34,743 research outputs found
Towards Decision Support Technology Platform for Modular Systems
The survey methodological paper addresses a glance to a general decision
support platform technology for modular systems (modular/composite
alterantives/solutions) in various applied domains. The decision support
platform consists of seven basic combinatorial engineering frameworks (system
synthesis, system modeling, evaluation, detection of bottleneck,
improvement/extension, multistage design, combinatorial evolution and
forecasting). The decision support platform is based on decision support
procedures (e.g., multicriteria selection/sorting, clustering), combinatorial
optimization problems (e.g., knapsack, multiple choice problem, clique,
assignment/allocation, covering, spanning trees), and their combinations. The
following is described: (1) general scheme of the decision support platform
technology; (2) brief descriptions of modular (composite) systems (or composite
alternatives); (3) trends in moving from chocie/selection of alternatives to
processing of composite alternatives which correspond to hierarchical modular
products/systems; (4) scheme of resource requirements (i.e., human,
information-computer); and (5) basic combinatorial engineering frameworks and
their applications in various domains.Comment: 10 pages, 9 figures, 2 table
An Introduction to Software Engineering and Fault Tolerance
This book consists of the chapters describing novel approaches to integrating
fault tolerance into software development process. They cover a wide range of
topics focusing on fault tolerance during the different phases of the software
development, software engineering techniques for verification and validation of
fault tolerance means, and languages for supporting fault tolerance
specification and implementation. Accordingly, the book is structured into the
following three parts: Part A: Fault tolerance engineering: from requirements
to code; Part B: Verification and validation of fault tolerant systems; Part C:
Languages and Tools for engineering fault tolerant systems
On whether and how D-RISC and Microgrids can be kept relevant (self-assessment report)
This report lays flat my personal views on D-RISC and Microgrids as of March
2013. It reflects the opinions and insights that I have gained from working on
this project during the period 2008-2013. This report is structed in two parts:
deconstruction and reconstruction. In the deconstruction phase, I review what I
believe are the fundamental motivation and goals of the D-RISC/Microgrids
enterprise, and identify what I judge are shortcomings: that the project did
not deliver on its expectations, that fundamental questions are left
unanswered, and that its original motivation may not even be relevant in
scientific research any more in this day and age. In the reconstruction phase,
I start by identifying the merits of the current D-RISC/Microgrids technology
and know-how taken at face value, re-motivate its existence from a different
angle, and suggest new, relevant research questions that could justify
continued scientific investment.Comment: 45 pages, 5 figures, 2 table
Middleware Building Blocks for Workflow Systems
This paper describes a building blocks approach to the design of scientific
workflow systems. We discuss RADICAL-Cybertools as one implementation of the
building blocks concept, showing how they are designed and developed in
accordance with this approach. This paper offers three main contributions: (i)
showing the relevance of the design principles underlying the building blocks
approach to support scientific workflows on high performance computing
platforms; (ii) illustrating a set of building blocks that enable multiple
points of integration, "unifying" conceptual reasoning across otherwise very
different tools and systems; and (iii) case studies discussing how
RADICAL-Cybertools are integrated with existing workflow, workload, and general
purpose computing systems and used to develop domain-specific workflow systems
(Auto)Focus approaches and their applications: A systematic review
Focus, a framework for formal specification and development of interactive
systems, was introduced approx. 25 years ago. Since then this approach was
broadly used in academic and industrial studies, as well as provided a basis
for a number of another frameworks focusing on particular domains, and for the
AF3 modelling tool. In this paper we provide a literature review of the
corresponding approaches, academic case studies and industrial applications of
these methods
Exploiting the power of multiplicity: a holistic survey of network-layer multipath
The Internet is inherently a multipath network---for an underlying network
with only a single path connecting various nodes would have been debilitatingly
fragile. Unfortunately, traditional Internet technologies have been designed
around the restrictive assumption of a single working path between a source and
a destination. The lack of native multipath support constrains network
performance even as the underlying network is richly connected and has
redundant multiple paths. Computer networks can exploit the power of
multiplicity to unlock the inherent redundancy of the Internet. This opens up a
new vista of opportunities promising increased throughput (through concurrent
usage of multiple paths) and increased reliability and fault-tolerance (through
the use of multiple paths in backup/ redundant arrangements). There are many
emerging trends in networking that signify that the Internet's future will be
unmistakably multipath, including the use of multipath technology in datacenter
computing; multi-interface, multi-channel, and multi-antenna trends in
wireless; ubiquity of mobile devices that are multi-homed with heterogeneous
access networks; and the development and standardization of multipath transport
protocols such as MP-TCP.
