Software engineering with abstractions
Software engineering with abstractions
Replicated Process Allocation for Load Distribution in Fault-Tolerant Multicomputers
IEEE Transactions on Computers
Synthetic Workload Generation for Load-Balancing Experiments
IEEE Parallel & Distributed Technology: Systems & Technology
How Network Topology Affects Dynamic Load Balancing
IEEE Parallel & Distributed Technology: Systems & Technology
IEEE Transactions on Computers
An Event-Based Architecture Definition Language
IEEE Transactions on Software Engineering
The Thread-Based Protocol Engines for CC-NUMA Multiprocessors
ICPP '00 Proceedings of the Proceedings of the 2000 International Conference on Parallel Processing
Object Model Driven Code Generation for the Enterprise
RSP '01 Proceedings of the 12th International Workshop on Rapid System Prototyping
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
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Many systems have very predictable points in time where the usage of a network changes. These systems are usually characterized by shift changes where the manning and functions performed change from shift to shift. We propose a pro-active optimization approach that uses predictable indicators like manning schedules, season, mission, and other foreseeable periodic events to configure distributed object servers. Object-Oriented computing is fast becoming the de-facto standard for software development and distributed object servers are becoming more common as transaction rates increase.Optimal deployment strategies for object servers change due to variations in object servers, client applications, operational missions, hardware modifications, and various other changes to the environment.As distributed object servers become more prevalent, there is more need to optimize the deployment of object servers to best serve the end user's changing needs. A system that automatically generates object server deployment strategies would allow users to take full advantage of their network of computers.The proposed method profiles object servers, client applications, user inputs and network resources. These profiles determine an optimization model that is solved to produce an optimal deployment strategy for the predicted upcoming usage by the users of the system of computers and servers.The validity of the model was tested by experimental measurement. A test bed was created and different manning schedules were simulated. The results of the experimentation showed that the average response time for a user could be improved by altering the deployment of the servers according to the scheduled manning of the system. The model was robust in the sense that the deployments that produced optimal response times in the model also produced optimal or near-optimal response times in the actual implementation of the test-bed.