440 research outputs found

    Optimal Control for Generalized Network-Flow Problems

    Get PDF
    We consider the problem of throughput-optimal packet dissemination, in the presence of an arbitrary mix of unicast, broadcast, multicast, and anycast traffic, in an arbitrary wireless network. We propose an online dynamic policy, called Universal Max-Weight (UMW), which solves the problem efficiently. To the best of our knowledge, UMW is the first known throughput-optimal policy of such versatility in the context of generalized network flow problems. Conceptually, the UMW policy is derived by relaxing the precedence constraints associated with multi-hop routing and then solving a min-cost routing and max-weight scheduling problem on a virtual network of queues. When specialized to the unicast setting, the UMW policy yields a throughput-optimal cycle-free routing and link scheduling policy. This is in contrast with the well-known throughput-optimal back-pressure (BP) policy which allows for packet cycling, resulting in excessive latency. Extensive simulation results show that the proposed UMW policy incurs a substantially smaller delay as compared with the BP policy. The proof of throughput-optimality of the UMW policy combines ideas from the stochastic Lyapunov theory with a sample path argument from adversarial queueing theory and may be of independent theoretical interest

    Robust Queueing Theory

    Get PDF
    We propose an alternative approach for studying queues based on robust optimization. We model the uncertainty in the arrivals and services via polyhedral uncertainty sets, which are inspired from the limit laws of probability. Using the generalized central limit theorem, this framework allows us to model heavy-tailed behavior characterized by bursts of rapidly occurring arrivals and long service times. We take a worst-case approach and obtain closed-form upper bounds on the system time in a multi-server queue. These expressions provide qualitative insights that mirror the conclusions obtained in the probabilistic setting for light-tailed arrivals and services and generalize them to the case of heavy-tailed behavior. We also develop a calculus for analyzing a network of queues based on the following key principles: (a) the departure from a queue, (b) the superposition, and (c) the thinning of arrival processes have the same uncertainty set representation as the original arrival processes. The proposed approach (a) yields results with error percentages in single digits relative to simulation, and (b) is to a large extent insensitive to the number of servers per queue, network size, degree of feedback, and traffic intensity; it is somewhat sensitive to the degree of diversity of external arrival distributions in the network

    Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

    Get PDF
    Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

    Work-Conserving Distributed Schedulers

    Get PDF
    Buffered multistage interconnection networks offer one of the most scalable and cost-effective approaches to building high capacity routers and switches. Unfortunately, the performance of such systems has been difficult to predict in the presence of the extreme traffic conditions that can arise in Internet routers. Recent work introduced the idea of distributed scheduling, to regulate the flow of traffic in such systems. This work demonstrated (using simulation and experimental measurements) that distributed scheduling can en-able robust performance, even in the presence of adversarial traffic patterns. In this paper, we show that appropriately designed distributed scheduling algorithms are provably work-conserving for speedups of 2 or more. Two of the three algorithms presented were inspired by algorithms previously developed for crossbar scheduling. The third has no direct counterpart in the crossbar scheduling context. In our analysis, we show that distributed schedulers based on blocking flows in small-depth acyclic flow graphs can be work-conserving, just as certain crossbar schedulers based on maximal bipartite matchings have been shown to be work-conserving. We also study the performance of practical variants of the work-conserving algorithms with speedups less than 2, using simulation. These studies demonstrate that distributed scheduling ensures excellent performance under extreme traffic conditions for speedups of less than 1.5
    • 

    corecore