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In this paper, we describe the main properties that a network workload generator should have today, and we present a tool for the generation of realistic network workload that can be used for the study of emerging networking scenarios.
Under the same simulation condition, SRNoC could cut down the total network workload to 48.67% at least.
The functionality of the co-processors is dynamically reconfigured to meet the requirements of the network workload.
A numerical example illustrates that the predictor-based control allows one to significantly increase the network-induced delays, whereas the event-triggering mechanism significantly reduces the network workload.
The aim of this investigation is to build a design with a control unit that manages the architecture and resource allocation according to the neural network workload prediction.
In particular, we discuss (i) how it tackles the main issues challenging the representative replication of network workload, and (ii) our design choices and its advanced features that make it suitable to analyze complex and emerging network scenarios.
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Simulation results validate that our algorithm can find a set of Pareto-optimal security policies under different network workloads, which can be provided to the potential users as the differentiated security preferences.
Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites.
These add-in cards or chips on POWER architecture give machine learning, big data, security and networks workloads super-human powers over straight CPU acceleration.
In [21], the authors propose new methods for computing upper and lower bounds on the optimal throughput for any given network and workload.
The scale comprises 22 items, dealing with the caregiver's health, feeling of psychological well-being, relations, social network, physical workload, and environmental aspects.
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