Popularity-Based Cache Placement for Fog Networks

Althamary, Ibrahim; Huang, Chih‐Wei; Lin, Pengzhi; Yang, Shun-Ren; Cheng, Chien-Wei

doi:10.1109/iwcmc.2018.8450495

Cited by 23 publications

(13 citation statements)

References 12 publications

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“…C/Off/S/nM [11] C R , C N Proposes an efficient cache placement strategy based on content popularity to reduce energy consumption in Fog networks.…”

Section: Category Reference Requirementscontributionsmentioning

confidence: 99%

An Overview of Service Placement Problem in Fog and Edge Computing

2020

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To support the large and various applications generated by the Internet of Things (IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement in such infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new classification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.

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“…C/Off/S/nM [11] C R , C N Proposes an efficient cache placement strategy based on content popularity to reduce energy consumption in Fog networks.…”

Section: Category Reference Requirementscontributionsmentioning

confidence: 99%

An Overview of Service Placement Problem in Fog and Edge Computing

2020

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“…The problem of deciding which services to place on edge clouds in MEC is commonly known as the service placement problem. Many works study the problem with the goal to optimize resource utilization, energy consumption [5], [6], and Quality-of-Service (QoS) [7], [8]. Some works consider a static placement decision where the placement decision is made in a single shot [9], [10].…”

Section: Related Workmentioning

confidence: 99%

QoS-Aware Placement of Deep Learning Services on the Edge with Multiple Service Implementations

Hudson¹,

Khamfroush²,

Lucani³

2021

Preprint

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Mobile edge computing pushes computationallyintensive services closer to the user to provide reduced delay due to physical proximity. This has led many to consider deploying deep learning models on the edge -commonly known as edge intelligence (EI). EI services can have many model implementations that provide different QoS. For instance, one model can perform inference faster than another (thus reducing latency) while achieving less accuracy when evaluated. In this paper, we study joint service placement and model scheduling of EI services with the goal to maximize Quality-of-Servcice (QoS) for end users where EI services have multiple implementations to serve user requests, each with varying costs and QoS benefits. We cast the problem as an integer linear program and prove that it is NP-hard. We then prove the objective is equivalent to maximizing a monotone increasing, submodular set function and thus can be solved greedily while maintaining a (1 − 1/e)-approximation guarantee. We then propose two greedy algorithms: one that theoretically guarantees this approximation and another that empirically matches its performance with greater efficiency. Finally, we thoroughly evaluate the proposed algorithm for making placement and scheduling decisions in both synthetic and realworld scenarios against the optimal solution and some baselines. In the real-world case, we consider real machine learning models using the ImageNet 2012 data-set for requests. Our numerical experiments empirically show that our more efficient greedy algorithm is able to approximate the optimal solution with a 0.904 approximation on average, while the next closest baseline achieves a 0.607 approximation on average.

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“…Authors in [39] proposed location customized regression based caching algorithm to predict the future content demands. Authors in [40] distinguished requests on three different popularity levels and then strategically cached data at the fog nodes according to various activity levels.…”

Section: Execution Of Commandsmentioning

confidence: 99%

Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

Tapwal¹,

Gupta²,

Xin³

2020

Preprint

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<div>IoT devices (wireless sensors, actuators, computer devices) produce large volume and variety of data and the data</div><div>produced by the IoT devices are transient. In order to overcome the problem of traditional IoT architecture where</div><div>data is sent to the cloud for processing, an emerging technology known as fog computing is proposed recently.</div><div>Fog computing brings storage, computing and control near to the end devices. Fog computing complements the</div><div>cloud and provide services to the IoT devices. Hence, data used by the IoT devices must be cached at the fog nodes</div><div>in order to reduce the bandwidth utilization and latency. This chapter discusses the utility of data caching at the</div><div>fog nodes. Further, various machine learning techniques can be used to reduce the latency by caching the data</div><div>near to the IoT devices by predicting their future demands. Therefore, this chapter also discusses various machine</div><div>learning techniques that can be used to extract the accurate data and predict future requests of IoT devices.</div>

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Popularity-Based Cache Placement for Fog Networks

Cited by 23 publications

References 12 publications

An Overview of Service Placement Problem in Fog and Edge Computing

An Overview of Service Placement Problem in Fog and Edge Computing

QoS-Aware Placement of Deep Learning Services on the Edge with Multiple Service Implementations

Data Caching at Fog Nodes Under IoT Networks: Review of Machine Learning Approaches

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