Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud
[EN] The increasing popularity of cloud computing has forced cloud providers to build economies of scale to meet the growing demand. Nowadays, data-centers include thousands of physical machines, each hosting many virtual machines (VMs), which share the main system resources, causing interference th...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/200990 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/200990 |
| Access Level: | acceso abierto |
| Palabra clave: | Cloud computing Public cloud Virtualization Interference Performance estimation QoS Tail latency Latency-critical workloads ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES |
| Sumario: | [EN] The increasing popularity of cloud computing has forced cloud providers to build economies of scale to meet the growing demand. Nowadays, data-centers include thousands of physical machines, each hosting many virtual machines (VMs), which share the main system resources, causing interference that can significantly impact on performance. Frequently, these data-centers run latency-critical workloads, whose performance is determined by tail latency, which is very sensitive to the interference of co-running workloads. To prevent QoS violations, cloud providers adopt overprovisioning strategies but they reduce the server utilization and increase the costs. A mechanism that accurately estimates performance degradation dynamically in a production system would allow cloud providers to improve the servers' utilization. In this work we propose Cloud White, an approach that is able to detect the inter-VM interference in scenarios with multiple co-located latency-critical VMs and estimate the performance degradation using multi-variable regression models. Unlike previous proposals, Cloud White is built taking into account the limitations of a public cloud production system. Experimental results show that Cloud White is able to estimate performance degradation with a small overall prediction error of 5%. |
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