An instruction set architecture extension for supporting fine-grain thread scheduling and execution is proposed.
Modern developments in information technologies such as RFID Radio Frequency Identificationn) provide a constructive basis to incorporate the stages of SC scheduling and execution under the attracting adaptation methods.
In this study, we investigate the issues of establishing adaptive feedbacks between SC scheduling and execution from the perspectives of modern control theory.
The scheduling and execution of bag-of-tasks applications (BoTs) in Clouds is performed on sets of virtualized Cloud resources that start being exhausted right after their allocation disregarding whether tasks are being executed.
Scheduling and execution of remote processes are integral parts of designing grid and cloud computing systems, which aim to utilize the idle CPU and memory resources of a node.
It offers distinct advantages over synchronous time stepping: (i) updates of individual macro-particles and discrete field elements are performed asynchronously, (ii) local time increments are determined and self-adaptively adjusted in time through scheduling and execution of physically meaningful local updates ("events").
In using optimal control for scheduling stage, feedback adaptive control for execution stage, and attainable sets for analysis of the achievement of the planned performance in a real execution environment, we pro-vide a mathematically unified framework for SC scheduling and execution control.
Moreover, an agent-based Cloud BoT scheduling approach that supports concurrent and parallel scheduling and execution of BoTs, and concurrent and parallel dynamic selection and composition of Cloud resources (by making use of the well-known contract net protocol) from multiple and distributed Cloud providers is designed and implemented.
Following the availability of this conceptual model, the next step in our future work would be to develop a scheduling and execution component that would allow the user to easily submit an instantiated conceptual model (a specific environmental application bind to the conceptual model) to one or more available computing infrastructures.
Businesses and governments seeking to make improvements to their processes may model them for the purpose of seeking improvements in schedule and task execution, product quality, risk reduction, and lifecycle/operating costs.
