Re-Stream
TL;DR: The experimental results demonstrate that the Re-Stream has the ability to improve energy efficiency of a big data stream computing system, and to reduce average response time.
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About: This article is published in Information Sciences. The article was published on 20 Oct 2015. and is currently open access. The article focuses on the topics: Stream & Data stream clustering.
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References
Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing
TL;DR: An architectural framework and principles for energy-efficient Cloud computing are defined and the proposed energy-aware allocation heuristics provision data center resources to client applications in a way that improves energy efficiency of the data center, while delivering the negotiated Quality of Service (QoS).
2.8K
Discretized streams: fault-tolerant streaming computation at scale
Matei Zaharia,Tathagata Das,Haoyuan Li,Timothy Hunter,Scott Shenker,Ion Stoica +5 more
- 03 Nov 2013
TL;DR: D-Streams enable a parallel recovery mechanism that improves efficiency over traditional replication and backup schemes, and tolerates stragglers, and can easily be composed with batch and interactive query models like MapReduce, enabling rich applications that combine these modes.
S4: Distributed Stream Computing Platform
Leonardo Neumeyer,Bruce Robbins,Anish Nair,Anand Kesari +3 more
- 13 Dec 2010
TL;DR: The architecture resembles the Actors model, providing semantics of encapsulation and location transparency, thus allowing applications to be massively concurrent while exposing a simple programming interface to application developers.
The information sciences
TL;DR: Through a human centered design project focused on an information science problem, students will gain experience and a better understanding of the process to develop an innovative solution addressing a societal need.
1K
Naiad: a timely dataflow system
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- 03 Nov 2013
TL;DR: It is shown that many powerful high-level programming models can be built on Naiad's low-level primitives, enabling such diverse tasks as streaming data analysis, iterative machine learning, and interactive graph mining.
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