36,255 research outputs found
Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures
Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs
DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams
In a data stream management system (DSMS), users register continuous queries,
and receive result updates as data arrive and expire. We focus on applications
with real-time constraints, in which the user must receive each result update
within a given period after the update occurs. To handle fast data, the DSMS is
commonly placed on top of a cloud infrastructure. Because stream properties
such as arrival rates can fluctuate unpredictably, cloud resources must be
dynamically provisioned and scheduled accordingly to ensure real-time response.
It is quite essential, for the existing systems or future developments, to
possess the ability of scheduling resources dynamically according to the
current workload, in order to avoid wasting resources, or failing in delivering
correct results on time. Motivated by this, we propose DRS, a novel dynamic
resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental
challenges: (a) how to model the relationship between the provisioned resources
and query response time (b) where to best place resources; and (c) how to
measure system load with minimal overhead. In particular, DRS includes an
accurate performance model based on the theory of \emph{Jackson open queueing
networks} and is capable of handling \emph{arbitrary} operator topologies,
possibly with loops, splits and joins. Extensive experiments with real data
confirm that DRS achieves real-time response with close to optimal resource
consumption.Comment: This is the our latest version with certain modificatio
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On Optimal and Fair Service Allocation in Mobile Cloud Computing
This paper studies the optimal and fair service allocation for a variety of
mobile applications (single or group and collaborative mobile applications) in
mobile cloud computing. We exploit the observation that using tiered clouds,
i.e. clouds at multiple levels (local and public) can increase the performance
and scalability of mobile applications. We proposed a novel framework to model
mobile applications as a location-time workflows (LTW) of tasks; here users
mobility patterns are translated to mobile service usage patterns. We show that
an optimal mapping of LTWs to tiered cloud resources considering multiple QoS
goals such application delay, device power consumption and user cost/price is
an NP-hard problem for both single and group-based applications. We propose an
efficient heuristic algorithm called MuSIC that is able to perform well (73% of
optimal, 30% better than simple strategies), and scale well to a large number
of users while ensuring high mobile application QoS. We evaluate MuSIC and the
2-tier mobile cloud approach via implementation (on real world clouds) and
extensive simulations using rich mobile applications like intensive signal
processing, video streaming and multimedia file sharing applications. Our
experimental and simulation results indicate that MuSIC supports scalable
operation (100+ concurrent users executing complex workflows) while improving
QoS. We observe about 25% lower delays and power (under fixed price
constraints) and about 35% decrease in price (considering fixed delay) in
comparison to only using the public cloud. Our studies also show that MuSIC
performs quite well under different mobility patterns, e.g. random waypoint and
Manhattan models
ReSHAPE: A Framework for Dynamic Resizing and Scheduling of Homogeneous Applications in a Parallel Environment
Applications in science and engineering often require huge computational
resources for solving problems within a reasonable time frame. Parallel
supercomputers provide the computational infrastructure for solving such
problems. A traditional application scheduler running on a parallel cluster
only supports static scheduling where the number of processors allocated to an
application remains fixed throughout the lifetime of execution of the job. Due
to the unpredictability in job arrival times and varying resource requirements,
static scheduling can result in idle system resources thereby decreasing the
overall system throughput. In this paper we present a prototype framework
called ReSHAPE, which supports dynamic resizing of parallel MPI applications
executed on distributed memory platforms. The framework includes a scheduler
that supports resizing of applications, an API to enable applications to
interact with the scheduler, and a library that makes resizing viable.
Applications executed using the ReSHAPE scheduler framework can expand to take
advantage of additional free processors or can shrink to accommodate a high
priority application, without getting suspended. In our research, we have
mainly focused on structured applications that have two-dimensional data arrays
distributed across a two-dimensional processor grid. The resize library
includes algorithms for processor selection and processor mapping. Experimental
results show that the ReSHAPE framework can improve individual job turn-around
time and overall system throughput.Comment: 15 pages, 10 figures, 5 tables Submitted to International Conference
on Parallel Processing (ICPP'07
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