145 research outputs found
From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey
Context data is in demand more than ever with the rapid increase in the
development of many context-aware Internet of Things applications. Research in
context and context-awareness is being conducted to broaden its applicability
in light of many practical and technical challenges. One of the challenges is
improving performance when responding to large number of context queries.
Context Management Platforms that infer and deliver context to applications
measure this problem using Quality of Service (QoS) parameters. Although
caching is a proven way to improve QoS, transiency of context and features such
as variability, heterogeneity of context queries pose an additional real-time
cost management problem. This paper presents a critical survey of
state-of-the-art in adaptive data caching with the objective of developing a
body of knowledge in cost- and performance-efficient adaptive caching
strategies. We comprehensively survey a large number of research publications
and evaluate, compare, and contrast different techniques, policies, approaches,
and schemes in adaptive caching. Our critical analysis is motivated by the
focus on adaptively caching context as a core research problem. A formal
definition for adaptive context caching is then proposed, followed by
identified features and requirements of a well-designed, objective optimal
adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys
Journal at this time of publishing in arxiv.or
A comprehensive survey on Fog Computing: State-of-the-art and research challenges
Cloud computing with its three key facets (i.e.,
Infrastructure-as-a-Service, Platform-as-a-Service, and Softwareas-
a-Service) and its inherent advantages (e.g., elasticity and
scalability) still faces several challenges. The distance between
the cloud and the end devices might be an issue for latencysensitive
applications such as disaster management and content
delivery applications. Service level agreements (SLAs) may also
impose processing at locations where the cloud provider does not
have data centers. Fog computing is a novel paradigm to address
such issues. It enables provisioning resources and services outside
the cloud, at the edge of the network, closer to end devices, or
eventually, at locations stipulated by SLAs. Fog computing is not
a substitute for cloud computing but a powerful complement. It
enables processing at the edge while still offering the possibility
to interact with the cloud. This paper presents a comprehensive
survey on fog computing. It critically reviews the state of
the art in the light of a concise set of evaluation criteria. We
cover both the architectures and the algorithms that make fog
systems. Challenges and research directions are also introduced.
In addition, the lessons learned are reviewed and the prospects
are discussed in terms of the key role fog is likely to play in
emerging technologies such as tactile Internet
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