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ScalabilityAvailability
 

ScalabilityAvailability

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    ScalabilityAvailability ScalabilityAvailability Presentation Transcript

    • Scalability & Availability Paul Greenfield
    • Building Real Systems
      • Scalable
        • Handle expected load with acceptable levels of performance
        • Grow easily when load grows
      • Available
        • Available enough of the time
      • Performance and availability cost
        • Aim for ‘enough’ of each but not more
        • Have to be ‘architected’ in… not added
    • Scalable
      • Scale-up or…
        • Use bigger and faster systems
      • … Scale-out
        • Systems working together to handle load
          • Server farms
          • Clusters
      • Implications for application design
        • Especially state management
        • And availability as well
    • Available
      • Goal is 100% availability
        • 24x7 operations
        • Including time for maintenance
      • Redundancy is the key to availability
        • No single points of failure
        • Spare everything
          • Disks, disk channels, processors, power supplies, fans, memory, ..
          • Applications, databases, …
            • Hot standby, quick changeover on failure
    • Performance
      • How fast is this system?
        • Not the same as scalability but related
        • Measured by response time and throughput
      • How scalable is this system?
        • Scalability is concerned with the upper limits to performance
        • How big can it grow?
        • How does it grow? (evenly, lumpily?)
    • Performance Measures
      • Response time
        • What delay does the user see?
        • Instantaneous is good
          • 95% under 2 seconds is acceptable?
          • Consistency is important psychologically
        • Response time varies with ‘heaviness’ of transactions
          • Fast read-only transactions
          • Slower update transactions
          • Effects of resource/database contention
    • Response Time
      • Each transaction takes…
        • Processor time
          • Application, system services, database, …
          • Shared amongst competing processes
        • I/O time
          • Largely disk reads/writes
          • Large DB caches reduce # of I/Os
            • 2TB in IBM’s top TPCC entry
        • Wait time for shared resources
          • Locks, shared structures, …
    • Response Times
    • Response Times
    • Response Times
    • Throughput
      • How many transactions can be handled in some period of time
        • Transactions/second or tpm, tph or tpd
        • A measure of overall capacity
        • Inverse of response time
      • Transaction Processing Council
        • Standard benchmarks for TP systems
        • www.tpc.org
        • TPC-C models typical transaction system
          • Current record is 4,092,799 tpmc (HP)
        • TPC-E approved as TPC-C replacement (2/07)
    • Throughput
      • Increases until resource saturation
        • Start waiting for resources
          • Processor, disk & network bandwidth
          • Increasing response time with load
        • Slowly decreases with contention
          • Overheads of sharing, interference
        • Some resources share/overload badly
          • Contention for shared locks
          • Ethernet network performance degrades
          • Disk degrades with sharing
    • Throughput
    • System Capacity?
      • How many clients can you support?
        • Name an acceptable response time
        • Average 95% under 2 secs is common
          • And what is ‘average’?
        • Plot response time vs # of clients
      • Great if you can run benchmarks
        • Reason for prototyping and proving proposed architectures before leaping into full-scale implementation
    • System Capacity
    • Scaling Out
      • More boxes at every level
        • Web servers (handling user interface)
        • App servers (running business logic)
        • Database servers (perhaps… a bit tricky?)
        • Just add more boxes to handle more load
      • Spread load out across boxes
        • Load balancing at every level
        • Partitioning or replication for database?
        • Impact on application design?
        • Impact on system management
        • All have impacts on architecture & operations
    • Scaling Out
    • ‘ Load Balancing’
      • A few different but related meanings
        • Distributing client bindings across servers or processes
          • Needed for stateful systems
          • Static allocation of client to server
        • Balancing requests across server systems or processes
          • Dynamically allocating requests to servers
          • Normally only done for stateless systems
    • Static Load Balancing Client Client Client Name Server Server process Load balancing across application process instances within a server Server process Advertise service Request server reference Return server reference Call server object’s methods Get server object reference
    • Load Balancing in CORBA
      • Client calls on name server to find the location of a suitable server
        • CORBA terminology for object directory
      • Name server can spread client objects across multiple servers
        • Often ‘round robin’
      • Client is bound to server and stays bound forever
        • Can lead to performance problems if server loads are unbalanced
    • Name Servers
      • Server processes call name server as part of their initialisation
        • Advertising their services/objects
      • Clients call name server to find the location of a server process/object
        • Up to the name server to match clients to servers
      • Client then directly calls server process to create or link to objects
        • Client-object binding usually static
    • Dynamic Stateful?
      • Dynamic load balancing with stateful servers/objects?
        • Clients can throw away server objects and get new ones every now and again
          • In application code or middleware
          • Have to save & restore state
        • Or object replication in middleware
