Optimizing Edge and Fog Computing Applications with AI and Metaheuristic Algorithms
暫譯: 利用AI和元啟發式演算法優化邊緣與霧計算應用程式

H. S., Madhusudhan, Gupta, Punit, Kumar Saini, Dinesh

  • 出版商: Auerbach Publication
  • 出版日期: 2025-09-16
  • 售價: $6,480
  • 貴賓價: 9.5$6,156
  • 語言: 英文
  • 頁數: 260
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1041003544
  • ISBN-13: 9781041003540
  • 相關分類: 雲端運算Machine Learning
  • 海外代購書籍(需單獨結帳)

商品描述

Fog and edge computing are two paradigms that have emerged to address the challenges associated with processing and managing data in the era of the Internet of Things (IoT). Both models involve moving computation and data storage closer to the source of data generation, but they have subtle differences in their architectures and scopes. These differences are one of the subjects covered in Optimizing Edge and Fog Computing Applications with AI and Metaheuristic Algorithms. Other subjects covered in the book include:

  • Designing machine learning (ML) algorithms that are aware of the resource constraints at the edge and fog layers ensures efficient use of computational resources
  • Resource-aware models using ML and deep leaning models that can adapt their complexity based on available resources and balancing the load, allowing for better scalability
  • Implementing secure ML algorithms and models to prevent adversarial attacks and ensure data privacy
  • Securing the communication channels between edge devices, fog nodes, and the cloud to protect model updates and inferences
  • Kubernetes container orchestration for fog computing
  • Federated learning that enables model training across multiple edge devices without the need to share raw data

The book discusses how resource optimization in fog and edge computing is crucial for achieving efficient and effective processing of data close to the source. It explains how both fog and edge computing aim to enhance system performance, reduce latency, and improve overall resource utilization. It examines the combination of intelligent algorithms, effective communication protocols, and dynamic management strategies required to adapt to changing conditions and workload demands. The book explains how security in fog and edge computing requires a combination of technological measures, advanced techniques, user awareness, and organizational policies to effectively protect data and systems from evolving security threats. Finally, it looks forward with coverage of ongoing research and development, which are essential for refining optimization techniques and ensuring the scalability and sustainability of fog and edge computing environments.

商品描述(中文翻譯)

霧計算和邊緣計算是為了解決物聯網(IoT)時代中處理和管理數據所面臨的挑戰而出現的兩種範式。這兩種模型都涉及將計算和數據存儲移近數據生成的來源,但在其架構和範疇上存在微妙的差異。這些差異是書籍《使用 AI 和元啟發式演算法優化邊緣和霧計算應用》中探討的主題之一。書中還涵蓋的其他主題包括:

- 設計能夠考慮邊緣和霧層資源限制的機器學習(ML)演算法,以確保計算資源的有效使用
- 使用 ML 和深度學習模型的資源感知模型,能根據可用資源調整其複雜性並平衡負載,從而實現更好的可擴展性
- 實施安全的 ML 演算法和模型,以防止對抗性攻擊並確保數據隱私
- 確保邊緣設備、霧節點和雲之間的通信通道安全,以保護模型更新和推斷
- 用於霧計算的 Kubernetes 容器編排
- 聯邦學習,使得在多個邊緣設備之間進行模型訓練而無需共享原始數據

本書討論了霧計算和邊緣計算中的資源優化對於實現接近數據來源的高效和有效數據處理的重要性。它解釋了霧計算和邊緣計算如何旨在提高系統性能、減少延遲並改善整體資源利用率。書中探討了結合智能演算法、有效通信協議和動態管理策略所需的組合,以適應不斷變化的條件和工作負載需求。書中還解釋了霧計算和邊緣計算中的安全性需要結合技術措施、高級技術、用戶意識和組織政策,以有效保護數據和系統免受不斷演變的安全威脅。最後,書中展望了持續的研究和開發,這對於完善優化技術以及確保霧計算和邊緣計算環境的可擴展性和可持續性至關重要。

作者簡介

Madhusudhan H S is an associate professor in the Department of Computer Science and Engineering at Vidyavardhaka College of Engineering, Mysuru, India.

Punit Gupta is an associate professor in the Department of Computer and Communication Engineering at Pandit Deendayal Energy University, Gujarat, India.

Dinesh Kumar Saini is a full professor at the School of Computing and Information Technology, Manipal University, Jaipur, India.

作者簡介(中文翻譯)

Madhusudhan H S 是印度邁索爾Vidyavardhaka工程學院計算機科學與工程系的副教授。

Punit Gupta 是印度古吉拉特邦Pandit Deendayal能源大學計算機與通信工程系的副教授。

Dinesh Kumar Saini 是印度Jaipur的Manipal大學計算與信息技術學院的正教授。