Name
Abstract | ||
|---|---|---|
Cloud architectures achieve scaling through two main functions: (i) load-balancers, which dispatch queries among replicated virtualized application instances, and (ii) autoscalers, which automatically adjust the number of replicated instances to accommodate variations in load patterns. These functions are often provided through centralized load monitoring, incurring operational complexity. This article introduces a unified and centralized-monitoring-free architecture achieving both autoscaling and load-balancing, reducing operational overhead while increasing response time performance. Application instances are virtually ordered in a chain, and new queries are forwarded along this chain until an instance, based on its local load, accepts the query. Autoscaling is triggered by the last application instance, which inspects its average load and infers if its chain is under- or over-provisioned. An analytical model of the system is derived, and proves that the proposed technique can achieve asymptotic zero-wait time with high (and controlable) probability. This result is confirmed by extensive simulations, which highlight close-to-ideal performance in terms of both response time and resource costs. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TNSM.2020.3045059 | IEEE Transactions on Network and Service Management |
Keywords | DocType | Volume |
Load balancing,auto-scaling,segment routing,application-aware,performance analysis | Journal | 18 |
Issue | ISSN | Citations |
1 | 1932-4537 | 1 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yoann Desmouceaux | 1 | 1 | 0.35 |
| Marcel Enguehard | 2 | 1 | 0.35 |
| Thomas Clausen | 3 | 2068 | 141.73 |