25,404 research outputs found
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
Semantic-based policy engineering for autonomic systems
This paper presents some important directions in the use of ontology-based semantics in achieving the vision of Autonomic Communications. We examine the requirements of Autonomic Communication with a focus on the demanding needs of ubiquitous computing environments, with an emphasis on the requirements shared with Autonomic Computing. We observe that ontologies provide a strong mechanism for addressing the heterogeneity in user task requirements, managed resources, services and context. We then present two complimentary approaches that exploit ontology-based knowledge in support of autonomic communications: service-oriented models for policy engineering and dynamic semantic queries using content-based networks. The paper concludes with a discussion of the major research challenges such approaches raise
Knowledge Representation Concepts for Automated SLA Management
Outsourcing of complex IT infrastructure to IT service providers has
increased substantially during the past years. IT service providers must be
able to fulfil their service-quality commitments based upon predefined Service
Level Agreements (SLAs) with the service customer. They need to manage, execute
and maintain thousands of SLAs for different customers and different types of
services, which needs new levels of flexibility and automation not available
with the current technology. The complexity of contractual logic in SLAs
requires new forms of knowledge representation to automatically draw inferences
and execute contractual agreements. A logic-based approach provides several
advantages including automated rule chaining allowing for compact knowledge
representation as well as flexibility to adapt to rapidly changing business
requirements. We suggest adequate logical formalisms for representation and
enforcement of SLA rules and describe a proof-of-concept implementation. The
article describes selected formalisms of the ContractLog KR and their adequacy
for automated SLA management and presents results of experiments to demonstrate
flexibility and scalability of the approach.Comment: Paschke, A. and Bichler, M.: Knowledge Representation Concepts for
Automated SLA Management, Int. Journal of Decision Support Systems (DSS),
submitted 19th March 200
Undermining User Privacy on Mobile Devices Using AI
Over the past years, literature has shown that attacks exploiting the
microarchitecture of modern processors pose a serious threat to the privacy of
mobile phone users. This is because applications leave distinct footprints in
the processor, which can be used by malware to infer user activities. In this
work, we show that these inference attacks are considerably more practical when
combined with advanced AI techniques. In particular, we focus on profiling the
activity in the last-level cache (LLC) of ARM processors. We employ a simple
Prime+Probe based monitoring technique to obtain cache traces, which we
classify with Deep Learning methods including Convolutional Neural Networks. We
demonstrate our approach on an off-the-shelf Android phone by launching a
successful attack from an unprivileged, zeropermission App in well under a
minute. The App thereby detects running applications with an accuracy of 98%
and reveals opened websites and streaming videos by monitoring the LLC for at
most 6 seconds. This is possible, since Deep Learning compensates measurement
disturbances stemming from the inherently noisy LLC monitoring and unfavorable
cache characteristics such as random line replacement policies. In summary, our
results show that thanks to advanced AI techniques, inference attacks are
becoming alarmingly easy to implement and execute in practice. This once more
calls for countermeasures that confine microarchitectural leakage and protect
mobile phone applications, especially those valuing the privacy of their users
Scather: programming with multi-party computation and MapReduce
We present a prototype of a distributed computational infrastructure, an associated high level programming language, and an underlying formal framework that allow multiple parties to leverage their own cloud-based computational resources (capable of supporting MapReduce [27] operations) in concert with multi-party computation (MPC) to execute statistical analysis algorithms that have privacy-preserving properties. Our architecture allows a data analyst unfamiliar with MPC to: (1) author an analysis algorithm that is agnostic with regard to data privacy policies, (2) to use an automated process to derive algorithm implementation variants that have different privacy and performance properties, and (3) to compile those implementation variants so that they can be deployed on an infrastructures that allows computations to take place locally within each participantâs MapReduce cluster as well as across all the participantsâ clusters using an MPC protocol. We describe implementation details of the architecture, discuss and demonstrate how the formal framework enables the exploration of tradeoffs between the efficiency and privacy properties of an analysis algorithm, and present two example applications that illustrate how such an infrastructure can be utilized in practice.This work was supported in part by NSF Grants: #1430145, #1414119, #1347522, and #1012798
Ontology Summit 2008 Communiqué: Towards an open ontology repository
Each annual Ontology Summit initiative makes a statement appropriate to each Summits theme as part of our general advocacy designed to bring ontology science and engineering into the mainstream. The theme this year is "Towards an Open Ontology Repository". This communiqué represents the joint position of those who were engaged in the year's summit discourse on an Open Ontology Repository (OOR) and of those who endorse below. In this discussion, we have agreed that an "ontology repository is a facility where ontologies and related information artifacts can be stored, retrieved and managed."
We believe in the promise of semantic technologies based on logic, databases and the Semantic Web, a Web of exposed data and of interpretations of that data (i.e., of semantics), using common standards. Such technologies enable distinguishable, computable, reusable, and sharable meaning of Web and other artifacts, including data, documents, and services. We also believe that making that vision a reality requires additional supporting resources and these resources should be open, extensible, and provide common services over the ontologies
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