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Figure 14 presents results for the other metric, average request tardiness.
The proposed solution was shown to be advantageous in terms of minimizing both average request tardiness and blocking percentage for multiple cloud network scenarios.
For QoS, we use the request tardiness, defined as the ratio of the end-to-end response time to the deadline, rather than the usual metric that counts missed deadlines.
Moving to the second metric, Fig. 18 shows the performance of the three algorithms in terms of average request tardiness while changing the allowed tardiness levels (or request lifetime).
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Fig. 12 Request average tardiness results for scheduling methods (allowed tardiness/request =25 time units).
The experimentation results for a diverse set of network loads show that the relaxed solution has achieved promising levels for connection request average tardiness.
This makes it a strong candidate to be used in cloud scenarios where the focus is on metrics like more accepted connection requests and less network congestion or request average tardiness (better quality of service conditions for cloud users).
VM represent the VM set, N represent the set of nodes, S is the set of servers, R is the set of connection requests, T is the allowed tardiness per request and D is the average duration of a connection.
Considering this scenario where requests with high tardiness are blocked presents a trade off between average connection tardiness and the percentage (or number) of blocked connections.
A central controller manages these requests with the objective of minimizing average tardiness and request blocking.
Regarding the other performance metric, average tardiness per request, the measurements are shown in Fig. 12.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com