Abstract

In the context of LeGE-WG we expect to see the Open Grid Services Architecture (OGSA) being used as a guiding framework for the future deployment of Distributed Learning Environment (DLEs). OGSA is totally service-oriented and it has been known for sometime that Quality of Service (QoS) is key to the success of DLEs. Delay in particular, as experienced by the end user, is one of the key QoS parameters for a DLE. This paper describes techniques for identifying sources of such delay, and a model for its analysis. Four major sources of delay are distinguished: Server, Client, Network and Protocol. The analysis techniques are illustrated in a case study of the traffic associated with a set of operational DLEs serving six Universities over a period of several months. This paper contributes towards the use of OGSA for e-learning by providing a detailed understanding of the QoS requirements for an OGSA compliant e-learning service.

Highlights

  • It is important from the perspective of good educational practice that online learning should be interactive, responsive and engaging

  • TAGS [3] is a framework for the research, development and deployment of DLEs which differs from conventional online learning packages in that it is Quality of Service (QoS) aware [4]

  • We have described the characteristics of DLEs constructed using the TAGS framework and noted that: i) they are highly interactive distributed applications, and ii) that delay, as experienced by the user, is a key QoS parameter

Read more

Summary

INTRODUCTION

It is important from the perspective of good educational practice that online learning should be interactive, responsive and engaging. Slow responses can quickly dissuade teachers and learners alike from investing their time in the use of DLE services. In this paper a methodology for assessing the factors that contribute to the delay experienced by users is presented and the implications that flow from the results obtained by its application are discussed. TAGS [3] is a framework for the research, development and deployment of DLEs which differs from conventional online learning packages in that it is QoS aware [4]. Reports of some users experiencing significant delays while using TAGS prompted us to return to work in analysing the source of delay in network service previously published in [5]

THE DELAY COMPONENT MODEL
THE TAGS CASE STUDY
Traffic Measurements and Analysis
CLIENT LIMITATION
NETWORK LIMITATION
PROTOCOL LIMITATION
SERVER LIMITATION
Findings
CONCLUSION

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.