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    Presentation Presentation Presentation Transcript

    • IBM SmartCloud Camp 2011 Presentation
      Team 3:
      Terry Chang
      Deepak
      Wai Phyo Kyaw
      Charlotte Ng
      Desmond See
    • Scenario 1: Problem Definition
      To design 3-tier Application with features:
      load balancing
      failover
      redundancy
      scalability
      secure
      Solve single point of failure in our systems
    • Scenario 1: Architectural Design
      IBM HTTP SERVER
      with WAS Plugin
      IBM HTTP SERVER
      with WAS Plugin
      LOADBALANCER
      LOADBALANCER
      INTERNET
      Websphere Application Server Cluster
      App: Clone 1
      App: Clone 2
      Node 1
      FIREWALL
      App: Clone 3
      App: Clone 4
      IBM DB2 Server Cluster
      IBM SAN (Redundant Disks: RAID 10)
      Instance 1
      Node 2
      stores data
      Instance 2
      Deployment Manager
      *instances will be created in different data centres around the world
    • Scenario 1: Configuration in Instances
      Assign Virtual IP
      Install Pacemaker
      Configure Heartbeat communication layer
      Provision at least 2 number of VMs instances and get ready
      In time of failure:
      Reconfigure Virtual IP for the alive instance
      Automatic direct traffic to the alive nodes
      Fault-tolerance, fast recovery times, session replication
    • Scenario 1: Advantages of IBM Cloud Systems
      What and how did we leverage IBM Technologies?
      IBM HTTP Server
      Workload Management + Loadbalancer
      IBM Websphere Application Server
      Clustering, Automatic failover
      IBM DB2 (with HADR/v9.5+)
      Database Client Nodes Clustering with HADR
      SQL Replication, Shared Disks/SAN Support
      DB2 HA: DB2 High Availability Instance Configuration Utility (db2haicu)
      IBM Storage Area Network SAN: RAID 1 or RAID 10 (not RAID 5)
      Shared Disk redundancy
    • Scenario 1: Design Considerations
      Different Physical Locations of Instances
      Automatic Trigger, Notification and Configuration
      Data integrity after recovery/during failover/in SAN
      Security in configuring/writing scripts
    • Scenario 1: Project Management
      Scope & Deliverables:
      Complete implementation of high availability data recovery three-tier application on SCE : from architecture to scripting configurations
      Configure WAS, DB2 instances and SAN nodes?
      Configure automated scripts, Provision monitoring instances
      Proposed Timeline:
      100 man hours
      5 persons team
      Resource Estimation:
      2 x firewall+loadbalancer, 2x IBM HTTP Server, 2x WAS Nodes, 2x DB2 Instances, 2x SAN Sites
      Test Plan
      Test instances failure (kill the services), Test recovery process (automation, time, data integrity)
    • Scenario 1: Project Risks
      Technical
      Configured incorrectly during failover and recovery
      Data integrity issues, session not replicated, data not copied properly
      SCE issues?
      Recovery time exceeds minimum as stated in SLA
      Team
      Inadequate skills, inexperience
      Manpower shortage
    • Scenario 2: Problem Definition
      To build a scalable and multi-tenancy web portal as a platform
      Customer self-management portal system
      To provide SaaS to customer as an Independent Software Vendor
      Simplified and standardized technical setup of the software for the customers
    • Scenario 2: Intended Design
      New User Registration/Login
      APIs
      - our own Business Logic (authentication, creating new account in DB)
    • Scenario 2: Intended Design
      Dashboard for Customer
      ** First, Create DeveloperCloudClient to execute requests against the Cloud | DeveloperCloudClientgetClient()
    • Scenario 2: Intended Design
      Billing information
    • Scenario 2: Project Management
      Scope
      Web portal
      DB2 – (customer authentication and data storage)
      Proxy server
      Business logic
      Deliverables
      Create portal system with fully functioning interface
      Create database integrated system + a relational database
      Add security features to protect customers’ data
      Modularized and loosely coupled system(Use of RESTful Services)
    • Scenario 2: Test Process
      Create multiple same Customer IDs
      Create > 5 instances of VM which is over the limitation
      Create Failover at either the SCE side or Proxy server.
      Give wrong user credentials
      Accuracy of the billings
    • Scenario 2: Project Risks
      Technical
      Security – authentication between users and proxy server or proxy server and SCE
      Failover and workload balance at SCE, proxy server
      Connection timeout between client, proxy and SCE
      Configuration error in creating the instances of the vm or application.
      SCE unable to create instances or access
      Team
      Skill and knowledge inadequate
      Illness, MC
      Underestimation of projected timeline
      Service level agreement
    • Scenario 2: Advantages of Cloud Systems
      Low total cost of ownership of the equipment
      Flexible usage of the services : Pay Per Use, Utility Billing
      High availability
      Handle Variable Demand (Dynamic Load)
      Pervasiveness (Anytime, anywhere)
    • Scenario 2: Design Considerations
      Customers information security
      Standardization and automation
      User friendly interfaces
      Cost saving
    • Scenario 3: Problem Definition
      Aim: Provide information to managers and identifying poor/well-performing branches (acc to branch, then country, then continent)
      Business problem: Unable to make adequate decisions because data is confusing and not presented in a readable format for further analysis
    • Scenario 3: Dummy Data
      Extracted, transformed and selected data from OLTP (ETL Process)
    • Scenario 3: How Cognos can help Rainbow Food
      Shopping experience:
      Identify, report on and analyze trends
      Use predictive models and association rules
      Gain insight into customer perceptions of service, store, products and merchandising
      Promotion and merchandise planning:
      Optimize merchandise levels and inventory
      Conduct market basket analysis
      Develop plans for key financial indicators
      Smarter operations:
      Set, measure and monitor key performance metrics based on standard financial statements.
      Use predictive models to improve recruitment and optimize staffing decisions.
      Gain visibility into key metrics across the chain: sales, labor, inventory and promotions
      Monitor turnover and employee productivity
    • Scenario 3: Generated Reports
      Expense and Revenue across Shifts
      Product Sales over Five Years
    • Scenario 3: Generated Reports
      Geospatial analysis of performance of Rainbow Food outlets
      for products, for staffing
    • Scenario 3: Intelligence
      Staff underutilization:
      understand which area is understaffed during particular shifts
      with maps and other outlets’ realtime data, we can relocate staff
      instead of retrenching or letting them idle
      Non-selling products:
      different regions have different tastes
      varies from time to time as well
      remove unpopular food items from menu
      analyze new food trends
      Mashup (interconnected) intelligence:
      import external datasets (eating habits, demographics, research agencies)
    • Scenario 3: Leveraging Cloud Systems
      Why Cognos, instead of Excel?
      can handle large datasets
      can draw data from different sources real-time
      multiple parties can gain insights at the same time, share data
      all stakeholders can access anytime anywhere
      use LotusLive for collaboration (sharing of reports)
      more transparency
    • Scenario 3: Project Management
      Scope: Create intelligent reports based on fusion of diverse data
      Deliverables: User-friendly, collaborative, interactive reports
      Timeline: 1 week (training) + 2 weeks (collection) + 1 weeks (report design) + 2 weeks (validation of reports)
      Resources: Reliable datasets, BI Training and Tools, Commitment of analysts
      Risks: Unfamiliar with Cognos, Garbage data, Context of data (food poisoning)
    • Thank you :)