Online flow size prediction for improved network routing

A 14% improvement is observed when the size of the three first packets is used along with the header information.

“A 14% improvement is observed when the size of the three first packets is used along with the header information.”

Poupart et al. explore the use of different ML techniques for flow size prediction and elephant flow detection, including GPR, oBMM and a (106, 60, 40, 1) MLP-NN.

“Poupart et al. [365] explore the use of different ML techniques for flow size prediction and elephant flow detection.These techniques include gaussian processes regression (GPR), online bayesian moment matching (oBMM) and a (106, 60, 40, 1) MLP-NN.”

The experiments show noticeable discrepancies in the performance of the approaches with varying datasets.

“The experiments show noticeable discrepancies in the performance of the approaches with varying datasets.”

Although NN outperforms GPR in terms of robustness to class imbalance by looking at the consistency of its TPR with varying flow size threshold, it has the lowest average TNR of below 80% in all datasets.

“Although NN outperforms GPR in terms of robustness to class imbalance by looking at the consistency of its TPR with varying flow size threshold, it has the lowest average TNR of below 80% in all datasets.”

oBMM outperforms all other approaches in terms of average TNR in all 3 datasets.

“However, it is worth noting that oBMM outperforms all other approaches in terms of average TNR in all 3 datasets.”

According to the authors, GPR improves the completion time by 6.6% in average for 99% of elephant flows when only the first packet header information is considered.

“According to the authors, GPR improves the completion time by 6.6% in average for 99% of elephant flows when only the first packet header information is considered.”

NN and GPR have an average TPR consistently above 80%.

“On the other hand, NN and GPR, have an average TPR consistently above 80%.”

The motive for flow size prediction in [365] is to discriminate elephant flows from mice flows in routing to speed up elephant flow completion time.

“The motive for flow size prediction in [365], is to discriminate elephant flows from mice flows in routing to speed up elephant flow completion time.”

Although oBMM outperforms all other approaches in one dataset with an average TPR and TNR very close to 100%, it fails miserably in the other datasets with an average TPR below 50% for one dataset.

“Although oBMM outperforms all other approaches in one dataset with an average TPR and TNR very close to 100%, it fails miserably in the other datasets with an average TPR below 50% for one dataset.”

Considering the size of the first three packets alone leads to over 13.5% improvement, regardless of whether GPR or oBMM is used, with a very slight impact on the completion time of mice flows.

“It is also noticed that considering the size of the first three packets alone leads to over 13.5% improvement, regardless of whether GPR or oBMM is used, with a very slight impact on the completion time of mice flows.”

Non-TSF approaches were investigated in [94,274,365] to infer traffic volumes from flow count and packet header fields.

“Non-TSF approaches were investigated in [94,274,365] to infer traffic volumes from flow count and packet header fields.”

According to [365], a more complex MLP-NN (in terms of number of layers and neurons) might be required to achieve better accuracy in a non-TSF setting, and the performance of supervised learning techniques varies when tested on different datasets.

“According to [365] a more complex MLP-NN (in terms of number of layers and neurons) might be required to achieve better accuracy in a non-TSF setting, and the performance of supervised learning techniques varies when tested on different datasets.”