54,197 research outputs found
Modelling interdependencies between the electricity and information infrastructures
The aim of this paper is to provide qualitative models characterizing
interdependencies related failures of two critical infrastructures: the
electricity infrastructure and the associated information infrastructure. The
interdependencies of these two infrastructures are increasing due to a growing
connection of the power grid networks to the global information infrastructure,
as a consequence of market deregulation and opening. These interdependencies
increase the risk of failures. We focus on cascading, escalating and
common-cause failures, which correspond to the main causes of failures due to
interdependencies. We address failures in the electricity infrastructure, in
combination with accidental failures in the information infrastructure, then we
show briefly how malicious attacks in the information infrastructure can be
addressed
Predicting Scheduling Failures in the Cloud
Cloud Computing has emerged as a key technology to deliver and manage
computing, platform, and software services over the Internet. Task scheduling
algorithms play an important role in the efficiency of cloud computing services
as they aim to reduce the turnaround time of tasks and improve resource
utilization. Several task scheduling algorithms have been proposed in the
literature for cloud computing systems, the majority relying on the
computational complexity of tasks and the distribution of resources. However,
several tasks scheduled following these algorithms still fail because of
unforeseen changes in the cloud environments. In this paper, using tasks
execution and resource utilization data extracted from the execution traces of
real world applications at Google, we explore the possibility of predicting the
scheduling outcome of a task using statistical models. If we can successfully
predict tasks failures, we may be able to reduce the execution time of jobs by
rescheduling failed tasks earlier (i.e., before their actual failing time). Our
results show that statistical models can predict task failures with a precision
up to 97.4%, and a recall up to 96.2%. We simulate the potential benefits of
such predictions using the tool kit GloudSim and found that they can improve
the number of finished tasks by up to 40%. We also perform a case study using
the Hadoop framework of Amazon Elastic MapReduce (EMR) and the jobs of a gene
expression correlations analysis study from breast cancer research. We find
that when extending the scheduler of Hadoop with our predictive models, the
percentage of failed jobs can be reduced by up to 45%, with an overhead of less
than 5 minutes
On the Identification of Agents in the Design of Production Control Systems
This paper describes a methodology that is being developed for designing and building agent-based systems for the domain of production control. In particular, this paper deals with the steps that are involved in identifying the agents and in specifying their responsibilities. The methodology aims to be usable by engineers who have a background in production control but who have no prior experience in agent technology. For this reason, the methodology needs to be very prescriptive with respect to the agent-related aspects of design
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