Scaling social media applications into geo-distributed clouds

Abstract

Federation of geo-distributed cloud services is a trend in cloud computing which, by spanning multiple data centers at different geographical locations, can provide a cloud platform with much larger capacities. Such a geo-distributed cloud is ideal for supporting large-scale social media streaming applications (e.g., YouTube-like sites) with dynamic contents and demands, owing to its abundant on-demand storage/bandwidth capacities and geographical proximity to different groups of users. Although promising, its realization presents challenges on how to efficiently store and migrate contents among different cloud sites (i.e. data centers), and to distribute user requests to the appropriate sites for timely responses at modest costs. These challenges escalate when we consider the persistently increasing contents and volatile user behaviors in a social media application. By exploiting social influences among users, this paper proposes efficient proactive algorithms for dynamic, optimal scaling of a social media application in a geo-distributed cloud. Our key contribution is an online content migration and request distribution algorithm with the following features: (1) future demand prediction by novelly characterizing social influences among the users in a simple but effective epidemic model; (2) oneshot optimal content migration and request distribution based on efficient optimization algorithms to address the predicted demand, and (3) a ∆(t)-step look-ahead mechanism to adjust the one-shot optimization results towards the offline optimum. We verify the effectiveness of our algorithm using solid theoretical analysis, as well as large-scale experiments under dynamic realistic settings on a home-built cloud platform.

Exploiting Geo-Distributed Clouds for a E-Health Monitoring System With Minimum Service Delay and Privacy Preservation

“…Proof: We prove theorem 1 through showing that (14) satisfies the sufficient condition (9). Equation (14) could be written as…”

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“…Equation (20) could also be written in the form of (9). Thus, the relationship between M j,i and U i,j could be described by…”

Adaptive Data Placement for Improving Performance of Online Social Network Services in a Multicloud Environment

“…These are actual locations of Microsoft Azure [7]. In order to make the distance between a user and a cloud seem to be geographically distant, we used formula (5), which creates a latency delay according to the distance between the user and the cloud [23] RTT (ms) = 0.02 × Distance (km) + 5.…”

A GGQS-based hybrid algorithm for inter-cloud time-critical event dissemination

“…Each cloud site resides in one data center, and contains a collection of interconnected and virtualized servers.The cloud architecture is illustrated in Figure 3.1. If a time-critical event is occurred in a cloud, the event must be disseminated to all clouds via the inter-cloud overlay (Wu et al, 2012).…”