Abstract:
Task offloading results in the remote execution of
tasks, thereby reducing the load on the lower capacity devices
and mobile instruments. The offloaded tasks to edge servers in
RANs get executed in container-based virtualization technologies.
In this paper, we examine traffic offloading and scheduling, where
we investigate QoS-based traffic assignment to edge servers in
network slices. We propose an ensemble method for classifying
through Multiple Attribute Decision Making (MADM), Single
Attribute Categorization (SAC), and fuzzy rules. Then, we apply
enhanced weighted Borda scoring to categorize the task into its
priority class, which are placed in their respective Kafka topics.
Finally, we present a probabilistic, priority-driven Kafka-topic
consumer which schedules the offloaded tasks in the edge containers. The slice-based setup constitutes of Flowvisor, Mininet,
Beacon and Pox controllers, Kafka, and Docker engine. The
proposed ensemble categorization exhibits 26% and 12.5% better
accuracy than simple additive and multiplicative exponential
weighting MADM methods. Experimental results show that the
proposed scheduling methodology on average reduces long piling
of medium and low priority tasks by a factor of 7% and 12%
respectively.
