1. INFO 320 Server Technology I
Week 2
Server architectures
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2. OS and Server Architecture
Last week we outlined the basic functions of an operating system
Since an OS exists to serve as a connection between apps and the hardware, what kind of hardware is available and how it’s used are critical things to consider
…So what is a server architecture?
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3. Server Architecture Key issues in server architecture include
What is the extent of centralization or distribution of functions?
There are many possible answers, not just A or B
The main functions are managing data, performing processing (e.g. running apps), and determining how to display the results to the user
i.e. Who does what where in your system?
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4. Server Architecture Other issues to keep mind could include
Reliability
Availability
Security
Performance
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5. Server Architecture
So defining server architecture is a key step in the larger process of designing a network
Once the architecture is set, then can work on details such as
How many of each server are needed?
How big are they (CPUs, RAM, storage)?
What kind of links are needed among them?
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6. Centralization
Centralizing all or some aspects of a system can be good
Take advantage of economies of scale
Easier to staff support people
Easier to control procurement
Easier to enforce programming and data structure standards
Easier to manage security
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7. Centralization
We can centralize computers, as was done with mainframes
Centralization doesn’t necessarily apply to the entire system though
We could centralize processing
Data processing, payroll, apps unique to a given department (CAD) might be centralized
We could centralize data
Big database server(s)
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8. Distributed data processing
Distributed data processing (DDP) is a possible step away from centralization
Servers are distributed throughout the organization in order to meet operational, economic, and/or geographic needs
Could still have a larger central facility with satellite facilities, or all peer facilities
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9. Distributed data processing
DDP advantages include
Responsiveness to local needs
Higher availability, more redundancy to minimize impact of a single system failure
Resource sharing can still be done with expensive hardware
Incremental growth is easier
Avoids all or nothing upgrades
More user involvement, control, productivity
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10. Distributed data processing
DDP operating systems need
Good networking capability to exchange data
Ability to cluster machines for high availability and high performance
To manage processes across the distributed environment
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11. Distributed processing overview
We’ll look at critical technologies in distributed processing
Client/server computing
Distributed message passing
Remote procedure calls
Clusters
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12. Client/server computing
In a client/server environment, a client requests information from the servers
An API (Applications Programming Interface), drivers, or other forms of middleware allows communication between them
Clients present the information in a user-cuddly GUI format
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13. Client/server computing
Servers exist to provide shared services to clients
What kind of servers could we see?
Also keep in mind the network connecting the clients and servers
Is it a LAN, WAN, the Internet, or ???
We need to be aware of the amount of traffic we expect the network to bear
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14. Client/server characteristics
A client/server architecture differs from other distributed processing in many ways
Strong emphasis on user-friendly apps for the user on their system
Often centralize database, network management, and utility functions to control overhead and support costs
Open and modular systems are increasingly common – mix products from various vendors
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15. Client/server characteristics
Networking is critical, hence focus a lot of attention to network management and security issues
Client/server apps communicate directly, depending on the network protocols (TCP/IP) to make that possible
Even though the client and server often have different platforms and OS’s
Client/server apps look like Internet apps!
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16. Client/server characteristics
Images from (Stallings, 2009)
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17. Client/server database
A common client/server app is to use a database server
The DBMS resides on the server, and is called by the application logic
Part of the app design challenge is to make sure the network isn’t overwhelmed by the data transfer expectations
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18. Client/server database
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19. Client/server database
The first example is good use of client/server, since
The server has the job of sorting through one million records, at which a desktop system might cringe
The network doesn’t have to support moving the entire database across itself
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20. Client/server classes
Four classes of client/server (C/S) apps
Host-based processing, much like a mainframe & dumb terminal, is not really C/S
Server-based processing, the most server-heavy class of C/S processing
Cooperative processing, processing is locally optimized on the client
Client-based processing, the most fair split of workload
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21. Client/server classes
(b) Is a “thin” client app
(c) and (d) are “fat” client apps
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22. Three-tier client/server architecture
In three-tier C/S, we now have a client, a middle tier server, and a backend server
The client is typically a thin client
The middle tier is often an application server
It acts as a server to the client, and as a client to the backend server
The backend server is often one or more database servers
The app server chooses which one is needed
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23. File consistency
Clients and servers often cache files which are frequently used
When a file or database record is being changed, the cache can be inconsistent with the correct version
Often address this by locking files or records, hence the level at which data is locked can be a key performance issue
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24. What is middleware?
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25. Middleware
Development of C/S apps has exceeded anyone’s ability to make standardized application support tools
APIs and other programming interfaces help address this, and are generically known as middleware
‘Common definitions are that middleware is the "glue" between software components or between software and the network or it is the slash in Client/Server.’ From here
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26. Middleware
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27. Middleware
Middleware describes software that connects two or more software applications so they can exchange data
There are many types of middleware, hence the confusion
Message Oriented Middleware, Object Middleware, RPC Middleware, Database Middleware, Transaction Middleware, Portals
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28. Distributed message passing
Within one computer, processes can pass messages via semaphores
In distributed systems, processes are on different systems, so that isn’t possible
One issue is message reliability (did it get there?)
Can processing continue before getting a response? (if so, called nonblocking or asynchronous)
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29. Distributed message passing
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30. Distributed message passing
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31. Remote procedure calls
Remote procedure calls (RPCs) allow distributed systems to communicate as though they were on the same machine
A remote interface can have named operations with specific types
Allows clearly defined documentation and static error checking
Helps generate code automatically, and port code to different platforms and OS’s
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32. Remote procedure calls
This expands on image (b) on slide 29.
