Extending OpenStack Monasca for Predictive Elasticity Control  

作　　者：Giacomo Lanciano Filippo Galli Tommaso Cucinotta Davide Bacciu Andrea Passarella 

机构地区：[1]Scuola Normale Superiore,Pisa 56126,Italy [2]the Real-Time Systems Laboratory(RETIS),Telecommunications,Computer Engineering,and Photonics Institute(TeCIP),Scuola Superiore Sant’Anna,Pisa 56127,Italy [3]Department of Computer Science,University of Pisa,Pisa 56127,Italy [4]National Research Council,Pisa56127,Italy

出　　处：《Big Data Mining and Analytics》2024年第2期315-339,共25页大数据挖掘与分析（英文）

基　　金：supported by the PNRR-M4C2-Investimento 1.3,Partenariato Esteso(No.PE00000013-FAIR).

摘　　要：Traditional auto-scaling approaches are conceived as reactive automations,typically triggered when predefined thresholds are breached by resource consumption metrics.Managing such rules at scale is cumbersome,especially when resources require non-negligible time to be instantiated.This paper introduces an architecture for predictive cloud operations,which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the system.In this way,they can anticipate load peaks and trigger appropriate scaling actions in advance,such that new resources are available when needed.The proposed architecture is implemented in OpenStack,extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard metrics.We use our architecture to implement predictive scaling policies leveraging on linear regression,autoregressive integrated moving average,feed-forward,and recurrent neural networks(RNN).Then,we evaluate their performance on a synthetic workload,comparing them to those of a traditional policy.To assess the ability of the different models to generalize to unseen patterns,we also evaluate them on traces from a real content delivery network(CDN)workload.In particular,the RNN model exhibites the best overall performance in terms of prediction error,observed client-side response latency,and forecasting overhead.The implementation of our architecture is open-source.

关 键 词：OPENSTACK MONITORING elasticity control auto-scaling predictive operations Monasca 

分 类 号：R563.1[医药卫生—呼吸系统] R181.8[医药卫生—内科学] O212.1[医药卫生—临床医学]