Dynamic_Cloud_Application_Redistribution_Performance_Optimization
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The Cloud computing paradigm emerged by establishing new resources provisioning and consumption models. Together with the improvement of resource management techniques, these models have contributed to an increase in the number of application developers that are strong supporters of partially or completely migrating their application to a highly scalable and pay-per-use infrastructure. In this paper we derive a set of functional and non-functional requirements and propose a process-based approach to support the optimal distribution of an application in the Cloud in order to handle fluctuating over time workloads. Using the TPC-H workload as the basis, and by means of empirical workload analysis and characterization, we evaluate the application persistence layer's performance under different deployment scenarios using generated workloads with particular behavior characteristics.
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Dynamic_Cloud_Application_Redistribution_Performance_Optimization
- 1. Research University of Stuttgart Universitätsstr. 38 70569 Stuttgart Germany Phone +49-711-685 88337 Fax +49-711-685 88472 Santiago Gómez Sáez, Vasilios Andrikopoulos, Frank Leymann, and Steve Strauch Institute of Architecture of Application Systems {gomez-saez, andrikopoulos, leymann, strauch}@iaas.uni-stuttgart.de Towards Dynamic Application Distribution Support for Performance Optimization in the Cloud IEEE CLOUD 2014
- 2. Research © Santiago Gómez Sáez 2 Agenda Motivation Experiments Methodology & Setup Application Persistence Analysis & Evaluation Performance-aware Application (Re-)Distribution Process Conclusion & Future Work
- 3. 33© Santiago Gómez Sáez Research Motivation – Efficient Application Distribution WebShop: WAR Apache_Tomcat: Servlet_Container Ubuntu10.04: Virt_Linux_OS IBM_Server: Physical_Server Product_DB: SQL_DB MySQL: SQL_RDBMS_Server AWS_EC2_m1. xlarge: AWS_EC2 Ubuntu13.10: Virt_Linux_OS MySQL: SQL_RDBMS_Server AWS_RDS_mediu mDB: AWS_RDS MySQL: SQL_DBaaS AWS_EC2_ m1.medium: AWS_EC2 Ubuntu13.0: Virt_Linux_OS AWS_Elastic_BeansTalk: Application_Container Partial vs. Complete Migration Multiple deployment options Multi-dimensional & Evolving problem Application workload behavior fluctuations Resources Demands Evolution Performance-aware Information WL Specification alt_hosted_on hosted_on interacts-with Andrikopoulos et al.: Optimal Distribution of Applications in the Cloud. In: Proceedings of CAiSE’14
- 4. 44© Santiago Gómez Sáez Research Motivation - Perspectives & Approaches Performance-aware Specification Workload Model Derivation & Characterization Application Workload Evolution Monitoring Top-down Bottom-up
- 5. 55© Santiago Gómez Sáez Research App. Persistence Experiments – Methodology & Setup (1) Evaluate the application persistence layer performance Under different deployment scenarios For different workload characteristics Towards maximizing its performance Emulated a three-layered application Consolidate the top-down and bottom-up performance analysis approaches over time Schema & Empirical analysis Derive the workload behavior model Characterize the different operations which constitute the workload Generate workloads with different characteristics Performance Analysis
- 6. 66© Santiago Gómez Sáez Research WebShop: WAR … Ubuntu10.04: Virt_Linux_OS IBM_Server: Physical_Server Product_DB: SQL_DB MySQL: SQL_RDBMS_Server AWS_EC2_m1. xlarge: AWS_EC2 Ubuntu13.10: Virt_Linux_OS AWS_RDS_xlarge DB: AWS_RDS MySQL: SQL_DBaaS alt_hosted_on hosted_on WL Specification Ubuntu10.04: Virt_Linux_OS FlexiScale4vCPU: FlexiScale_VM on-premise Interacts-with IaaS DBaaS App. Persistence Experiments – Methodology & Setup (2)
- 7. 77© Santiago Gómez Sáez Research App. Persistence Experiments – Methodology & Setup (3) Using the TPC-H Benchmark workload & data Initial workload -> 23 SQL (SELECT) queries 1 GB data Apache JMeter 2.9 as the load driver Measurements & Rounds Throughput (Req./s) 10 Rounds/day during one month (Q4 2013) Characterization of the Workload items Generated multiple workloads with different characteristics (CL, CM, and CH) Probability of occurrences of queries QCL, QCM, and QCH Analyze the variation with respect to the initial workload behavior model Analyzing the performance improvement/degradation for the persistence layer deployment alternatives
- 8. 88© Santiago Gómez Sáez Research App. Persistence Experiments – Sample Query Table joints Subqueries Embedded operations Conditional selection Ordering & Limit
- 9. 99© Santiago Gómez Sáez Research App. Persistence Analysis – Workload Analysis
- 10. 1010© Santiago Gómez Sáez Research App. Persistence Analysis – Workload Characterization
- 11. 1111© Santiago Gómez Sáez Research App. Persistence Analysis – Performance Improvement
- 12. 1212© Santiago Gómez Sáez Research Synthetic Workload Generation
- 13. 1313© Santiago Gómez Sáez Research Generated Workload Analysis – Cumulative Distribution Fit (i) On-premise (ii) DBaaS (iii) IaaS
- 14. 1414© Santiago Gómez Sáez Research Generated Workload – Alternative Topologies Evaluation
- 15. 1515© Santiago Gómez Sáez Research Participants Application Developer Application design and realization Application topology specification, e.g. using TOSCA Application Cloud Distribution Design Support System Application (Re-)distribution towards proactively react to fluctuating workloads Topology+ : enriched application topology model with performance awareness (e.g. expected throughput/operation or component, resource consumption) application workload characteristics (e.g. probability matrix of operations) Topology*: viable topology describing application distribution alternatives and specifying Cloud services dynamic resource adaptation configurations Approach – Performance Aware Application (Re-)Distribution Process
