Throughput maximization of real-time scheduling with batching

Author:

Bar-Noy Amotz1,Guha Sudipto2,Katz Yoav3,Naor Joseph (Seffi)4,Schieber Baruch5,Shachnai Hadas4

Affiliation:

1. Brooklyn College, Bedford Avenue Brooklyn, NY

2. University of Pennsylvania, Philadelphia, PA

3. IBM Haifa Research Lab, Haifa, Israel

4. Technion, Israel

5. IBM T.J. Watson Research Center, Yorktown Heights, NY

Abstract

We consider the following scheduling with batching problem that has many applications, for example, in multimedia-on-demand and manufacturing of integrated circuits. The input to the problem consists of n jobs and k parallel machines. Each job is associated with a set of time intervals in which it can be scheduled (given either explicitly or nonexplicitly), a weight, and a family. Each family is associated with a processing time. Jobs that belong to the same family can be batched and executed together on the same machine. The processing time of each batch is the processing time of the family of jobs it contains. The goal is to find a nonpreemptive schedule with batching that maximizes the weight of the scheduled jobs. We give constant factor (4 or 4 + ε) approximation algorithms for two variants of the problem, depending on the precise representation of the input. When the batch size is unbounded and each job is associated with a time window in which it can be processed, these approximation ratios reduce to 2 and 2 + ε, respectively. We also give approximation algorithms for two special cases when all release times are the same.

Funder

Sixth Framework Programme

Publisher

Association for Computing Machinery (ACM)

Subject

Mathematics (miscellaneous)

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1. On Batching Task Scheduling;2022 IEEE Real-Time Systems Symposium (RTSS);2022-12

2. A state-of-art review and a simple meta-analysis on deterministic scheduling of diffusion furnaces in semiconductor manufacturing;International Journal of Production Research;2022-08-06

3. From Spectrum Bonding to Contiguous-Resource Batching Task Scheduling;IEEE/ACM Transactions on Networking;2022-06

4. Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces;Supply Chain Management in Manufacturing and Service Systems;2021

5. Minimizing the maximum flow time in batch scheduling;Operations Research Letters;2016-11

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