TY - GEN
T1 - Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing
AU - Podolskiy, Vladimir
AU - Mayo, Michael
AU - Koey, Abigail
AU - Gerndt, Michael
AU - Patros, Panos
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - With changing workloads, cloud service providers can leverage vertical container scaling (adding/removing resources) so that Service Level Objective (SLO) violations are minimized and spare resources are maximized. In this paper, we investigate a solution to the self-adaptive problem of vertical elasticity for co-located containerized applications. First, the system learns performance models that relate SLOs to workload, resource limits and service level indicators. Second, it derives limits that meet SLOs and minimize resource consumption via a combination of optimization and restricted brute-force search. Third, it vertically scales containers based on the derived limits. We evaluated our technique on a Kubernetes private cloud of 8 nodes with three deployed applications. The results registered two SLO violations out of 16 validation tests; acceptably low derivation times facilitate realistic deployment. Violations are primarily attributed to application specifics, such as garbage collection, which require further research to be circumvented.
AB - With changing workloads, cloud service providers can leverage vertical container scaling (adding/removing resources) so that Service Level Objective (SLO) violations are minimized and spare resources are maximized. In this paper, we investigate a solution to the self-adaptive problem of vertical elasticity for co-located containerized applications. First, the system learns performance models that relate SLOs to workload, resource limits and service level indicators. Second, it derives limits that meet SLOs and minimize resource consumption via a combination of optimization and restricted brute-force search. Third, it vertically scales containers based on the derived limits. We evaluated our technique on a Kubernetes private cloud of 8 nodes with three deployed applications. The results registered two SLO violations out of 16 validation tests; acceptably low derivation times facilitate realistic deployment. Violations are primarily attributed to application specifics, such as garbage collection, which require further research to be circumvented.
KW - Autoscaling
KW - Cloud Computing
KW - Data Driven Adaptation
KW - Performance Interference
KW - Resource Sharing
KW - Self Adaptive Systems
UR - http://www.scopus.com/inward/record.url?scp=85070557495&partnerID=8YFLogxK
U2 - 10.1109/SASO.2019.00018
DO - 10.1109/SASO.2019.00018
M3 - Conference contribution
AN - SCOPUS:85070557495
T3 - International Conference on Self-Adaptive and Self-Organizing Systems, SASO
SP - 72
EP - 81
BT - Proceedings - 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019
PB - IEEE Computer Society
T2 - 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2019
Y2 - 16 June 2019 through 20 June 2019
ER -