Chapter 5: Challenges and Solutions in Edge Computing
Abstract
This chapter tackles the pivotal technical challenges in edge computing and offers potential solutions for building robust, efficient systems. Beginning with programmability, it explores new programming models like the Firework model and the need for automatic program partitioning to deploy applications across diverse edge nodes. It discusses the importance of standardized naming mechanisms, data abstraction, and resource optimization to manage device identification, data processing, and latency reduction. Further, the chapter addresses load balancing through data offloading and efficient scheduling strategies. Critical issues such as privacy, security, and service management are examined, and solutions like edge-based data processing and blockchain for enhanced security are proposed. The chapter concludes with insights into hardware and software selection, practical deployment of artificial intelligence (AI) on edge devices, and the functional theories and business models that support the continued growth of edge computing.
📝 Practice Questions
1. How does edge caching contribute to reducing latency in edge computing?
2. Discuss the trade-offs between security and performance in edge computing.
3. What are the challenges in implementing resource management in large-scale edge computing environments?
📘 Course Projects
1. Design a dynamic data management framework that supports real-time data ingestion, processing, and retrieval at the edge. Use open-source tools such as Apache Kafka (https://kafka.apache.org/) for real-time data streaming, and Apache Cassandra (https://cassand ra.apache.org) for distributed storage to build the framework. Evaluate the performance in handling large-scale data streams from IoT devices, focusing on latency, throughput, and data consistency.
2. Explore resource allocation strategies in edge-cloud environments and implement a resource allocation mechanism that dynamically balances workloads between edge devices and the cloud, based on real-time conditions like network latency and device capabilities. Use simulation tools such as CloudSim (https://github.com/Cloudslab/cloudsim) or iFogSim (https://github.com/Cloudslab/iFogSim) to model the edge-cloud environment and implement the resource allocation mechanism. Evaluate the mechanism's effectiveness by running simulations with varying network conditions, task complexities, and resource constraints.
3. Design a privacy-preserving mechanism suitable for an edge computing scenario, such as secure data processing in healthcare. Implement the proposed mechanism using opensource frameworks like EdgeX Foundry or Open Horizon (https://lfedge.org/projects/open-horizon/), focusing on encryption, data anonymization, or secure data transmission. Simulate potential security threats and assess the effectiveness of the proposed mechanism.
4. Explore the integration of edge computing with existing software and hardware solutions in a specific industry (e.g., manufacturing, healthcare, and 5G) and develop a prototype that demonstrates this integration. Use open-source frameworks like EdgeX Foundry (https://lfedge.org/projects/edgex-foundry/) to integrate edge computing with selected software and hardware solutions.
📚 Suggested Papers
1. Sumit Maheshwari et al. "Scalability and performance evaluation of edge cloud systems for latency constrained applications". In: 2018 IEEE/ACM Symposium on Edge Computing (SEC) . IEEE. 2018, pp. 286–299 | Paper.
2. Lanyu Xu, Arun Iyengar, and Weisong Shi. "CHA: A caching framework for home-based voice assistant systems". In: 2020 IEEE/ACM Symposium on Edge Computing (SEC). IEEE. 2020, pp. 293–306 | Paper.
3. Lanyu Xu, Arun Iyengar, and Weisong Shi. "ChatCache: A hierarchical semantic redundancy cache system for conversational services at edge". In: 2021 IEEE 14th International Conference on Cloud Computing (CLOUD). IEEE. 2021, pp. 85–95 | Paper.
