Cooperative Edge Caching in User-Centric Clustered Mobile Networks

Zhang, Shan; He, Peter; Suto, Katsuya; Yang, Peng; Zhao, Lian; Shen, Xuemin

doi:10.1109/tmc.2017.2780834

Cited by 250 publications

(104 citation statements)

References 42 publications

Supporting

Mentioning

104

Contrasting

Order By: Relevance

“…It studies how to design a caching pool to store the frequently visited data and services. Service caching can also be studied in a cooperative way [17]. It opens research opportunities where multi-agent learning can be utilized to optimize QoE in large-scale edge computing systems.…”

Section: B a Recapitulation Of Iecmentioning

confidence: 99%

“…A wide range of optimization problems on service caching are proposed to endow edge servers with learning capability. Sadeghi et al study a sequential fetch-cache decision based on dynamic prices and user requests [17]. This paper endows SBSs with efficient fetch-cache decision-making schemes operating in dynamic settings.…”

Section: Problem Definition Model Construction Algorithm Designmentioning

confidence: 99%

See 1 more Smart Citation

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Deng

Zhao

Fang

et al. 2020

IEEE Internet Things J.

717

224

View full text Add to dashboard Cite

Along with the deepening development in communication technologies and the surge of mobile devices, a brandnew computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the upgrade of hardware architectures. Billions of bytes of data, generated at the network edge, put great demands on data processing and structural optimization. Therefore, there exists a strong demand to integrate Edge Computing and AI, which gives birth to Edge Intelligence. In this article, we divide Edge Intelligence into AI for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial Intelligence on Edge). The former focuses on providing a more optimal solution to the key concerns in Edge Computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on edge. This article focuses on giving insights into this new inter-disciplinary field from a broader vision and perspective. It discusses the core concepts and the research road-map, which should provide the necessary background for potential future research programs in Edge Intelligence.

show abstract

Section: B a Recapitulation Of Iecmentioning

confidence: 99%

Section: Problem Definition Model Construction Algorithm Designmentioning

confidence: 99%

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Deng

Zhao

Fang

et al. 2020

IEEE Internet Things J.

717

224

View full text Add to dashboard Cite

show abstract

“…12,[24][25][26][27][28] It has also been applied to CDN to reduce the number of duplicated files. [30][31][32] To tackle these challenges, terminal-and edge-side cooperative storage techniques are being studied extensively in the edge/mobile computing community. Shah and de Veciana 29 proposed a stochastic model for a file-server system where servers can work together to form a pooled resource.…”

Section: Related Workmentioning

confidence: 99%

Cooperative storage by exploiting graph‐based data placement algorithm for edge computing environment

Jin

Luo

2018

Concurrency and Computation

View full text Add to dashboard Cite

Edge computing is a new computing paradigm that performs data processing at the edge of the network (ie, edge servers) to lower data processing latency. Prior research significantly focused on offloading tasks from terminals to edge servers, yet most ignored how to store task's necessary data (such as databases and pretrained machine-learning models) on edge servers. Today, as data-intensive tasks such as deep learning and augmented reality become common, large data storage and powerful computation resources are needed. This is a cumbersome challenge, because many lightweight edge servers have limited resources. If an edge server does not have a task's necessary data, then it needs to offload the task to cloud datacenters or download the necessary data from the cloud. Either case could increase data processing latency. To address this problem, this paper proposes an edge-side collaborative storage framework called Edge-side Cooperative Storage (ECS). In ECS, edge servers collaboratively store and process data-intensive tasks's necessary data. Here, we particularly focus on how to effectively place data on ECS, using an approach that differs from existing works (that model data placement problems as linear/integer programming problems). Our work models cooperative storage as a graph and solves the data placement problem by using a graph-based iterative algorithm. This algorithm easily extends to a distributed version, so distributed ECSworks efficiently without a centralized scheduler. We also evaluate ECS's effectiveness and convergence through simulations. Simulation results show that ECSis 2× better than a traditional nonshared storage framework in terms of the cache hit rate. KEYWORDScooperative storage, data placement, edge computing INTRODUCTIONEdge computing 1,2 is a new computing paradigm that performs data processing at the network's edge (ie, edge servers). In an edge computing system, terminals with limited computation and storage resources can offload tasks to edge servers to improve terminals' performance. As Figure 1 shows, a typical edge computing system consists of three layers, ie, the terminal layer, edge server layer, and cloud datacenter layer. Terminals, such as Raspberry Pi, mobile phones, and Google Glass, are small end devices that have limited computation, storage, and energy resources. 3 Recently, applications running on terminals, such as object detection, 4 indoor localization, 5,6 and encrypting, 7,8 are increasingly data-intensive and latency-critical.For example, Google Glass needs to classify the objects in pictures it takes, and augmented reality games need indoor localization information to render real-life environments on mobile phones. Because the terminals' computation and storage resources cannot satisfy these applications' requirements, often, computation tasks are offloaded to nearby edge servers. Edge servers are small computing facilities, such as smart network routers, base stations, and small datacenters, which are located near terminal devices. Edge servers connect via ...

show abstract

“…The research on the performance of caching in wireless communication systems may adopt various metrics. The focus can be decreasing the content delivery latency [4], alleviating congestion over the backhaul [5], reducing energy consumption [6], or a combination of the above [7]. While metrics can be different, the underlying caching performance is largely centered around one measurement, i.e., the cache hit ratio.…”

Section: Introductionmentioning

confidence: 99%

The Study of Dynamic Caching via State Transition Field—the Case of Time-Invariant Popularity

Gao¹,

Zhao²,

Xuemin

et al. 2019

IEEE Trans. Wireless Commun.

Self Cite

View full text Add to dashboard Cite

This two-part paper investigates cache replacement schemes with the objective of developing a general model to unify the analysis of various replacement schemes and illustrate their features. To achieve this goal, we study the dynamic process of caching in the vector space and introduce the concept of state transition field (STF) to model and characterize replacement schemes. In the first part of this work, we consider the case of time-invariant content popularity based on the independent reference model (IRM). In such case, we demonstrate that the resulting STFs are static, and each replacement scheme leads to a unique STF. The STF determines the expected trace of the dynamic change in the cache state distribution, as a result of content requests and replacements, from any initial point. Moreover, given the replacement scheme, the STF is only determined by the content popularity. Using four example schemes including random replacement (RR) and least recently used (LRU), we show that the STF can be used to analyze replacement schemes such as finding their steady states, highlighting their differences, and revealing insights regarding the impact of knowledge of content popularity. Based on the above results, STF is shown to be useful for characterizing and illustrating replacement schemes.Extensive numeric results are presented to demonstrate analytical STFs and STFs from simulations for the considered example replacement schemes. Index Termscache replacement policy, probabilistic caching, cache state transition, IRM, online caching, mobile edge caching.

show abstract

Cooperative Edge Caching in User-Centric Clustered Mobile Networks

Cited by 250 publications

References 42 publications

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

Cooperative storage by exploiting graph‐based data placement algorithm for edge computing environment

The Study of Dynamic Caching via State Transition Field—the Case of Time-Invariant Popularity

Contact Info

Product

Resources

About