A novel robust on-line protocol for load-balancing in structured peer-to-peer systems

Tsatsanifos, G; Samoladas, V

dc.contributor.author	Tsatsanifos, G	en
dc.contributor.author	Samoladas, V	en
dc.date.accessioned	2014-03-01T11:46:38Z
dc.date.available	2014-03-01T11:46:38Z
dc.date.issued	2012	en
dc.identifier.issn	0010485X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/37988
dc.subject	Distributed hash-tables	en
dc.subject	Load-balancing	en
dc.subject	Multiple realities	en
dc.subject	Range queries	en
dc.subject	Replication	en
dc.subject	Structured peer-to-peer systems	en
dc.subject	Virtual nodes	en
dc.title	A novel robust on-line protocol for load-balancing in structured peer-to-peer systems	en
heal.type	other	en
heal.identifier.primary	10.1007/s00607-012-0199-7	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00607-012-0199-7	en
heal.publicationDate	2012	en
heal.abstract	In this paper, we revisit the problem of load-balancing structured peer-to-peer systems with on-line protocols. Load-balancing is of major significance for large-scale decentralized networks in terms of enhanced scalability and performance. The main incentives behind balancing schemes are under-utilization of bandwidth and computer resources. Therefore, our methods focus mainly on task-skew. Specifically, we address the problem with on-line protocols on the basis of migration and enhanced availability. In particular, the cornerstones of our methods are the notions of virtual nodes, replication and Multiple realities, combined altogether with allocation techniques based on balls-in-bins games. The rationale of our dynamic protocol to depend exclusively on peer load distribution preserves intact the structural properties and search efficiency of the overlay used as an indexing infrastructure, while preserving the semantic information of the data (e.g., range partitioned network). We also propose an effective load-aware mechanism to facilitate robust operations that counteract against contingent churn failures. Finally, our work is complemented with extensive experiments using both real and synthetic data sets. © 2012 Springer-Verlag.	en
heal.journalName	Computing	en
dc.identifier.doi	10.1007/s00607-012-0199-7	en
dc.identifier.spage	1	en
dc.identifier.epage	32	en