SplayNet: Towards Locally Self-Adjusting Networks

Schmid, Stefan; Avin, Chen; Scheideler, Christian; Borokhovich, Michael; Haeupler, Bernhard; Lotker, Zvi

doi:10.1109/tnet.2015.2410313

Cited by 59 publications

(50 citation statements)

References 20 publications

(38 reference statements)

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“…SASL 2 comes with theoretical guarantees which allow us to tie it to the complexity map and to our generative model. That said, we note that similar claims could also be made for other network proposals, e.g., SplayNets [64].…”

Section: From Structure To Optimization: a Case Studysupporting

confidence: 81%

“…Our case study is situated in the context of emerging reconfigurable optical networks: networks whose topologies can be adjusted to meet the demand they serve [8,9,21,34,39,41,42,64,80]. We refer to such networks as self-adjusting networks.…”

Section: From Structure To Optimization: a Case Studymentioning

confidence: 99%

“…A possible partition of the complexity map into regions of dominating designs, for expected path length. We hypothesize the locations of Xpaner [48], fat-tree [2], RotorNet [57], ProjecTor [39], DAN [9], SASL 2 [12] and SplayNet [64].…”

Section: From Structure To Optimization: a Case Studymentioning

confidence: 99%

See 2 more Smart Citations

On the Complexity of Traffic Traces and Implications

Avin

Ghobadi

Griner

et al. 2020

Proc. ACM Meas. Anal. Comput. Syst.

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This paper presents a systematic approach to identify and quantify the types of structures featured by packet traces in communication networks. Our approach leverages an information-theoretic methodology, based on iterative randomization and compression of the packet trace, which allows us to systematically remove and measure dimensions of structure in the trace. In particular, we introduce the notion of \emphtrace complexity which approximates the entropy rate of a packet trace. Considering several real-world traces, we show that trace complexity can provide unique insights into the characteristics of various applications. Based on our approach, we also propose a traffic generator model able to produce a synthetic trace that matches the complexity levels of its corresponding real-world trace. Using a case study in the context of datacenters, we show that insights into the structure of packet traces can lead to improved demand-aware network designs: datacenter topologies that are optimized for specific traffic patterns.

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Section: From Structure To Optimization: a Case Studysupporting

confidence: 81%

Section: From Structure To Optimization: a Case Studymentioning

confidence: 99%

See 1 more Smart Citation

On the Complexity of Traffic Traces and Implications

Avin

Ghobadi

Griner

et al. 2020

Proc. ACM Meas. Anal. Comput. Syst.

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“…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)). Additionally the optimal binary search tree can be computed efficiently for every D using dynamic programming [21]. In the current work we extend this line of research by studying more general distributions and a larger family of host networks.…”

Section: Putting Things Into Perspective and Related Workmentioning

confidence: 99%

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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“…In terms of reconfigurable networks, there exist several static [7,10] and dynamic [20,17] algorithms for constant-degree networks, as well as hybrid variants [14] which combine static and reconfigurable links. However, these solutions do not apply to the line and do not provide performance guarantees over time; the latter also applies to recent work on node migration models on the grid [6].…”

Section: Related Workmentioning

confidence: 99%

Self-adjusting Linear Networks

Avin

Duijn

Schmid

2019

Lecture Notes in Computer Science

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Emerging networked systems become increasingly flexible and "reconfigurable". This introduces an opportunity to adjust networked systems in a demand-aware manner, leveraging spatial and temporal locality in the workload for online optimizations. However, it also introduces a tradeoff: while more frequent adjustments can improve performance, they also entail higher reconfiguration costs.This paper initiates the formal study of linear networks which self-adjust to the demand in an online manner, striking a balance between the benefits and costs of reconfigurations. We show that the underlying algorithmic problem can be seen as a distributed generalization of the classic dynamic list update problem known from self-adjusting datastructures: in a network, requests can occur between node pairs. This distributed version turns out to be significantly harder than the classical problem in generalizes. Our main results are a Ω(log n) lower bound on the competitive ratio, and a (distributed) online algorithm that is O(log n)-competitive if the communication requests are issued according to a linear order.We presented a first and asymptotically tight, i.e., Θ(log n)-competitive online algorithm for self-adjusting reconfigurable line networks with linear demand. Both our lower and upper bounds are non-trivial, and we believe that our work opens several interesting directions for future research. In particular, it would be very interesting to shed light on the competitive ratio achievable in more general network topologies, and to study randomized algorithms.

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SplayNet: Towards Locally Self-Adjusting Networks

Cited by 59 publications

References 20 publications

On the Complexity of Traffic Traces and Implications

On the Complexity of Traffic Traces and Implications

Demand-aware network designs of bounded degree

Self-adjusting Linear Networks

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