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Article

AI-Driven QoS-Aware Scheduling for Serverless Video Analytics at the Edge

by
Dimitrios Giagkos
,
Achilleas Tzenetopoulos
*,
Dimosthenis Masouros
,
Sotirios Xydis
,
Francky Catthoor
and
Dimitrios Soudris
School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
*
Author to whom correspondence should be addressed.
Information 2024, 15(8), 480; https://doi.org/10.3390/info15080480
Submission received: 12 July 2024 / Revised: 7 August 2024 / Accepted: 7 August 2024 / Published: 13 August 2024

Abstract

Today, video analytics are becoming extremely popular due to the increasing need for extracting valuable information from videos available in public sharing services through camera-driven streams in IoT environments. To avoid data communication overheads, a common practice is to have computation close to the data source rather than Cloud offloading. Typically, video analytics are organized as separate tasks, each with different resource requirements (e.g., computational- vs. memory-intensive tasks). The serverless computing paradigm forms a promising approach for mapping such types of applications, enabling fine-grained deployment and management in a per-function, and per-device manner. However, there is a tradeoff between QoS adherence and resource efficiency. Performance variability due to function co-location and prevalent resource heterogeneity make maintaining QoS challenging. At the same time, resource efficiency is essential to avoid waste, such as unnecessary power consumption and CPU reservation. In this paper, we present Darly, a QoS-, interference- and heterogeneity-aware Deep Reinforcement Learning-based Scheduler for serverless video analytics deployments on top of distributed Edge nodes. The proposed framework incorporates a DRL agent that exploits performance counters to identify the levels of interference and the degree of heterogeneity in the underlying Edge infrastructure. It combines this information along with user-defined QoS requirements to improve resource allocations by deciding the placement, migration, or horizontal scaling of serverless functions. We evaluate Darly on a typical Edge cluster with a real-world workflow composed of commonly used serverless video analytics functions and show that our approach achieves efficient scheduling of the deployed functions by satisfying multiple QoS requirements for up to 91.6% (Profile-based) of the total requests under dynamic conditions.
Keywords: edge computing; serverless computing; deep reinforcement learning; IoT video processing; resource management edge computing; serverless computing; deep reinforcement learning; IoT video processing; resource management

Share and Cite

MDPI and ACS Style

Giagkos, D.; Tzenetopoulos, A.; Masouros, D.; Xydis, S.; Catthoor, F.; Soudris, D. AI-Driven QoS-Aware Scheduling for Serverless Video Analytics at the Edge. Information 2024, 15, 480. https://doi.org/10.3390/info15080480

AMA Style

Giagkos D, Tzenetopoulos A, Masouros D, Xydis S, Catthoor F, Soudris D. AI-Driven QoS-Aware Scheduling for Serverless Video Analytics at the Edge. Information. 2024; 15(8):480. https://doi.org/10.3390/info15080480

Chicago/Turabian Style

Giagkos, Dimitrios, Achilleas Tzenetopoulos, Dimosthenis Masouros, Sotirios Xydis, Francky Catthoor, and Dimitrios Soudris. 2024. "AI-Driven QoS-Aware Scheduling for Serverless Video Analytics at the Edge" Information 15, no. 8: 480. https://doi.org/10.3390/info15080480

APA Style

Giagkos, D., Tzenetopoulos, A., Masouros, D., Xydis, S., Catthoor, F., & Soudris, D. (2024). AI-Driven QoS-Aware Scheduling for Serverless Video Analytics at the Edge. Information, 15(8), 480. https://doi.org/10.3390/info15080480

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