A proactive energy-aware auto-scaling solution for edge-based infrastructures

Proactive auto-scaling mechanisms in edge-based infrastructures can anticipate user service requests by allocating computing resources while supporting the quality of service needed by a vast range of applications requiring, e.g., a low latency or response time.  However, managing the dynamic needs of user service requests is challenging due to the edge infrastructure's heterogeneity and dynamic nature. Also, minimizing global energy consumption is a must in today’s systems, which should be addressed inherently as part of any resource scaling solution. This paper presents a proactive horizontal auto-scaling framework for edge infrastructures, which takes into account both the base (idle) and dynamic (due to application execution) energy consumption of edge nodes, as well as of the node scaling mechanism. Simulations were performed with the EdgeCloudSim simulator with a workload provided by Shanghai Telecom and the results show up to a 92.5\% decrease in energy consumption, a failed request rate of up to 0\%, and reasonable execution times of the auto-scaling process for different problem sizes.

This work is supported by the European Union's H2020 research and innovation program under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 (MCI/AEI) and RHEA P18-FR-1081.