2,174 research outputs found
End-to-End Design for Self-Reconfigurable Heterogeneous Robotic Swarms
More widespread adoption requires swarms of robots to be more flexible for
real-world applications. Multiple challenges remain in complex scenarios where
a large amount of data needs to be processed in real-time and high degrees of
situational awareness are required. The options in this direction are limited
in existing robotic swarms, mostly homogeneous robots with limited operational
and reconfiguration flexibility. We address this by bringing elastic computing
techniques and dynamic resource management from the edge-cloud computing domain
to the swarm robotics domain. This enables the dynamic provisioning of
collective capabilities in the swarm for different applications. Therefore, we
transform a swarm into a distributed sensing and computing platform capable of
complex data processing tasks, which can then be offered as a service. In
particular, we discuss how this can be applied to adaptive resource management
in a heterogeneous swarm of drones, and how we are implementing the dynamic
deployment of distributed data processing algorithms. With an elastic drone
swarm built on reconfigurable hardware and containerized services, it will be
possible to raise the self-awareness, degree of intelligence, and level of
autonomy of heterogeneous swarms of robots. We describe novel directions for
collaborative perception, and new ways of interacting with a robotic swarm
Future directions in networked sensing
Started one decade ago as a wild academic idea, wireless sensor and actuator networks turned into a vibrant research area with a large R&D community dealing with a commercially highly relevant technology. The new paradigm in networked sensing has received significant attention because of the unprecedented benefits it promises in many areas.\ud
Seamless integration of computing with the physical world via sensors and actuators will generate higher living standards, greater safety, more comfort and more efficiency in a more sustainable world. Emerging applications include environmental monitoring, health, sport, transport, public and industrial safety, manufacturing plants and ambient assisted living. However, in spite of the significant research efforts that have been spent world-wide since the start, realization of all these great opportunities turned out to be harder than expected. \ud
In this Strategic Research and Technology Agenda (SRA), the Dutch sensor network community presents its vision on the further development of this important technology.\u
TriG - A GNSS Precise Orbit and Radio Occultation Space Receiver
The GPS radio occultation (RO) technique [1] produces
measurements in the ionosphere and neutral atmosphere
[2] that contribute to monitoring space weather and climate
change; and improving operational weather prediction.
The high accuracy of RO soundings, traceable to SI standards,
makes them ideal climate benchmark observations. For
weather applications, RO observations improve the accuracy
of weather forecasts by providing temperature and moisture
profiles of sub-km vertical resolution, over land and ocean
and in the presence of clouds.
JPL is currently flying a handful of RO instruments [3] on
various satellites in Low Earth Orbit (LEO). Although these
receivers have served to pioneer occultation measurements,
various advances in technology and understanding of the RO
technique along with availability of new signals from GPS and
other GNSS satellites allow us to design an improved next
generation space-based Precise Orbit Determination (POD)
and RO receiver, the TriG receiver. The paper describes the
architecture and implementation of the JPL TriG receiver as
well as results obtained with a prototype receiver demonstrating
key technologies necessary for a next-generation space
science receiver
Reconfigurable Processing for Satellite On-Board Automatic Cloud Cover Assessment (ACCA)
Clouds have a critical role in many studies such as weather- and climate-related investigations. However, they represent a source of errors in many applications, and the presence of cloud contamination can hinder the use of satellite data. In addition, sending cloudy data to ground stations can result in an inefficient utilization of the communication bandwidth. This requires satellite on-board cloud detection capability to mask out cloudy pixels from further processing. Remote sensing satellite missions have always required smaller size, lower cost, more flexibility, and higher computational power. Reconfigurable Computers (RCs) combine the flexibility of traditional microprocessors with the power of Field Programmable Gate Arrays (FPGAs). Therefore, RCs are a promising candidate for on-board preprocessing. This paper presents the design and implementation of an RC-based real-time cloud detection system. We investigate the potential of using RCs for on-board preprocessing by prototyping the Landsat 7 ETM+ ACCA algorithm on one of the state-of-the-art reconfigurable platforms, SRC-6. It will be shown that our work provides higher detection accuracy and over one order of magnitude improvement in performance when compared to previously reported investigations
Hybrid Optical and Electrical Network Flows Scheduling in Cloud Data Centres
Hybrid intra-data centre networks, with optical and electrical capabilities,
are attracting research interest in recent years. This is attributed to the
emergence of new bandwidth greedy applications and novel computing paradigms. A
key decision to make in networks of this type is the selection and placement of
suitable flows for switching in circuit network. Here, we propose an efficient
strategy for flow selection and placement suitable for hybrid Intra-cloud data
centre networks. We further present techniques for investigating bottlenecks in
a packet networks and for the selection of flows to switch in circuit network.
The bottleneck technique is verified on a Software Defined Network (SDN)
testbed. We also implemented the techniques presented here in a scalable
simulation experiment to investigate the impact of flow selection on network
performance. Results obtained from scalable simulation experiment indicate a
considerable improvement on average throughput, lower configuration delay, and
stability of offloaded flowsComment: 17 pages 11 figures, Journal pape
Reconfigurable Voxels: A New Representation for LiDAR-Based Point Clouds
LiDAR is an important method for autonomous driving systems to sense the
environment. The point clouds obtained by LiDAR typically exhibit sparse and
irregular distribution, thus posing great challenges to the detection of 3D
objects, especially those that are small and distant. To tackle this
difficulty, we propose Reconfigurable Voxels, a new approach to constructing
representations from 3D point clouds. Specifically, we devise a biased random
walk scheme, which adaptively covers each neighborhood with a fixed number of
voxels based on the local spatial distribution and produces a representation by
integrating the points in the chosen neighbors. We found empirically that this
approach effectively improves the stability of voxel features, especially for
sparse regions. Experimental results on multiple benchmarks, including
nuScenes, Lyft, and KITTI, show that this new representation can remarkably
improve the detection performance for small and distant objects, without
incurring noticeable overhead costs
An Open-Source Benchmark Suite for Cloud and IoT Microservices
Cloud services have recently started undergoing a major shift from monolithic
applications, to graphs of hundreds of loosely-coupled microservices.
