In Fog-assisted IoT systems, it is a common practice to cache popular content at the network edge to achieve high quality of service. Due to uncertainties in practice such as unknown file popularities, cache placement scheme design is still an open problem with unresolved challenges: 1) how to maintain time-averaged storage costs under budgets, 2) how to incorporate online learning to aid cache placement to minimize performance loss (a.k.a. regret), and 3) how to exploit offline historical information to further reduce regret. In this paper, we formulate the cache placement problem with unknown file popularities as a constrained combinatorial multi-armed bandit (CMAB) problem. To solve the problem, we employ virtual queue techniques to manage time-averaged storage cost constraints, and adopt history-aware bandit learning methods to integrate offline historical information into the online learning procedure to handle the exploration-exploitation tradeoff. With an effective combination of online control and history-aware online learning, we devise a Cache Placement scheme with History-aware Bandit Learning called CPHBL. Our theoretical analysis and simulations show that CPHBL achieves a sublinear time-averaged regret bound. Moreover, the simulation results verify CPHBL's advantage over the deep reinforcement learning based approach.
Recent years have witnessed a remarkable development in communication and computing systems, mainly driven by the increasing demands of data and processing intensive applications such as virtual reality, M2M, connected vehicles, IoT services, to name a few. Massive amounts of data will be collected by various mobile and fixed terminals that will need to be processed in order to extract knowledge from the data. Traditionally, a centralized approach is taken for processing the collected data using large data centers connected to a core network. However, due to the scale of the Internet-connected…
We introduce the problem of optimal congestion control in cache networks, whereby \emph{both} rate allocations and content placements are optimized \emph{jointly}. We formulate this as a maximization problem with non-convex constraints, and propose solving this problem via (a) a Lagrangian barrier algorithm and (b) a convex relaxation. We prove different optimality guarantees for each of these two algorithms; our proofs exploit the fact that the non-convex constraints of our problem involve DR-submodular functions.
Caching high-frequency reuse contents at the edge servers in the mobile edge computing (MEC) network omits the part of backhaul transmission and further releases the pressure of data traffic. However, how to efficiently decide the caching contents for edge servers is still an open problem, which refers to the cache capacity of edge servers, the popularity of each content, and the wireless channel quality during transmission. In this paper, we discuss the influence of unknown user density and popularity of content on the cache placement solution at the edge server. Specifically, towards the imp…
Caching can be leveraged to significantly improve network performance and mitigate congestion. However, characterizing the optimal tradeoff between routing cost and cache deployment cost remains an open problem. In this paper, for a network with arbitrary topology and congestion-dependent nonlinear cost functions, we aim to jointly determine the cache deployment, content placement, and hop-by-hop routing strategies, so that the sum of routing cost and cache deployment cost is minimized. We tackle this NP-hard problem starting with a fixed-routing setting, and then to a general dynamic-routing …
Edge caching via the placement of distributed storages throughout the network is a promising solution to reduce latency and network costs of content delivery. With the advent of the upcoming 5G future, billions of F-RAN (Fog-Radio Access Network) nodes will created and used for for the purpose of Edge Caching. Hence, the total amount of memory deployed at the edge is expected to increase 100 times. Currently, used DRAM-based caches in CDN (Content Delivery Networks) are extremely power-hungry and costly. Our purpose is to reduce the cost of ownership and recurring costs (of power consumption) …
Similarity caching systems have recently attracted the attention of the scientific community, as they can be profitably used in many application contexts, like multimedia retrieval, advertising, object recognition, recommender systems and online content-match applications. In such systems, a user request for an object $o$, which is not in the cache, can be (partially) satisfied by a similar stored object $o$', at the cost of a loss of user utility. In this paper we make a first step into the novel area of similarity caching networks, where requests can be forwarded along a path of caches to ge…
Mobility-aware Caching Scheduling for Fog Computing in mmWave Band
作为云计算的延伸，网络边缘的雾计算提供了低延迟、位置感知和实时交互等特性。同时，毫米波（mmWave）通信能够提供具有大可用带宽的定向多千兆比特传输速率。基于用户在一个区域内的移动轨迹，可以获得用户经常经过的几个活动热点。通过在热点附近的边缘节点缓存热门内容，用户可以在短距离内直接下载缓存内容，用户体验得到显著提升。考虑到一个区域内存在多个热点，如何有效地调度传输以满足边缘节点的缓存需求成为一个关键问题。在本文中，我们专注于在热点附近的边缘节点的缓存的移动性感知的传输调度问题，并利用多跳中继和并发传输，以实现更好的性能。在将最优调度问题表示为随机非线性混合整数规划之后，我们提出了一种移动性感知的缓存调度方案，称为MHRC（Multi-Hop Relaying based Caching），其中为边缘节点建立多跳D2 D路径，并且在边缘节点处的缓存调度中利用并发传输。广泛的性能评估表明，MHRC实现了超过1倍的预期缓存数据量相比，国家的最先进的计划。
This paper investigates a setup consisting of multiple transmitters serving multiple cache-enabled clients through a linear network, which covers both wired and wireless transmission situations. We investigate decentralized coded caching scenarios in which there is either no cooperation or limited cooperation between the clients at the cache content placement phase. For the fully decentralized caching case (i.e., no cooperation) we analyze the performance of the system in terms of the Coding Delay metric. Furthermore, we investigate a hybrid cache content placement scenario in which there are …
We propose a decentralized caching policy for wireless heterogeneous networks that makes content placement decisions based on pairwise interactions between cache nodes. We call our proposed scheme γ-exclusion cache placement (GEC), where a parameter γ controls an exclusion radius that discourages nearby caches from storing redundant content. GEC takes into account item popularity and the nodes' caching priorities and leverages negative dependence to relax the classic 0-1 knapsack problem to yield spatially balanced sampling across caches. We show that GEC guarantees a better concentration (red…
With constrained resources, what, where, and how to cache at the edge is one of the key challenges for edge computing systems. The cached items include not only the application data contents but also the local caching of edge services that handle incoming requests. However, current systems separate the contents and services without considering the latency interplay of caching and queueing. Therefore, in this paper, we propose a novel class of stochastic models that enable the optimization of content caching and service placement decisions jointly. We first explain how to apply layered queueing…
Modern connected vehicles (CVs) frequently require diverse types of content for mission-critical decision-making and onboard users' entertainment. These contents are required to be fully delivered to the requester CVs within stringent deadlines that the existing radio access technology (RAT) solutions may fail to ensure. Motivated by the above consideration, this paper exploits content caching in vehicular edge networks (VENs) with a software-defined user-centric virtual cell (VC) based RAT solution for delivering the requested contents from a proximity edge server. Moreover, to capture the he…
