A Forecasting Methodology for Workload

A Forecasting Methodology for Workload

Forecasting in Cloud Systems

Forecastingis the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might beestimationof some variable of interest at some specified future date.Predictionis a similar, but more general term. Both might refer to formal statistical methods employingtime series,cross-sectionalorlongitudinaldata, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, inhydrologythe terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specificfuturetimes, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period.Cloud Computing is an essential paradigm of computing services based on the “elasticity” property, where available resources are adapted efficiently to different workloads over time. In elastic platforms, the forecasting component can be considered by far the most important element and the differentiating factor when comparing such systems, being workload forecasting one of the problems to solve if we want to achieve a truly elastic system.. As there is no general methodology in the literature that addresses this problem analytically and from a time series forecasting perspective (even less in the cloud field), we propose a combination of these tools based on a stateof- the-art forecasting methodology which we have enhanced with some elements, such as: a specific cost function, statistical tests, visual analysis, etc. The insights obtained from this analysis are used to detect the asymmetrical nature of the forecasting problem and to find the best forecasting from the viewpoint of the current state of the art in time series forecasting. From an operational point of view the most interesting forecast is a short-time horizon, so we focus on this. To show the feasibility of this methodology, The results show that the analyzed series are non-linear in nature, and that no seasonal patterns can be found. Moreover, on the analyzed datasets, the penalty cost as usually included in the SLA can be reduced down to a 30% on average.

SYSTEM ANALYSIS

EXISTING SYSTEM

While there are many solutions in the literature based on existing forecasting tools, there is no general framework or methodology that addresses the workload forecasting problem analytically from a state-of-the-art time series forecasting perspective; even less in the cloud field, where we perceive a gap between forecasters and domain experts, in the sense that well-structured stateof- the-art forecasting methodology is not commonly employed in this area.

For example, one of the main results of the M3 forecasting competition is that simple methods often outperform more complex ones [6], and model performance depends on the underlying data of a forecasting problem. More recently, the sNN3 competition [7] yielded as a result that none of the participating Machine Learning (ML) methods was able to outperform the theta method, which is equivalent to simple exponential smoothing with drift, which is a relatively simple standard method in time series forecasting

PROPOSED SYSTEM

In conclusion, the application of the proposed methodology has led us to gain some useful insight regarding CWF time series properties, i.e., no seasonality, nor trend is found, and they are non-linear. This helps to discard some models and focus on others. In addition, by using the forecasting models developed here, a significant reduction in under-provisioning and over-provision costs can be achievedcontrol inherently. The proposed protocols also protect the privacy of filenames.

PROPOSED SYSTEM ALGORITHMS

Time Series:

Time-series forecasting methods use historical information only to produce estimates of future values. Time-series forecasting techniques assume the data's past pattern will continue in the future and include specific measures of error which can help users understand how accurate the forecast has been. Some techniques seek to identify underlying patterns such as trend or seasonal adjustments; others attempt to be self-correcting by including calculations about past periods' error in future forecasts.

The code included here addresses several of the most common time-series forecasting techniques, including naive/Bayes, simple moving average, weighted moving average, exponential smoothing, and adaptive rate smoothing.

In the naive/Bayes approach, the current period's value is used as the forecast for the upcoming period.

In a simple moving average, the prior n-number of values are averaged together with equal weight to produce a value for the upcoming period.

Machine Learning Algorithm:

It proposes a system with two modules of forecasting: one for short-term forecasting’s while the other one is being trained, and another one for long-termforecasting’s.Machine learning borrows from the field of statistics, but gives new approaches for modeling problems.The fundamental problem for machine learning and time series is the same: to predict new outcomes based on previously known results.

Modules:

Workload

Load the work,web and database server instances for the current observed workload.Web infrastructures, the forecasting part does not represent a general framework for workload forecasting.

Seasonal Study

The automatic building procedure for ETS does not choose seasonal models. However, ARIMA can choose new seasonal and differenced models. After modeling, likewise, if the original series are non-stationary, we also perform the seasonal process on the differenced version of the series.

In seasonal time, load the product is to be added. Product is demand to the user.

Non Seasonal

In seasonal time, load the product is to be added. Product is more but the period of time is less.

Time Series

It depicts the complete workload system series for the dataset hourly CPU load 2. In the figure, we see an oscillating pattern with a period length of approx. one day, which could indicate a seasonal pattern of daily fluctuation.

Products will be loaded a period of time. The time series will be calculated by the system.

Forecasting:

Forecastingis the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might beestimationof some variable of interest at some specified future date.

SYSTEM SPECIFICATION

Hardware Requirements:

•System: Pentium IV 2.4 GHz.

•Hard Disk : 40 GB.

•Floppy Drive: 1.44 Mb.

•Monitor : 14’ Colour Monitor.

•Mouse: Optical Mouse.

•Ram : 512 Mb.

Software Requirements:

•Operating system : Windows 7 Ultimate.

•Coding Language: MVC 4 Razor

•Front-End: Visual Studio 2010 Professional.

•Data Base: SQL Server 2008.

Conclusion:

The Conclusion of Cloud Computing, as well as the relevance of having a proper forecasting module, as part of a complete elastic system. We have seen that workload forecasting in cloud platforms is a problem that needs a preliminary stage to analyze and model properly the properties of the time series.The effectiveness of this approach has been evaluated through its application to workloads of four different cloud datacenters. The insights gained from the empirical results have allowed to find the best model architecture, so that the best forecasts from the viewpoint of the current state of the art in time series forecasting have been achieved.