OBST: A self-adjusting peer-to-peer overlay based on multiple BSTs

Avin, Chen; Borokhovich, Michael; Schmid, Stefan

doi:10.1109/p2p.2013.6688721

Cited by 4 publications

(6 citation statements)

References 26 publications

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“…In particular, it leads us to a third connection, namely to information and coding theory, see also [3] for a code-based network deisgn of arbitrary degree. It is known that the expected path length in binary search trees [23] as well as in network designs providing local routing [2,3,21] is related to the entropy H(X) (over the elements X in the data structure) resp. conditional entropy of the distribution: H(X|Y ) + H(Y |X) is a lower bound on the expected path length of local routing tree designs [21] where X, Y are the random variables distributed according to the marginal distribution of the sources and destinations in D. This bound is tight for the limited case where D is a product distribution (i.e., p(i, j) = p(i)p(j)).…”

Section: Putting Things Into Perspective and Related Workmentioning

confidence: 99%

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Demand-aware network designs of bounded degree

2019

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Traditionally, networks such as datacenter interconnects are designed to optimize worst-case performance under arbitrary traffic patterns. Such network designs can however be far from optimal when considering the actual workloads and traffic patterns which they serve. This insight led to the development of demand-aware datacenter interconnects which can be reconfigured depending on the workload.Motivated by these trends, this paper initiates the algorithmic study of demand-aware networks (DANs) designs, and in particular the design of boundeddegree networks. The inputs to the network design problem are a discrete communication request distribution, D, defined over communicating pairs from the node set V , and a bound, ∆, on the maximum degree. In turn, our objective is to design an (undirected) demand-aware network N = (V, E) of bounded-degree ∆, which provides short routing paths between frequently communicating nodes distributed across N . In particular, the designed network should minimize the expected path length on N (with respect to D), which is a basic measure of the efficiency of the network.We show that this fundamental network design problem exhibits interesting connections to several classic combinatorial problems and to information theory. We derive a general lower bound based on the entropy of the communication pattern D, and present asymptotically optimal network-aware design algorithms for important distribution families, such as sparse distributions and distributions of locally bounded doubling dimensions.

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Section: Putting Things Into Perspective and Related Workmentioning

confidence: 99%

“…Our objective is to minimize the expected path length [1,2,21] of the designed host network N ∈ N : For u, v ∈ V (N ), let d N (u, v) denote the shortest path between u and v in N . Given a distribution D over V × V and a graph N over V , the Expected Path Length (EPL) of route requests is defined as:…”

Section: Introductionmentioning

confidence: 99%

Demand-aware network designs of bounded degree

2019

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“…Preliminary versions of the results presented in this paper appeared at DISC 2012 [20], IEEE IPDPS 2013 [6], and IEEE P2P 2013 [4].…”

Section: Related Workmentioning

confidence: 99%

SplayNet: Towards Locally Self-Adjusting Networks

Schmid

Avin

Scheideler

et al. 2016

IEEE/ACM Trans. Networking

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Abstract-This paper initiates the study of locally selfadjusting networks: networks whose topology adapts dynamically and in a decentralized manner, to the communication pattern σ. Our vision can be seen as a distributed generalization of the selfadjusting datastructures introduced by Sleator and Tarjan [22]: In contrast to their splay trees which dynamically optimize the lookup costs from a single node (namely the tree root), we seek to minimize the routing cost between arbitrary communication pairs in the network.As a first step, we study distributed binary search trees (BSTs), which are attractive for their support of greedy routing. We introduce a simple model which captures the fundamental tradeoff between the benefits and costs of self-adjusting networks. We present the SplayNet algorithm and formally analyze its performance, and prove its optimality in specific case studies. We also introduce lower bound techniques based on interval cuts and edge expansion, to study the limitations of any demand-optimized network. Finally, we extend our study to multi-tree networks, and highlight an intriguing difference between classic and distributed splay trees.

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“…Compute the approximate median priority M by using the algorithm AMF. 5 Compute |L low |, |L high |, and |g s | using the balanced skip list formed by AMF if the condition in Equation 2 is true. 6 Determine the membership vector bit V x d by using P (x), M , |L low |, |L high |, and |g s | and update is-dominating-group D x d .…”

Section: E Assignment Of New Timestampsmentioning

confidence: 99%

Locally Self-Adjusting Skip Graphs

Huq

Ghosh

2017

2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)

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We present a distributed self-adjusting algorithm for skip graphs that minimizes the average routing costs between arbitrary communication pairs by performing topological adaptation to the communication pattern. Our algorithm is fully decentralized, conforms to the CON GEST model (i.e. uses O(log n) bit messages), and requires O(log n) bits of memory for each node, where n is the total number of nodes. Upon each communication request, our algorithm first establishes communication by using the standard skip graph routing, and then locally and partially reconstructs the skip graph topology to perform topological adaptation. We propose a computational model for such algorithms, as well as a yardstick (working set property) to evaluate them. Our working set property can also be used to evaluate self-adjusting algorithms for other graph classes where multiple tree-like subgraphs overlap (e.g. hypercube networks). We derive a lower bound of the amortized routing cost for any algorithm that follows our model and serves an unknown sequence of communication requests. We show that the routing cost of our algorithm is at most a constant factor more than the amortized routing cost of any algorithm conforming to our computational model. We also show that the expected transformation cost for our algorithm is at most a logarithmic factor more than the amortized routing cost of any algorithm conforming to our computational model.

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OBST: A self-adjusting peer-to-peer overlay based on multiple BSTs

Cited by 4 publications

References 26 publications

Demand-aware network designs of bounded degree

Demand-aware network designs of bounded degree

SplayNet: Towards Locally Self-Adjusting Networks

Locally Self-Adjusting Skip Graphs

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