VIP: Joint traffic engineering and caching in Named Data Networks

Yeh, Edmund; Ho, Tracey; Cui, Ying; Burd, Michael; Liu, Ran; Leong, Derek

doi:10.1109/iccnc.2015.7069430

Cited by 27 publications

(44 citation statements)

References 4 publications

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“…Our framework, and adaptive algorithm, leaves many open questions, including jointly optimizing caching and routing decisions; this is even more pertinent in the presence of congestion, as in [38]. The excellent performance of the greedy heuristic in our simulations further attests to the need for establishing its performance formally.…”

Section: Discussionmentioning

confidence: 81%

Adaptive Caching Networks with Optimality Guarantees

Ioannidis

Yeh

2016

Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science

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190

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We study the problem of optimal content placement over a network of caches, a problem naturally arising in several networking applications, including ICNs, CDNs, and P2P systems. Given a demand of content request rates and paths followed, we wish to determine the content placement that maximizes the expected caching gain, i.e., the reduction of routing costs due to intermediate caching. The offline version of this problem is NP-hard and, in general, the demand and topology may be a priori unknown. Hence, a distributed, adaptive, constant approximation content placement algorithm is desired. We show that path replication, a simple algorithm frequently encountered in literature, can be arbitrarily suboptimal when combined with traditional eviction policies, like LRU, LFU, or FIFO. We propose a distributed, adaptive algorithm that performs stochastic gradient ascent on a concave relaxation of the expected caching gain, and constructs a probabilistic content placement within 1−1/e factor from the optimal, in expectation. Motivated by our analysis, we also propose a novel greedy eviction policy to be used with path replication, and show through numerical evaluations that both algorithms significantly outperform path replication with traditional eviction policies over a broad array of network topologies.

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Section: Discussionmentioning

confidence: 81%

Adaptive Caching Networks with Optimality Guarantees

Ioannidis

Yeh

2016

Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science

Self Cite

190

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“…This latter link is a "profitable" link for forwarding the Interest Packet at time slot t, from the standpoint of reducing delays and congestion. If either condition (i) or (ii) does not hold, then forward the Interest Packet on the link used by node n to forward the most recent Interest Packet for a chunk of object k. 10 If the head-of-the-queue Interest Packet at node n at time t is an interest for a chunk of data object k which is not the starting chunk, then forward the Interest Packet on the link used by node n to forward the most recent Interest Packet for a chunk of object k.…”

Section: Forwarding Of Interest Packetsmentioning

confidence: 99%

Vip

Yeh

Cui

et al. 2014

Proceedings of the 1st ACM Conference on Information-Centric Networking

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Emerging information-centric networking architectures seek to optimally utilize both bandwidth and storage for e cient content distribution. This highlights the need for joint design of tra c engineering and caching strategies, in order to optimize network performance in view of both current tra c loads and future tra c demands. We present a systematic framework for joint dynamic interest request forwarding and dynamic cache placement and eviction, within the context of the Named Data Networking (NDN) architecture. The framework employs a virtual control plane which operates on the user demand rate for data objects in the network, and an actual plane which handles Interest Packets and Data Packets. We develop distributed algorithms within the virtual plane to achieve network load balancing through dynamic forwarding and caching, thereby maximizing the user demand rate that the NDN network can satisfy. Numerical experiments within a number of network settings demonstrate the superior performance of the resulting algorithms for the actual plane in terms of low user delay and high rate of cache hits.

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“…Similarly, in [12], authors suggest to forward Interests to the neighboring node that advertised the highest hit probability for the requested content object. A closer work to ours is [14], where an optimization framework is defined to jointly handle backpressure-based forwarding and LFU-like content placement. The work designs a control plane that feeds the actual chunk-level data plane with flow rates and queue sizes in order to operate optimal content placement and request forwarding.…”

Section: Caching and Forwarding Interactionmentioning

confidence: 99%