Microservices fundamentally change a lot of assumptions current cloud systems
are designed with, and present both opportunities and challenges when
optimizing for quality of service (QoS) and utilization. In this paper we
explore the implications microservices have across the cloud system stack. We
first present DeathStarBench, a novel, open-source benchmark suite built with
microservices that is representative of large end-to-end services, modular and
extensible. DeathStarBench includes a social network, a media service, an
e-commerce site, a banking system, and IoT applications for coordination
control of UAV swarms. We then use DeathStarBench to study the architectural
characteristics of microservices, their implications in networking and
operating systems, their challenges with respect to cluster management, and
their trade-offs in terms of application design and programming frameworks.
Finally, we explore the tail at scale effects of microservices in real
deployments with hundreds of users, and highlight the increased pressure they
put on performance predictability
Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds
Cloud based medical image analysis has become popular recently due to the
high computation complexities of various deep neural network (DNN) based
frameworks and the increasingly large volume of medical images that need to be
processed. It has been demonstrated that for medical images the transmission
from local to clouds is much more expensive than the computation in the clouds
itself. Towards this, 3D image compression techniques have been widely applied
to reduce the data traffic. However, most of the existing image compression
techniques are developed around human vision, i.e., they are designed to
minimize distortions that can be perceived by human eyes. In this paper we will
use deep learning based medical image segmentation as a vehicle and demonstrate
that interestingly, machine and human view the compression quality differently.
Medical images compressed with good quality w.r.t. human vision may result in
inferior segmentation accuracy. We then design a machine vision oriented 3D
image compression framework tailored for segmentation using DNNs. Our method
automatically extracts and retains image features that are most important to
the segmentation. Comprehensive experiments on widely adopted segmentation
frameworks with HVSMR 2016 challenge dataset show that our method can achieve
significantly higher segmentation accuracy at the same compression rate, or
much better compression rate under the same segmentation accuracy, when
compared with the existing JPEG 2000 method. To the best of the authors'
knowledge, this is the first machine vision guided medical image compression
framework for segmentation in the clouds.Comment: IEEE Computer Society Conference on Computer Vision and Pattern
Recognition(CVPR), Long Beach, CA, 201
Leveraging Deep Learning to Improve the Performance Predictability of Cloud Microservices
Performance unpredictability is a major roadblock towards cloud adoption, and
has performance, cost, and revenue ramifications. Predictable performance is
even more critical as cloud services transition from monolithic designs to
microservices. Detecting QoS violations after they occur in systems with
microservices results in long recovery times, as hotspots propagate and amplify
across dependent services. We present Seer, an online cloud performance
debugging system that leverages deep learning and the massive amount of tracing
data cloud systems collect to learn spatial and temporal patterns that
translate to QoS violations. Seer combines lightweight distributed RPC-level
tracing, with detailed low-level hardware monitoring to signal an upcoming QoS
violation, and diagnose the source of unpredictable performance. Once an
imminent QoS violation is detected, Seer notifies the cluster manager to take
action to avoid performance degradation altogether. We evaluate Seer both in
local clusters, and in large-scale deployments of end-to-end applications built
with microservices with hundreds of users. We show that Seer correctly
anticipates QoS violations 91% of the time, and avoids the QoS violation to
begin with in 84% of cases. Finally, we show that Seer can identify
application-level design bugs, and provide insights on how to better architect
microservices to achieve predictable performance
GPU PaaS Computation Model in Aneka Cloud Computing Environment
Due to the surge in the volume of data generated and rapid advancement in
Artificial Intelligence (AI) techniques like machine learning and deep
learning, the existing traditional computing models have become inadequate to
process an enormous volume of data and the complex application logic for
extracting intrinsic information. Computing accelerators such as Graphics
processing units (GPUs) have become de facto SIMD computing system for many big
data and machine learning applications. On the other hand, the traditional
computing model has gradually switched from conventional ownership-based
computing to subscription-based cloud computing model. However, the lack of
programming models and frameworks to develop cloud-native applications in a
seamless manner to utilize both CPU and GPU resources in the cloud has become a
bottleneck for rapid application development. To support this application
demand for simultaneous heterogeneous resource usage, programming models and
new frameworks are needed to manage the underlying resources effectively. Aneka
is emerged as a popular PaaS computing model for the development of Cloud
applications using multiple programming models like Thread, Task, and MapReduce
in a single container .NET platform. Since, Aneka addresses MIMD application
development that uses CPU based resources and GPU programming like CUDA is
designed for SIMD application development, here, the chapter discusses GPU PaaS
computing model for Aneka Clouds for rapid cloud application development for
.NET platforms. The popular opensource GPU libraries are utilized and
integrated it into the existing Aneka task programming model. The scheduling
policies are extended that automatically identify GPU machines and schedule
respective tasks accordingly. A case study on image processing is discussed to
demonstrate the system, which has been built using PaaS Aneka SDKs and CUDA
library.Comment: Submitted as book chapter, under processing, 32 page
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