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A Novel Multilevel Index Model for Distributed service Repositories

Tsinghua Science and Technology 2017年第3期22卷 273-281页

作者： Zhao Xu Yan Wu Dejun Miao Lu Liu School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China Department of ComputerScience Boise State UniversityID 83725USA Department of computing and Mathematics University of DerbyDerbyDE22 1GBUK Department of computing and Mathematics University of DerbyDerbyDE22 1GBUK and the School of Computer Science and Communication EngineeringJiangsu UniversityZhenjiang 212013China

This study, based on the theory of equivalence relations, proposes a novel multilevel index model for decentralized service repositories to eliminate redundant information and enhance the time-management quality of the service retrieval process of the service repository architecture. An efficient resource discovery algorithm based on Discrete Hash Tables is presented to enable efficient and effective retrieval services among different distributed repositories. The performance of the proposed model and the supporting algorithms have been evaluated in a distributed environment. Experimental results validate the effectiveness of our proposed indexing model and search algorithm.

关键词： service computing distributed algorithm service retrieval service storage

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QoS-Aware Virtual Machine Scheduling for Video Streaming services in Multi-Cloud

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Tsinghua Science and Technology 2013年第3期18卷 308-317页

作者： Wei Chen Junwei Cao Yuxin Wan the Research Institute of Information Technology Tsinghua National Laboratory for Information Science and TechnologyTsinghua University

Video streaming services are trending to be deployed on cloud. Cloud computing offers better stability and lower price than traditional IT facilities. Huge storage capacity is essential for video streaming service. More and more cloud providers appear so there are increasing cloud platforms to choose. A better choice is to use more than one data center, which is called multi-cloud. In this paper a closed-loop approach is proposed for optimizing Quality of service （QoS） and cost. Modules of monitoring and controlling data centers are required as well as the application feedback such as video streaming services. An algorithm is proposed to help choose cloud providers and data centers in a multi-cloud environment as a video service manager. Performance with different video service workloads are evaluated. Compared with using only one cloud provider, dynamically deploying services in multi-cloud is better in aspects of both cost and QoS. If cloud service costs are different among data centers, the algorithm will help make choices to lower the cost and keep a high QoS.

关键词： cloud computing dynamic scheduling data centers video streaming service computing performance evaluation Quality of service (QoS)

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维普期刊数据库

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Performance Evaluation and Dynamic Optimization of Speed Scaling on Web Servers in Cloud computing

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Tsinghua Science and Technology 2013年第3期18卷 298-307页

作者： Yuan Tian Chuang Lin Zhen Chen Jianxiong Wan Xuehai Peng the Department of Computer Science and Technology Tsinghua University the Research Institute of Information Technology and Tsinghua National Laboratory for Information Science and Technology (TNList) Tsinghua University the College of Information Engineering Inner Mongolia University of TechnologyJinchuan Development Area Hohhot Inner Mongolia 010080 China the Beijing Municipal Commission of Economy and Information

The energy consumption in large-scale data centers is attracting more and more attention today with the increasing data center energy costs making the enhanced performance very expensive. This is becoming a bottleneck to further developments in terms of both scale and performance of cloud computing. Thus, the reduction of the energy consumption by data centers is becoming a key research topic in green IT and green computing. The web servers providing cloud service computing run at various speeds for different scenarios. By shifting among these states using speed scaling, the energy consumption is proportional to the workload, which is termed energy-proportionality. This study uses stochastic service decision nets to investigate energy-efficient speed scaling on web servers. This model combines stochastic Petri nets with Markov decision process models. This enables the model to dynamically optimize the speed scaling strategy and make performance evaluations. The model is graphical and intuitive enough to characterize complicated system behavior and decisions. The model is service-oriented using the typical service patterns to reduce the complex model to a simple model with a smaller state space. Performance and reward equivalent analyse substantially reduces the system behavior sub-net. The model gives the optimal strategy and evaluates performance and energy metrics more concisely.

关键词： cloud computing green IT energy consumption data centers stochastic Petri nets performance evaluation dynamic optimization service computing

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