26,501 research outputs found
Pinwheel Scheduling for Fault-tolerant Broadcast Disks in Real-time Database Systems
The design of programs for broadcast disks which incorporate real-time and fault-tolerance requirements is considered. A generalized model for real-time fault-tolerant broadcast disks is defined. It is shown that designing programs for broadcast disks specified in this model is closely related to the scheduling of pinwheel task systems. Some new results in pinwheel scheduling theory are derived, which facilitate the efficient generation of real-time fault-tolerant broadcast disk programs.National Science Foundation (CCR-9308344, CCR-9596282
Energy efficient mining on a quantum-enabled blockchain using light
We outline a quantum-enabled blockchain architecture based on a consortium of
quantum servers. The network is hybridised, utilising digital systems for
sharing and processing classical information combined with a fibre--optic
infrastructure and quantum devices for transmitting and processing quantum
information. We deliver an energy efficient interactive mining protocol enacted
between clients and servers which uses quantum information encoded in light and
removes the need for trust in network infrastructure. Instead, clients on the
network need only trust the transparent network code, and that their devices
adhere to the rules of quantum physics. To demonstrate the energy efficiency of
the mining protocol, we elaborate upon the results of two previous experiments
(one performed over 1km of optical fibre) as applied to this work. Finally, we
address some key vulnerabilities, explore open questions, and observe
forward--compatibility with the quantum internet and quantum computing
technologies.Comment: 25 pages, 5 figure
Discriminative models for multi-instance problems with tree-structure
Modeling network traffic is gaining importance in order to counter modern
threats of ever increasing sophistication. It is though surprisingly difficult
and costly to construct reliable classifiers on top of telemetry data due to
the variety and complexity of signals that no human can manage to interpret in
full. Obtaining training data with sufficiently large and variable body of
labels can thus be seen as prohibitive problem. The goal of this work is to
detect infected computers by observing their HTTP(S) traffic collected from
network sensors, which are typically proxy servers or network firewalls, while
relying on only minimal human input in model training phase. We propose a
discriminative model that makes decisions based on all computer's traffic
observed during predefined time window (5 minutes in our case). The model is
trained on collected traffic samples over equally sized time window per large
number of computers, where the only labels needed are human verdicts about the
computer as a whole (presumed infected vs. presumed clean). As part of training
the model itself recognizes discriminative patterns in traffic targeted to
individual servers and constructs the final high-level classifier on top of
them. We show the classifier to perform with very high precision, while the
learned traffic patterns can be interpreted as Indicators of Compromise. In the
following we implement the discriminative model as a neural network with
special structure reflecting two stacked multi-instance problems. The main
advantages of the proposed configuration include not only improved accuracy and
ability to learn from gross labels, but also automatic learning of server types
(together with their detectors) which are typically visited by infected
computers
Blazes: Coordination Analysis for Distributed Programs
Distributed consistency is perhaps the most discussed topic in distributed
systems today. Coordination protocols can ensure consistency, but in practice
they cause undesirable performance unless used judiciously. Scalable
distributed architectures avoid coordination whenever possible, but
under-coordinated systems can exhibit behavioral anomalies under fault, which
are often extremely difficult to debug. This raises significant challenges for
distributed system architects and developers. In this paper we present Blazes,
a cross-platform program analysis framework that (a) identifies program
locations that require coordination to ensure consistent executions, and (b)
automatically synthesizes application-specific coordination code that can
significantly outperform general-purpose techniques. We present two case
studies, one using annotated programs in the Twitter Storm system, and another
using the Bloom declarative language.Comment: Updated to include additional materials from the original technical
report: derivation rules, output stream label
Reservation-Based Federated Scheduling for Parallel Real-Time Tasks
This paper considers the scheduling of parallel real-time tasks with
arbitrary-deadlines. Each job of a parallel task is described as a directed
acyclic graph (DAG). In contrast to prior work in this area, where
decomposition-based scheduling algorithms are proposed based on the
DAG-structure and inter-task interference is analyzed as self-suspending
behavior, this paper generalizes the federated scheduling approach. We propose
a reservation-based algorithm, called reservation-based federated scheduling,
that dominates federated scheduling. We provide general constraints for the
design of such systems and prove that reservation-based federated scheduling
has a constant speedup factor with respect to any optimal DAG task scheduler.
Furthermore, the presented algorithm can be used in conjunction with any
scheduler and scheduling analysis suitable for ordinary arbitrary-deadline
sporadic task sets, i.e., without parallelism
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