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Cyber foraging techniques have been proposed in edge computing to support resource-intensive and latency-sensitive mobile applications. In a natural or man-made disaster scenario, all cyber foraging challenges are exacerbated by two problems: edge nodes are scarce and hence easily overloaded and failures are common due to the ad-hoc hostile conditions. In this paper, we study the use of efficient load profiling and migration strategies to mitigate such problems. In particular, we propose FORMICA, an architecture for cyber foraging orchestration, whose goal is to minimize the completion time of a set of jobs offloaded from mobile devices. Existing service offloading solutions are mainly concerned with outsourcing a job out of the mobile responsibility. Our architecture supports both mobile-based offloading and backend-driven onloading i.e., the offloading decision is taken by the edge infrastructure and not by the mobile node. FORMICA leverages Gelenbe networks to estimate the load profile of each node of the edge computing infrastructure to make proactive load profiling decisions. Our evaluation on a proof-of-concept implementation shows the benefits of our policy-based architecture in several (challenged disaster) scenarios but its applicability is broad to other IoT-based latency-sensitive applications.
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This content will become publicly available on June 30, 2026
Sustainable Dependent Sub-Tasks Orchestration at Extreme Edge Computing: A Partitioning-based Deep Reinforcement Learning Approach
Extreme Edge Computing (EEC) promotes sustainable computing by reducing reliance on centralized data centres and decreasing their environmental impact. By using extreme edge devices to handle computing requests, the EEC reduces the energy demands for data transmission and execution, thereby reducing carbon footprints. However, EEC introduces challenges due to the mobile, heterogeneous, and resource-limited nature of these devices. Additionally, tasks are often complex and interdependent, complicating offloading and workload orchestration. The dynamicity of EEC systems, where both task generation and resources can be mobile, alongside task inter-dependencies, escalates the complexity of task offloading and workload management. To tackle these complexities, task partitioning emerges as a viable strategy. Moreover, in dynamic edge computing scenarios, resource demand remains unpredictable, emphasizing the critical need to optimize resource utilization efficiently. In this article, we investigate the problem of tasks with inter-dependencies offloading in an EEC environment where mobile and resource-constrained edge devices are employed as computing resources. In this regard, a partitioning-based Deep Reinforcement Learning (DRL) for Dependent sub-Task Orchestration (DeTOrch) model is proposed. DeTOrch uses a state-of-the-art partitioning method for decomposing tasks and proposes a novel mobility task-orchestration mechanism to minimize the task completion time and maximize the use of edge devices’ resource. The simulation results show that the proposed model can significantly improve the task success rate and decrease task completion time. In addition, in various scenarios with different levels of mobility, the proposed model outperforms the baselines while utilizing the resource of edge devices.
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- Award ID(s):
- 2048266
- PAR ID:
- 10657099
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Journal on Computing and Sustainable Societies
- Volume:
- 3
- Issue:
- 2
- ISSN:
- 2834-5533
- Page Range / eLocation ID:
- 1 to 31
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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