Abstract

Multi-access edge computing (MEC) applications are often implemented in the form of task offloading, which results in an unprecedented demand for data transfers among MEC servers. However, the combination of expensive and limited bandwidth, growing peak demand, and heterogeneous requirements of mixed traffic has posed a great challenge in terms of task offloading. In this study, we present a storage-assisted optical upstream transport scheme (SOUT) to overcome this challenge. Latency-critical (LC) tasks are given preemptive priority over delay-tolerant (DT) tasks. To reduce peak demand, the storage of an MEC server is introduced to temporarily store DT tasks. Resource partitioning is performed with an adjustable boundary based on traffic fluctuation. Analytic models are presented to investigate the interplay between SOUT and the performance of tasks. Our key findings reveal that there exist two trade-offs to be considered in SOUT. To balance the trade-offs, we formulate the spectrum partitioning and storage assignment problem as an optimization model and solve it using a heuristic approach. Studies show that SOUT provides lower blocking probability for both LC and DT tasks at the cost of slight preemption and limited storage usage when compared with two state-of-the-art optical transport schemes. We further show that 60% of network expenditures can be saved by trading cost-efficient storage for expensive link spectrum resources under a certain network scenario. Overall, this study aims to provide useful insights into task offloading over elastic optical links.

© 2022 Optical Society of America

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