Keskeiset käsitteet
The proposed FairBatch algorithm strikes a balance between efficiency and fairness in batch processing by dynamically assigning priorities, employing a dynamic time slice, and utilizing periodic sorting.
Tiivistelmä
The content discusses the importance of efficient CPU utilization and the need to address both efficiency and fairness in batch processing environments. It provides a comprehensive overview of classical CPU scheduling algorithms and their limitations, highlighting the lack of a universally accepted fairness metric for single-batch processing.
The authors propose the FairBatch algorithm, which aims to revitalize the single batch processing paradigm. FairBatch incorporates dynamic time slicing, a preemption mechanism, and periodic sorting of processes to optimize CPU time allocation and achieve a balance between efficiency and fairness.
The key aspects of the FairBatch algorithm include:
- Balanced selection of jobs: The algorithm considers both shorter and longer processes to ensure a well-rounded mix, promoting fair distribution of CPU resources and improving response time for longer processes.
- Progress tracking in the fairness ratio: The algorithm acknowledges the importance of honoring the progress made by each process, prioritizing processes that have made substantial progress to avoid unnecessary interruptions and context switches.
- Limiting preemption using the fairness ratio: The algorithm promotes efficiency by reducing unnecessary preemption, minimizing CPU overhead, and enhancing responsiveness.
The authors provide a detailed analysis of the algorithm's behavior, including formulations for waiting time and response time, and discuss the importance of a suitable time quantum. They also propose an optimization technique to reduce the computational overhead of the algorithm.
The experimental setup involves a comprehensive evaluation of the proposed algorithm against classical scheduling algorithms, using a diverse set of job clusters generated from various probability distributions. The authors emphasize the importance of adhering to practical constraints and normalizing the job clusters to ensure a robust and realistic assessment.
Tilastot
The average waiting time and average turnaround time are indicators of efficiency, while the average response time reflects the fairness of the algorithms.
Lainaukset
"Efficient scheduling not only impacts system performance but also has significant economic implications. In today's digital era, where computational power is a valuable resource, optimising CPU scheduling can lead to substantial cost savings."
"Fairness is often viewed as a subjective metric lacking a universally agreed-upon definition in various task-scheduling contexts."
"Our work addresses this question by first analysing these measures and then analyzing the classical algorithms commonly used in time-sharing and multi-programming systems with respect to these measures to understand how they perform in terms of both efficient and fair distribution of job selections across a diverse set of job clusters."