MinDelay: Low-Latency Joint Caching and Forwarding for Multi-Hop Networks

Mahdian, Milad; Yeh, Edmund

doi:10.1109/icc.2018.8422990

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…Recently, information centric networking (ICN) architectures have put emphasis on the traffic engineering and caching problems [13], [25] to effectively use both bandwidth and storage for efficient content distribution [26], and optimize the network performance [27]. Alternatively, there have been works focusing on jointly optimizing the caching gain and resource usage, e.g., a decentralized SC caching optimization, i.e., femtocaching, to minimize the download delay [2], distributed optimization of caching gain given routing [13], minimizing the total cost incurred in storing and accessing objects by building the Steiner trees [28], jointly optimizing caching and routing to provide latency guarantees [29], and minimizing delay by taking into account congestion [30], and elastic and inelastic traffic [31].…”

Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

Joint Power Control and Caching for Transmission Delay Minimization in Wireless HetNets

Malak,

Mutlu,

Zhang

et al. 2024

IEEE/ACM Trans. Networking

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A fundamental challenge in wireless heterogeneous networks (HetNets) is to effectively utilize the limited transmission and storage resources in the presence of increasing deployment density and backhaul capacity constraints. To alleviate bottlenecks and reduce resource consumption, we design optimal caching and power control algorithms for multi-hop wireless HetNets. We formulate a joint optimization framework to minimize the average transmission delay as a function of the caching variables and the signal-to-interference-plus-noise ratios (SINR) which are determined by the transmission powers, while explicitly accounting for backhaul connection costs and the power constraints. Using convex relaxation and rounding, we obtain a reduced-complexity formulation (RCF) of the joint optimization problem, which can provide a constant factor approximation to the globally optimal solution. We then solve RCF in two ways: 1) alternating optimization of the power and caching variables by leveraging biconvexity, and 2) joint optimization of power control and caching. We characterize the necessary (KKT) conditions for an optimal solution to RCF, and use quasi-convexity to show that the KKT points are Pareto optimal for RCF. We then devise a subgradient projection algorithm to jointly update the caching and power variables under general SINR conditions. Finally, our analytical findings are supported by results from extensive numerical experiments.

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Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

Joint Power Control and Caching for Transmission Delay Minimization in Wireless HetNets

Malak,

Mutlu,

Zhang

et al. 2024

IEEE/ACM Trans. Networking

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“…The same problem was also studied, albeit in a different model, by Dehghan et al [28]. Online cache allocation algorithms exist, e.g., for maximizing throughput [15], [29], or minimizing delay [30]. Ioannidis and Yeh [9] study a similar problem as Shanmugam et al [25] for networks with arbitrary topology and linear link costs, seeking cache allocations that minimize routing costs across multiple hops.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to this paper, many consider fixed routing for requests. Although one can incorporate routing decisions into the problem, not doing that does not make the optimal cache allocation problem trivial, and the problem is still NP-complete [9], [25]- [28], [30], [32], [33]. TTL caches.…”

Section: Related Workmentioning

confidence: 99%

Rate Allocation and Content Placement in Cache Networks

Kamran¹,

Moharrer²,

Ioannidis³

et al. 2021

Preprint

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“…Managing the congestion while devising a queuing mechanism is an important effort to preserve time-sensitive properties of the network. Towards this end, the study by Mahdian et al [42] has presented a framework for caching networks to jointly optimize forwarding and caching strategies for minimizing congestion-dependent network cost. Caching variables are typically integer valued which results in an NPhard optimization problem.…”

Section: B Queuing and Forwardingmentioning

confidence: 99%

TSN Algorithms for Large Scale Networks: A Survey and Conceptual Comparison

Nasrallah¹,

Venkatraman²,

Thyagaturu³

et al. 2019

Preprint

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This paper provides a comprehensive survey of queueing and scheduling mechanisms for supporting large scale deterministic networks (LDNs). The survey finds that extensive mechanism design research and standards development for LDNs has been conducted over the past few years. However, these mechanism design studies have not been followed up with a comprehensive rigorous evaluation. The main outcome of this survey is a clear organization of the various research and standardization efforts towards queueing and scheduling mechanisms for LDNs as well as the identification of the main strands of mechanism development and their interdependencies. Based on this survey, it appears urgent to conduct a comprehensive rigorous simulation study of the main strands of mechanisms.

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MinDelay: Low-Latency Joint Caching and Forwarding for Multi-Hop Networks

Cited by 13 publications

References 14 publications

Joint Power Control and Caching for Transmission Delay Minimization in Wireless HetNets

Joint Power Control and Caching for Transmission Delay Minimization in Wireless HetNets

Rate Allocation and Content Placement in Cache Networks

TSN Algorithms for Large Scale Networks: A Survey and Conceptual Comparison

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