A hotspot-based probabilistic cache placement policy for ICN in MANETs

Zhang, Cheng; Xia, Chunhe; Li, Yu; Wang, Haiquan; Li, Xiaojian

doi:10.1186/s13638-019-1466-5

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…Various cache access and placement strategies and their performances are analyzed in [74]. A gain-based cache replacement policy named, Min-SAUD [75] and a hotspot-based caching scheme [76] are adopted to satisfy the caching replacement requirements. In [77], authors have proposed a cache replacement and content delivery strategy for regularly updating cached contents.…”

Section: A Caching Placement Strategymentioning

confidence: 99%

A Survey on Cache-Aided NOMA for 6G Networks

Bepari¹,

Mondal²,

Chandra³

et al. 2022

Preprint

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Contrary to orthogonal multiple-access (OMA), non-orthogonal multiple-access (NOMA) schemes can serve a pool of users without exploiting the scarce frequency or time domain resources. This is useful in meeting the sixth generation (6G) network requirements, such as, low latency, massive connectivity, users' fairness, and high spectral efficiency. On the other hand, content caching restricts duplicate data transmission by storing popular contents in advance at the network edge which reduces 6G data traffic. In this survey, we focus on cache-aided NOMA-based wireless networks which can reap the benefits of both cache and NOMA; switching to NOMA from OMA enables cache-aided networks to push additional files to content servers in parallel and improve the cache hit probability. Beginning with fundamentals of cache-aided NOMA technology, we summarize the performance goals of cache-aided NOMA systems, present the associated design challenges, and categorize related recent literature based on their application verticals. Concomitant standardization activities and open research challenges are highlighted as well.

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Section: A Caching Placement Strategymentioning

confidence: 99%

A Survey on Cache-Aided NOMA for 6G Networks

Bepari¹,

Mondal²,

Chandra³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…b) If the small base station hits the cache, the small base station immediately responds to the user; c) If the macro base station also does not hit, the macro base station forwards the user request to the mobile core network until the target content. Deploying a cache at a base station can greatly relieve the pressure on backhaul links and mobile core networks and reduce network latency [35]. After the base station deployment cache, the mobile base station network can be abstracted into a distributed network model.…”

Section: B Network-based Caching 1) Base Station Cachementioning

confidence: 99%

Optimization of Network-Based Caching and Forwarding Using Mobile Edge Computing

Liu

Shi

2019

IEEE Access

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The rapid growth of mobile network traffic has brought great pressure and challenge to the current mobile network, and the backhaul network pressure is huge. The surge in mobile network traffic makes the mobile backhaul link under pressure and the bandwidth resources are very tight. The content is transmitted repeatedly which can increase the load of network resources. The current mobile network also adopts the end-to-end transmission mechanism, which makes a large number of popular content repeated transmission. The network delay is large, and the user experience is not so good. Thus, this study proposes to use mobile edge computing to solve this issue. To this issue, the deployment of computing and caching resources using mobile edge computing has become the focus of academic and industry attention. This technology can provide users the access to the nearest network and business services. In order to avoid network congestion and load balancing, the joint optimization of data caching and request forwarding is considered, and the random optimization model is used to limit network stability to minimize average transmission cost. This study also proposed an optimization technology to largely improve the performance of network. Using Liapunov optimization technology, the joint optimization problem is changed to linear planning problems, and the design of real-time caching and forwarding online algorithms. The results of the simulation experiment show that the algorithm can realize congestion avoidance and load balancing with reducing transmission cost. INDEX TERMS Edge computing, caching and forwarding, Lyapunov optimization, stability.

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An energy efficient and scalable endpoint linked green content caching for Named Data Network based Internet of Things

Shrisha

Boregowda

2022

Results in Engineering

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A hotspot-based probabilistic cache placement policy for ICN in MANETs

Cited by 7 publications

References 23 publications

A Survey on Cache-Aided NOMA for 6G Networks

A Survey on Cache-Aided NOMA for 6G Networks

Optimization of Network-Based Caching and Forwarding Using Mobile Edge Computing

An energy efficient and scalable endpoint linked green content caching for Named Data Network based Internet of Things

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