55,351 research outputs found
Local Greedy Approximation for Scheduling in Multi-hop Wireless Networks
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
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
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
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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PGGA: A predictable and grouped genetic algorithm for job scheduling
This paper presents a predictable and grouped genetic algorithm (PGGA) for job scheduling. The novelty of the PGGA is twofold: (1) a job workload estimation algorithm is designed to estimate a job workload based on its historical execution records, (2) the divisible load theory (DLT) is employed to predict an optimal fitness value by which the PGGA speeds up the convergence process in searching a large scheduling space. Comparison with traditional scheduling methods such as first-come-first-serve (FCFS) and random scheduling, heuristics such as a typical genetic algorithm, Min-Min and Max-Min indicates that the PGGA is more effective and efficient in finding optimal scheduling solutions
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