Scheduling in variable-core collaborative systems
Proceedings of the ACM 2011 conference on Computer supported cooperative work
| Hi-index | 0.00 |
Two important performance metrics in collaborative systems are local and remote response times. For certain classes of applications, it is possible to meet response time requirements better than existing systems through a new system without requiring hardware, network, or user-interface changes. This self-optimizing system improves response times by automatically making runtime adjustments to three aspects of a collaborative application. One of these aspects is the collaboration architecture. Previous work has shown that dynamically switching architectures at runtime can improve response times; however, no previous work performs the switch automatically. The thesis shows that (a) another important performance parameter is whether multicast or unicast is used to transmit commands, and (b) response times can be noticeably better with multicast than with unicast when transmission costs are high. Traditional architectures, however, support only unicast – a computer that processes input commands must also transmit commands to all other computers. To support multicast, a new bi-architecture model of collaborative systems is introduced in which two separate architectures govern the processing and transmission tasks that each computer must perform.The thesis also shows that another important performance aspect is the order in which a computer performs these tasks. These tasks can be scheduled sequentially or concurrently on a single-core, or in parallel on multiple cores. As the thesis shows, existing single-core policies trade-off noticeable improvements in local (remote) for noticeable degradations in remote (local) response times. A new lazy policy for scheduling these tasks on a single-core is introduced that trades-off an unnoticeable degradation in performance of some users for a much larger noticeable improvement in performance of others. The thesis also shows that on multi-core devices, the tasks should always be scheduled on separate cores.The self-optimizing system adjusts the processing architecture, communication architecture, and scheduling policy based on response time predictions given by a new analytical model. Both the analytical model and the self-optimizing system are validated through simulations and experiments in practical scenarios.