Name
Title | ||
|---|---|---|
Co-Design Implications Of Cost-Effective On-Demand Acceleration For Cloud Healthcare Analytics: The Aegle Approach |
Abstract | ||
|---|---|---|
Nowadays, big data and machine learning are transforming the way we realize and manage our data. Even though the healthcare domain has recognized big data analytics as a prominent candidate, it has not yet fully grasped their promising benefits that allow medical information to be converted to useful knowledge. In this paper, we introduce AEGLE's big data infrastructure provided as a Platform as a Service. Utilizing the suite of genomic analytics from the Chronic Lymphocytic Leukaemia (CLL) use case, we show that on-demand acceleration is profitable w.r.t a pure software cloud-based solution. However, we further show that on-demand acceleration is not offered as a "free-lunch" and we provide an in-depth analysis and lessons learnt on the co-design implications to be carefully considered for enabling cost-effective acceleration at the cloud-level. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/DATE.2019.8714934 | 2019 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE) |
Keywords | Field | DocType |
Cloud, Platform as a Service (PaaS), Big Data Framework, Big Data Analytics, Co-design, On-demand Acceleration, Genomic | Data science,Co-design,On demand,Computer science,Healthcare analytics,Real-time computing,Acceleration,Cloud computing | Conference |
ISSN | Citations | PageRank |
1530-1591 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimosthenis Masouros | 1 | 12 | 6.37 |
| Konstantina Koliogeorgi | 2 | 1 | 2.06 |
| Georgios Zervakis | 3 | 49 | 8.33 |
| Alexandra Kosvyra | 4 | 0 | 1.35 |
| Achilleas Chytas | 5 | 0 | 0.34 |
| Sotirios Xydis | 6 | 144 | 31.51 |
| Ioanna Chouvarda | 7 | 155 | 38.47 |
| Dimitrios Soudris | 8 | 82 | 18.71 |