.png){kind=icon}
Return
Dependent Application Offloading in Edge Computing
.png){kind=icon}
PRE
AI
DOI：10.1109/TCC.2023.3290777.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Task offloading offloads latency-sensitive and computation-intensive applications from resource-constrained terminal devices to relatively resource-rich edge servers to meet users' demands for latency and energy consumption, which has attracted extensive attention from academia and industry. However, most of the existing researches only considers offloading dependent tasks within a single application or multiple independent applications, while ignoring the dependencies between applications. To this end, this paper proposes an offloading strategy for distributed dependent applications under the condition of limited computing and cache resources. The goal of the proposed strategy is to minimize the weighted sum of latency and energy to complete all applications while solving the offloading and resource allocation problems of dependent applications. However, the dual dependencies between applications and tasks within the application complicate offloading tasks. To accommodate this issue, we represent the dual dependencies as a directed acyclic graph. Then, we design the offloading strategy as follows: First, we transform the formulated non-convex problem into convex optimization subproblems. Second, we iteratively calculate the task priority and obtain the optimal offloading decision of the task according to the priority. Finally, we perform validation on real datasets. Compared with several state-of-the-art methods, our proposed strategy can significantly reduce the weighted sum of latency and energy.
Keywords:
Convex optimization
dependent application
edge computing
resource allocation
task offloading
Journal
I
IF:
5
Papers:
1.9K
Citations:
4.3K
Organization
View more organizations
Cited Papers
ShareSave
ShareSaveShareSaveShareSaveShareSaveDeep Learning-Based Dynamic Computation Task Offloading for Mobile Edge Computing Networks.png)
SENSORS
OAAI
SENSORSIF3.5
OAAIShareSaveShareSaveShareSave View more