Scaling Service-oriented Applications into Geo-distributed Clouds
Refereed conference paper presented and published in conference proceedings

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AbstractWith the significant prevalence of cloud computing, more and more data centers are built to host and deliver various online services. However, a key challenge faced by service providers is how to scale their applications into geo-distributed data centers to improve application performance as well as minimizing the operational cost. While most existing deployment methods ignore the service dependencies in an application, this paper proposes a general dynamic service deployment framework to bridge this gap, in which a deployment manager and a local scheduler are designed to optimize data center selection and auto-scale the service instances in each data center respectively. More specifically, we formulate the deployment problem across multiple data centers as a compact minimization model, which can be solved efficiently by a genetic algorithm. To evaluate the performance of our approach, extensive experiments are conducted based on a large-scale real-world latency dataset. The experimental results show that our approach substantially outperforms the other existing methods.
All Author(s) ListZhu JM, Zheng ZB, Zhou YF, Lyu MR
Name of ConferenceIEEE 7th International Symposium on Service-Oriented System Engineering (SOSE)
Start Date of Conference25/03/2013
End Date of Conference28/03/2013
Place of ConferenceRedwood City
Country/Region of ConferenceUnited States of America
Pages335 - 340
LanguagesEnglish-United Kingdom
KeywordsDynamic deployment; genetic algorithm; geo-distributed clouds; service-oriented application
Web of Science Subject CategoriesComputer Science; Computer Science, Hardware & Architecture; Engineering; Engineering, Electrical & Electronic

Last updated on 2021-15-10 at 00:05