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Delay analysis for max weight opportunistic scheduling in wireless systems. arXiv:0806.2345v1
, 2008
"... Abstract—We consider the delay properties of maxweight opportunistic scheduling in a multiuser ON/OFF wireless system, such as a multiuser downlink or uplink. It is well known that maxweight scheduling stabilizes the network (and hence yields maximum throughput) whenever input rates are inside t ..."
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Cited by 22 (3 self)
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Abstract—We consider the delay properties of maxweight opportunistic scheduling in a multiuser ON/OFF wireless system, such as a multiuser downlink or uplink. It is well known that maxweight scheduling stabilizes the network (and hence yields maximum throughput) whenever input rates are inside the network capacity region. We show that when arrival and channel processes are independent, average delay of the maxweight policy is orderoptimal, in the sense that it does not grow with the number of network links. While recent queuegrouping algorithms are known to also yield orderoptimal delay, this is the first such result for the simpler class of maxweight policies. I.
ON THE POWER OF (EVEN A LITTLE) RESOURCE POOLING ∗†
, 2011
"... We propose and analyze a multiserver model that captures a performance tradeoffbetweencentralized anddistributedprocessing. In our model, a fraction p of an available resource is deployed in a centralized manner (e.g., to serve a mostloaded station) while the remaining fraction 1−p is allocated t ..."
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Cited by 6 (1 self)
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We propose and analyze a multiserver model that captures a performance tradeoffbetweencentralized anddistributedprocessing. In our model, a fraction p of an available resource is deployed in a centralized manner (e.g., to serve a mostloaded station) while the remaining fraction 1−p is allocated to local servers that can only serve requests addressed specifically to their respective stations. Using a fluid model approach, we demonstrate a surprising phase transition in the steadystate delay scaling, as p changes: in the limit of a large number of stations, and when any amount of centralization is available (p>0), the average queue length in steady state scales as log 1
On the power of (even a little) centralization in distributed processing
, 2011
"... We propose and analyze a multiserver model that captures a performance tradeoff between centralized and distributed processing. In our model, a fraction p of an available resource is deployed in a centralized manner (e.g., to serve a mostloaded station) while the remaining fraction 1 − p is alloc ..."
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Cited by 4 (0 self)
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We propose and analyze a multiserver model that captures a performance tradeoff between centralized and distributed processing. In our model, a fraction p of an available resource is deployed in a centralized manner (e.g., to serve a mostloaded station) while the remaining fraction 1 − p is allocated to local servers that can only serve requests addressed specifically to their respective stations. Using a fluid model approach, we demonstrate a surprising phase transition in the steadystate delay, as p changes: in the limit of a large number of stations, and when any amount of centralization is available (p> 0), the average when the queue length in steady state scales as log 1
1 Delay Analysis for Max Weight Opportunistic Scheduling in Wireless Systems
, 806
"... Abstract—We consider the delay properties of maxweight opportunistic scheduling in a multiuser ON/OFF wireless system, such as a multiuser downlink or uplink. It is well known that maxweight scheduling stabilizes the network (and hence yields maximum throughput) whenever input rates are inside t ..."
Abstract
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Abstract—We consider the delay properties of maxweight opportunistic scheduling in a multiuser ON/OFF wireless system, such as a multiuser downlink or uplink. It is well known that maxweight scheduling stabilizes the network (and hence yields maximum throughput) whenever input rates are inside the network capacity region. We show that when arrival and channel processes are independent, average delay of the maxweight policy is orderoptimal, in the sense that it does not grow with the number of network links. While recent queuegrouping algorithms are known to also yield orderoptimal delay, this is the first such result for the simpler class of maxweight policies. We then consider multirate transmission models and show that average delay in this case typically does increase with the network size due to queues containing a small number of “residual ” packets. I.