Abstract
Cooperative edge offloading to nearby end devices via Device-to-Device (D2D) links in edge networks with sliced computing resources has mainly been studied for end devices (helper nodes) that are stationary (or follow predetermined mobility paths) and for independent computation tasks. However, end devices are often mobile, and a given application request commonly requires a set of dependent computation tasks. We formulate a novel model for the cooperative edge offloading of dependent computation tasks to mobile helper nodes. We model the task dependencies with a general task dependency graph. Our model employs the state-of-the-art deep-learning-based PECNet mobility model and offloads a task only when the sojourn time in the coverage area of a helper node or Multi-access Edge Computing (MEC) server is sufficiently long. We formulate the minimization problem for the consumed battery energy for task execution, task data transmission, and waiting for offloaded task results on end devices. We convert the resulting non-convex mixed integer nonlinear programming problem into an equivalent quadratically constrained quadratic programming (QCQP) problem, which we solve via a novel Energy-Efficient Task Offloading (EETO) algorithm. The numerical evaluations indicate that the EETO approach consistently reduces the battery energy consumption across a wide range of task complexities and task completion deadlines and can thus extend the battery lifetimes of mobile devices operating with sliced edge computing resources.
Highlights
We developed and evaluated the novel Energy-Efficient Task Offloading (EETO)
EETO accommodates arbitrary task dependencies that are characterized by a general task dependency graph and employs a deep-learning-based trajectory prediction for the device sojourn times in the wireless transmission ranges
EETO outperforms benchmarks with a limited set of offloading decision options; we considered benchmarks that allow helper nodes to only function as task processing nodes (CPCO) or to only function as task communication nodes for offloading to an Multi-access Edge Computing (MEC) server (CMCO)
Summary
In order to provide these edge network services in an economical manner, the slicing of the edge network communication, computing, and storage resources and the efficient management of the sliced resources are critical [14,15]. This study focuses on the efficient resource management of sliced computing resources in edge networks. This study considers sliced computing resources that are provided by installed MEC infrastructures as well as sliced computing resources that are provided by the mobile end devices with spare computing resources that are in the vicinity of a mobile device with a set of demanding computing tasks. The computing and communication resources of the adjacent end devices can be utilized to enhance the total system performance and spectral efficiency [16]. The present study seeks to minimize the battery energy consumption on wireless end devices that operate within the deviceenhanced MEC paradigm.
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