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An Edge-centric Ensemble Scheme for Queries Assignment

The new era of the Internet of Things (IoT) reveals 
new potentials for the management of numerous devices. Such 
devices produce data streams that are guided to the Cloud for 
further processing. However, any processing in the Cloud, even if 
it is supported by increased computational resources, suffers from 
increased latency. For minimizing the latency we can perform 
data processing at the edge of the network, i.e., at the edge nodes. 
The aim is to provide analytics and build knowledge on top of 
the collected data in the minimum time. In this paper, we deal 
with the problem of allocating queries, defined for producing 
knowledge, to a number of edge nodes. The aim is to further 
reduce the latency by allocating queries to nodes that exhibit 
low load (the current and the estimated), thus, they can provide 
the final response in the minimum time. However, before the 
allocation, we should decide the computational burden that a 
query will add. The allocation is concluded by the assistance of an 
ensemble similarity scheme responsible to deliver the complexity 
class for each query. The complexity class, thus, can be matched 
against the current load of every edge node. We discuss our 
scheme and through a large set of simulations and the adoption 
of benchmarking queries, we reveal the potentials of the proposed 
model supported by numerical results.