CloudPick: a framework for QoS‐aware and ontology‐based service deployment across clouds. (15th September 2014)

• Record Type: Journal Article
• Authors: Dastjerdi, Amir Vahid; Garg, Saurabh Kumar; Rana, Omer F.; Buyya, Rajkumar
• Is Part Of: Software, practice & experience. Volume 45:Number 2(2015)
• Page Start: 197
• Page End: 231
• Publication Date: 2014-09-15
• Subjects: Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2288
• Languages: English
• ISSNs: 0038-0644

Abstract

<abstract abstract-type="main" id="spe2288-abs-0001"> <title>SUMMARY</title> <p id="spe2288-para-0001">The cloud computing paradigm allows on‐demand access to computing and storage services over the Internet. Multiple providers are offering a variety of software solutions in the form of virtual appliances and computing units in the form of virtual machines with different pricing and QoS in the market. Thus, it is important to exploit the benefit of hosting virtual appliances on multiple providers to not only reduce the cost and provide better QoS but also achieve failure‐resistant deployment. This paper presents a framework called CloudPick to simplify cross‐cloud deployment and particularly focuses on QoS modeling and deployment optimization. For QoS modeling, cloud services have been automatically enriched with semantic descriptions using our translator component to increase precision and recall in discovery and benefit from descriptive QoS from multiple domains. In addition, an optimization approach for deploying networks of appliances is required to guarantee minimum cost, low latency, and high reliability. We propose and compare two different deployment optimization approaches: genetic‐based and forward‐checking‐based backtracking. They take into account QoS criteria such as reliability, data communication cost, and latency between multiple clouds to select the most appropriate combination of virtual machines and appliances. We evaluate our approach using a real case study and different request types. Experimental results suggest that both algorithms reach near‐optimal solution. Further, we investigate the effects of factors such as latency, reliability requirements, and data communication between appliances on the performance of the algorithms and placement of appliances across multiple clouds. The results show the efficiency of optimization algorithms depends on the data transfer rate between appliances. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>