64,856 research outputs found
Interpreting Deep Learning-Based Networking Systems
While many deep learning (DL)-based networking systems have demonstrated
superior performance, the underlying Deep Neural Networks (DNNs) remain
blackboxes and stay uninterpretable for network operators. The lack of
interpretability makes DL-based networking systems prohibitive to deploy in
practice. In this paper, we propose Metis, a framework that provides
interpretability for two general categories of networking problems spanning
local and global control. Accordingly, Metis introduces two different
interpretation methods based on decision tree and hypergraph, where it converts
DNN policies to interpretable rule-based controllers and highlight critical
components based on analysis over hypergraph. We evaluate Metis over several
state-of-the-art DL-based networking systems and show that Metis provides
human-readable interpretations while preserving nearly no degradation in
performance. We further present four concrete use cases of Metis, showcasing
how Metis helps network operators to design, debug, deploy, and ad-hoc adjust
DL-based networking systems.Comment: To appear at ACM SIGCOMM 202
Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks
Without any doubt, Machine Learning (ML) will be an important driver of
future communications due to its foreseen performance when applied to complex
problems. However, the application of ML to networking systems raises concerns
among network operators and other stakeholders, especially regarding
trustworthiness and reliability. In this paper, we devise the role of network
simulators for bridging the gap between ML and communications systems. In
particular, we present an architectural integration of simulators in ML-aware
networks for training, testing, and validating ML models before being applied
to the operative network. Moreover, we provide insights on the main challenges
resulting from this integration, and then give hints discussing how they can be
overcome. Finally, we illustrate the integration of network simulators into
ML-assisted communications through a proof-of-concept testbed implementation of
a residential Wi-Fi network
Advanced Cyberinfrastructure for Science, Engineering, and Public Policy
Progress in many domains increasingly benefits from our ability to view the
systems through a computational lens, i.e., using computational abstractions of
the domains; and our ability to acquire, share, integrate, and analyze
disparate types of data. These advances would not be possible without the
advanced data and computational cyberinfrastructure and tools for data capture,
integration, analysis, modeling, and simulation. However, despite, and perhaps
because of, advances in "big data" technologies for data acquisition,
management and analytics, the other largely manual, and labor-intensive aspects
of the decision making process, e.g., formulating questions, designing studies,
organizing, curating, connecting, correlating and integrating crossdomain data,
drawing inferences and interpreting results, have become the rate-limiting
steps to progress. Advancing the capability and capacity for evidence-based
improvements in science, engineering, and public policy requires support for
(1) computational abstractions of the relevant domains coupled with
computational methods and tools for their analysis, synthesis, simulation,
visualization, sharing, and integration; (2) cognitive tools that leverage and
extend the reach of human intellect, and partner with humans on all aspects of
the activity; (3) nimble and trustworthy data cyber-infrastructures that
connect, manage a variety of instruments, multiple interrelated data types and
associated metadata, data representations, processes, protocols and workflows;
and enforce applicable security and data access and use policies; and (4)
organizational and social structures and processes for collaborative and
coordinated activity across disciplinary and institutional boundaries.Comment: A Computing Community Consortium (CCC) white paper, 9 pages. arXiv
admin note: text overlap with arXiv:1604.0200
Deploying AI Frameworks on Secure HPC Systems with Containers
The increasing interest in the usage of Artificial Intelligence techniques
(AI) from the research community and industry to tackle "real world" problems,
requires High Performance Computing (HPC) resources to efficiently compute and
scale complex algorithms across thousands of nodes. Unfortunately, typical data
scientists are not familiar with the unique requirements and characteristics of
HPC environments. They usually develop their applications with high-level
scripting languages or frameworks such as TensorFlow and the installation
process often requires connection to external systems to download open source
software during the build. HPC environments, on the other hand, are often based
on closed source applications that incorporate parallel and distributed
computing API's such as MPI and OpenMP, while users have restricted
administrator privileges, and face security restrictions such as not allowing
access to external systems. In this paper we discuss the issues associated with
the deployment of AI frameworks in a secure HPC environment and how we
successfully deploy AI frameworks on SuperMUC-NG with Charliecloud.Comment: 6 pages, 2 figures, 2019 IEEE High Performance Extreme Computing
Conferenc
Complex regional innovation networks and HEI engagement the case of Chicago
This article considers how HEIs engage within local complex development networks in order to develop the urban metropolis, using the case of Chicago as a specific example. It focuses on three main issues: how collaboration occurs amongst regional stakeholders; how goals are set and how shared goals have been created; and the extent to which there exist conflicting views amongst stakeholders, and their capability to create solutions where there are disagreements and clashing purposes. Chicago is in the middle of making a paradigm shift, with at its core an open system approach that includes a variety of ways to engage citizen-users as co-creators, including through user-driven innovation and digitalised services. In the metropolitan area there is a widely shared goal amongst stakeholders to develop and improve novel approaches for regional engagement to enhance innovativeness and competitiveness. The paradigm shift in regional engagement from building co-operation clusters to one of organisational betweenness and open systemic thinking requires new skills in management and leadership centred on interaction, co-creation and sharing of knowledge
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
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