Cloud Analytics for Capacity Planning and Instant VM Provisioning

Cloud Analytics for Capacity Planning and Instant VM Provisioning Yexi Jiang Florida International University Advisor: Dr. Tao Li Collaborator: Dr. Charles Perng, Dr. Rong Chang

Presentation Outline Background Cloud Capacity Prediction Predict provisioning resource demand Estimate de-provisioning requests Experimental evaluation results Instant Cloud Provisioning Predict VM provisioning demand Experimental evaluation results 1 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Background What is Cloud Analytics? Rapidly identify cloud resource or application trouble spots so you can solve the problem. What is the objective of cloud analytics? The cloud platform itself. What can cloud analytics do? Workload analysis System fault diagnostics 2 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Smart Cloud Enterprise trace data 5 month, 35k+ requests, 120+ image types, 20+ features each record Important Features: Image Name, Owner, Start Time, End Time, ID 3 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Aggregating the Raw Data weekly Cannot reflect real capacity daily Just right hourly 4 Yexi Jiang http://users.cis.fiu.edu/~yjian004/ 5

Aggregating the Raw Data weekly Cannot reflect real capacity daily Just right hourly Measurement Weekly Daily Hourly Coefficient of Variance (CV) 0.5606 0.7915 1.2249 Skewness 0.3295 1.5644 5.4464 Too irregular Kurtosis 1.62 5.8848 52.4103 5 Yexi Jiang http://users.cis.fiu.edu/~yjian004/ 6

Presentation Outline Background Cloud Capacity Prediction Predict provisioning resource demand Estimate de-provisioning requests Experimental evaluation results Instant Cloud Provisioning Predict VM provisioning demand Experimental evaluation results 6 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Cost of Data Centers * From James Hamilton's Blog 31% of the cost is related to power. As hardware price continuously decreases, the proportion would further increase. The US EPA estimates the energy usage at data centers is experiencing successive doubling every five years. (7.4 billion in 2011) 7 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Motivation Reduce power cost via capacity prediction Cost of the Cloud Provider Prepared Resource Real Requirement 8 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Motivation Reduce power cost via capacity prediction Cost of the Cloud Provider Prepared Resource Predicted Resource Real Requirement 9 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Candidate Time Series Capacity time series Non-stationary. Difficult to model directly Provisioning /de-provisioning time series Obvious temporal pattern Better candidate 10 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Basic Idea Capacity = (# existing VMs) + (# provisioning) - (# de-provisioning) Predicted Provisioning - Predicted Deprovisioning + Existing VM in cloud Predicted Capacity 11 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Predicting Provisioning Ensemble method for time series prediction Individual prediction techniques used: Moving Average. Naïve predictor. Auto Regression. Linear predictor. Neural Network. Non-linear predictor. Gene Expression Programming. Genetic algorithm. Support Vector Machine. Linear predictor with non-linear kernel. Dynamic weighted linear combination Weight update Demands w p (t) weight of predictor p v p predicted value of individual predictor p c p (t) cost of predictor p at time t e (t) error of individual predictor p 12 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Cloud Prediction Cost C = R( v( t), v ~ ( t)) + T ( v( t), v ~ ( t)) Over-prediction: cost of resource waste. R function: Under-prediction: cost of SLA penalty. T function: Property: Non-negative, Monotonic. 13 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Prediction Result Ensemble has the best average performance. 14 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Predicting De-provisioning Use the life span CDF F(x) of VMs to estimate number of deprovisioning requests Estimation of distribution: step-wise function. * n i # of VMs with life span t (t1 < t < t2) 15 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

De-provisioning evaluation Test data: last 60 day. Test methods: 1. No preparation at all (None) 2. Always prepare the maximum capacity (Maximum) 3. Time series prediction (Time Series) 4. Life span distribution despite of image 60 days of data (Dist 60) 90 days of data (Dist 90) Global distribution estimation method outperforms the time series prediction method. 16 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Presentation Outline Background Cloud Capacity Prediction Predict provisioning resource demand Estimate de-provisioning requests Experimental evaluation results Instant Cloud Provisioning Predict VM provisioning demand Experimental evaluation results 17 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Motivation Problem: Existing clouds are not instant, not suitable for mid-job scaling and urgent tasks. VM preparation is fast, but patching, security assurance, manual process and other processes cost time. Known solutions: Prepare extreme large number of different types of VMs. Waste resource Ask customers to provide schedule. Impractical Our Idea: Make good use of the customer historical requests to infer the future demand. Reduce the average VM provisioning fulfillment time. 18 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Core Idea Model and predict demands Predict Results Pre-provision at suitable time Wait for Requests Assign VMs to customers 19 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Focus on individual types No obvious temporal patterns for individual image type. Ensemble is still required. 20 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Focus on popular VM types 1) About 10% (12) of the 124 VM types consists more than 80% requests 2) Inflection point divides the VM types into popular group and rare group 3) Requests for rare image types appear randomly. 21 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

22 Yexi Jiang http://users.cis.fiu.edu/~yjian004/ Workflow Overview

Experimental Evaluation Ensemble method have the best performance in reducing waiting time and resource waste. 23 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Conclusion Capacity Prediction The demand of cloud capacity can be estimated by predicting provisioning and deprovisioning requests Use time series ensemble method for provisioning prediction Use VM life span model for de-provisioning prediction Instant cloud provisioning Pre-provision VMs before requests arrive Predict VM provision requests use time series ensemble method The average provisioning fulfillment time can be reduced by 85%+ Future work Improve prediction with user profile Fine-grain adjustment with control theory 24 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Thank you! 25 Yexi Jiang http://users.cis.fiu.edu/~yjian004/

Thank you Related Paper: Intelligent Cloud Capacity Management. (NOMS 2012) ASAP: A Self-Adaptive Prediction System for Instant Cloud Resource Demand Provisioning. (ICDM 2011) Patent: Cloud Provisioning Accelerator, Serial # 13306506, Pending 26 Yexi Jiang http://users.cis.fiu.edu/~yjian004/