Triangle Enumeration on Massive Graphs using AWS Lambda Functions
| dc.contributor.author | Yu, Tengkai | |
| dc.contributor.supervisor | Thomo, Alex | |
| dc.contributor.supervisor | Srinivasan, Venkatesh | |
| dc.date.accessioned | 2020-05-01T04:44:36Z | |
| dc.date.available | 2020-05-01T04:44:36Z | |
| dc.date.copyright | 2020 | en_US |
| dc.date.issued | 2020-04-30 | |
| dc.degree.department | Department of Computer Science | en_US |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | Triangle enumeration is a fundamental task in graph data analysis with many applications. Recently, Park et al. proposed a distributed algorithm, PTE (Pre-partitioned Triangle Enumeration), that, unlike previous works, scales well using multiple high end machines and can handle very large real-world networks. This work presents a serverless implementation of the PTE algorithm using the AWS Lambda platform. Our experiments take advantage of the high concurrency of the Lambda instances to compete with the expensive server-based experiments of Park et al. Our analysis shows the trade-off between the time and cost of triangle enumeration and the numbers of tasks generated by the distributed algorithm. Our results reveal the importance of using a higher number of tasks in order to improve the efficiency of PTE. Such an analysis can only be performed using a large number of workers which is indeed possible using AWS Lambda but not easy to achieve using few servers as in the case of Park et al. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/11707 | |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | triangle enumeration | en_US |
| dc.subject | massive graphs | en_US |
| dc.subject | distributed system | en_US |
| dc.subject | AWS Lambda | en_US |
| dc.title | Triangle Enumeration on Massive Graphs using AWS Lambda Functions | en_US |
| dc.type | project | en_US |