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Automated planning and scheduling, sometimes denoted as simply AI planning, [1] is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles.
The algorithm can function with any pipeline architecture and thus software requires few architecture-specific modifications. [3]: 183 Many modern processors implement dynamic scheduling schemes that are variants of Tomasulo's original algorithm, including popular Intel x86-64 chips. [5] [failed verification] [6]
Master/Agent architecture — the historic architecture for Job Scheduling software. The Job Scheduling software is installed on a single machine (Master), while on production machines only a very small component (Agent) is installed that awaits commands from the Master, executes them, then returns the exit code back to the Master. Cooperative ...
Artificial Intelligence is thought to potentially lead to and ensue major changes in architecture. [1] [2] [3] AI's potential in optimization of design, planning and productivity have been noted as accelerators in the field of architectural work. The ability of AI to potentially amplify an architect's design process has also been noted.
The scheduler is an operating system module that selects the next jobs to be admitted into the system and the next process to run. Operating systems may feature up to three distinct scheduler types: a long-term scheduler (also known as an admission scheduler or high-level scheduler), a mid-term or medium-term scheduler, and a short-term scheduler.
This is a list of notable job scheduler software. Job scheduling applications are designed to carry out repetitive tasks as defined in a schedule based upon calendar and event conditions. This category of software is also called workload automation. Only products with their own article are listed: ActiveBatch; Apache Airflow; Cron; DIET; HTCondor
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Open Automated Demand Response (OpenADR) is a research and standards development effort for energy management led by North American research labs and companies. The typical use is to send information and signals to cause electrical power-using devices to be turned off during periods of high demand.