynamics and we prove its convergence to generalized Nash equilibria.Finally, we numerically evaluate equilibria in terms of their efficiency with respect to the social optimum of the Cloud by varying our algorithm initial solution. Numerical results show that our algorithm is scalable and very efficient and thus can be adopted for the run-time management of very large scale systems.

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“…Datacenter, VM and SLA modeling, and resource management solutions inside a datacenter are extensively discussed in the previous work, cf. [12,13,14,15,16,17,18]. In this section, a review of the most relevant work to the GLB problem is provided.…”

Section: Related Workmentioning

confidence: 99%

Geographical Load Balancing for Online Service Applications in Distributed Datacenters

Goudarzi

Pedram

2013

2013 IEEE Sixth International Conference on Cloud Computing

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-This work focuses on the load balancing problem for online service applications (which are response time-sensitive) considering a distributed cloud system comprised of geographically dispersed, heterogeneous datacenters. An offline solution based on force-directed scheduling is presented, which can determine the application placement for long periods of time. The solution is then extended to do online application placement and migration for geographically distributed datacenters based on predictions about the application lifetimes, workload intensities, dynamic energy prices, and renewable energy generation capacities at different datacenters in the cloud system. The simulation results demonstrate 27% to 40% improvement using the proposed algorithms with respect to the method that does not consider the geographical load balancing.

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Energy-Aware Autonomic Resource Allocation in Multitier Virtualized Environments

Cited by 166 publications

References 32 publications

Green-Aware Virtual Machine Migration Strategy in Sustainable Cloud Computing Environments

Green-Aware Virtual Machine Migration Strategy in Sustainable Cloud Computing Environments

Generalized Nash equilibria for SaaS/PaaS Clouds

Geographical Load Balancing for Online Service Applications in Distributed Datacenters

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