Response time and display rate in human performance with computers
ACM Computing Surveys (CSUR)
Groupware: some issues and experiences
Communications of the ACM
Real time groupware as a distributed system: concurrency control and its effect on the interface
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Log-based collaborative infrastructure
Log-based collaborative infrastructure
Using cursor prediction to smooth telepointer jitter
GROUP '03 Proceedings of the 2003 international ACM SIGGROUP conference on Supporting group work
Measurement based analysis, modeling, and synthesis of the internet delay space
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Improving network efficiency in real-time groupware with general message compression
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Modeling the effects of delayed haptic and visual feedback in a collaborative virtual environment
ACM Transactions on Computer-Human Interaction (TOCHI)
Lazy scheduling of processing and transmission tasks in collaborative systems
Proceedings of the ACM 2009 international conference on Supporting group work
COLCOM '07 Proceedings of the 2007 International Conference on Collaborative Computing: Networking, Applications and Worksharing
Fiia: user-centered development of adaptive groupware systems
Proceedings of the 1st ACM SIGCHI symposium on Engineering interactive computing systems
Towards self-optimizing frameworks for collaborative systems
Towards self-optimizing frameworks for collaborative systems
Towards self-optimizing collaborative systems
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
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The performance of a collaborative system depends on how two mandatory collaborative tasks, processing and transmission of user commands, are scheduled. We have developed multiple policies for scheduling these tasks on computers that have (a) one processing element on the network interface card and (b) one or more processing cores on the CPU. To compare these policies, we have a developed a formal analytical model that predicts their performance. It shows that the optimal scheduling policy depends on several factors including the number of cores that is available. We have implemented a system that supports all of the policies and performed experiments to validate the formal model. This system is a component of a self-optimizing scheduler we have developed that improves response times by automatically choosing the scheduling policy based on number of cores and other factors.