OnDisc: Online Latency-Sensitive Job Dispatching and Scheduling in Heterogeneous Edge-Clouds

Air-to-Air Collaborative Learning: A Multi-Task Orchestration in Federated Aerial Computing

“…The total number of tasks deployed in Drone-5 cluster is 38, while total of 77 tasks are deployed in Drone-6 cluster. The task dependency depth of each job is in the range of (1,16]. In these two clusters, AerialEdge use up to 13% less resources compared with the baselines and the Random approach.…”

“…Closest is a widely adopted heuristic or principle in distributed systems, since mobile devices often need to communicate only with the closest or nearest edge-clouds. Most of the works on edge-clouds, e.g., [8], [9], [16], adopt the closest principle as the task offloading policy.…”

AirEdge: A Dependency-Aware Multi-Task Orchestration in Federated Aerial Computing

“…Existing works on multi-UAV or drone-based task offloading and execution in EC systems [10], [12]- [17] do not consider drones' flight time and assume a drone can fly for unlimited amount of time, which can lead to loss of job due to drones' limited flight time [11]. Some researches [43], [44] have proposed a greedy approach to deploy a job to an edge which brings the least increase to the response time. But this approach can lead to job waiting at the server due to insufficient resource availability, which is not suitable for latency-sensitive jobs.…”

“…Closest is a widely adopted heuristic or principle in distributed systems, since mobile devices often need to communicate only with the closest or nearest edge-clouds. Most of the works on edge-clouds, e.g., [29], [30], [43], [44], adopt the closest principle as the task offloading policy. end if 9: end for Algorithm 2 describes the dispatching procedure in 3 steps.…”

SPMOO: A Multi-Objective Offloading Algorithm for Dependent Tasks in IoT Cloud-Edge-End Collaboration

“…However, it remains a challenge to effectively offload in a cloud-edge-end-computing environment due to resource heterogeneity, the diversity of user requirements, network complexity and task dependencies. At present, a part of the related work does not consider the complexity of tasks in realistic scenarios and only considers each computing task as an indivisible whole when performing offloading [14][15][16][17][18][19][20][21], which is obviously unreasonable. Another part of the related work considers the complexity of the tasks and partitions the tasks, but the complexity of calculating the communication cost between subtasks makes it extremely difficult to perform offloading research in cloud-edge-end architectures [22][23][24][25][26], so their work is often performed in two-tier offloading architectures (cloud-edge architecture or edge-end architecture, etc.).…”