Abstract
Edge computing represents the future of computing paradigms that perform tasks near the user plane. The integration of blockchain with edge computing provides the added advantage of secured and trusted communication. In this paper, we propose a blockchain-based service migration by developing edge clusters using NVIDIA Jetson boards in an embedded edge environment, using containers and Kubernetes as a container orchestration capable of handling real-time computation-intensive deep learning tasks. Resource constraints in the edge and client movement are the proposed scenarios for service migration. Container migration due to mobile clients is integrated with blockchain to find a suitable destination, meta-based node evaluation, and secured data transfer in the connected car environment. Each service request migration takes, on average, 361 ms. The employed container migration method takes 75.11 s and 70.46 s to migrate application containers that use NVIDIA CUDA Toolkit. Finally, we evaluate the efficiency of blockchain to find the destination node through performance parameters such as latency, throughput, storage, and bandwidth.
Highlights
Blockchain and edge-computing paradigms emerged in Internet of Things (IoT) data-processing in recent years, with latency, security, and bandwidth advantages
The purpose of the experiments was to evaluate the performance of the proposed algorithms in an Advanced RISC Machines (ARM)-based embedded edge environment that runs deep learning applications using the Compute Unified Device Architecture (CUDA) Toolkit in a container environment
This was supported by NVIDIA Jetpack and DeepStream Software Development Kits (SDKs), as well as CUDA, CUDA Deep Neural Network library, and TensorRT software libraries
Summary
Blockchain and edge-computing paradigms emerged in IoT data-processing in recent years, with latency, security, and bandwidth advantages. These two technologies mainly have different aims. Billions of devices are connected to the internet, requesting a zillion bytes of data This increases the demand for computation; extensive research is being conducted every day. A recent advancement in computing, brings services and utilities closer to the user, reducing response times and latency [5,6] It provides multiple advantages, including better bandwidth, support for mobile clients, and location awareness for real-life scenarios where the application response time is an essential factor and a fundamental requirement [7]. Researchers evaluated edge-computing performance, focusing on the network operational cost, and analyzing performance parameters, such as end-to-end latency, bandwidth utilization, resource utilization, and infrastructure cost [12]
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