75 research outputs found

    Cache-aided Interference Management Using Hypercube Combinatorial Cache Designs

    Full text link
    We consider a cache-aided interference network which consists of a library of NN files, KTK_T transmitters and KRK_R receivers (users), each equipped with a local cache of size MTM_T and MRM_R files respectively, and connected via a discrete-time additive white Gaussian noise channel. Each receiver requests an arbitrary file from the library. The objective is to design a cache placement without knowing the receivers' requests and a communication scheme such that the sum Degrees of Freedom (sum-DoF) of the delivery is maximized. This network model has been investigated by Naderializadeh {\em et al.}, who proposed a prefetching and a delivery schemes that achieves a sum-DoF of min{MTKT+KRMRN,KR}\min\{\frac{{M_TK_T+K_RM_R}}{{N}}, K_R\}. One of biggest limitations of this scheme is the requirement of high subpacketization level. This paper is the first attempt in the literature (according to our knowledge) to reduce the file subpacketization in such a network. In particular, we propose a new approach for both prefetching and linear delivery schemes based on a combinatorial design called {\em hypercube}. We show that required number of packets per file can be exponentially reduced compared to the state of the art scheme proposed by Naderializadeh {\em et al.}, or the NMA scheme. When MTKT+KRMRKRM_TK_T+K_RM_R \geq K_R, the achievable one-shot sum-DoF using this approach is MTKT+KRMRN\frac{{M_TK_T+K_RM_R}}{{N}} , which shows that 1) the one-shot sum-DoF scales linearly with the aggregate cache size in the network and 2) it is within a factor of 22 to the information-theoretic optimum. Surprisingly, the identical and near optimal sum-DoF performance can be achieved using the hypercube approach with a much less file subpacketization.Comment: 6 pages, 4 figures, accepted by ICC 201

    Edge Computing in the Dark: Leveraging Contextual-Combinatorial Bandit and Coded Computing

    Full text link
    With recent advancements in edge computing capabilities, there has been a significant increase in utilizing the edge cloud for event-driven and time-sensitive computations. However, large-scale edge computing networks can suffer substantially from unpredictable and unreliable computing resources which can result in high variability of service quality. Thus, it is crucial to design efficient task scheduling policies that guarantee quality of service and the timeliness of computation queries. In this paper, we study the problem of computation offloading over unknown edge cloud networks with a sequence of timely computation jobs. Motivated by the MapReduce computation paradigm, we assume each computation job can be partitioned to smaller Map functions that are processed at the edge, and the Reduce function is computed at the user after the Map results are collected from the edge nodes. We model the service quality (success probability of returning result back to the user within deadline) of each edge device as function of context (collection of factors that affect edge devices). The user decides the computations to offload to each device with the goal of receiving a recoverable set of computation results in the given deadline. Our goal is to design an efficient edge computing policy in the dark without the knowledge of the context or computation capabilities of each device. By leveraging the \emph{coded computing} framework in order to tackle failures or stragglers in computation, we formulate this problem using contextual-combinatorial multi-armed bandits (CC-MAB), and aim to maximize the cumulative expected reward. We propose an online learning policy called \emph{online coded edge computing policy}, which provably achieves asymptotically-optimal performance in terms of regret loss compared with the optimal offline policy for the proposed CC-MAB problem

    Center for Aeronautics and Space Information Sciences

    Get PDF
    This report summarizes the research done during 1991/92 under the Center for Aeronautics and Space Information Science (CASIS) program. The topics covered are computer architecture, networking, and neural nets
    corecore