Selective Data replication for Online Social Networks with Distributed Datacenters

Liu, Guoxin; Shen, Haiying; Chandler, Harrison

doi:10.1109/icnp.2013.6733582

Cited by 44 publications

(22 citation statements)

References 19 publications

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“…Recent work proposes maintaining social locality to address this issue: SPAR [24] minimizes the total number of slave replicas while maintaining social locality for every user; S-CLONE [28] maximizes the number of users whose social locality can be maintained, given a fixed number of replicas per user. For OSN across multiple sites, some propose selective replication of data across data centers to reduce the total inter-data-center traffic [20], and others propose a framework that captures and optimizes multiple dimensions of the OSN system objectives simultaneously [15]. The work in [24] and [28] does not have the concern of QoS as in our geo-distribution case.…”

Section: Related Workmentioning

confidence: 99%

“…The work in [24] and [28] does not have the concern of QoS as in our geo-distribution case. Besides, the cost models in all the aforementioned existing work, except [15], do not capture the monetary expense and cannot fit the cloud scenario, while [15] and [20] do not explore social locality to optimize the multi-data-center OSN service.…”

Section: Related Workmentioning

confidence: 99%

“…Existing work on OSN service provisioning either pursues least cost in a single site without the QoS concern as in the geo-distribution case [24], [28] or aims for least inter-data-center traffic in the case of multiple data centers without considering other dimensions of the service [20], e.g., data availability. More importantly, the models in all such work do not capture the monetary cost of resource usage and thus cannot fit the cloud scenario.…”

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confidence: 99%

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Optimizing Cost for Online Social Networks on Geo-Distributed Clouds

Jiao

et al. 2016

IEEE/ACM Trans. Networking

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Geo-distributed clouds provide an intriguing platform to deploy online social network (OSN) services. To leverage the potential of clouds, a major concern of OSN providers is optimizing the monetary cost spent in using cloud resources while considering other important requirements, including providing satisfactory quality of service (QoS) and data availability to OSN users. In this paper, we study the problem of cost optimization for the dynamic OSN on multiple geo-distributed clouds over consecutive time periods while meeting predefined QoS and data availability requirements. We model the cost, the QoS, as well as the data availability of the OSN, formulate the problem, and design an algorithm named . We carry out extensive experiments with a large-scale real-world Twitter trace over 10 geo-distributed clouds all across the US. Our results show that, while always ensuring the QoS and the data availability as required,can reduce much more one-time cost than the state-of-the-art methods, and it can also significantly reduce the accumulative cost when continuously evaluated over 48 months, with OSN dynamics comparable to real-world cases.Index Terms-Cloud computing, online social network, optimization models and methods, performance analysis and evaluation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimizing Cost for Online Social Networks on Geo-Distributed Clouds

Jiao

et al. 2016

IEEE/ACM Trans. Networking

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“…Cassandra performs random data partitioning and replication that are blind to social locality. As a result, it leads to high inter-server traffic caused by user operations in OSNs [6], [7], [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…Aggressively striving for maximum social locality in replication does not necessarily optimize the total inter-server traffic. A recent SD 3 mechanism [9] proposes to create replicas only when they save more read-incurred traffic than the write-incurred traffic produced. Nevertheless, it assumes fixed data partitions.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Inter-server Communication for Online Social Networks

Tang

Yuan

2015

2015 IEEE 35th International Conference on Distributed Computing Systems

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Distributed storage systems are the key infrastructures for hosting the user data of large-scale Online Social Networks (OSNs). The amount of inter-server communication is an important scalability indicator for these systems. Data partitioning and replication are two inter-related issues affecting the inter-server traffic caused by user-initiated read and write operations. This paper investigates the problem of minimizing the total inter-server traffic among a cluster of OSN servers through joint partitioning and replication optimization. We propose a Traffic-Optimized Partitioning and Replication (TOPR) method based on an analysis of how replica allocation affects the interserver communication. Lightweight algorithms are developed to adjust partitioning and replication dynamically according to data read and write rates. Evaluations with real Facebook and Twitter social graphs show that TOPR significantly reduces the interserver communication compared with state-of-the-art methods.

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Towards traffic minimization for data placement in online social networks

Zhou

Fan

Wang

et al. 2016

Concurrency and Computation

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With the increasing number of users and a huge scale of data, the service providers of Online Social Networks (OSNs) are facing the problem of how to place users' data to multiple servers. Key-value stores solve the problem based on consistent hashing, and have become a defacto standard. However, random placement manner of hashing cannot preserve social locality, which leads to high intra-data center traffic and unpredictable response time. Many existing works solve the problem by using graph partitioning algorithms. These works have two drawbacks: First, the social graph is constructed with ordinary pairwise graph that cannot fully reflect multi-participant interactions often occurring in OSNs. Second, the underlying network topologies of data center have never been considered. This paper investigates the problem of traffic minimization for OSNs data storage. Motivated by maximally preserving both social locality and distance locality, we formulate the problem as two sub-problems -hypergraph partitioning and partition-to-server mapping, and propose a two-phase data placement (TDP) scheme. Specifically we present two algorithms to solve partition-to-server mapping over two widely used network topologies (i.e., tree and BCube). Evaluations with a large scale Facebook trace show that TDP significantly reduces intra-data center traffic as well as load balancing across servers.

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Selective Data replication for Online Social Networks with Distributed Datacenters

Cited by 44 publications

References 19 publications

Optimizing Cost for Online Social Networks on Geo-Distributed Clouds

Optimizing Cost for Online Social Networks on Geo-Distributed Clouds

Optimizing Inter-server Communication for Online Social Networks

Towards traffic minimization for data placement in online social networks

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