Energy-Efficient Framework for Task Caching and Computation Offloading in Multi-tier Vehicular Edge-Cloud Systems

Ibrahim A. Elgendy; Abdukodir Khakimov; Ammar Muthanna

doi:10.1007/978-3-031-80853-1_4

Energy-Efficient Framework for Task Caching and Computation Offloading in Multi-tier Vehicular Edge-Cloud Systems

Ibrahim A. Elgendy
, Abdukodir Khakimov^*
, Ammar Muthanna

^*Corresponding author for this work

Interdisciplinary Research Center for Finance and Digital Economy

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The proliferation of mobile Internet of Things (IoT) applications like autonomous vehicles and augmented reality demands processing power beyond traditional devices. Vehicular Edge-Cloud Computing (VECC) emerges as a solution, leveraging distributed computing resources at the network’s edge (e.g., roadside units) and the cloud for remote task execution. However, energy efficiency remains a concern. This paper proposes an energy-efficient framework for VECC. To optimize resource utilization, a caching mechanism stores completed tasks at the edge server for faster retrieval. Additionally, an optimization model minimizes energy consumption while adhering to latency constraints during task offloading and resource allocation. Simulations demonstrate significant energy savings compared to existing benchmarks. This framework addresses both energy efficiency and resource allocation challenges in VECC systems.

Original language	English
Title of host publication	Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers
Editors	Vladimir M. Vishnevsky, Konstantin E. Samouylov, Dmitry V. Kozyrev
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	42-53
Number of pages	12
ISBN (Print)	9783031808524
DOIs	https://doi.org/10.1007/978-3-031-80853-1_4
State	Published - 2025
Event	27th International Conference on Distributed Computer and Communication Networks, DCCN 2024 - Moscow, Russian Federation Duration: 23 Sep 2024 → 27 Sep 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15460 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Distributed Computer and Communication Networks, DCCN 2024
Country/Territory	Russian Federation
City	Moscow
Period	23/09/24 → 27/09/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Keywords

Autonomous Vehicles
Task Caching
Task Offloading
Vehicular Edge-Cloud Computing

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-80853-1_4

Cite this

Elgendy, I. A., Khakimov, A., & Muthanna, A. (2025). Energy-Efficient Framework for Task Caching and Computation Offloading in Multi-tier Vehicular Edge-Cloud Systems. In V. M. Vishnevsky, K. E. Samouylov, & D. V. Kozyrev (Eds.), Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers (pp. 42-53). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15460 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-80853-1_4

Elgendy, Ibrahim A. ; Khakimov, Abdukodir ; Muthanna, Ammar. / Energy-Efficient Framework for Task Caching and Computation Offloading in Multi-tier Vehicular Edge-Cloud Systems. Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers. editor / Vladimir M. Vishnevsky ; Konstantin E. Samouylov ; Dmitry V. Kozyrev. Springer Science and Business Media Deutschland GmbH, 2025. pp. 42-53 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Elgendy, IA, Khakimov, A & Muthanna, A 2025, Energy-Efficient Framework for Task Caching and Computation Offloading in Multi-tier Vehicular Edge-Cloud Systems. in VM Vishnevsky, KE Samouylov & DV Kozyrev (eds), Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15460 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 42-53, 27th International Conference on Distributed Computer and Communication Networks, DCCN 2024, Moscow, Russian Federation, 23/09/24. https://doi.org/10.1007/978-3-031-80853-1_4

Energy-Efficient Framework for Task Caching and Computation Offloading in Multi-tier Vehicular Edge-Cloud Systems. / Elgendy, Ibrahim A.; Khakimov, Abdukodir; Muthanna, Ammar.
Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers. ed. / Vladimir M. Vishnevsky; Konstantin E. Samouylov; Dmitry V. Kozyrev. Springer Science and Business Media Deutschland GmbH, 2025. p. 42-53 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15460 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Elgendy, Ibrahim A.

AU - Khakimov, Abdukodir

AU - Muthanna, Ammar

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

PY - 2025

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N2 - The proliferation of mobile Internet of Things (IoT) applications like autonomous vehicles and augmented reality demands processing power beyond traditional devices. Vehicular Edge-Cloud Computing (VECC) emerges as a solution, leveraging distributed computing resources at the network’s edge (e.g., roadside units) and the cloud for remote task execution. However, energy efficiency remains a concern. This paper proposes an energy-efficient framework for VECC. To optimize resource utilization, a caching mechanism stores completed tasks at the edge server for faster retrieval. Additionally, an optimization model minimizes energy consumption while adhering to latency constraints during task offloading and resource allocation. Simulations demonstrate significant energy savings compared to existing benchmarks. This framework addresses both energy efficiency and resource allocation challenges in VECC systems.

AB - The proliferation of mobile Internet of Things (IoT) applications like autonomous vehicles and augmented reality demands processing power beyond traditional devices. Vehicular Edge-Cloud Computing (VECC) emerges as a solution, leveraging distributed computing resources at the network’s edge (e.g., roadside units) and the cloud for remote task execution. However, energy efficiency remains a concern. This paper proposes an energy-efficient framework for VECC. To optimize resource utilization, a caching mechanism stores completed tasks at the edge server for faster retrieval. Additionally, an optimization model minimizes energy consumption while adhering to latency constraints during task offloading and resource allocation. Simulations demonstrate significant energy savings compared to existing benchmarks. This framework addresses both energy efficiency and resource allocation challenges in VECC systems.

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Elgendy IA, Khakimov A, Muthanna A. Energy-Efficient Framework for Task Caching and Computation Offloading in Multi-tier Vehicular Edge-Cloud Systems. In Vishnevsky VM, Samouylov KE, Kozyrev DV, editors, Distributed Computer and Communication Networks - 27th International Conference, DCCN 2024, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2025. p. 42-53. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-80853-1_4