The full text of this article:

Adaptive time/space sharing with SCOJO
by Angela C. Sodan, Xuemin Huang
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 4, No. 5/6, 2006
Abstract: Time-shared execution of parallel jobs via gang scheduling is known to yield better average response times than space sharing. We incorporate adaptive CPU/node-resource allocation to consider varying system load and to reduce fragmentation. As main innovations, our SCOJO approach provides a clear model how to adapt, and considers realistic job mixes with moldable, malleable and rigid jobs. Our adaptive SCOJO significantly decreases response times and average slowdowns, while using a lower multiprogramming level than standard gang scheduling uses best and, therefore, decreasing the memory pressure. These benefits apply though the realistic job mixes limit the flexibility in resource allocation.

is only available to individual subscribers or to users at subscribing institutions.

Pay per view: If you are not a Subscriber and you just want to read the full contents of this article, please click here to purchase online access to the full-text of this article. Please allow 3 days + mailing time. Current price for article is Thirty Euros (€30)

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of High Performance Computing and Networking (IJHPCN) journal, that have been redirected here, please check if you have a registered username/password subscription with Inderscience. If that is the case, please Login:

If you are not yet a Subscriber to International Journal of High Performance Computing and Networking (IJHPCN) journal, you can subscribe by following a few simple and quick steps. A subscription will give you complete access to all articles in the current issue, as well as to all articles in the previous three years, where applicable. Click here to subscribe.

Should you experience further difficulties or have any enquiries, please email subs@inderscience.com