TY - GEN
T1 - EdgeBench
T2 - Future of Information and Communication Conference, FICC 2024
AU - Yang, Qirui
AU - Jin, Runyu
AU - Gandhi, Nabil
AU - Ge, Xiongzi
AU - Khouzani, Hoda Aghaei
AU - Zhao, Ming
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Edge computing has been developed to utilize heterogeneous computing resources from different physical locations for privacy, cost, and Quality of Service (QoS) reasons. Edge workloads have the characteristics of data-driven, latency-sensitive, and privacy-critical. As a result, edge systems have been developed to be both heterogeneous and distributed to utilize different computing tiers’ resources and features. The unique characteristics of edge workloads and edge systems have motivated EdgeBench, a workflow-based benchmark aiming to provide the ability to explore the full design space of edge applications and edge systems. EdgeBench is both customizable and representative. It allows users to customize the workflow logic of edge workloads, the data storage backends, and the distribution of the individual workflow function to different computing tiers. To illustrate the usability of EdgeBench, we implement two representative edge workflows, a video analytics workflow, and an IoT hub workflow that represent a large portion of today’s edge applications. Both workflows are evaluated using the workflow-level and system-level metrics reported by EdgeBench. We show that EdgeBench can effectively discover the performance bottlenecks and provide improvement implications for the edge workloads and the edge systems.
AB - Edge computing has been developed to utilize heterogeneous computing resources from different physical locations for privacy, cost, and Quality of Service (QoS) reasons. Edge workloads have the characteristics of data-driven, latency-sensitive, and privacy-critical. As a result, edge systems have been developed to be both heterogeneous and distributed to utilize different computing tiers’ resources and features. The unique characteristics of edge workloads and edge systems have motivated EdgeBench, a workflow-based benchmark aiming to provide the ability to explore the full design space of edge applications and edge systems. EdgeBench is both customizable and representative. It allows users to customize the workflow logic of edge workloads, the data storage backends, and the distribution of the individual workflow function to different computing tiers. To illustrate the usability of EdgeBench, we implement two representative edge workflows, a video analytics workflow, and an IoT hub workflow that represent a large portion of today’s edge applications. Both workflows are evaluated using the workflow-level and system-level metrics reported by EdgeBench. We show that EdgeBench can effectively discover the performance bottlenecks and provide improvement implications for the edge workloads and the edge systems.
KW - Cloud computing
KW - Edge benchmark
KW - Edge computing
KW - Edge system
KW - Function as a service
UR - http://www.scopus.com/inward/record.url?scp=85189314583&partnerID=8YFLogxK
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U2 - 10.1007/978-3-031-54053-0_12
DO - 10.1007/978-3-031-54053-0_12
M3 - Conference contribution
AN - SCOPUS:85189314583
SN - 9783031540523
T3 - Lecture Notes in Networks and Systems
SP - 150
EP - 170
BT - Advances in Information and Communication - Proceedings of the 2024 Future of Information and Communication Conference FICC
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 April 2024 through 5 April 2024
ER -