Local Search-based Approach for Cost-effective Job Assignment on Large Language Models
Abstract
References
Index Terms
- Local Search-based Approach for Cost-effective Job Assignment on Large Language Models
Recommendations
Optimizing the Utilization of Large Language Models via Schedule Optimization: An Exploratory Study
ESEM '24: Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and MeasurementBackground: Large Language Models (LLMs) have gained significant attention in machine-learning-as-a-service (MLaaS) offerings. In-context learning (ICL) is a technique that guides LLMs towards accurate query processing by providing additional ...
A hybrid local search algorithm for scheduling real-world job shops with batch-wise pending due dates
This paper aims at solving a real-world job shop scheduling problem with two characteristics, i.e., the existence of pending due dates and job batches. Due date quotation is an important decision process for contemporary companies that adopt the MTO (...
Joint cache partition and job assignment on multi-core processors
WADS'13: Proceedings of the 13th international conference on Algorithms and Data StructuresMulticore shared cache processors pose a challenge for designers of embedded systems who try to achieve minimal and predictable execution time of workloads consisting of several jobs. To address this challenge the cache is statically partitioned among ...
Comments
Information & Contributors
Information
Published In
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Poster
Conference
Acceptance Rates
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 48Total Downloads
- Downloads (Last 12 months)48
- Downloads (Last 6 weeks)4
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in