On the Optimal Cache Allocation in Information-Centric Networking

Hagikura, Jo; Nakamura, Ryô; Ohsaki, Hiroyuki

doi:10.1109/compsac48688.2020.0-101

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…In Reference 16, authors studied how the optimal cache allocation of routers was affected by several factors, and a heuristic algorithm for performance analysis and traditional optimization problems was designed. In Reference 17, a novel scheme was proposed for energy‐efficient MAR services in MEC, which jointly considered mobile cache and power management.…”

Section: Related Workmentioning

confidence: 99%

“…In Reference 17, a novel scheme was proposed for energy‐efficient MAR services in MEC, which jointly considered mobile cache and power management. In References 16 and 17, the authors considered the work of caching in MEC, however ignored the target of the user's interest. In Reference 18, users were divided into different clusters according to their interests and preferences.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Cache allocation policy based on user preference using reinforcement learning in mobile edge computing

Li,

Zhai,

Song

et al. 2023

Concurrency and Computation

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SummaryIn mobile edge computing (MEC), due to the limited computing resources and power of mobile augmented reality (MAR) devices, cache identification results which can reduce power consumption and executing time of mobile devices are the solution to process MAR tasks. In this paper, we study an allocation cache problem in MAR systems. The allocation cache problem is formulated as maximizing the cache utility of cache hit ratio and user preference factor. To solve this problem, a cache resource allocation and cache space adjustment policy for edge computing systems is proposed. We also propose an improved double deep Q‐network (DDQN) algorithm to learn this policy. Simulation results show that the policy greatly improves the cache hit ratio compared with the traditional caching policy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Cache allocation policy based on user preference using reinforcement learning in mobile edge computing

Li,

Zhai,

Song

et al. 2023

Concurrency and Computation

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“…Rossi D et al [14] studied the difference of performance based on different centrality metrics and proved that Degree-Centrality is the best performance metric for cache allocation. Jo H et al [15] studied the relationship between network performance of optimal cache allocation and several factors including network topology, network space, and etc. And this paper proposed an approximate model to present the unpredictability of optimal cache allocation.…”

Section: Related Workmentioning

confidence: 99%

“…The optimal solution is obtained by solving the knapsack problem, which used a heuristic method based on the expected efficiency of the ant colony. Since these methods obtain solutions by formulating cache allocation as the optimization problem, and optimization algorithms require numerous computations [15,22], this category of methods is computationally complex. And cache allocation solutions obtained by these methods cannot reach a completely optimal network, due to multiple conflicting performance metrics, such as hit ratio, throughput, and energy consumption.…”

Section: Related Workmentioning

confidence: 99%

An Ant Colony Inspired Cache Allocation Mechanism for Heterogeneous Information Centric Network

Zhou

et al. 2021

IEEE Access

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The cache allocation of in-network caching is fundamental to the quality of service in heterogeneous information centric network (ICN). The aim of the cache allocation is to efficiently allocate appropriate cache capacity to each router for storing content, avoiding the long-distance transmission cost from clients to servers. Existing works focus on the analysis of network topology. However, this way is computation-expensive for such analysis, at the same time it cannot reach a completely optimal network, due to its used multiple conflicting performance metrics. In this paper, we propose an ant colony inspired cache allocation mechanism (ACCM) for heterogeneous ICN, which is able to deal with the cache budget constraint. Specifically, we first construct an evaluation model of network performance, in terms of hit ratio, energy consumption, time latency, and throughput. Then, based on the evaluation model, we devise an ant colony inspired cache allocation mechanism, where the ICN topology system is mapped into an ant colony system and the cache allocation process is simulated by the ant colony foraging behavior. Our theoretical analysis shows that the proposed mechanism can converge to the best solution. Finally, we conduct simulations on AttMpls and TW Telecom topologies with real datasets in YouTube. Simulation results show the effectiveness and efficiency of the proposed mechanism on the simulation instances in terms of hit ratio, energy consumption, time latency, and throughput.

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