Enhancing Datacenter Resource Management through Temporal Logic Constraints Conference Paper

abstract

• Resource management of modern datacenters needs to consider multiple competing objectives that involve complex system interactions. In this work, Linear Temporal Logic (LTL) is adopted in describing such interactions by leveraging its ability to express complex properties. Further, LTL-based constraints are integrated with reinforcement learning according the recent progress on control synthesis theory. The LTL-constrained reinforcement learning facilitates desired balance among the competing objectives in managing resources for datacenters. The effectiveness of this new approach is demonstrated by two scenarios. In datacenter power management, the LTL-constrained manager reaches the best balance among power, performance and battery stress compared to the previous work and other alternative approaches. In multitenant job scheduling, 200 MapReduce jobs are emulated on the Amazon AWS cloud. The LTL-constrained scheduler achieves the best balance between system performance and fairness compared to several other methods including three Hadoop schedulers.

name of conference

• 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)

authors

• Da Silva, Dilma

published proceedings

• 2017 31ST IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS)

author list (cited authors)

• He, H., Hu, J., & Da Silva, D.

citation count

• 2

complete list of authors

• He, Hao||Hu, Jiang||Da Silva, Dilma

publication date

• May 2017

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Proceedings of the International Parallel and Distributed Processing Symposium, IPDPS  Journal

keywords

• 46 Information And Computing Sciences
• 4602 Artificial Intelligence

Digital Object Identifier (DOI)

• 10.1109/IPDPS.2017.27

start page

• 133

end page

• 142

URL

• http://dx.doi.org/10.1109/ipdps.2017.27