Energy Efficient Offline Parallel Scheduling in Cloud Computing by Reducing Total Busy Time - Jason, Sebagenzi (International Journal of Computer Sciences and Engineering, 2022)
The basic scheduling issue is examined in this chapter. On n identical computers with bounded capacity, n deterministic jobs need to be scheduled offline. Each work has a start time, a finish time, a processing time, and a machine capacity requirement. The purpose is to schedule all of the jobs no proactively in their start-time–end-time windows, subject to machine capacity limits so that the overall busy time of the machines is minimized. Minimizing the overall busy time for the scheduling of several identical machines is the name we give to this issue (MinTBT). Power-aware scheduling for Cloud computing, optical network design, customer service systems, and other relevant fields can all benefit from solving this issue. In the particular case where all jobs have the same process time and can be scheduled in a set time interval, scheduling to reduce busy time is already NP-hard. The 5-approximation approach for exceptional situations utilizing the first-fit-decreasing (FFD) algorithm is one of the best-known solutions to this problem. In this chapter, we suggest and demonstrate a modified first-fit-decreasing-earliest 3-approximation technique for the general case and gain further results for particular situations. Then, we demonstrate how our findings might be used in cloud computing to increase energy efficiency.