Finding the projection on a polytope: An iterative method

“…The projection operation in (27) is implemented in order to satisfy the constraints (3) to (6). At each iteration, node i projects the outcome of the RCO update (18) onto either the set X 0 , if the iterate pertains to another node's optimal action, or the set X i , if the iterate pertains to node i's own action.…”

Section: 2 Amentioning

confidence: 99%

“…Since the sets X 0 and X i , ∀i ∈ V defined in (28) and (29) are polytopes in R 4 , each defined as the intersection of finitely many halfspaces, the projection operation may be implemented using the algorithm described in [27], for example.…”

Section: 2 Amentioning

confidence: 99%

“…Using the RCObased update rule (27), the nodes collectively update a total of 28 real values, and exchange a total of 32 real values at each iteration. By comparison, in implementing an algorithm based on (9), they would collectively update a total of 36 real values, and exchange a total of 48 real values at each iteration.…”

Section: Remark 51 (The Benefits Of Rco)mentioning

confidence: 99%

“…We now investigate the behavior of the decentralized content caching update rules (27), implemented by the nodes in Figure 3, with individual costs assigned as in (21) to (23). All simulations are based on the following set of parameters: Problem Parameters:…”

Section: B Simulationsmentioning

confidence: 99%

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We consider the problem of designing contentcaching strategies for the energy-efficient delivery of content such as video, over an internet-style network. We propose a method for the design of decentralized caching strategies that can adapt to real-time changes in regional content popularity. This design method is based on a recently proposed reduced consensus-optimization scheme wherein a number of agents networked over a general mesh topology cooperate in locating the optimum of the sum of their individual, privately known objective functions. The agents (i.e. network nodes with caching capabilities) achieve the collectively optimal caching configuration via nearest-neighbor interactions and measurements of local content request rates only. The caching behavior of individual nodes, which dynamically trades transport and caching energy costs in response to fluctuations in content demand, is designed to optimize the performance of the network as a whole.

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Qiu

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Trans Emerging Tel Tech

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Fifth-generation (5G) networks expect to see explosive growth of mobile data. In this context, 5G information-centric networking (5G-ICN) architecture is proposed to provide native support for this trend. However, because of the huge amount of energy consumed by high-density data and extremely dynamic network topology in 5G networks, the current content caching strategies (CCSs) in ICN are not appropriate in 5G-ICN architecture. In this paper, through balancing the trade-off between data caching energy consumption and data transport energy consumption, an optimization problem is formulated with the consideration of dynamic network topology.By leveraging a dynamic self-optimization algorithm, the formulated optimization problem is solved and the green dynamic self-optimizing in-network CCS (GDSoC) is therefore proposed. To demonstrate the performance of GDSoC, extensive simulations are performed in terms of caching hit ratio and energy consumption based on different network topologies (including CERNET, GÉANT, and Abilene topologies) through comparing with several well-known caching strategies.

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Finding the projection on a polytope: An iterative method

Cited by 8 publications

References 6 publications

Efficient Computation of the Hausdorff Distance Between Polytopes by Exterior Random Covering

Efficient Computation of the Hausdorff Distance Between Polytopes by Exterior Random Covering

Decentralized caching strategies for energy-efficient content delivery

GDSoC: Green dynamic self‐optimizing content caching in ICN‐based 5G network

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