WAC: A Workload analysis and classification tool for automatic selection of cloud auto-scaling methods
(English)Manuscript (preprint) (Other academic)
Autoscaling algorithms for elastic cloud infrastructures dynami-cally change the amount of resources allocated to a service ac-cording to the current and predicted future load. Since there areno perfect predictors, no single elasticity algorithm is suitable foraccurate predictions of all workloads. To improve the quality ofworkload predictions and increase the Quality-of-Service (QoS)guarantees of a cloud service, multiple autoscalers suitable for dif-ferent workload classes need to be used. In this work, we intro-duce WAC, a Workload Analysis and Classification tool that as-signs workloads to the most suitable elasticity autoscaler out of aset of pre-deployed autoscalers. The workload assignment is basedon the workload characteristics and a set of user-defined Business-Level-Objectives (BLO). We describe the tool design and its maincomponents. We implement WAC and evaluate its precision us-ing various workloads, BLO combinations and state-of-the-art au-toscalers. Our experiments show that, when the classifier is tunedcarefully, WAC assigns between 87% and 98.3% of the workloadsto the most suitable elasticity autoscaler.
IdentifiersURN: urn:nbn:se:umu:diva-108396OAI: oai:DiVA.org:umu-108396DiVA: diva2:852772
FunderSwedish Research CouncilEU, European Research Council