55,351 research outputs found

    Local Greedy Approximation for Scheduling in Multi-hop Wireless Networks

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    In recent years, there has been a significant amount of work done in developing low-complexity scheduling schemes to achieve high performance in multi-hop wireless networks. A centralized sub-optimal scheduling policy, called Greedy Maximal Scheduling (GMS) is a good candidate because its empirically observed performance is close to optimal in a variety of network settings. However, its distributed realization requires high complexity, which becomes a major obstacle for practical implementation. In this paper, we develop simple distributed greedy algorithms for scheduling in multi-hop wireless networks. We reduce the complexity by relaxing the global ordering requirement of GMS, up to near-zero. Simulation results show that the new algorithms approximate the performance of GMS, and outperform the state-of-the-art distributed scheduling policies

    Efficient Task Replication for Fast Response Times in Parallel Computation

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    One typical use case of large-scale distributed computing in data centers is to decompose a computation job into many independent tasks and run them in parallel on different machines, sometimes known as the "embarrassingly parallel" computation. For this type of computation, one challenge is that the time to execute a task for each machine is inherently variable, and the overall response time is constrained by the execution time of the slowest machine. To address this issue, system designers introduce task replication, which sends the same task to multiple machines, and obtains result from the machine that finishes first. While task replication reduces response time, it usually increases resource usage. In this work, we propose a theoretical framework to analyze the trade-off between response time and resource usage. We show that, while in general, there is a tension between response time and resource usage, there exist scenarios where replicating tasks judiciously reduces completion time and resource usage simultaneously. Given the execution time distribution for machines, we investigate the conditions for a scheduling policy to achieve optimal performance trade-off, and propose efficient algorithms to search for optimal or near-optimal scheduling policies. Our analysis gives insights on when and why replication helps, which can be used to guide scheduler design in large-scale distributed computing systems.Comment: Extended version of the 2-page paper accepted to ACM SIGMETRICS 201

    Low-complexity medium access control protocols for QoS support in third-generation radio access networks

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    One approach to maximizing the efficiency of medium access control (MAC) on the uplink in a future wideband code-division multiple-access (WCDMA)-based third-generation radio access network, and hence maximize spectral efficiency, is to employ a low-complexity distributed scheduling control approach. The maximization of spectral efficiency in third-generation radio access networks is complicated by the need to provide bandwidth-on-demand to diverse services characterized by diverse quality of service (QoS) requirements in an interference limited environment. However, the ability to exploit the full potential of resource allocation algorithms in third-generation radio access networks has been limited by the absence of a metric that captures the two-dimensional radio resource requirement, in terms of power and bandwidth, in the third-generation radio access network environment, where different users may have different signal-to-interference ratio requirements. This paper presents a novel resource metric as a solution to this fundamental problem. Also, a novel deadline-driven backoff procedure has been presented as the backoff scheme of the proposed distributed scheduling MAC protocols to enable the efficient support of services with QoS imposed delay constraints without the need for centralized scheduling. The main conclusion is that low-complexity distributed scheduling control strategies using overload avoidance/overload detection can be designed using the proposed resource metric to give near optimal performance and thus maintain a high spectral efficiency in third-generation radio access networks and that importantly overload detection is superior to overload avoidance

    On the Complexity of Scheduling in Wireless Networks

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    We consider the problem of throughput-optimal scheduling in wireless networks subject to interference constraints. We model the interference using a family of K-hop interference models, under which no two links within a K-hop distance can successfully transmit at the same time. For a given K, we can obtain a throughput-optimal scheduling policy by solving the well-known maximum weighted matching problem. We show that for K > 1, the resulting problems are NP-Hard that cannot be approximated within a factor that grows polynomially with the number of nodes. Interestingly, for geometric unit-disk graphs that can be used to describe a wide range of wireless networks, the problems admit polynomial time approximation schemes within a factor arbitrarily close to 1. In these network settings, we also show that a simple greedy algorithm can provide a 49-approximation, and the maximal matching scheduling policy, which can be easily implemented in a distributed fashion, achieves a guaranteed fraction of the capacity region for "all K." The geometric constraints are crucial to obtain these throughput guarantees. These results are encouraging as they suggest that one can develop low-complexity distributed algorithms to achieve near-optimal throughput for a wide range of wireless networksopen1
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