Abstract
The adoption of next-generation applications, such as Augmented Reality and Internet-of-Things (IoT), is expected to grow rapidly in the years to come. The number of IoT devices is expected to reach 75 billion by 2025, while the scale of potential use-cases (e.g., smart cities with hundreds of thousands or millions of residents) is also expected to grow. In such use-cases, users may request the processing of data with temporal, spatial, or semantic similarity by servers physically close to them. The repeated processing of considerably similar data may result in the same processing outputs and thus the execution of duplicate (redundant) computation. This project advances our understanding of the notion of “computation reuse”, where the processing outputs are reused/shared among processing requests for similar data, having the potential to: (i) reduce the time for the processing of similar data and thus the overall latency perceived by next-generation applications; and (ii) eliminate the execution of redundant computation, so that the available computing resources are effectively utilized. Our project will result in a novel network framework, so that requests for the processing of similar data are identified and forwarded to the same servers, facilitating the reuse of computation in realistic infrastructure deployments. The research outcomes of this project will be incorporated into university course curriculum, while this project will also offer opportunities that broaden participation and diversity in STEM.
This project aims to advance the state-of-the art in edge computing architectures in order to facilitate the reuse of computation through a novel, hybrid network framework based on Information-Centric Networking (ICN)/Named-Data Networking (NDN) and Software-Defined Networking (SDN). The proposed framework capitalizes on the best of both ICN/NDN and SDN to realize a service-centric, computation reuse-aware, and “softwarized” architecture through: (i) the semantically meaningful ICN/NDN naming and stateful forwarding plane for the seamless and adaptive task forwarding towards edge servers that can reuse similar previously executed tasks; and (ii) the separation of forwarding and control planes and the programmable (logically) centralized SDN intelligence for the orchestration of the computation reuse semantics. This project will tackle fundamental challenges in the design and development of network systems for the realization of computation reuse in realistic edge computing environments, where multiple edge servers may be available for redundancy, fault tolerance, and load balancing purposes, facilitating the pervasive edge computing deployment.
Publications
(Best Paper Award Runner-Up) M.W. Al Azad and S. Mastorakis, "Reservoir: Named Data for Pervasive Computation Reuse at the Network Edge", IEEE International Conference on Pervasive Computing and Communications (PerCom), 2022 (Preprint) Acceptance rate: 24/178 = 13.48% (10.67% for long papers)
M.W. Al Azad, R. Tourani, A. Mtibaa, S. Mastorakis, "Harpocrates: Anonymous Data Publication in Named Data Networking", ACM Symposium on Access Control Models and Technologies (SACMAT), 2022 (Preprint)
M. W. Al Azad*, S. Shannigrahi, N. Stergiou, F. Ortega, S. Mastorakis, "CLEDGE: A Hybrid Cloud-Edge Computing Framework over Information Centric Networking", IEEE Conference on Local Computer Networks (LCN), 2021 (Preprint)
A. Benmoussa, C. A. Kerrache, N. Lagraa, S. Mastorakis, A. Lakas, A. Tahari, "Interest Flooding Attacks in Named Data Networking: A Survey of Existing Solutions, Open Issues, Requirements, and Future Directions", ACM Computing Surveys, 2022. (Q1/Impact Factor: 10.282) (PDF)
M. W. Al Azad, S. Mastorakis, "The Promise and Challenges of Computation Deduplication and Reuse at the Network Edge," IEEE Wireless Communications Magazine, 2022. (Q1/Impact Factor: 11.979) (Preprint)
M. Babar, M. Jan, X. He, M. Usman, S. Mastorakis, R. Alturki, "IoT-Enabled Data Analytics Using Machine Learning for Edge-Cloud Computing", IEEE Internet of Things Journal, 2022 (Q1/Impact Factor: 9.471) (Preprint)
R. Thompson, E. Lyons, I. Dasgupta, S. Mastorakis, M. Zink, S. Shannigrahi, "An Information Centric Framework for Weather Sensing Data", IEEE International Conference on Communications (ICC) Workshops, 2022 (Preprint)
S. Taki and S. Mastorakis, "An NDN-Enabled Fog Radio Access Network Architecture With Distributed In-Network Caching", IEEE International Conference on Communications (ICC), 2023 (Preprint)