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33. Remote procedure calls
Issues with using RPC include
Passing parameters by value or pointer
Representation of parameters (int, float, $, …)
Client/server binding
Nonpersistent (always make new connection)
Persistent (keep the same binding until it expires)
Asynchronous (let other processes continue) or synchronous (block everything until done)
Object-oriented RPC (see OLE or CORBA)
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34. SMP
In order to get lots of computational power, symmetric multiprocessing (SMP) was the first option
SMP has multiple processors
They share main memory (RAM) and I/O
They are connected by a bus
Are processors are the same type (hence the ‘symmetric part’)
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35. Clustering
As the need for more computational power grew, clustering was developed
What kind of problems need massive CPU power?
Clustering is a group of interconnected standalone computers working together as one
Each computer in a cluster is a node
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36. Clustering Clustering has several benefits
Absolute scalability – can keep adding more systems to get as much power as you can afford
Incremental scalability – you can add a little more power as well, avoiding complex upgrade paths
High availability – lots of separate computers means if one fails it’s not a big deal
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37. Clustering
Superior price/performance since cheap computers can be clustered
Clusters can be classified based on whether they share hard disks (among other ways)
In the first approach, each standby server has separate disks, and they communicate via a high speed link
In the second approach, they share a RAID array
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38. Clustering
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39. Clustering
A better approach for cluster classification is by functionality
Passive Standby
Active secondary
Separate servers
Servers connected to disks
Servers share disks
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40. Clustering Passive Standby
A second server takes over if the primary fails
Easy to implement
Wastes second server since it’s mostly unused
Doesn’t improve performance over a single server
Often not considered a true cluster
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41. Clustering Active secondary
The second server is also used for processing tasks
Cheaper since second server is now used
Increased complexity
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42. Clustering Separate servers (is (a) on slide 38)
Servers have separate disks
Data is copied from primary to second server
Gives high availability and high performance
High network and server overhead due to copying
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43. Clustering Servers connected to disks
Also called the shared nothing approach
Servers are connected to a set of disks, but each server has its own disks in that set
Reduces need for copying among servers
Often needs mirroring or RAID in case of disk failure
Windows Cluster Server is an example
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44. Clustering Servers share disks (is (b) on slide 38)
Multiple servers share a set of disks
Low network and server overhead
Reduced chance of disk failure
Requires lock manager software, plus mirroring and/or RAID
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45. Clustering and the OS Clustering produces interesting OS problems
Failure management
Either a high availability approach or a fault tolerant approach can be used
The latter is better at handling partial transactions if a system fails
Failover is the function of handing off an app and its data when there’s a failure; the opposite is failback
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46. Clustering and the OS Load balancing Parallelized computation
How do you balance how much work each system is performing?
A load-balancing facility must handle this and schedule tasks accordingly
Parallelized computation
How is the application run on multiple systems?
Could have a parallelizing compiler
A parallelized application is written to run on a cluster
Parametric computing tools can be used for simulations that require a lot of similar runs with different conditions
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47. Clustering architecture
A cluster presents itself to the user as a single system, the single-system image
This is possible thanks to the clustering middleware
The middleware also may perform load balancing and respond to system failures
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48. Clustering architecture
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49. Clustering architecture
The single-system image ensures that
Single entry point
The user logs into the cluster, not a machine
Single file hierarchy
The user sees files in a single file structure
Single control point
There is a default node used to manage the cluster
Single virtual networking
Any node can access the rest of the cluster
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50. Clustering architecture
Single memory space
Distributed shared memory allows programs to share variables
Single job management system
A user can commit a job to run without specifying where it runs (which node)
Single user interface
The same GUI supports users regardless of where they log into the cluster
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51. Clustering architecture
To improve availability, the OS allows
Single I/O space
Any node can access any I/O peripheral or disk device no matter where it is
Single process space
A uniform process identification scheme is used
Checkpointing
Saves process state and data in case of failure
Process migration
Enables load balancing
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52. SMP versus clustering
SMP is more mature technology, is easier to manage and configure than a cluster
SMP takes less space and power
Clusters win when scalability, either absolute or incremental, is critical
Availability for clusters is also higher
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53. Clustering examples
Windows Cluster Server is a shared-nothing approach
Sun Cluster is an object-oriented approach using CORBA
The object framework handles calls to other nodes
A virtual node (vnode) file system is used
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54. Beowulf
Beowulf (no, not Beowulf) is one of the oldest clustering approaches, started in 1994 using clustered PCs
Most Beowulf clusters use Linux systems, connected by Ethernet (LAN) or via TCP/IP
Each node runs an autonomous Linux kernel, yet participates in global namespaces
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55. Beowulf Key pieces of Beowulf software are
BPROC, the distributed process space package, which allows a process to span multiple nodes and can allow a new process to be created on other nodes
Ethernet Channel Bonding, which joins multiple local networks into one high speed network and does load balancing
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56. Beowulf
Pvmsync is a programming environment which helps perform synchronization and shares data objects among processes
EnFuzion is a set of tools for parametric computing; creating a lot of jobs with different input parameters or initial conditions
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57. References
Operating Systems Internals and Design Principles, by William Stallings, 6th Ed, Pearson/Prentice Hall ISBN
His web site
What is Middleware?
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