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Resource Allocation and Task Off-Loading for 6G Enabled Smart Edge Environments
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DOI：10.1109/ACCESS.2022.3203711.png)
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Abstract
En 中文
There has been an enormous increase in information flow and communication data due to the rapid rise in the number of Internet of Everything (IoE) devices and the development of cutting-edge technologies such as the rollout of the Sixth Generation (6G) network. The rising and inevitable off-loading requirements of IoE devices have resulted in an unprecedented increase in the reliance on edge and cloud paradigms. However, such a reliance on far-end technologies to access already scarce resources can often result in increased latency and unstable connection issues due to limited bandwidth. In this paper, we investigate the solution for such a stringent network design by presenting a conceptual cloud architecture based on key components such as resource allocation, scheduling and task off-loading for IoE devices. The IoE devices utilise a scheduler to access resources from nearby higher resourced IoE devices for their task computation, where the scheduler allocates incoming requests according to the availability of resources within a cluster of devices or to other devices in nearby clusters. Motivated by these design characteristics, we propose a design of a novel Main Task Off-loading Scheduling Algorithm (MTOSA) for efficient task allocation and dissemination. We present a theoretical analysis of five different scheduling policies namely Round Robin (RR), Strongest Channel (SC), Max Rate (MR), Proportional Fair (PF) and Priority Base (PB) scheduling to find an optimal technique for task off-loading in futuristic networks. Furthermore, we compare the performance of these five scheduling policies with the two existing scheduling policies from the literature. It is shown through various experiments that the proposed MTOSA algorithm performs better when compared with the existing schemes for different performance parameters.
Keywords:
Task analysis
Resource management
6G mobile communication
Scheduling
Cloud computing
Scheduling algorithms
Load modeling
Edge computing
Edge-based cloud
6G
IoE
smart devices
AI
QoS
task off-loading
machine learning (ML)
scheduling
resource allocation