Load-Balancing Classes Load Balancing
10.8.2 Load-Balancing Classes
If you need to implement your own queuing system, you have to consider whether the queue controls the request processors, or whether the processors access the queue. The latter model is how the socket model works: each request processor sits on the queue and waits for it to pass a request. This looks rather like the following class: public class PassiveRequestQueue { The queue of requests FIFO_Queue queue = new FIFO_Queue ; public synchronized void acceptRequestRequest r { Add to the queue, then notify all processors waiting on the releaseRequest method queue.addr; notify ; } public synchronized Request releaseRequest { for;; { if the queue is empty, just go back into the wait call if queue.isEmpty try {wait ;} catch InterruptedException e{} Need to check again if the queue is empty, in case we were interrupted if queue.isEmpty return Request queue.pop ; } } } The former model, in which the request processors are passive and the queue actively manages them, looks more like the following class: public class ActiveRequestQueue subclass the passive queue which holds the behavior needed for managing the queue of requests extends PassiveRequestQueue and make us able to run in our own thread implements Runnable { int MAXIMUM_NUM_SERVERS=3; Data for the public queue - a list of private servers ActiveRequestQueue[] servers; Data for the private internal queues the RequestProcessor RequestProcessor requestProcessor; Retain a handle on my thread so that we can easily access it if we need control Thread myThread; Keep a handle on the public queue - the one which actually holds the objects ActiveRequestQueue queueServer; Availability boolean isAvailable = true; - 226 - Internal queue object - processes requests private ActiveRequestQueueActiveRequestQueue q { queueServer = q; requestProcessor=new RequestProcessor ; } External queue object - accepts requests and manages a queue of them public ActiveRequestQueueint num_servers { Create a pool of queue servers and start them in their own threads servers = new ActiveRequestQueue[num_servers]; Thread t; for int i = servers.length-1; i=0 ; i-- { servers[i] = new ActiveRequestQueuethis; t = new Threadservers[i].start ; servers[i].myThread = t; } } public synchronized void acceptRequestRequest r { Override the super class accept to increase the number of servers if they are all busy If we already have the maximum number of threads, just queue the request if servers.length = MAXIMUM_NUM_SERVERS { super.acceptRequestr; return; } otherwise, if one of the servers is available, just queue the request for int i = servers.length-1; i=0 ; i-- { if servers[i].isAvailable { super.acceptRequestr; return; } } otherwise, increase the server pool by one, then queue the request Thread t; ActiveRequestQueue[] tmp_servers = servers; servers = new ActiveRequestQueue[tmp_servers.length+1]; System.arraycopytmp_servers, 0, servers, 0, tmp_servers.length; servers[tmp_servers.length] = new ActiveRequestQueuethis; t = new Threadservers[tmp_servers.length].start ; servers[tmp_servers.length].myThread = t; super.acceptRequestr; } public void run { Request request; RequestResult result; Private queues use this method. - 227 - Basically, we just ask the public server for a request. The releaseRequest method blocks until one is available. Then we process it and start again. for;; { request = queueServer.releaseRequest ; isAvailable = false; result = requestProcessor.processRequestrequest; returnResultresult; isAvailable = true; } } public boolean isAvailable { return isAvailable;} public void returnResultRequestResult r {} } Note that the server classes as they stand can be tested with the following minimal implementations for support classes: class FIFO_Queue { java.util.Stack v = new java.util.Stack ; public void addObject o{v.pusho;} public Object pop {return v.pop ;} public boolean isEmpty {return v.isEmpty ;} } class RequestProcessor { public RequestResult processRequestRequest r { System.out.printlnProcessing request: + r; try{Thread.sleep2000;}catchInterruptedException e{} return new RequestResult ; } } class RequestResult {} class Request {}10.8.3 A Load-Balancing Example
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
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» What to Measure Introduction
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» Java 2 cpu=samples Profile Output
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» VM Speed Variations VMs with JIT Compilers
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» 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
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» 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
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» String canonicalization Changeable objects
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» Converting doubles to Strings
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» 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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