Online Balancing of Range-Partitioned Data with Applications to Peer-to-Peer Systems

GANESAN, P; BAWA, M; GARCIAMOLINA, H

doi:10.1016/b978-012088469-8/50041-3

Cited by 92 publications

(158 citation statements)

References 10 publications

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“…Aspnes et al [36] and Ganesan et al [37] propose mechanisms to improve the load balance of range queries. Sahin et al [38] present a scheme for caching range queries.…”

Section: Customized Schemesmentioning

confidence: 99%

Efficient range query processing over DHTs based on the balanced Kautz tree

Zhang

Liu

et al. 2010

Concurrency and Computation

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SUMMARY Distributed Hash Tables (DHTs) are scalable, self-organizing, and adaptive to underlying topology changes, thus being a promising infrastructure for hosting large-scale distributed applications. The ever-wider use of DHT infrastructures has found more and more applications that require support for range queries. Recently, a number of DHT-based range query schemes have been proposed. However, most of them suffer from high query delay or imbalanced load distribution. To address these problems, in this paper we first present an efficient indexing structure called Balanced Kautz (BK) tree that uniformly maps the m-dimensional data space onto DHT nodes, and then propose a BK tree-based range query scheme called ERQ that processes range queries in a parallel fashion and guarantees to return the results in a bounded delay. In a DHT with N nodes, ERQ can answer any range of query in less than log N (2 log log N +1) hops in a load-balanced manner, irrespective of the queried range, the whole space size, or the number of queried attributes. The effectiveness of our proposals is demonstrated through experiments.

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“…Aspnes et al [36] and Ganesan et al [37] propose mechanisms to improve the load balance of range queries. Sahin et al [38] present a scheme for caching range queries.…”

Section: Customized Schemesmentioning

confidence: 99%

Efficient range query processing over DHTs based on the balanced Kautz tree

Zhang

Liu

et al. 2010

Concurrency and Computation

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“…Load Balancing. Past research on load balancing methods for distributed databases resulted in a number of methods for balancing storage load by managing the partitioning of the data [19,20]. Mariposa [21] offered load balancing by providing marketplace rules where data providers use bidding mechanisms.…”

Section: Data Streams and Continuous Queriesmentioning

confidence: 99%

Dynamic Query Processing for P2P Data Services in the Cloud

Jurczyk

Xiong

2009

Lecture Notes in Computer Science

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Abstract. With the trend of cloud computing, data and computing are moved away from desktop and are instead provided as a service from the cloud. Data-as-a-service enables access to a wealth of data across distributed and heterogeneous data sources in the cloud. We designed and developed DObjects, a general-purpose P2P-based query and data operations infrastructure that can be deployed in the cloud. This paper presents the details of the dynamic query execution engine within our data query infrastructure that dynamically adapts to network and node conditions. The query processing is capable of fully benefiting from all the distributed resources to minimize the query response time and maximize system throughput. We present a set of experiments using both simulations and real implementation and deployment.

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“…NEIX balances load through iterative item exchanges between neighboring nodes (see Figure 1, where iterative key exchanges between (A,B), (B,C) and (C,D) node pairs produce a balanced load). The majority of proposed approaches utilize a version of these two schemes in order to finally balance load among peers each responsible for a given range of the data [5][6][7]. While they both achieve their goal, their effectiveness and cost greatly vary, making a method that utilizes only one of them inefficient for all cases.…”

Section: Introductionmentioning

confidence: 99%

Measuring the cost of online load-balancing in distributed range-queriable systems

Konstantinou

Tsoumakos

Koziris

2009

2009 IEEE Ninth International Conference on Peer-to-Peer Computing

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Distributed systems such as Peer-to-Peer overlays have been shown to efficiently support the processing of range queries over large numbers of participating hosts. In such systems, uneven load allocation has to be effectively tackled in order to minimize overloaded peers and optimize their performance. In this work, we detect and analyze the two basic methodologies used to achieve load-balancing: Iterative key re-distribution between neighbors and node migration. Based on this analysis, we propose a hybrid method that adaptively utilizes these two extremes to achieve both fast and cost-effective load-balancing in distributed systems that support range queries. As a case study, we offer an implementation on top of a Skip Graph, where we validate our findings in a variety of workloads. Our experimental analysis shows that the hybrid method converges 10% faster than simple neighbor item exchanges and is more than 70% bandwidth efficient compared to simple node migrations.

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Online Balancing of Range-Partitioned Data with Applications to Peer-to-Peer Systems

Cited by 92 publications

References 10 publications

Efficient range query processing over DHTs based on the balanced Kautz tree

Efficient range query processing over DHTs based on the balanced Kautz tree

Dynamic Query Processing for P2P Data Services in the Cloud

Measuring the cost of online load-balancing in distributed range-queriable systems

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