Social Community-Partition Aware Replica Allocation in Ad-Hoc Social Networks

Ahmed, Ahmedin Mohammed; Xia, Feng; Asabere, Nana Yaw; Liaqat, Hannan Bin; Li, Jie

doi:10.1109/greencom-ithings-cpscom.2013.147

Cited by 3 publications

(1 citation statement)

References 25 publications

(31 reference statements)

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“…The existence of communities in a social structure or network system is unquestionable. Therefore, previous works [8], [9], [10], [11] define community as a structural subunit that can be represented as a set of individuals belonging to a social network with a high density of internal links.…”

Section: Community Formationmentioning

confidence: 99%

Community-Based Event Dissemination with Optimal Load Balancing

Xia

Ahmed

Yang

et al. 2015

IEEE Trans. Comput.

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Distributed publish/subscribe systems are poised with challenges of performance degradation and poor scalability. This is typically caused by an uneven load distribution of real-world applications and the susceptibility of link failure in networks. Partitioning and replication techniques have been implemented by exploring community-based load balancing to cope with such issues. The novel approach herein exploits offloading at the inter-community level as well as filter replication at the intra-community level. This results in the dynamic distribution and forwarding of publication and subscription services among brokers during run time. The proposed method, Co-Lab (COmmunity-based LoAd Balancing), seeks to improve the network performance by clustering brokers in a community by taking into consideration interest similarity and filter replication. It attempts to effectively achieve a more consistent and uniform load distribution among brokers and to circumvent the occurrence of highly overloaded brokers. Performance evaluations indicate that Co-Lab has promising advantages by achieving relatively better load balance, reduced overall load and robustness against failures.

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