Scaling Design and Architecture
13.4.2 Scaling
The performance characteristics of an application vary with the number of different factors the application can deal with. These variable factors can include the number of users, the amount of data dealt with, the number of objects used by the application, etc. During the design phase, whenever considering performance, you should consider how the performance scales as the load on the application varies. It is usually not possible to predict or measure the performance for all possible variations of these factors. But you should select several representative sets of values for the factors, and predict and measure performance for these sets. The sets should include factors for when the application: • Has a light load • Has a medium load • Has a heavy load • Has a varying load predicted to represent normal operating conditions • Has spiked loads where the load is mostly normal but occasionally spikes to the maximum supported • Consistently has the maximum load the application was designed to support You need to ensure that your scaling conditions include variations in threads, objects and users, and variations in network conditions if appropriate. Measure response times and throughput for the various different scenarios and decide whether any particular situation needs optimizing for throughput of the system as a whole or for response times for individual users . It is clear that many extra factors need to be taken into account during scaling. The tools you have for profiling scaling behavior are fairly basic: essentially, only graphs of response times or throughput against scaled parameters. It is typical to have a point at which the application starts to have bad scaling behavior: the knee or elbow in the response-time curve. At that point, the application has probably reached some serious resource conflict that requires tuning so that nice scaling behavior can be extended further. Clearly, tuning for scaling behavior is likely to be a long process, but you cannot shortcut this process if you want to be certain your application scales. [12] [12] By including timer-based delays in the application code, at least one multiuser application has deliberately slowed response times for low-scaled situations. The artificial delay is reduced or cut out at higher scaling values. The users perceive a system with a similar response time under most loads.13.4.3 Distributed Applications
Parts
» OReilly.Java.performance tuning
» The Tuning Game System Limitations and What to Tune
» A Tuning Strategy Introduction
» Threading to Appear Quicker Streaming to Appear Quicker
» User Agreements Starting to Tune
» Setting Benchmarks Starting to Tune
» The Benchmark Harness Starting to Tune
» Taking Measurements Starting to Tune
» What to Measure Introduction
» Dont Tune What You Dont Need to Tune
» Measurements and Timings Profiling Tools
» Garbage Collection Profiling Tools
» Profiling Methodology Method Calls
» Java 2 cpu=samples Profile Output
» HotSpot and 1.3 -Xprof Profile Output
» JDK 1.1.x -prof and Java 2 cpu=old Profile Output
» Object-Creation Profiling Profiling Tools
» Monitoring Gross Memory Usage
» Replacing Sockets ClientServer Communications
» Performance Checklist Profiling Tools
» Garbage Collection Underlying JDK Improvements
» Replacing JDK Classes Underlying JDK Improvements
» VM Speed Variations VMs with JIT Compilers
» Other VM Optimizations Faster VMs
» Inline calls Remove dynamic type checks Unroll loops Code motion
» Literal constants are folded String concatenation is sometimes folded Constant fields are inlined
» Optimizations Performed When Using the -O Option
» Performance Effects From Runtime Options
» Compile to Native Machine Code
» Native Method Calls Underlying JDK Improvements
» Uncompressed ZIPJAR Files Underlying JDK Improvements
» Performance Checklist Underlying JDK Improvements
» Object-Creation Statistics Object Creation
» Pool Management Object Reuse
» Reusable Parameters Object Reuse
» String canonicalization Changeable objects
» Weak references Canonicalizing Objects
» Avoiding Garbage Collection Object Creation
» Preallocating Objects Lazy Initialization
» Performance Checklist Object Creation
» The Performance Effects of Strings
» Compile-Time Versus Runtime Resolution of Strings
» Converting bytes, shorts, chars, and booleans to Strings Converting floats to Strings
» Converting doubles to Strings
» Converting Objects to Strings
» Word-Counting Example Strings Versus char Arrays
» Line Filter Example HotSpot 1.0
» String Comparisons and Searches
» Sorting Internationalized Strings Strings
» The Cost of try-catch Blocks Without an Exception
» The Cost of try-catch Blocks with an Exception
» Using Exceptions Without the Stack Trace Overhead Conditional Error Checking
» no JIT 1.3 Variables Strings
» Method Parameters Performance Checklist
» Exception-Terminated Loops Loops and Switches
» no JIT 1.3 Loops and Switches
» Recursion Loops and Switches
» no HotSpot 1.0 2nd Loops and Switches
» Recursion and Stacks Loops and Switches
» Performance Checklist Loops and Switches
» Replacing System.out IO, Logging, and Console Output
» Logging From Raw IO to Smokin IO
» no JIT HotSpot 1.0 no JIT HotSpot 1.0 Serialization
» no IO, Logging, and Console Output
» Clustering Objects and Counting IO Operations
» Compression IO, Logging, and Console Output
» Performance Checklist IO, Logging, and Console Output
» Avoiding Unnecessary Sorting Overhead
» An Efficient Sorting Framework
» no HotSpot Better Than Onlogn Sorting
» User-Interface Thread and Other Threads
» Desynchronization and Synchronized Wrappers
» Avoiding Serialized Execution HotSpot 1.0
» no JIT no JIT HotSpot 1.0 Timing Multithreaded Tests
» Atomic Access and Assignment
» Free Load Balancing from TCPIP
» Load-Balancing Classes Load Balancing
» A Load-Balancing Example Load Balancing
» Threaded Problem-Solving Strategies Threading
» Collections Appropriate Data Structures and Algorithms
» Java 2 Collections Appropriate Data Structures and Algorithms
» Hashtables and HashMaps Appropriate Data Structures and Algorithms
» Cached Access Appropriate Data Structures and Algorithms
» Caching Example I Appropriate Data Structures and Algorithms
» Caching Example II Appropriate Data Structures and Algorithms
» Finding the Index for Partially Matched Strings
» Search Trees Appropriate Data Structures and Algorithms
» Comparing Communication Layers Distributed Computing
» Batching I Application Partitioning
» Compression Caching Low-Level Communication Optimizations
» Transfer Batching Low-Level Communication Optimizations
» Batching II Distributed Garbage Collection
» Performance Checklist Distributed Computing
» When Not to Optimize Tuning Class Libraries and Beans
» Scaling Design and Architecture
» Distributed Applications Design and Architecture
» Object Design Design and Architecture
» Use simulations and benchmarks Consider the total work done and the design overhead
» Tuning After Deployment When to Optimize
» User Interface Usability Training Server Downtime
» Performance Checklist When to Optimize
» Clustering Files Cached Filesystems RAM Disks, tmpfs, cachefs
» Disk Fragmentation Disk Sweet Spots
» RAM Underlying Operating System and Network Improvements
» Network Bottlenecks Network IO
» Performance Checklist Underlying Operating System and Network Improvements
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