107,521 research outputs found
The Continuous Stream Model of Computation for Real-Time Control
This paper presents a new Model of Computation (MoC) for real-time tasks used in control systems. This new model, named continuous stream task model, relaxes some of the constraints imposed by the traditional hard and soft real-time task models. A key advantage of the model is the possibility to easily analyse the probabilistic evolution of the delays. This leads to an easy formalisation of necessary and sufficient conditions for the stochastic stability of the closed loop system producing considerable savings in the amount of CPU bandwidth required to stabilise the system. This fact is confirmed by an extensive set of simulations. © 2013 IEEE
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
Streamlines for Motion Planning in Underwater Currents
Motion planning for underwater vehicles must consider the effect of ocean
currents. We present an efficient method to compute reachability and cost
between sample points in sampling-based motion planning that supports
long-range planning over hundreds of kilometres in complicated flows. The idea
is to search a reduced space of control inputs that consists of stream
functions whose level sets, or streamlines, optimally connect two given points.
Such stream functions are generated by superimposing a control input onto the
underlying current flow. A streamline represents the resulting path that a
vehicle would follow as it is carried along by the current given that control
input. We provide rigorous analysis that shows how our method avoids exhaustive
search of the control space, and demonstrate simulated examples in complicated
flows including a traversal along the east coast of Australia, using actual
current predictions, between Sydney and Brisbane.Comment: 7 pages, 4 figures, accepted to IEEE ICRA 2019. Copyright 2019 IEE
Engineering Crowdsourced Stream Processing Systems
A crowdsourced stream processing system (CSP) is a system that incorporates
crowdsourced tasks in the processing of a data stream. This can be seen as
enabling crowdsourcing work to be applied on a sample of large-scale data at
high speed, or equivalently, enabling stream processing to employ human
intelligence. It also leads to a substantial expansion of the capabilities of
data processing systems. Engineering a CSP system requires the combination of
human and machine computation elements. From a general systems theory
perspective, this means taking into account inherited as well as emerging
properties from both these elements. In this paper, we position CSP systems
within a broader taxonomy, outline a series of design principles and evaluation
metrics, present an extensible framework for their design, and describe several
design patterns. We showcase the capabilities of CSP systems by performing a
case study that applies our proposed framework to the design and analysis of a
real system (AIDR) that classifies social media messages during time-critical
crisis events. Results show that compared to a pure stream processing system,
AIDR can achieve a higher data classification accuracy, while compared to a
pure crowdsourcing solution, the system makes better use of human workers by
requiring much less manual work effort
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