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Ioannis Pogkas

dblp:54/7602 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-authorSoftware engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Interconnection networks and networks-on-chip · 77% Cloud and datacenter computing · 23%
Software engineering, system software, and programming languages
1 paper
Services computing and microservices · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interconnection networks and networks-on-chip
hypercube network
0.212014
Decentralized Enactment of BPEL Processes · IEEE Trans. Serv. Comput. 2014
Cloud and datacenter computing › resource allocation
workload allocation
0.112014
Decentralized Enactment of BPEL Processes · IEEE Trans. Serv. Comput. 2014

Methods — techniques the papers use, named apart from their topics

hypercube peer-to-peer topology · 0.4
YearPublicationVenuePosition
2014 Decentralized Enactment of BPEL Processes
abstract
This article presents BPELcube, a framework comprising a scalable architecture and a set of distributed algorithms, which support the decentralized enactment of BPEL processes. In many application domains, BPEL processes are long-running, involve the exchange of voluminous data with external Web services, and are concurrently accessed by large numbers of users. In such context, centralized BPEL process execution engines pose considerable limitations in terms of scalability and performance. To overcome such problems, a scalable hypercube peer-to-peer topology is employed by BPELcube in order to organize an arbitrary number of nodes, which can then collaborate in the decentralized execution and monitoring of BPEL processes. Contrary to traditional clustering approaches, each node does not fully take charge of executing the whole process; rather, it contributes to the overall process execution by running a subset of the process activities and maintaining a subset of the process variables. Hence, the hypercube-based infrastructure acts as a single execution engine, where workload is evenly distributed among the participating nodes in a fine-grained manner. An experimental evaluation of BPELcube and a comparison with centralized and clustered BPEL engine architectures demonstrate that the decentralized approach yields improved process execution times and throughput.
Michael Pantazoglou, Ioannis Pogkas, Aphrodite Tsalgatidou
IEEE Trans. Serv. Comput.2
2009 Adaptive neighborhood selection in peer-to-peer networks based on content similarity and reputation
Ioannis Pogkas, Vassil Kriakov, Zhongqiang Chen, Alex Delis
Peer-to-Peer Netw. Appl.1