36,742 research outputs found
Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation
A human computation system can be viewed as a distributed system in which the
processors are humans, called workers. Such systems harness the cognitive power
of a group of workers connected to the Internet to execute relatively simple
tasks, whose solutions, once grouped, solve a problem that systems equipped
with only machines could not solve satisfactorily. Examples of such systems are
Amazon Mechanical Turk and the Zooniverse platform. A human computation
application comprises a group of tasks, each of them can be performed by one
worker. Tasks might have dependencies among each other. In this study, we
propose a theoretical framework to analyze such type of application from a
distributed systems point of view. Our framework is established on three
dimensions that represent different perspectives in which human computation
applications can be approached: quality-of-service requirements, design and
management strategies, and human aspects. By using this framework, we review
human computation in the perspective of programmers seeking to improve the
design of human computation applications and managers seeking to increase the
effectiveness of human computation infrastructures in running such
applications. In doing so, besides integrating and organizing what has been
done in this direction, we also put into perspective the fact that the human
aspects of the workers in such systems introduce new challenges in terms of,
for example, task assignment, dependency management, and fault prevention and
tolerance. We discuss how they are related to distributed systems and other
areas of knowledge.Comment: 3 figures, 1 tabl
Resilience markers for safer systems and organisations
If computer systems are to be designed to foster resilient
performance it is important to be able to identify contributors to resilience. The
emerging practice of Resilience Engineering has identified that people are still a
primary source of resilience, and that the design of distributed systems should
provide ways of helping people and organisations to cope with complexity.
Although resilience has been identified as a desired property, researchers and
practitioners do not have a clear understanding of what manifestations of
resilience look like. This paper discusses some examples of strategies that
people can adopt that improve the resilience of a system. Critically, analysis
reveals that the generation of these strategies is only possible if the system
facilitates them. As an example, this paper discusses practices, such as
reflection, that are known to encourage resilient behavior in people. Reflection
allows systems to better prepare for oncoming demands. We show that
contributors to the practice of reflection manifest themselves at different levels
of abstraction: from individual strategies to practices in, for example, control
room environments. The analysis of interaction at these levels enables resilient
properties of a system to be āseenā, so that systems can be designed to explicitly
support them. We then present an analysis of resilience at an organisational
level within the nuclear domain. This highlights some of the challenges facing
the Resilience Engineering approach and the need for using a collective
language to articulate knowledge of resilient practices across domains
A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing
Data Grids have been adopted as the platform for scientific communities that
need to share, access, transport, process and manage large data collections
distributed worldwide. They combine high-end computing technologies with
high-performance networking and wide-area storage management techniques. In
this paper, we discuss the key concepts behind Data Grids and compare them with
other data sharing and distribution paradigms such as content delivery
networks, peer-to-peer networks and distributed databases. We then provide
comprehensive taxonomies that cover various aspects of architecture, data
transportation, data replication and resource allocation and scheduling.
Finally, we map the proposed taxonomy to various Data Grid systems not only to
validate the taxonomy but also to identify areas for future exploration.
Through this taxonomy, we aim to categorise existing systems to better
understand their goals and their methodology. This would help evaluate their
applicability for solving similar problems. This taxonomy also provides a "gap
analysis" of this area through which researchers can potentially identify new
issues for investigation. Finally, we hope that the proposed taxonomy and
mapping also helps to provide an easy way for new practitioners to understand
this complex area of research.Comment: 46 pages, 16 figures, Technical Repor
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