By Wolfgang Effelsberg, Ralf Steinmetz, Thorsten Strufe
Peer-to-peer platforms at the moment are primary and became the focal point of cognizance for lots of researchers over the last decade. a few algorithms for decentralized seek, content material distribution, and media streaming were constructed. This e-book offers basic techniques for the benchmarking of these algorithms in peer-to-peer platforms. It additionally incorporates a choice of attribute benchmarking effects. The chapters of the e-book were equipped in 3 topical sections on: basics of Benchmarking in P2P structures; artificial Benchmarks for Peer-to-Peer structures; and alertness Benchmarks for Peer-to-Peer structures. they're preceded via an in depth advent to the topic.
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Extra info for Benchmarking Peer-to-Peer Systems: Understanding Quality of Service in Large-Scale Distributed Systems (Lecture Notes in Computer Science)
Panitzek, and M. Lehn is defined by certain constraints, which have to be fulfilled. For instance, a distributed database consistency constraint could demand that content within a part of the distributed database is not conflicting with any data in the whole system at any time. A weaker requirement of consistency is coherence, which demands that every response for one specific request may not lead to a conflict, and, therefore, the system must never deliver contradictory results to different peers or users.
In the following, commonly used metrics are presented to quantify the workload-independent quality aspects performance, costs, efficiency, and fairness. 3, performance corresponds to the combination of responsiveness, throughput, and validity. Responsiveness r of a distributed hash table can be expressed by the metric response time r. Usually, the response time is measured at individual peers as r(p,t) = sr − si with the timestamps si and sr of the request initiation and received response, respectively.
3, performance corresponds to the combination of responsiveness, throughput, and validity. Responsiveness r of a distributed hash table can be expressed by the metric response time r. Usually, the response time is measured at individual peers as r(p,t) = sr − si with the timestamps si and sr of the request initiation and received response, respectively. To average the response time over the entire system r has to be calculated as x described above. An alternative quantification of performance is the metric throughput h.