Publication Date: 8/22/2022
Event: ACM SIGCOMM 2022 Workshop on Network-Application Integration (NAI 2022)
Reference: pp. 1-7, 2022
Authors: Anousheh Gholami, NEC Laboratories America, Inc., University of Maryland; Kunal Rao, NEC Laboratories America, Inc.; Wang-Pin Hsiung, NEC Laboratories America, Inc.; Oliver Po, NEC Laboratories America, Inc.; Murugan Sankaradas, NEC Laboratories America, Inc.; John S. Baras, University of Maryland; Srimat T. Chakradhar, NEC Laboratories America, Inc.
Abstract: 5G services and applications explicitly reserve compute and network resources in today’s complex and dynamic infrastructure of multi-tiered computing and cellular networking to ensure application-specific service quality metrics, and the infrastructure providers charge the 5G services for the resources reserved. A static, one-time reservation of resources at service deployment typically results in extended periods of under-utilization of reserved resources during the lifetime of the service operation. This is due to a plethora of reasons like changes in content from the IoT sensors (for example, change in number of people in the field of view of a camera) or a change in the environmental conditions around the IoT sensors (for example, time of the day, rain or fog can affect data acquisition by sensors). Under-utilization of a specific resource like compute can also be due to temporary inadequate availability of another resource like the network bandwidth in a dynamic 5G infrastructure. We propose a novel Reinforcement Learning-based online method to dynamically adjust an application’s compute and network resource reservations to minimize under-utilization of requested resources, while ensuring acceptable service quality metrics. We observe that a complex application-specific coupling exists between the compute and network usage of an application. Our proposed method learns this coupling during the operation of the service, and dynamically modulates the compute and network resource requests to mimimize under-utilization of reserved resources. Through experimental evaluation using real-world video analytics application, we show that our technique is able to capture complex compute-network coupling relationship in an online manner i.e. while the application is running, and dynamically adapts and saves up to 65% compute and 93% network resources on average (over multiple runs), without significantly impacting application accuracy.
Publication Link: https://dl.acm.org/doi/10.1145/3538401.3546598