9,330 research outputs found
Transparent multi-core speculative parallelization of DES models with event and cross-state dependencies
In this article we tackle transparent parallelization of Discrete Event Simulation (DES) models to be run on top of multi-core machines according to speculative schemes. The innovation in our proposal lies in that we consider a more general programming and execution model, compared to the one targeted by state of the art PDES platforms, where the boundaries of the state portion accessible while processing an event at a specific simulation object do not limit access to the actual object state, or to shared global variables. Rather, the simulation object is allowed to access (and alter) the state of any other object, thus causing what we term cross-state dependency. We note that this model exactly complies with typical (easy to manage) sequential-style DES programming, where a (dynamically-allocated) state portion of object A can be accessed by object B in either read or write mode (or both) by, e.g., passing a pointer to B as the payload of a scheduled simulation event. However, while read/write memory accesses performed in the sequential run are always guaranteed to observe (and to give rise to) a consistent snapshot of the state of the simulation model, consistency is not automatically guaranteed in case of parallelization and concurrent execution of simulation objects with cross-state dependencies. We cope with such a consistency issue, and its application-transparent support, in the context of parallel and optimistic executions. This is achieved by introducing an advanced memory management architecture, able to efficiently detect read/write accesses by concurrent objects to whichever object state in an application transparent manner, together with advanced synchronization mechanisms providing the advantage of exploiting parallelism in the underlying multi-core architecture while transparently handling both cross-state and traditional event-based dependencies. Our proposal targets Linux and has been integrated with the ROOT-Sim open source optimistic simulation platform, although its design principles, and most parts of the developed software, are of general relevance. Copyright 2014 ACM
DeSyRe: on-Demand System Reliability
The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
- …