The aim of this paper is to provide a comprehensive survey of the literature
on network-layer multipath solutions. We will present a detailed investigation
of two important design issues, namely the control plane problem of how to
compute and select the routes, and the data plane problem of how to split the
flow on the computed paths. The main contribution of this paper is a systematic
articulation of the main design issues in network-layer multipath routing along
with a broad-ranging survey of the vast literature on network-layer
multipathing. We also highlight open issues and identify directions for future
work
Triangular Dynamic Architecture for Distributed Computing in a LAN Environment
A computationally intensive large job, granulized to concurrent pieces and
operating in a dynamic environment should reduce the total processing time.
However, distributing jobs across a networked environment is a tedious and
difficult task. Job distribution in a Local Area Network based on Triangular
Dynamic Architecture (TDA) is a mechanism that establishes a dynamic
environment for job distribution, load balancing and distributed processing
with minimum interaction from the user. This paper introduces TDA and discusses
its architecture and shows the benefits gained by utilizing such architecture
in a distributed computing environment.Comment: Publishe
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
With the breakthroughs in deep learning, the recent years have witnessed a
booming of artificial intelligence (AI) applications and services, spanning
from personal assistant to recommendation systems to video/audio surveillance.
More recently, with the proliferation of mobile computing and
Internet-of-Things (IoT), billions of mobile and IoT devices are connected to
the Internet, generating zillions Bytes of data at the network edge. Driving by
this trend, there is an urgent need to push the AI frontiers to the network
edge so as to fully unleash the potential of the edge big data. To meet this
demand, edge computing, an emerging paradigm that pushes computing tasks and
services from the network core to the network edge, has been widely recognized
as a promising solution. The resulted new inter-discipline, edge AI or edge
intelligence, is beginning to receive a tremendous amount of interest. However,
research on edge intelligence is still in its infancy stage, and a dedicated
venue for exchanging the recent advances of edge intelligence is highly desired
by both the computer system and artificial intelligence communities. To this
end, we conduct a comprehensive survey of the recent research efforts on edge
intelligence. Specifically, we first review the background and motivation for
artificial intelligence running at the network edge. We then provide an
overview of the overarching architectures, frameworks and emerging key
technologies for deep learning model towards training/inference at the network
edge. Finally, we discuss future research opportunities on edge intelligence.
We believe that this survey will elicit escalating attentions, stimulate
fruitful discussions and inspire further research ideas on edge intelligence.Comment: Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang,
"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge
Computing," Proceedings of the IEE
Asynchronous Complex Analytics in a Distributed Dataflow Architecture
Scalable distributed dataflow systems have recently experienced widespread
adoption, with commodity dataflow engines such as Hadoop and Spark, and even
commodity SQL engines routinely supporting increasingly sophisticated analytics
tasks (e.g., support vector machines, logistic regression, collaborative
filtering). However, these systems' synchronous (often Bulk Synchronous
Parallel) dataflow execution model is at odds with an increasingly important
trend in the machine learning community: the use of asynchrony via shared,
mutable state (i.e., data races) in convex programming tasks, which has---in a
single-node context---delivered noteworthy empirical performance gains and
inspired new research into asynchronous algorithms. In this work, we attempt to
bridge this gap by evaluating the use of lightweight, asynchronous state
transfer within a commodity dataflow engine. Specifically, we investigate the
use of asynchronous sideways information passing (ASIP) that presents
single-stage parallel iterators with a Volcano-like intra-operator iterator
that can be used for asynchronous information passing. We port two synchronous
convex programming algorithms, stochastic gradient descent and the alternating
direction method of multipliers (ADMM), to use ASIPs. We evaluate an
implementation of ASIPs within on Apache Spark that exhibits considerable
speedups as well as a rich set of performance trade-offs in the use of these
asynchronous algorithms
A Hardware-Software Blueprint for Flexible Deep Learning Specialization
Specialized Deep Learning (DL) acceleration stacks, designed for a specific
set of frameworks, model architectures, operators, and data types, offer the
allure of high performance while sacrificing flexibility. Changes in
algorithms, models, operators, or numerical systems threaten the viability of
specialized hardware accelerators. We propose VTA, a programmable deep learning
architecture template designed to be extensible in the face of evolving
workloads. VTA achieves this flexibility via a parametrizable architecture,
two-level ISA, and a JIT compiler. The two-level ISA is based on (1) a task-ISA
that explicitly orchestrates concurrent compute and memory tasks and (2) a
microcode-ISA which implements a wide variety of operators with single-cycle
tensor-tensor operations. Next, we propose a runtime system equipped with a JIT
compiler for flexible code-generation and heterogeneous execution that enables
effective use of the VTA architecture. VTA is integrated and open-sourced into
Apache TVM, a state-of-the-art deep learning compilation stack that provides
flexibility for diverse models and divergent hardware backends. We propose a
flow that performs design space exploration to generate a customized hardware
architecture and software operator library that can be leveraged by mainstream
learning frameworks. We demonstrate our approach by deploying optimized deep
learning models used for object classification and style transfer on edge-class
FPGAs.Comment: 6 pages plus references, 8 figure
- …