Name
Abstract | ||
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Real-time transfer optimization approaches offer promising solutions as they can discover optimal transfer configuration in the runtime without requiring an upfront work or making assumptions about underlying system architectures. On the other hand, existing implementations suffer from slow convergence speed due to running many sample transfers with suboptimal configurations. In this work, we evaluate time-series models to minimize the impact of sample transfers with suboptimal configurations by shortening the transfer duration without degrading the accuracy. The results gathered in various networks with rich set of transfer configurations indicate that, in most cases, Autoregressive model can accurately estimate sample transfer throughput in less than 5 seconds which is up-to 4x improvement over the state-of-the-art solution. We also realized that while the most common transfer applications report transfer throughput at most once a second, decreasing the reporting interval is the key to further reduce the impact of sample transfers by quickly determining their performance.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3322798.3329256 | Proceedings of the ACM Workshop on Systems and Network Telemetry and Analytics |
Keywords | Field | DocType |
real-time optimization, sample transfer, time-series analysis | Time series,Computer science,Algorithm | Conference |
ISBN | Citations | PageRank |
978-1-4503-6761-5 | 2 | 0.39 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hemanta Sapkota | 1 | 4 | 0.76 |
| Bahadir A. Pehlivan | 2 | 6 | 0.82 |
| Engin Arslan | 3 | 116 | 12.12 |