KPI-Agnostic Control for Fine-Grained Vertical Elasticity

Lakew, Ewnetu Bayuh; Papadopoulos, Alessandro Vittorio; Maggio, Martina; Klein, Cristian; Elmroth, Erik

doi:10.1109/ccgrid.2017.71

Cited by 23 publications

(10 citation statements)

References 25 publications

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“…The problem of the scalability of resource allocation for virtual machines can be addressed by solving an optimization problem in a distributed peer-to-peer manner over a scale-free overlay as shown in [6]. The vertical elasticity of VMs to meet SLOs imposed on cloud applications was studied from the control-theoretic point of view in [7]. The proposed approach to vertical scaling was extended with probabilistic cloning of requests in [8], which improved SLO-compliance of running applications and reduced over-provisioning in comparison to [9].…”

Section: Background and Related Workmentioning

confidence: 99%

Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing

Podolskiy

Mayo

Koey

et al. 2019

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

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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.

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Section: Background and Related Workmentioning

confidence: 99%

Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing

Podolskiy

Mayo

Koey

et al. 2019

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

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“…This latter makes the system essentially different from other, extensively explored, problems in IT infrastructure. Such is the case of Cloud systems [13], [14], [15], where there is no such differentiation in the grid between CiGri and user jobs. MapReduce is a more distributed parallel-oriented framework that targets large parallelizable problems, while CiGri focuses on independent parametric tasks.…”

Section: A Experimental Setupmentioning

confidence: 99%

A control-theory approach for cluster autonomic management: maximizing usage while avoiding overload

Yabo

Robu

Richard

et al. 2019

2019 IEEE Conference on Control Technology and Applications (CCTA)

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“…Thus, a more efficient vertical scaling can resolve the insufficiency of a specific resource. Nowadays, diverse vertical scaling techniques implemented on cloud services have been developed by academic researchers or industries . Kalyvianaki et al designed a controller using Kalman filtering to control CPU allocation based on the CPU utilization.…”

Section: Related Workmentioning

confidence: 99%

Automatic cloud service testing and bottleneck detection system with scaling recommendation

Liu

Sheu

et al. 2019

Concurrency and Computation

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Performance problems in a cloud service are difficult to diagnose because they may be caused by various system components. This study proposes an automatic cloud service testing and bottleneck detection system that is applicable to different types of services. With the proposed test module, the user can customize test scenarios to automatically test and collect the corresponding metrics of the target service. Afterward, the proposed bottleneck detection algorithm analyzes the collected metrics and determines whether a bottleneck is presented in the target system. The bottleneck detection module also provides a scaling recommendation for the service provider to facilitate the service system reconfiguration. The experimental results reveal that the proposed system could detect a potential bottleneck in a service system accurately. In accordance with the scaling recommendation, the performance of the target cloud service can be improved efficiently after reconfiguration. Therefore, usage of the proposed system can ensure a high quality of service, and the service level objective could be fulfilled.

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KPI-Agnostic Control for Fine-Grained Vertical Elasticity

Cited by 23 publications

References 25 publications

Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing

Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing

A control-theory approach for cluster autonomic management: maximizing usage while avoiding overload

Automatic cloud service testing and bottleneck detection system with scaling recommendation

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