- 16. 1616© Santiago Gómez Sáez Research Model Application Topology Specify Performance- awareness Discover Application Distribution Evaluate Application & Distribute Register & Monitor Performance Re-distribution • Requirements •Capabilities •Constraints • Expected Performance •Workload Behavior Specification •Topology+ • Workload Model Derivation • Cloud Offerings Matching • Similarity & utility-based Analysis • Topologies* • Topology* instance • Synthetic Workload Generation • Distribution Performance Evaluation • Demanded vs. Provided Performance • Workload behavior Patterns • Statistical Classification Collaborative Loop • Proactiveness • Workload Fluctuation • Application Performance Evolution • Optimize performance vs. cost tradeoff Resources Adaptation Application Developer Distribution Support System
- 17. 1717© Santiago Gómez Sáez Research Conclusions Collaboration Loop Experiments focusing on the Application persistence Deriving the workload model Generating workloads with different characteristics Multiple deployment scenarios Process-based approach to distribute the application towards optimizing its performance Functional & Non-functional aspects Identifying the need to enrich the topology Focusing on the application workload behavior evolution
- 18. 1818© Santiago Gómez Sáez Research Future Work Reuse existing tools & implement new ones of the proposed tool chain Performance-aware specification language Evaluate the performance of the overall process Experiments in the application upper layers Santiago Gómez Sáez E-mail: gomez-saez@iaas.uni-stuttgart.de Institute of Architecture of Applications Systems (IAAS) University of Stuttgart (Germany)
- 19. 19 Thanks for your attention!!
Editor's Notes
- Make an animation here with the perspective and the background
- - Empirical Cumulative Distribution Function: An empirical cumulative distribution function (CDF) is a non-parametric estimator of the underlying CDF of a random variable. It assigns a probability of to each datum, orders the data from smallest to largest in value, and calculates the sum of the assigned probabilities up to and including each datum. The result is a step function that increases by at each datum. The empirical CDF is usually denoted by or , and is defined as - Derive the workload behavior model -> The performance of a system depends on the workload that it must serve. For example, if a work is evenly distributed the performance will be better than if it comes in unpredictable bursts that lead to congestion. Therefore, performance evaluations require the use of representative workloads towards producing dependable results. This can be achieved by collecting data about real workloads, and creating statistical models that capture their features. - Characterization -> Exploratory Data Analysis: exploring data sets to summarize their main characteristics
- TPC Benchmark™ H is comprised of a set of business queries designed to exercise system functionalities in a manner representative of complex business analysis applications. These queries have been given a realistic context, portraying the activity of a wholesale supplier to help the reader relate intuitively to the components of the benchmark Workload model: Two ways to use a measured workload to analyze and evaluate the system: 1) use traced workload to drive a simulation, or 2) create a model from the trace and use this model for analysis or simulation. Real world: testing vs. production. e.g. if a new job is created, then the workload model has to be derived again. Need large amounts of real data to be able to optimize the emulation. Degree of details:
- C -> if AVG throughut Qi < median -> H if AVG throughput Qi > Mean & % Logical Evaluations wrt Benchmark > Average Table Access -> M else -> L - In statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample.
- C -> if AVG throughut Qi < median -> H if AVG throughput Qi > Mean & % Logical Evaluations wrt Benchmark > Average Table Access -> M else -> L - In statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample. - Explain that not all queries were in an appropriate time frame executed. We incorporate to the workload analysis the successfully executed in a reasonable time frame. Some queries need more than an hour to be executed. This is another parameter to take into account when distributing the application, as some queries need too much time in some deployment scenarios and relative ok time for other scenarios Standard deviation: how much dispersion from the average exists. A low standard deviation indicates that most of the values are close to the mean.
- Distribution of CL is shifted to the left, and it increases fast as the probability of ocurrence of queries with a CL throughput is greater than the other ones. The probability to find a query CL is high, so it increases rapidly until the CL queries, and in the CL queries the probability between them is low, so that it increases slowly Distribution of CH is shifter to the right, as the probability of ocurrence of CL queries is really low, and most of the CH queries have an equal probability which is low but in conjunction sums up a high probability. Distribution of the CM is geared towards the initial load
- - Cloud service broker: intermediary between cloud consumer and cloud services. Functionalities: discovery and purchase, contract negotiation. A Cloud broker is a software application that facilitates the distribution of work between different cloud service providers.