Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.11861/7680
Title: | DJM: A global distributed virtual machine on the internet |
Authors: | Prof. LEUNG Kwong Sak Lee, Kin-Hong Wong, Yuk-Yin |
Issue Date: | 1998 |
Publisher: | John Wiley and Sons Ltd |
Source: | Software - Practice and Experience, 1998, vo. 28 (12), pp. 1269 - 1297 |
Journal: | Software - Practice and Experience |
Abstract: | In this paper, we present the design and implementation of a novel model of distributed computing on the Internet and Intranet environment. Our model is called the Distributed Java Machine (DJM). It is a global distributed virtual machine used to realize the concept of 'network is the computer'. DJM explores coarse-grained parallelism by using the under-utilized workstations on the network, combining the elements of object-oriented technology, distributed computing, World-Wide Web and Java programming. It can run on machines with heterogeneous hardware and software platforms without relinking or recompilation. DJM has two unique features. First, using an original applet helper mechanism, DJM allows machines without any DJM software and of different levels of 'trust' to work together. Secondly, DJM has implemented concurrency enhancement mechanisms (one-way message, future and redirected future) to increase the efficiency of method invocation. The prototype of DJM has been implemented and tested under both the Intranet and the Internet environments. Using the workstations from our teaching laboratories, which are already running under normal loading, experimental results show that we can achieve a speedup of about 5-8 times by 14 workstations in a Local Area Network (LAN) environment, and about 4.5 times speedup for eight workstations in a Wide Area Network (WAN) environment. © 1998 John Wiley & Sons, Ltd. |
Type: | Peer Reviewed Journal Article |
URI: | http://hdl.handle.net/20.500.11861/7680 |
ISSN: | 00380644 |
DOI: | 10.1002/(SICI)1097-024X(1998100)28:12<1269::AID-SPE202>3.0.CO;2-K |
Appears in Collections: | Applied Data Science - Publication |
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