Effective Handling of Urgent Jobs - Speed Up Scheduling for Computing Applications

TL;DR

This paper introduces algorithms for speed up scheduling that prioritize urgent jobs in computing environments without additional resources, demonstrated through simulations in web and CPU scheduling domains.

Contribution

It formulates the speed up scheduling problem without extra resources and applies it to web and CPU scheduling, providing new algorithms and evaluation methods.

Findings

Speed up scheduling effectively prioritizes urgent jobs.

Simulation results show improved completion times for urgent jobs.

The approach is practical for real-world computing systems.

Abstract

A queue is required when a service provider is not able to handle jobs arriving over the time. In a highly flexible and dynamic environment, some jobs might demand for faster execution at run-time especially when the resources are limited and the jobs are competing for acquiring resources. A user might demand for speed up (reduced wait time) for some of the jobs present in the queue at run time. In such cases, it is required to accelerate (directly sending the job to the server) urgent jobs (requesting for speed up) ahead of other jobs present in the queue for an earlier completion of urgent jobs. Under the assumption of no additional resources, such acceleration of jobs would result in slowing down of other jobs present in the queue. In this paper, we formulate the problem of Speed Up Scheduling without acquiring any additional resources for the scheduling of on-line speed up requests posed by a user at run-time and present algorithms for the same. We apply the idea of Speed Up Scheduling to two different domains -Web Scheduling and CPU Scheduling. We demonstrate our results with a simulation based model using trace driven workload and synthetic datasets to show the usefulness of Speed Up scheduling. Speed Up provides a new way of addressing urgent jobs, provides a different evaluation criteria for comparing scheduling algorithms and has practical applications.