Lu Zhang
ABSTRACT
The fast-growing Internet of Things (IoT) and Artificial intelligence (AI) applications mandate high-performance edge data analytics. This requirement cannot be fully fulfilled by prior works that focus on either small architectures (e.g., accelerators) or large infrastructure (e.g., cloud data centers). Sitting in between the edge and cloud, there have been many server-level designs for augmenting edge data processing. However, they often require specialized hardware resources and lack scalability as well as agility.
Other than reinventing the wheel, we explore tapping into underutilized network infrastructure in the incoming 5G era for augmenting edge data analytics. Specifically, we focus on efficiently deploying edge data processing applications on Network Function Virtualization (NFV) enabled commodity servers. In such a way, we can benefit from the service flexibility of NFV while greatly reducing the cost of many servers deployed in the edge network. We perform extensive experiments to investigate the characteristics of packet processing in a DPDK-based NFV platform and discover the resource under-utilization issue when using the DPDK polling-mode. Then, we propose a framework named EdgeMiner, which can harvest the potentially idle cycles of the cores for data processing purpose. Meanwhile, it can also guarantee the Quality of Service (QoS) of both the Virtualized Network Functions (VNFs) and Edge Data Processing (EDP) applications when they are co-running on the same server.
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Index Terms
- Characterizing and orchestrating NFV-ready servers for efficient edge data processing
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