Power-Aware Management in Cloud Data Centers

Milenković, Milan; Castro-Leon, Enrique; Blakley, James R.

doi:10.1007/978-3-642-10665-1_70

Cited by 16 publications

(11 citation statements)

References 6 publications

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“…They need to access large numbers of datasets, which may be replicated at different data centres (Milenkovic et al, 2009;Song et al, 2009;Winter, 2009). The economic cost of each data replica itself can never be overlooked because cloud computing is actually a model of business computing (Liu et al, 2011).…”

Section: Optimizations In Data-intensive Service Compositionmentioning

confidence: 99%

Bio-Inspired Cost-Effective Access to Big Data

Wang¹,

Shen

2014

Proceedings of the International Symposium for Next Generation Infrastructure

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With the rapid proliferation of services and cloud computing, Big Data has become a significant phenomenon across many scientific disciplines and sectors of society, wherever huge amounts of data are generated and processed daily. End users will always seek higher-quality data access at lower prices. This demand poses challenges to service composers, service providers and data providers, who should maintain their service and data provision as cost-effectively as possible. This paper will apply bio-inspired approaches to achieving equilibrium among the otherwise competitive stakeholders. In addition to novel models of cost for Big Data provision, bio-inspired algorithms will be developed and validated for dynamic optimisation. Furthermore, the optimised algorithms will also be applied in the data-mining research on the Alpha Magnetic Spectrometer (AMS) experiment, which is aiming to find dark matter in the universe. This experiment typically receives 200G and generates 700G data daily. ABSTRACTWith the rapid proliferation of services and cloud computing, Big Data has become a significant phenomenon across many scientific disciplines and sectors of society, wherever huge amounts of data are generated and processed daily. End users will always seek higher-quality data access at lower prices. This demand poses challenges to service composers, service providers and data providers, who should maintain their service and data provision as cost-effectively as possible. This paper will apply bio-inspired approaches to achieving equilibrium among the otherwise competitive stakeholders. In addition to novel models of cost for Big Data provision, bio-inspired algorithms will be developed and validated for dynamic optimisation. Furthermore, the optimised algorithms will also be applied in the data-mining research on the Alpha Magnetic Spectrometer (AMS) experiment, which is aiming to find dark matter in the universe. This experiment typically receives 200G and generates 700G data daily.

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Section: Optimizations In Data-intensive Service Compositionmentioning

confidence: 99%

Bio-Inspired Cost-Effective Access to Big Data

Wang¹,

Shen

2014

Proceedings of the International Symposium for Next Generation Infrastructure

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“…Each service requests data sets from the storage resources (or data servers). Each of these data sets may be replicated at several locations that are connected to each other and to the service endpoints through networks of varying capability [10], [15], [24]. When composing data-intensive services, optimizing the cost of data is a priority, as data play the dominant role in the data-intensive service composition.…”

Section: A An Economical Model Of Data-intensive Service Provisionmentioning

confidence: 99%

“…There are two branches in all the conditional patterns with the probability of 0.5. The price of a data set, the network bandwidth (Mbps) between each data server and service endpoint, the storage media speed (Mbps), the size (MB) of a data set and the number of data request in the waiting queue were randomly generated with uniform distribution from the following intervals: [1,100], [1,100], [1,100], [1000,10000] and [1,10].…”

Section: A Test Case Generationmentioning

confidence: 99%

An Improved Genetic Algorithm for Cost-Effective Data-Intensive Service Composition

Wang

Shen

Luo³

et al. 2013

2013 Ninth International Conference on Semantics, Knowledge and Grids

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The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of IT trends in the current decade. Designing workflow of data-intensive services requires data analysis from multiple sources to get required composite services. Composing such services requires effective transfer of large data. Thus many new challenges are posed to control the cost and revenue of the whole composition. This paper addresses the data-intensive service composition and presents an innovative data-intensive service selection algorithm based on a modified genetic algorithm. The performance of this new algorithm is also tested by simulations and compared against other traditional approaches, such as mix integer programming. The contributions of this paper are three folds: 1) An economical model for dataintensive service provision is proposed, 2) An extensible QoS model is also proposed to calculate the QoS values of data-intensive services, 3) Finally, a modified genetic algorithm-based approach is introduced to compose data-intensive services. A local selection method with modifications of crossover and mutation operators is adopted for this algorithm. The results of experiments will demonstrate the scalability and effectiveness of our proposed algorithm. Abstract-The explosion of digital data and the dependence on data-intensive services have been recognized as the most significant characteristics of IT trends in the current decade. Designing workflow of data-intensive services requires data analysis from multiple sources to get required composite services. Composing such services requires effective transfer of large data. Thus many new challenges are posed to control the cost and revenue of the whole composition. This paper addresses the data-intensive service composition and presents an innovative data-intensive service selection algorithm based on a modified genetic algorithm. The performance of this new algorithm is also tested by simulations and compared against other traditional approaches, such as mix integer programming. The contributions of this paper are three folds: 1) An economical model for data-intensive service provision is proposed; 2) An extensible QoS model is also proposed to calculate the QoS values of data-intensive services; 3) Finally, a modified genetic algorithmbased approach is introduced to compose data-intensive services. A local selection method with modifications of crossover and mutation operators is adopted for this algorithm. The results of experiments will demonstrate the scalability and effectiveness of our proposed algorithm.

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“…Live migration and placement optimizations of virtual machines (VM) have been used in earlier works to provide a mechanism to achieve energy efficiency [33,34,35]. Power aware provisioning and scheduling of VMs has also been used with DVFS techniques to reduce the overall power consumption [36,37].…”

Section: Related Workmentioning

confidence: 99%