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In the IoT and smart systems era, the massive amount of data generated from various IoT and smart devices are often sent directly to the cloud infrastructure for processing, analyzing, and storing. While handling this big data, conventional cloud infrastructure encounters many challenges, e.g., scarce bandwidth, high latency, real-time constraints, high power, and privacy issues. The edge-centric computing is transpiring as a synergistic solution to address these issues of cloud computing, by enabling processing/analyzing the data closer to the source of the data or at the network’s edge. This in turn allows real-time and in-situ data analytics and processing, which is imperative for many real-world IoT and smart systems, such as smart cars. Since the edge computing is still in its infancy, innovative solutions, models, and techniques are needed to support real-time and in-situ data processing and analysis of edge computing platforms. In this research work, we introduce a novel, unique, and efficient FPGA-HLS-based hardware accelerator for PCA+SVM model for real-time processing and analysis on edge computing platforms. This is inspired by our previous work on PCA+SVM models for edge computing applications. It was demonstrated that the amalgamation of principal component analysis (PCA) and support vector machines (SVM) leads to high classification accuracy in many fields. Also, machine learning techniques, such as SVM, can be utilized for many edge tasks, e.g. anomaly detection, health monitoring, etc.; and dimensionality reduction techniques, such as PCA, are often used to reduce the data size, which in turn vital for memory-constrained edge devices/platforms. Furthermore, our previous works demonstrated that FPGA’s many traits, including parallel processing abilities, low latency, and stable throughput despite the workload, make FPGAs suitable for real-time processing of edge computing applications/platforms. Our proposed FPGA-HLS-based PCA+SVM hardware IP achieves up to 254x speedup compared to its embedded software counterpart, while maintaining small area and low power requirements of edge computing applications. Our experimental results show great potential in utilizing FPGA-based architectures to support real-time processing on edge computing applications.
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CLEDGE: A Hybrid Cloud-Edge Computing Framework over Information Centric Networking
In today's era of Internet of Things (IoT), where massive amounts of data are produced by IoT and other devices, edge computing has emerged as a prominent paradigm for low-latency data processing. However, applications may have diverse latency requirements: certain latency-sensitive processing operations may need to be performed at the edge, while delay-tolerant operations can be performed on the cloud, without occupying the potentially limited edge computing resources. To achieve that, we envision an environment where computing resources are distributed across edge and cloud offerings. In this paper, we present the design of CLEDGE (CLoud + EDGE), an information-centric hybrid cloud-edge framework, aiming to maximize the on-time completion of computational tasks offloaded by applications with diverse latency requirements. The design of CLEDGE is motivated by the networking challenges that mixed reality researchers face. Our evaluation demonstrates that CLEDGE can complete on-time more than 90% of offloaded tasks with modest overheads.
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- PAR ID:
- 10296923
- Date Published:
- Journal Name:
- 2021 IEEE 46th Conference on Local Computer Networks (LCN)
- Page Range / eLocation ID:
- 589 to 596
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/lcn52139.2021.9525006
