Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 1 / 16 Model-based SOA Performance Profiling using CloudLauncher Other project members Francesco Caruso Josephine Micallef Sumant Tambe, Vanderbilt University, Nashville TN Summer Telcordia Technologies, June-Aug 2009

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 2 / 16 Outline  Background: Service-Oriented Architecture (SOA) and Quality of Service (QoS)  QoS Design Problem in SOA  Overview of State of the Art  Project Objectives  Solution: SOA Dynamic Designer for QoS  A model-driven deployment and performance profiling tool for SOA QoS design: CloudLauncher  Amazon Elastic Compute Cloud (EC2) Testbed  Concluding remarks

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 3 / 16 Service-Oriented Architecture (SOA) & QoS Implementation layer Services interface layer Business process layer..NET J2EELegacy composite services atomic services E2E QoS (SLA) Service QoS Environment QoS SOA System

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 4 / 16 Motivation  Mission-critical systems with high-assurance requirements – in defense, communications, healthcare, are increasingly adopting SOA  Often non-intuitive tradeoffs are necessary between multiple QoS (e.g., security, reliability, availability, performance)  Lack of engineering methodology for SOA system QoS  QoS design today is an ad-hoc, manual and costly one-off job for every SOA system  Increases risk and cost for successfully deploying these solutions  No procedures or tools available to intelligently explore the solution space to find the best tradeoffs

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 5 / 16 Overview of State of the Art  SOA standard specifications for security and reliable messaging policies are maturing  Implementations exist for enterprise middleware platforms  However, no support in higher-level QoS design methodology or tool  Several research projects addressing SOA QoS  Primary goal is for “service matching”– selecting the service that meets the required QoS ”– not system design  Provide point solutions to very specific problems – not “end-to-end”  QoS Modeling for Distributed Real-time Embedded (DRE) systems  E.g., Component QoS Modeling Language (CQML), Vanderbilt Univ.  Fault-tolerance, Network QoS etc.

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 6 / 16 Project Objectives  Provide system engineers with a methodology and supporting toolset to enable them to design SOA- based systems that achieve end-to-end QoS  Use model-driven techniques to generate configurations adapted to the target deployment environment that meet the QoS requirements  Reduce development burden and configuration errors  Facilitates deployment onto heterogeneous environments  Enable adaptation to changes in system’s QoS requirements or deployment environment by re- computing new target configuration  Incremental computation to minimize rework  Minimize configuration delta to reduce impact

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 7 / 16 Solution: SOA Dynamic Designer for QoS Model System End-to-End QoS Requirements Suggest Composition Patterns for QoS Build Service QoS Profile Deploy Promising Candidates SOA Dynamic Designer for QoS Design-Time  Intelligently present design options based on all constraints  Allows users to do “what-if” analysis on alternative designs  Automatically configure “tunables” on services and deployment environment

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 8 / 16 Order ManagerCredit Check Model-based Design Specification (1/3)  System model specifies composition of SOA services  Structure  Behavioral Abstractions  Parallel  Serial Inventory CheckCustomer DataInventory Data External Credit Check Serial Parallel E2E QoS: Support up to N orders/hour while responding with order confirmation in no more than X sec. Order Confirmation Response Time T = max ( T a, T b ) TaTa TbTb Credit Check Response Time T a = T x + T y TxTx TyTy T

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 9 / 16 Model-based Design Specification (2/3)  Specify QoS attributes (s ecure channel, reliable channel etc)  Apply composition patterns  Encryption strength  At least once, exactly once delivery, etc Secure Channel Reliable Channel Order ManagerCredit CheckInventory CheckCustomer DataInventory Data External Credit Check Encryption Mediator Persistent Message Queue

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 10 / 16 Collocated and Clustered Model-based Design Specification (3/3)  Specify deployment pattern alternatives  Single/multiple core(s), small/medium/large memory resources  Clustering Vs. standalone  Collocated Vs. distributed Order ManagerCredit CheckInventory CheckCustomer DataInventory Data External Credit Check Inventory CheckInventory DataInventory CheckInventory DataInventory CheckInventory Data Less Resourceful Machine Resourceful Machine

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 11 / 16 Build Service QoS Profile CloudLauncher  Model-based tool for empirical load testing  Use Amazon Elastic Compute Cloud (EC2) as testbed  Automatically instantiate  different deployment patterns  different deployment host resource settings  different QoS composition patterns (in progress)  Use Grinder for generating dynamic load (in progress)  Measure data points (response time, load, utilization)  Interpolate to estimate service QoS profile (in progress)

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 12 / 16 CloudLauncher Supported Deployment Patterns Amazon Cloud (US-East-1* Zone) Amazon EC2 Instance App Server: Order Manager DB: Customer Data JDBC Single Machine Instance App Server: Order Manager DB: Customer Data JDBC Distributed on 2 Machines DB: Customer Data App Server: Order Status App Server: Order Manager JDBC Load Balancer Clustered App Server App Server: Order Manager DB: Customer Data JDBC App Server: Order Manager DB: Customer Data JDBC App Server: Order Manager DB: Customer Data JDBC Load Balancer Replicated DB

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 13 / 16 Architecture of Grinder SOA Dynamic Designer for QoS Amazon Cloud (US-East-1* Zone) Amazon EC2 Instance The Grinder Controller Test Clients Distributes test plan to agents, And collects test statistics DB: Customer Data App Server: Order Status App Server: Order Manager JDBC Load Balancer Clustered App Server The Grinder Agent Generate

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 14 / 16 Example Service Profile for Different Deployment Patterns Response Time Load S Host L Host XL Host Response Time Load 1H-Cluster 4H-Cluster Response Time (Load) = a + b * Load 2H-Cluster Machine Type Clustered Configuration L1L1 L2L2 L1L1 L2L2 T1T1 T2T2 T1T1 T2T2

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 15 / 16 Concluding Remarks  SOA Dynamic Designer for QoS capabilities  Model end-to-end QoS requirements  Intelligently suggest QoS patterns  Generate configuration for target deployment environment  Build QoS profile of the system by empirical testing on cloud platforms

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 16 / 16 Thank you!

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 17 / 16 Extra slides

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 18 / 16 Generic Modeling Environment (GME)  Developed at Institute for Software Integrated Systems (ISIS), Vanderbilt University  Under development since 1995 (GME 9 in beta testing)  Enables layered, multi-view system modeling, model transformation, model analysis and validation, and model execution  API support in C++, Java, Python  Reference: Composing Domain-Specific Design Environments, Akos Ledeczi et al., IEEE Computer, Nov 2001 Parts browser with custom icons Tree view System Model

Copyright © 2009, Telcordia Technologies, Inc. All Rights Reserved. 19 / 16 Cloud Vs. Grid Computing 19 CloudGrid Transactional apps (e.g. web sites)High performance, one application Real-time resource allocation, Fork-a- server now! Best-effort scheduling of queued allocation requests Requests of up to nodesRequests of up to nodes Same administrative domain (so far)Sharing of resources belonging to different admin domains Pay-as-you-go (frequent scale out and scale down) infrequent Virtualization is crucialVirtualization is just entering Usability: Easy (PaaS, SaaS)Heavy