Self-adaptive workload classification and forecasting for proactive resource provisioning

Abstract

As modern enterprise software systems become increasingly dynamic, workload forecasting techniques are gaining in importance as a foundation for online capacity planning and resource management. Time series analysis offers a broad spectrum of methods to calculate workload forecasts based on history monitoring data. Related work in the field of workload forecasting mostly concentrates on evaluating specific methods and their individual optimisation potential or on predicting Quality-of-Service (QoS) metrics directly. As a basis, we present a survey on established forecasting methods of the time series analysis concerning their benefits and drawbacks and group them according to their computational overheads. In this paper, we propose a novel selfadaptive approach that selects suitable forecasting methods for a given context based on a decision tree and direct feedback cycles together with a corresponding implementation. The user needs to provide only his general forecasting objectives. In several experiments and case studies based on realworld workload traces, we show that our implementation of the approach provides continuous and reliable forecast results at run-time. The results of this extensive evaluation show that the relative error of the individual forecast points is significantly reduced compared to statically applied forecasting methods, e.g. in an exemplary scenario on average by 37%. In a case study, between 55% and 75% of the violations of a given service level agreement can be prevented by applying proactive resource provisioning based on the forecast results of our implementation.

Exploratory Learning of Resource Management in Private Cloud Environment

“…Proactive systems used different phases to provide a solution for managed resources in the cloud. In MAPE, an analysis process with load prediction [14], [23], [24] using proactive and reactive approaches discussed where a CPU as a predicted resource was considered. In MAPE, a planning process with various architectures are studied and scaling rules are presented [25].…”

Time Series Forecasting Using a Two-level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans

“…The ARIMA model is a stochastic process modelling framework [1] that is defined by three parameters ( , , ). The parameter stands for the order of the autoregressive ( ) process, for the order of integration (needed for the transformation into a stationary stochastic process), and for the order of the moving average ( ) process [5]. A stationary stochastic process means a process where the data properties have the same variance and autocorrelation [6].…”

Which Cloud Auto-Scaler Should I Use for my Application?

“…Elasticity allows resources to be provisioned and released to scale rapidly out ward and in ward according to demand. Tens -if not hundreds -of algorithms have been proposed in the literature to automatically achieve elastic provisioning [15,23,14,21,13,20,6,12,16,10]. These algorithms are typically referred to as elasticity algorithms, dynamic provisioning techniques or autoscalers.…”