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E-Kai Shen

dblp:69/1055 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2004
—ORCID · none

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

Systems, architecture and hardware · 2 · 1 first-author

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
Distributed systems · 100%
Software engineering, system software, and programming languages
1 paper
Services computing and microservices · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems › middleware
adaptive middleware
0.012000
High performance adaptive middleware for CORBA-based systems · PODC 2000
Distributed systems
middleware
0.012000
High performance adaptive middleware for CORBA-based systems · PODC 2000
Distributed systems › middleware › distributed object middleware
object request broker
0.012000
High performance adaptive middleware for CORBA-based systems · PODC 2000

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

handle-driven mode · 0.1forwarding mode · 0.1adaptive ORB · 0.1
YearPublicationVenuePosition
2004 Performance of adaptive CORBA middleware
Shikharesh Majumdar, E-Kai Shen, Istabrak Abdul-Fatah
J. Parallel Distributed Comput.2
2000 High performance adaptive middleware for CORBA-based systems
abstract
Middleware provides inter-operability and transparent location of servers in a heterogeneous distributed environment. A careful design of the middleware software is required however for achieving high performance. This research proposes an adaptive middleware architecture for CORBA-based systems. The adaptive middleware agent that maps an object name to the object reference has two modes of operations. In the handle-driven mode it returns a reference for the requested object to the client that uses this reference to re-send the request for the desired operation to the server whereas in the forwarding mode it forwards the entire client request to the server. The server upon invocation performs the desired operation and returns the results to the client. An adaptive ORB dynamically switches between these two modes depending on the current system load. Using a commercial middleware product called Orbix-MT we have implemented a skeletal performance prototype for the adaptive ORB. Based on measurements made on a network of workstations and a synthetic workload we observe that the adaptive ORB can produce a substantial benefit in performance in comparison to a pure handle-driven or a pure forwarding ORB. Our measurements provide valuable insights into system behavior and performance.
E-Kai Shen, Shikharesh Majumdar, Istabrak Abdul-Fatah
PODC1