17,152 research outputs found
Enabling Adaptive Grid Scheduling and Resource Management
Wider adoption of the Grid concept has led to an increasing amount of federated
computational, storage and visualisation resources being available to scientists and
researchers. Distributed and heterogeneous nature of these resources renders most of the
legacy cluster monitoring and management approaches inappropriate, and poses new
challenges in workflow scheduling on such systems. Effective resource utilisation monitoring
and highly granular yet adaptive measurements are prerequisites for a more efficient Grid
scheduler. We present a suite of measurement applications able to monitor per-process
resource utilisation, and a customisable tool for emulating observed utilisation models. We
also outline our future work on a predictive and probabilistic Grid scheduler. The research is
undertaken as part of UK e-Science EPSRC sponsored project SO-GRM (Self-Organising
Grid Resource Management) in cooperation with BT
DeepSecure: Scalable Provably-Secure Deep Learning
This paper proposes DeepSecure, a novel framework that enables scalable
execution of the state-of-the-art Deep Learning (DL) models in a
privacy-preserving setting. DeepSecure targets scenarios in which neither of
the involved parties including the cloud servers that hold the DL model
parameters or the delegating clients who own the data is willing to reveal
their information. Our framework is the first to empower accurate and scalable
DL analysis of data generated by distributed clients without sacrificing the
security to maintain efficiency. The secure DL computation in DeepSecure is
performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized
realization of various components used in DL. Our optimized implementation
achieves more than 58-fold higher throughput per sample compared with the
best-known prior solution. In addition to our optimized GC realization, we
introduce a set of novel low-overhead pre-processing techniques which further
reduce the GC overall runtime in the context of deep learning. Extensive
evaluations of various DL applications demonstrate up to two
orders-of-magnitude additional runtime improvement achieved as a result of our
pre-processing methodology. This paper also provides mechanisms to securely
delegate GC computations to a third party in constrained embedded settings
An Approximately Optimal Algorithm for Scheduling Phasor Data Transmissions in Smart Grid Networks
In this paper, we devise a scheduling algorithm for ordering transmission of
synchrophasor data from the substation to the control center in as short a time
frame as possible, within the realtime hierarchical communications
infrastructure in the electric grid. The problem is cast in the framework of
the classic job scheduling with precedence constraints. The optimization setup
comprises the number of phasor measurement units (PMUs) to be installed on the
grid, a weight associated with each PMU, processing time at the control center
for the PMUs, and precedence constraints between the PMUs. The solution to the
PMU placement problem yields the optimum number of PMUs to be installed on the
grid, while the processing times are picked uniformly at random from a
predefined set. The weight associated with each PMU and the precedence
constraints are both assumed known. The scheduling problem is provably NP-hard,
so we resort to approximation algorithms which provide solutions that are
suboptimal yet possessing polynomial time complexity. A lower bound on the
optimal schedule is derived using branch and bound techniques, and its
performance evaluated using standard IEEE test bus systems. The scheduling
policy is power grid-centric, since it takes into account the electrical
properties of the network under consideration.Comment: 8 pages, published in IEEE Transactions on Smart Grid, October 201
Grid-enabled Workflows for Industrial Product Design
This paper presents a generic approach for developing and using Grid-based workflow technology for enabling cross-organizational engineering applications. Using industrial product design examples from the automotive and aerospace industries we highlight the main requirements and challenges addressed by our approach and describe how it can be used for enabling interoperability between heterogeneous workflow engines
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