Evolutionary multiobjective optimization for green clouds

Phan, Dung H.; Suzuki, Junichi; Carroll, Raymond J.; Balasubramaniam, Sasitharan; Donnelly, William J.; Botvich, Dmitri

doi:10.1145/2330784.2330788

Cited by 36 publications

(24 citation statements)

References 18 publications

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“…Multiobjective GA, binary code to indicate whether a cloud provider is selected to the federation [Song et al 2009] ACO distributed schedule resources of various cloud providers in the federation, so as to balance the dynamic load of providers Multiobjective GA selects a partner to carry a service, balance energy consumption and response time [Phan et al 2012] Scheduling for data routing (How to find cloud resources fast? )…”

Section: Scheduling For Service Placementmentioning

confidence: 99%

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Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

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Section: Scheduling For Service Placementmentioning

confidence: 99%

“…Phan et al [2012] made decisions of service migration and placement with an evolutionary multiobjective GA. The chromosome code is of the same length as the number of the services, and each gene stands for the index of the cloud provider in the federation.…”

Section: Scheduling For Partner Federationmentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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“…Some examples of work that reports the results of efforts towards green mapping of workload to data centers are: [26,27,28,29,30,31,32,33]. On a related note, Sucevic et.…”

Section: Related Workmentioning

confidence: 99%

RED-BL: Evaluating dynamic workload relocation for data center networks

et al. 2014

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In this paper, we present RED-BL (Relocate Energy Demand to Better Locations), a framework to minimize the electricity cost for operating data center networks over consecutive intervals of fixed duration. Within each interval, RED-BL provides a mapping of workload to a set of geographically distributed data centers. To this end, RED-BL uses the geographical and temporal variations in electricity prices as exhibited by electrical energy markets. In addition, we incorporate the transition costs associated with a change in workload mapping from one interval to the next, over a planning window comprising multiple such intervals. This results in a sequence of workload mappings that is optimal over the entire planning window, even though the workload mapping in a given interval may not be locally optimal.Our evaluation of RED-BL uses electricity prices from the US markets and workload traces from live Internet applications with millions of users. We find that RED-BL can reduce the electric bill by as much as 45% compared to the case when the workload is uniformly distributed. When compared to existing workload relocation solutions, for a wide range of data center deployment sizes, RED-BL achieves electricity cost savings that are 8.28% higher, on average. This seemingly modest reduction can save millions of dollars for the operators. The cost of this saving is an inexpensive computation at the start of each planning window.

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“…In 2010, renewable energy provided 312 GW worldwide, which accounted for 3% of global electricity generation 4 . Green Monster is an optimization engine to operate a federation of clouds in a sustainable manner [20]. It dynamically moves services (i.e., workload) to clouds with more desirable energy profiles while their maintaining performance (e.g., response time).…”

Section: Eip-to-mule Transformationmentioning

confidence: 99%

“…CE implies the power usage effectiveness (PUE) of clouds [21], and USD represents the response time of services to users. See [20] for full discussion on the EMOA in Green Monster. Fig.…”

Section: Eip-to-mule Transformationmentioning

confidence: 99%

Model-driven integration for a service placement optimizer in a sustainable cloud of clouds

Suzuki

Phan

Higuchi³

et al. 2012

The 6th International Conference on Soft Computing and Intelligent Systems, and the 13th International Symposium on Advanced In

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Abstract-"Cloud of clouds" (or federated cloud) is an emerging style of software deployment and execution to interoperate application services and data across geographically-distributed clouds. One of key research issues to realize this notion is intercloud integration. This paper proposes a model-driven integration (MDI) framework for federated clouds. The proposed MDI framework consists of (1) a metamodel to graphically define intercloud integration models using Enterprise Integration Patterns (EIPs) and Cloud Computing Patterns (CCPs) and (2) an MDI tool that accepts an integration model defined with the proposed metamodel and transforms it to integration code (e.g., program code and deployment configurations). This paper presents the design of the proposed MDI framework and describes an integration case study to build a bio-inspired optimization engine for dynamic service placement in a sustainable cloud of clouds.Index Terms-Cloud of clouds, federated clouds, model-driven system integration and sustainable clouds

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Evolutionary multiobjective optimization for green clouds

Cited by 36 publications

References 18 publications

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

RED-BL: Evaluating dynamic workload relocation for data center networks

Model-driven integration for a service placement optimizer in a sustainable cloud of clouds

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