A cloud‐assisted publish/subscribe service for time‐critical dissemination of bulk content. (29th December 2016)

• Record Type: Journal Article
• Authors: Ma, Xingkong; Wang, Yijie; Pei, Xiaoqiang; Xu, Fangliang
• Is Part Of: Concurrency and computation. Volume 29:Number 8(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-12-29
• Subjects: cloud‐assisted -- cloud computing -- data dissemination -- publish/subscribe; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4047
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Characterized by the increasing arrival rate of live content, emergency applications pose a great challenge: how to disseminate data with diverse sizes to interested users in a real‐time manner. Most file sharing applications focus on the dissemination of bulk content with less consideration of users' interests. On the other hand, existing publish/subscribes are designed for notifying interested users with small‐sized content. To bridge this gap, we propose CAPS, a cloud‐assisted publish/subscribe service for time‐critical bulk content dissemination. In CAPS, a hybrid 2‐layer architecture is proposed to knit servers in the cloud and clients in the internet. Through dividing each event into attribute‐value pairs and the data content, CAPS provides both event matching service and data distribution in a parallel manner. To improve the upload bandwidth of data distribution, we propose a helper‐based content distribution protocol, where the servers not only guide the clients with similar interests to exchange their received data blocks but also contribute their own upload capacities to clients. Moreover, a volume‐aware helper renting scheme is proposed to adaptively adjust the scale of servers according to the churn of data volume, leading to a high‐performance price ratio. So as to evaluate the performance of CAPS, about 1000 virtual machines are deployed in our Cloud‐Stack testbed. Extensive experiments confirm that CAPS can linearly reduce the download completion time with the growing number of servers, adaptively adjust the upload capacity in tens of seconds according to the change of the workloads, and ensure reliable data dissemination even if a large number of nodes frequently churn or instantaneously fail. Compared with the state‐of‐the‐art approaches, CAPS demonstrates better performance under various parameter settings.