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

Abstract

The existing online social network (OSN) services in a multiple-cloud (Multicloud) environment use replications to store user data for improving the service performance. However, it not only generates tremendous traffic for synchronization between data but also stores considerable redundant data, thus causing large storage costs. In addition, it does not provide dynamic load balancing considering the resource status of each cloud. As a result, it cannot cope with the degradation of performance caused by the resource contention. We introduce an adaptive data placement algorithm without the replications for improving the performance of the OSN services in the Multicloud environment. Our approach is designed to avoid server overhead using data balancing technique, which locates data from a cloud to another according to the amount of traffic. To provide acceptable latency delay, it also considers the relationship between users and the distance between user and cloud when transferring data. To validate our approach, we experimented with actual users' locations and times of use collected from OSN services. Our findings indicate that this approach can reduce the resource contention by an average of more than 59%, reduce storage volume to at least 50%, and maintain the latency delay under 50 ms.

SpeCH: A scalable framework for data placement of data-intensive services in geo-distributed clouds

“…The data placement problem has been studied extensively in the literature spanning a wide-variety of research areas, both from the perspective of execution environments: ranging from distributed systems (Chervenak et al, 2007;Golab et al, 2014) to cloud computing environments (Li et al, 2018;Ferdaus et al, 2017;Yu et al, 2012;Guo and Wang, 2013); application areas: scientific workflows (Yuan et al, 2010;Ebrahimi et al, 2015;Liu and Datta, 2011), online social network services (Jiao et al, 2014;Han et al, 2017), location aware data placement for geo-distributed cloud services (Yu and Pan, 2017;Zhang et al, 2016;Pan, 2015, 2016;Agarwal et al, 2010), and many more. Here, we provide an overview of the existing research that overlaps with the work presented in this article.…”

Scalable Data Placement of Data-intensive Services in Geo-distributed Clouds

“…The data placement problem has been studied extensively in the literature spanning a wide-variety of research areas, both from the perspective of execution environments: ranging from distributed systems (Chervenak et al, 2007;Golab et al, 2014) to cloud computing environments (Yuan et al, 2010;Li et al, 2017;Ebrahimi et al, 2015;Liu and Datta, 2011); application areas: online social network services (Jiao et al, 2014;Han et al, 2017), location aware data placement for geo-distributed cloud services (Yu and Pan, 2017;Zhang et al, 2016;Yu and Pan, 2015;Yu and Pan, 2016;Agarwal et al, 2010), and many more. Here, we provide an overview the existing works that overlap with our problem.…”

“…(Jiao et al, 2014) formulates a multi-objective social network aware optimization problem that performs data placement by building a model framework, which takes multiple objectives into consideration. (Han et al, 2017) introduce an adaptive data placement algorithm for social network services in a multicloud environment, which adapts to the changing data traffic for performing intelligent data migration decisions. (Rochman et al, 2013) have focused on placing data in a distributed environment to ensure that a large fraction of region specific requests are served at a lower cost.…”

UnifyDR: A Generic Framework for Unifying Data and Replica Placement

“…In the past decade, the data placement problem has witnessed extensive research with a wide variety of techniques developed for different execution environments, namelydistributed computing [9], [17], grid computing [13], [26], [27], and cloud computing [16], [19], [30], [44]. Initially, the focus of these works was on relational workloads such as database joins [17] and scientific workloads [14], [31], [45], however, recently the focus has shifted towards workloads emanating from specialized applications such as OSN services [21], [24] and data intensive services in geo-distributed clouds [1], [41]- [43], [47]. Given that our focus in this work is to combine data and replica placement as a single joint optimization problem, we only present a review of the existing literature on data placement that is directly related to our work.…”

“…Shifting the focus of our discussion to multi-cloud environments, Jiao et al [24] present a technique that takes multiple optimization objectives, such as inter-cloud traffic and carbon footprint, into consideration to perform data placement in multi-clouds. Later, Han et al [21] proposed an algorithm for OSN service data migration, which can adapt to the variation in data traffic in multi-clouds.…”