          • Identical copies of objects on all servers
          • Replication of changes between servers
          • Clients have references to all copies
    • BEA WLS Load Balancing Clients Clients DBMS MACHINE B MACHINE A EJB Cluster HeartBeat via Multicast backbone EJB Servers instances EJB Servers instances
    • Threaded Servers
      • No need for load-balancing within a single system
        • Multithreaded server process
          • Thread pool servicing requests
        • All objects live in a single process space
        • Any request can be picked up by any thread
      • Used by modern app servers
    • Threaded Servers Client Client Client App DLL COM+ COM+ process Thread pool Shared object space Application code COM+ using thread pools rather than load balancing within a single system
    • Dynamic Load Balancing
      • Dynamically balance load across servers
        • Requests from a client can go to any server
      • Requests dynamically routed
        • Often used for Web Server farms
        • IP sprayer (Cisco etc)
        • Network Load Balancer etc
      • Routing decision has to be fast & reliable
        • Routing in main processing path
      • Applications normally stateless
    • Web Server Farms
      • Web servers are highly scalable
        • Web applications are normally stateless
          • Next request can go to any Web server
          • State comes from client or database
        • Just need to spread incoming requests
          • IP sprayers (hardware, software)
          • Or >1 Web server looking at same IP address with some coordination
    • Clusters
      • A group of independent computers acting like a single system
        • Shared disks
        • Single IP address
        • Single set of services
        • Fail-over to other members of cluster
        • Load sharing within the cluster
        • DEC, IBM, MS, …
    • Clusters Client PCs Server A Server B Disk cabinet A Disk cabinet B Heartbeat Cluster management
    • Clusters
      • Address scalability
        • Add more boxes to the cluster
        • Replication or shared storage
      • Address availability
        • Fail-over
        • Add & remove boxes from the cluster for upgrades and maintenance
      • Can be used as one element of a highly-available system
    • Scaling State Stores?
      • Scaling stateless logic is easy
      • … but how are state stores scaled?
      • Bigger, faster box (if this helps at all)
        • Could hit lock contention or I/O limits
      • Replication
        • Multiple copies of shared data
        • Apps access their own state stores
        • Change anywhere & send to everyone
    • Scaling State Stores
      • Partitioning
        • Multiple servers, each looking after a part of the state store
          • Separate customers A-M & N-Z
          • Split customers according to state
        • Preferably transparent to apps
          • e.g. SQL/Server partitioned views
      • Or combination of these approaches
    • Scaling Out Summary Districts 11-20 Districts 1-10 Web server farm (Network Load Balancing) Application farm (Component Load Balancing) Database servers (Cluster Services and partitioning) UI tier Business tier Data tier
    • Scale-up
      • No need for load-balancing
        • Just use a bigger box
        • Add processors, memory, ….
        • SMP (symmetric multiprocessing)
        • May not fix problem!
      • Runs into limits eventually
      • Could be less available
        • What happens on failures? Redundancy?
      • Could be easier to manage
    • Scale-up
      • eBay example
        • Server farm of Windows boxes (scale-out)
        • Single database server (scale-up)
          • 64-processor SUN box (max at time)
        • More capacity needed?
          • Easily add more boxes to Web farm
          • Faster DB box? (not available)
            • More processors? (not possible)
            • Split DB load across multiple DB servers?
        • See eBay presentation…
    • Available System Web Clients Web Server farm Load balanced using WLB App Servers farm using COM+ LB Database installed on cluster for high availability
    • Availability
      • How much?
        • 99% 87.6 hours a year
        • 99.9% 8.76 hours a year
        • 99.99% 0.876 hours a year
      • Need to consider operations as well
        • Not just faults and recovery time
        • Maintenance, software upgrades, backups, application changes
    • Availability
      • Often a question of application design
        • Stateful vs stateless
          • What happens if a server fails?
          • Can requests go to any server?
        • Synchronous method calls or asynchronous messaging?
          • Reduce dependency between components
          • Failure tolerant designs
        • And manageability decisions to consider
    • Redundancy=Availability
      • Passive or active standby systems
        • Re-route requests on failure
        • Continuous service (almost)
          • Recover failed system while alternative handles workload
          • May be some hand-over time (db recovery?)
          • Active standby & log shipping reduce this
            • At the expense of 2x system cost…
      • What happens to in-flight work?
        • State recovers by aborting in-flight ops & doing db recovery but …
    • Transaction Recovery
      • Could be handled by middleware
        • Persistent queues of accepted requests
        • Still a failure window though
      • Large role for client apps/users
        • Did the request get lost on failure?
        • Retry on error?
      • Large role for server apps
        • What to do with duplicate requests?
        • Try for idempotency (repeated txns OK)
        • Or track and reject duplicates
    • Fragility
      • Large, distributed, synchronous systems are not robust
        • Many independent systems & links…
          • Everything always has to be working
        • Rationale for Asynchronous Messaging
          • Loosen ‘coupling’ between components
          • Rely on guaranteed delivery instead
          • May just defer error handling though
            • Could be much harder to handle later
          • To be discussed next time…
    • Example