ART: sub-logarithmic decentralized range query processing with probabilistic guarantees

Abstract

We focus on range query processing on large-scale, typically distributed infrastructures, such as clouds of thousands of nodes of shared-datacenters, of p2p distributed overlays, etc. In such distributed environments, efficient range query processing is the key for managing the distributed data sets per se, and for monitoring the infrastructure's resources. We wish to develop an architecture that can support range queries in such large-scale decentralized environments and can scale in terms of the number of nodes as well as in terms of the data items stored. Of course, in the last few years there have been a number of solutions (mostly from researchers in the p2p domain) for designing such large-scale systems. However, these are inadequate for our purposes, since at the envisaged scales the classic logarithmic complexity (for point queries) is still too expensive while for range queries it is even more disappointing. In this paper we go one step further and achieve a sub-logarithmic complexity. We contribute the ART (Autonomous Range Tree) structure, which outperforms the most popular decentralized structures, including Chord (and some of its successors), BATON (and its successor) and Skip-Graphs. We contribute theoretical analysis, backed up by detailed experimental results, showing that the communication cost of query and update operations is O(log 2 b log N) hops, where the base b is a double-exponentially power of two and N is the total number of nodes. Moreover, ART is a fully dynamic and fault-tolerant structure, which supports the join/leave node operations in O(log log N) expected w.h.p. number of hops. Our experimental performance studies include a detailed performance comparison which showcases the improved performance, scalability, and robustness of ART.

A P2P Framework for Developing Bioinformatics Applications in Dynamic Cloud Environments

“…The direct method is to adjust the ID of each node [25, 26], whereas the indirect way is to add more parameters as filters when performing operations [27, 28]. Yamato et al used a Locator/ID separation approach with the hierarchical Kademlia for new generation networks, and mobile nodes could retrieve specific data without explicit mapping [25].…”

“…Sioutas et al randomly chose a node to store an index table of the level clustered nodes, and these nodes were assigned an ordered NodeID by a given autonomous ranging factor [26]. In this way, the data searching process with peer churn would only require O (log⁡·log⁡ n ) hops for n nodes.…”

Dream

“…Apart from the fact that a number of packages facilitate development, a number of different protocols are available as sample code and executables in order to allow familiarization with the development and usage of the simulator: Chord, Baton*, Nested Balanced Distributed Tree (NBDT), NBDT*, R-NBDT* with advanced load distribution and ART [11]. Moreover, the respective abstract classes and programming steps are depicted also at a simplistic dummy protocol in order to guide the programmers that first use the simulation framework proposed.…”

Sart

“…Most existing decentralized discovery solutions in practice are either DHT based, like Chord or hierarchical clustering based, like BATON [3], NBDT [14], ART [16] or Skip-Graphs [3]. The majority of existing P2P overlays for sensornets were designed in a DHT fashion and the best current solution is the TChord.…”

“…In particular, it is not able to support neither join of sensor nodes with energy level out of the ranges supported by the existing p2p overlay nor leave of empty overlay peers to which no sensor nodes are currently associated. On this purpose and based on the efficient P2P method presented in [16], we design a dynamic P2P overlay for Energy Level discovery in a sensornet, the so-called SART (Sensors' Autonomous Range Tree). The adaptation of the P2P index presented in [16] guarantees the best-known dynamic query performance of the above operation.…”