Scalability

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Scalability Availability

• Paul Greenfield
• CSIRO

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Building Real Systems

• Scalable
• Fast enough to handle expected load
• Grow easily when load grows
• Available
• Available enough of the time
• Performance and availability cost
• Aim for enough of each but not more

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Scalable

• Scale-up
• Bigger and faster systems
• Scale-out
• Systems working to handle load
• Server farms
• Clusters
• Implications for application design

4
Available

• Goal is 100 availability
• 24x7 operations
• Redundancy is the key
• No single points of failure
• Spare everything
• Disks, disk channels, processors, power supplies,
  fans, memory, ..
• Automated fail-over and recovery

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Performance

• How fast is this system?
• Not the same as scalability but related
• Scalability is concerned with the limits to
  possible performance
• Measured by response time and throughput
• Aim for enough performance
• Have a performance target
• Tune and add hardware until target hit
• Then worry about tomorrow

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Performance Measures

• Response time
• What delay does the user see?
• Instantaneous is good but 95 under 2 seconds is
  acceptable
• Response time varies with heaviness of
  transactions
• Fast read-only transactions
• Slower update transactions
• Effects of database contention

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Response Times
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Response Times
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Response Times
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Throughput

• How many transactions can be handled in some
  period of time
• Transactions/second or tpm, tph or tpd
• A measure of overall capacity
• Transaction Processing Council
• Standard benchmarks for TP systems
• TPCC for typical transaction system
• www.tpc.org
• Current record is 227,000 tpmc

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Throughput

• Throughput increases until some resource limit is
  hit
• Adding more clients just increases the response
  time
• Run out of processor, disk bandwidth, network
  bandwidth
• Some resources overload badly
• Ethernet network performance degrades

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Throughput
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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

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System Capacity
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Load Balancing I

• A few different but related meanings
• 1. Balancing across server processes
• CORBA-style where clients use objects that live
  inside server processes
• Want all server processes to be busy
• Client calls have to go to the process containing
  their object, even if this process is busy and
  others are idle

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Load Balancing I
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Load Balancing I

• Client calls on name server to find the location
  of a suitable server
• 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

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Load Balancing I
 
 
Initial
Later
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Load Balancing I

• Solution to static allocation problem is for
  clients to throw away their server objects and
  get new ones every now and again
• Application coding problem
• And can be objects be discarded?
• What kind of objects are they if they can be
  discarded?

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Name Servers

• Server processes call name server when they come
  up
• Advertising their services
• Clients call name server to find the location of
  a server process
• Up to the name server to match clients to servers
• Client calls server process to create objects

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Load Balancing I
Name Server
Client
Client
Server process
Client
Load balancing across processes within a server
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Load Balancing II

• What happens when our single system is full?
• Use faster systems
• Scale-up
• Use additional systems
• Scale-out
• Now load-balancing is used to spread load across
  systems

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Load Balancing II

• CORBA world
• Name server can distribute across server
  processes running on different systems
• Scales well
• Name server only involved when handing out a
  reference to a server, not on every method call

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Load Balancing II
Name Server
Server process
Client
Client
Client
Server process
Load balancing across multiple systems
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Load Balancing II

• COM world
• No need for load-balancing within a system
• Multithreaded server process
• All objects live in a single process space
• Component load balancing across systems
• Client calls router when creating object
• Router returns reference to an object in a COM
  server process
• Load balanced at time of object creation

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Load Balancing II
MTS process
DCOM/MTS
App DLL
Client
Client
Client
Thread pool
Shared object space
Application code
COM/MTS using thread pools rather than load
balancing within a single system
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COM Component Load Balancing
Client
Client
Client
COM CLB balancing load across multiple systems
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Load Balancing II

• COM scales well
• Router only involved when object is created
• May change in later release to support dynamic
  re-balancing as server load changes
• Method calls direct from client to server
• Allocation based on response time rather than
  round-robin
• Allocate to least-loaded server

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Load Balancing II

• No name server in COM world?
• COM/MTS clients know the name of the server
• Set at client installation time
• Can change using GUI tools
• Admin problem if server app is moved
• COM uses Active Directory to find services

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Load Balancing II

• Some systems involve the router in every method
  call/request
• Request goes to router process who then passes it
  on to a server process
• Scales poorly as the router can be a major
  bottle-neck
• Some availability concerns as well
• What happens if the router fails?

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Load Balancing II
Server process
Client
Router
Server process
Client
Client
Load balancing with router in main call path
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Scale-up

• No need for load-balancing across systems
• Just use a bigger box
• Add processors, memory, .
• SMP (symmetric multiprocessing)
• Runs into limits eventually
• Could be less available

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Scale-up

• Example from the Web
• Large auction site
• Server farm of NT boxes (scale-out)
• Single database server (scale-up)
• 64-processor SUN box
• More capacity needed?
• Add more NT boxes easily
• SUN box is full so have to shift some databases
  to another box

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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,

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Clusters
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Clusters

• Address scalability
• Add more boxes to the cluster
• 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

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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)
• gt1 Web server looking at same IP address with
  some coordination (see MS WLB docs)
• Same technique for other network apps

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Available System
Web Clients
Web Servers Load balanced using Convoy
App Servers use COM LB
Database is installed on Wolfpack cluster for
high availability
COM LBS router node
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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
• Maintenance, software upgrades, backups,
  application changes
• Not just faults and recovery time

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Availability and Scalability

• Often a question of application design
• Stateful vs stateless
• What happens if a server fails?
• Can requests go to any server?
• What language and database API
• Balance cost vs speed VB/C - ODBC/ADO
• Synchronous method calls or asynchronous
  messaging?
• Reduce dependency between components
• Failure tolerant designs

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Next Week

• Distributed application architectures
• How to design systems that will work, scale and
  be available
• Web-based systems
• Web technology