A Novel Caching Policy with Content Popularity Prediction and User Preference Learning in Fog-RAN

Jiang, Yanxiang; Ma, Miaoli; Bennis, Mehdi; Zheng, Fu-Chun; You, Xiaohu

doi:10.1109/glocomw.2017.8269166

Cited by 28 publications

(12 citation statements)

References 14 publications

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“…Up to now there are only a handful of proposals addressing the challenges of new flexible networking and cloud architectures accounting for content popularity. Exceptions include [24] in which a logistic-loss machine learning approach to content popularity prediction is applied for a Fog RAN environment, and, our recent papers [4] and [15]. In [4], the algorithm -outlined in [15] and presented extensively in the present -is integrated into an elastic CDN framework based on lightweight cloud capabilities using Unikernels.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis

2019

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Video content is responsible for more than 70% of the global IP traffic. Consequently, it is important for content delivery infrastructures to rapidly detect and respond to changes in content popularity dynamics. In this paper, we propose the employment of on-line change point (CP) analysis to implement real-time, autonomous and low-complexity video content popularity detection. Our proposal, denoted as realtime change point detector (RCPD), estimates the existence, the number and the direction of changes on the average number of video visits by combining: (i) off-line and on-line CP detection algorithms; (ii) an improved time-series segmentation heuristic for the reliable detection of multiple CPs; and (iii) two algorithms for the identification of the direction of changes. The proposed detector is validated against synthetic data, as well as a large database of real YouTube video visits. It is demonstrated that the RCPD can accurately identify changes in the average content popularity and the direction of change. In particular, the success rate of the RCPD over synthetic data is shown to exceed 94% for medium and large changes in content popularity. Additionally, the dynamic time warping distance, between the actual and the estimated changes, has been found to range between 20 samples on average, over synthetic data, to 52 samples, in real data. The rapid responsiveness of the RCPD is instrumental in the deployment of real-time, lightweight load balancing solutions, as shown in a real example.Index Terms-Video content popularity detection, change point analysis, on-line change point detection, binary segmentation algorithm, load balancing.

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Section: Related Workmentioning

confidence: 99%

Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis

2019

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“…An energy-aware offloading scheme was proposed to jointly optimize communication and computing resources with limited energy and delay sensitivity for time and energy reducing C. PREFERENCE OFFLOADING Some researches focused on offloading tasks according to some preferences. Jiang et al [32] studied the relationship between content popularity and user preference and task offloading in edge computing. The user popularity is predicted in the online phase and the user's preferences are learned in the offline phase.…”

Section: B Fully Offloadingmentioning

confidence: 99%

An Energy-Efficient Off-Loading Scheme for Low Latency in Collaborative Edge Computing

Wang

Liao

et al. 2019

IEEE Access

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Mobile terminal users applications, such as smartphones or laptops, have frequent computational task demanding but limited battery power. Edge computing is introduced to offload terminals' tasks to meet the quality of service requirements such as low delay and energy consumption. By offloading computation tasks, edge servers can enable terminals to collaboratively run the highly demanding applications in acceptable delay requirements. However, existing schemes barely consider the characteristics of the edge server, which leads to random assignment of tasks among servers and big tasks with high computational intensity (named as ''big task'') may be assigned to servers with low ability. In this paper, a task is divided into several subtasks and subtasks are offloaded according to characteristics of edge servers, such as transmission distance and central processing unit (CPU) capacity. With this multi-subtasks-to-multi-servers model, an adaptive offloading scheme based on Hungarian algorithm is proposed with low complexity. Extensive simulations are conducted to show the efficiency of the scheme on reducing the offloading latency with low energy consumption.INDEX TERMS Latency, energy, offloading, edge computing.

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“…Clearly, a caching policy for the SSC is based on a well-designed metric if (σ (λ)) is close to (σ opt ). A spontaneous observation, common to the context of content caching [14], [15], [21], [24], [36], is that a good caching policy needs to depend on the popularity of a task, intended as the frequency with which the task is requested with respect to the other tasks. Indeed, to reduce costs, we want to avoid to repeatedly and redundantly process frequently requested tasks.…”

Section: Policies For Computation Cachingmentioning

confidence: 99%

An Optimal Low-Complexity Policy for Cache-Aided Computation Offloading

Pietro

Strinati

2019

IEEE Access

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Computation caching is a novel strategy to improve the performance of computation offloading in wireless networks endowed with edge cloud or fog computing capabilities. It consists in preemptively storing in caches located at the edge of the network the results of computations that users offload to the edge cloud. The goal is to avoid redundant and repetitive processing of the same tasks, thus streamlining the offloading process and improving the exploitation of both the users' and the network's resources. In this paper, a novel computation caching policy is defined, investigated, and benchmarked against stateof-the-art solutions. The proposed new policy is built on three characterizing parameters of offloadable computational tasks: popularity, input size, and output size. This work proves the crucial importance of including these parameters altogether in the design of efficient policies. Our proposed policy has low computational complexity and is numerically shown to achieve optimality for several performance indicators and to yield significantly better results compared to the other analyzed policies. This is shown in both a singleand a multi-cell scenario, where a serving small cell has access to its neighboring cells' caches via backhaul. In this paper, the benefits of computation caching are highlighted and estimated through extensive numerical simulations in terms of reduction of uplink traffic, communication and computation costs, offloading delay, and computational resource outage. INDEX TERMS Computation caching, 5G, multi-access edge computing, MEC, fog computing, computation offloading, small cell, energy efficiency.

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A Novel Caching Policy with Content Popularity Prediction and User Preference Learning in Fog-RAN

Cited by 28 publications

References 14 publications

Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis

Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis

An Energy-Efficient Off-Loading Scheme for Low Latency in Collaborative Edge Computing

An Optimal Low-Complexity Policy for Cache-Aided Computation Offloading

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