Treffer: CloudSNAP: A transparent infrastructure for decentralized web deployment using distributed interception

Abstract: Over the last years we have seen the proliferation of many new popular web applications, which are commonly used on a daily basis by most of us. The challenges that have to be overcome by web application designers include how to make these applications support as much concurrent users as possible, without degrading application’s performance, and without single points of failure. Such complex task would be much easier to achieve if designers could concentrate on the application functionalities without worrying about its wide-area scope and derived problems. In this article, we introduce CloudSNAP, a decentralized web deployment platform. CloudSNAP allows transforming any actual web application into a globally-enabled and scalable one. By using a distributed Peer-to-Peer (P2P) Cloud interception middleware, all necessary functionalities are injected into existent web infrastructures in a transparent way. Therefore, CloudSNAP provides many benefits from P2P Cloud computing, like a decentralized deployment environment as well as a set of distributed mechanisms, like load balancing, fault tolerance, dynamic activation, persistence and replication. Moreover, our solution offers important advantages: (i) a high degree of transparency and decoupling in all provided services by means of distributed interception techniques, and (ii) the direct deployment of existent Java Enterprise Edition (Java EE) applications and services with practically no changes on them. In summary, CloudSNAP makes it easy to deploy any Java EE web application into a P2P Cloud infrastructure, and immediately benefit from all of its inherent services at a minimal development and infrastructure cost. [Copyright &y& Elsevier]

Copyright of Future Generation Computer Systems is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)