18,304 research outputs found
Delay, memory, and messaging tradeoffs in distributed service systems
We consider the following distributed service model: jobs with unit mean,
exponentially distributed, and independent processing times arrive as a Poisson
process of rate , with , and are immediately dispatched
by a centralized dispatcher to one of First-In-First-Out queues associated
with identical servers. The dispatcher is endowed with a finite memory, and
with the ability to exchange messages with the servers.
We propose and study a resource-constrained "pull-based" dispatching policy
that involves two parameters: (i) the number of memory bits available at the
dispatcher, and (ii) the average rate at which servers communicate with the
dispatcher. We establish (using a fluid limit approach) that the asymptotic, as
, expected queueing delay is zero when either (i) the number of
memory bits grows logarithmically with and the message rate grows
superlinearly with , or (ii) the number of memory bits grows
superlogarithmically with and the message rate is at least .
Furthermore, when the number of memory bits grows only logarithmically with
and the message rate is proportional to , we obtain a closed-form expression
for the (now positive) asymptotic delay.
Finally, we demonstrate an interesting phase transition in the
resource-constrained regime where the asymptotic delay is non-zero. In
particular, we show that for any given (no matter how small), if our
policy only uses a linear message rate , the resulting asymptotic
delay is upper bounded, uniformly over all ; this is in sharp
contrast to the delay obtained when no messages are used (), which
grows as when , or when the popular
power-of--choices is used, in which the delay grows as
Universality of Load Balancing Schemes on Diffusion Scale
We consider a system of parallel queues with identical exponential
service rates and a single dispatcher where tasks arrive as a Poisson process.
When a task arrives, the dispatcher always assigns it to an idle server, if
there is any, and to a server with the shortest queue among randomly
selected servers otherwise . This load balancing scheme
subsumes the so-called Join-the-Idle Queue (JIQ) policy and the
celebrated Join-the-Shortest Queue (JSQ) policy as two crucial
special cases. We develop a stochastic coupling construction to obtain the
diffusion limit of the queue process in the Halfin-Whitt heavy-traffic regime,
and establish that it does not depend on the value of , implying that
assigning tasks to idle servers is sufficient for diffusion level optimality
Ultra-Dense Networks: Is There a Limit to Spatial Spectrum Reuse?
The aggressive spatial spectrum reuse (SSR) by network densification using
smaller cells has successfully driven the wireless communication industry
onward in the past decades. In our future journey toward ultra-dense networks
(UDNs), a fundamental question needs to be answered. Is there a limit to SSR?
In other words, when we deploy thousands or millions of small cell base
stations (BSs) per square kilometer, is activating all BSs on the same
time/frequency resource the best strategy? In this paper, we present
theoretical analyses to answer such question. In particular, we find that both
the signal and interference powers become bounded in practical UDNs with a
non-zero BS-to-UE antenna height difference and a finite UE density, which
leads to a constant capacity scaling law. As a result, there exists an optimal
SSR density that can maximize the network capacity. Hence, the limit to SSR
should be considered in the operation of future UDNs.Comment: conference submission in Oct. 201
MOON: MapReduce On Opportunistic eNvironments
Abstract—MapReduce offers a flexible programming model for processing and generating large data sets on dedicated resources, where only a small fraction of such resources are every unavailable at any given time. In contrast, when MapReduce is run on volunteer computing systems, which opportunistically harness idle desktop computers via frameworks like Condor, it results in poor performance due to the volatility of the resources, in particular, the high rate of node unavailability. Specifically, the data and task replication scheme adopted by existing MapReduce implementations is woefully inadequate for resources with high unavailability. To address this, we propose MOON, short for MapReduce On Opportunistic eNvironments. MOON extends Hadoop, an open-source implementation of MapReduce, with adaptive task and data scheduling algorithms in order to offer reliable MapReduce services on a hybrid resource architecture, where volunteer computing systems are supplemented by a small set of dedicated nodes. The adaptive task and data scheduling algorithms in MOON distinguish between (1) different types of MapReduce data and (2) different types of node outages in order to strategically place tasks and data on both volatile and dedicated nodes. Our tests demonstrate that MOON can deliver a 3-fold performance improvement to Hadoop in volatile, volunteer computing environments
Spectral and Energy Efficiency in Cognitive Radio Systems with Unslotted Primary Users and Sensing Uncertainty
This paper studies energy efficiency (EE) and average throughput maximization
for cognitive radio systems in the presence of unslotted primary users. It is
assumed that primary user activity follows an ON-OFF alternating renewal
process. Secondary users first sense the channel possibly with errors in the
form of miss detections and false alarms, and then start the data transmission
only if no primary user activity is detected. The secondary user transmission
is subject to constraints on collision duration ratio, which is defined as the
ratio of average collision duration to transmission duration. In this setting,
the optimal power control policy which maximizes the EE of the secondary users
or maximizes the average throughput while satisfying a minimum required EE
under average/peak transmit power and average interference power constraints
are derived. Subsequently, low-complexity algorithms for jointly determining
the optimal power level and frame duration are proposed. The impact of
probabilities of detection and false alarm, transmit and interference power
constraints on the EE, average throughput of the secondary users, optimal
transmission power, and the collisions with primary user transmissions are
evaluated. In addition, some important properties of the collision duration
ratio are investigated. The tradeoff between the EE and average throughput
under imperfect sensing decisions and different primary user traffic are
further analyzed.Comment: This paper is accepted for publication in IEEE Transactions on
Communication
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