Working Set缓存算法(转)
为了加深对缓存算法的理解,特转此篇,又由于本文内容过多,故不做翻译,原文地址Working Set页面置换算法
In the purest form of paging, processes are started up with none of their pages in memory. As soon as the CPU tries to fetch the first instruction, it gets a page fault, causing the operating system to bring in the page containing the first instruction. Other page faults for global variables and the stack usually follow quickly. After a while, the process has most of the pages it needs and settles down to run with relatively few page faults. This strategy is called demand paging because pages are loaded only on demand, not in advance.
Of course, it is easy enough to write a test program that systematically reads all the pages in a large address space, causing so many page faults that there is not enough memory to hold them all. Fortunately, most processes do not work this way. They exhibit a locality of reference, meaning that during any phase of execution, the process references only a relatively small fraction of its pages. Each pass of a multipass compiler, for example, references only a fraction of all the pages, and a different fraction at that.
The set of pages that a process is currently using is called its working set (Denning, 1968a; Denning, 1980). If the entire working set is in memory, the process will run without causing many faults until it moves into another execution phase (e.g., the next pass of the compiler). If the available memory is too small to hold the entire working set, the process will cause many page faults and run slowly since executing an instruction takes a few nanoseconds and reading in a page from the disk typically takes 10 milliseconds. At a rate of one or two instructions per 10 milliseconds, it will take ages to finish. A program causing page faults every few instructions is said to be thrashing (Denning, 1968b).
In a multiprogramming system, processes are frequently moved to disk (i.e., all their pages are removed from memory) to let other processes have a turn at the CPU. The question arises of what to do when a process is brought back in again. Technically, nothing need be done. The process will just cause page faults until its working set has been loaded. The problem is that having 20, 100, or even 1000 page faults every time a process is loaded is slow, and it also wastes considerable CPU time, since it takes the operating system a few milliseconds of CPU time to process a page fault.
Therefore, many paging systems try to keep track of each process' working set and make sure that it is in memory before letting the process run. This approach is called the working set model (Denning, 1970). It is designed to greatly reduce the page fault rate. Loading the pages before letting processes run is also called prepaging. Note that the working set changes over time.
It has been long known that most programs do not reference their address space uniformly, but that the references tend to cluster on a small number of pages. A memory reference may fetch an instruction, it may fetch data, or it may store data. At any instant of time, t, there exists a set consisting of all the pages used by the k most recent memory references. This set, w(k, t), is the working set. Because the k= 1 most recent references must have used all the pages used by the k > 1 most recent references, and possibly others, w(k, t) is a monotonically nondecreasing function of k. The limit of w(k, t) as k becomes large is finite because a program cannot reference more pages than its address space contains, and few programs will use every single page. Figure 4-5 depicts the size of the working set as a function of k.

Figure 4-5. The working set is the set of pages used by the k most recent memory references. The function w(k, t) is the size of the working set at time t.
The fact that most programs randomly access a small number of pages, but that this set changes slowly in time explains the initial rapid rise of the curve and then the slow rise for large k. For example, a program that is executing a loop occupying two pages using data on four pages, may reference all six pages every 1000 instructions, but the most recent reference to some other page may be a million instructions earlier, during the initialization phase. Due to this asymptotic behavior, the contents of the working set is not sensitive to the value of k chosen. To put it differently, there exists a wide range of kvalues for which the working set is unchanged. Because the working set varies slowly with time, it is possible to make a reasonable guess as to which pages will be needed when the program is restarted on the basis of its working set when it was last stopped. Prepaging consists of loading these pages before the process is allowed to run again.
To implement the working set model, it is necessary for the operating system to keep track of which pages are in the working set. Having this information also immediately leads to a possible page replacement algorithm: when a page fault occurs, find a page not in the working set and evict it. To implement such an algorithm, we need a precise way of determining which pages are in the working set and which are not at any given moment in time.
As we mentioned above, the working set is the set of pages used in the k most recent memory references (some authors use the k most recent page references, but the choice is arbitrary). To implement any working set algorithm, some value of k must be chosen in advance. Once some value has been selected, after every memory reference, the set of pages used by the previous k memory references is uniquely determined.
Of course, having an operational definition of the working set does not mean that there is an efficient way to monitor it in real time, during program execution. One could imagine a shift register of length k, with every memory reference shifting the register left one position and inserting the most recently referenced page number on the right. The set of all k page numbers in the shift register would be the working set. In theory, at a page fault, the contents of the shift register could be read out and sorted. Duplicate pages could then be removed. The result would be the working set. However, maintaining the shift register and processing it at a page fault would both be prohibitively expensive, so this technique is never used.
Instead, various approximations are used. One commonly used approximation is to drop the idea of counting back k memory references and use execution time instead. For example, instead of defining the working set as those pages used during the previous 10 million memory references, we can define it as the set of pages used during the past 100 msec of execution time. In practice, such a definition is just as good and much easier to use. Note that for each process, only its own execution time counts. Thus if a process starts running at time T and has had 40 msec of CPU time at real time T + 100 msec, for working set purposes, its time is 40 msec. The amount of CPU time a process has actually used has since it started is often called its current virtual time. With this approximation, the working set of a process is the set of pages it has referenced during the past t seconds of virtual time.
Now let us look at a page replacement algorithm based on the working set. The basic idea is to find a page that is not in the working set and evict it. In Fig. 4-6 we see a portion of a page table for some machine. Because only pages that are in memory are considered as candidates for eviction, pages that are absent from memory are ignored by this algorithm. Each entry contains (at least) two items of information: the approximate time the page was last used and the R (Referenced) bit. The empty white rectangle symbolizes the other fields not needed for this algorithm, such as the page frame number, the protection bits, and the M (Modified) bit.

Figure 4-6. The working set algorithm.
The algorithm works as follows. The hardware is assumed to set the R and M bits, as we have discussed before. Similarly, a periodic clock interrupt is assumed to cause software to run that clears theReferenced bit on every clock tick. On every page fault, the page table is scanned to look for a suitable page to evict.
As each entry is processed, the R bit is examined. If it is 1, the current virtual time is written into the Time of last use field in the page table, indicating that the page was in use at the time the fault occurred. Since the page has been referenced during the current clock tick, it is clearly in the working set and is not a candidate for removal (t is assumed to span multiple clock ticks).
If R is 0, the page has not been referenced during the current clock tick and may be a candidate for removal. To see whether or not it should be removed, its age, that is, the current virtual time minus its Time of last use is computed and compared to t. If the age is greater than t, the page is no longer in the working set. It is reclaimed and the new page loaded here. The scan continues updating the remaining entries, however.
However, if R is 0 but the age is less than or equal to t, the page is still in the working set. The page is temporarily spared, but the page with the greatest age (smallest value of Time of last use) is noted. If the entire table is scanned without finding a candidate to evict, that means that all pages are in the working set. In that case, if one or more pages with R = 0 were found, the one with the greatest age is evicted. In the worst case, all pages have been referenced during the current clock tick (and thus all have R = 1), so one is chosen at random for removal, preferably a clean page, if one exists.
Working Set缓存算法(转)的更多相关文章
- Android ImageCache图片缓存,使用简单,支持预取,支持多种缓存算法,支持不同网络类型,扩展性强
本文主要介绍一个支持图片自动预取.支持多种缓存算法的图片缓存的使用及功能.图片较大需要SD卡保存情况推荐使用ImageSDCardCache. 与Android LruCache相比主要特性:(1). ...
- 缓存算法之belady现象
前言 在使用FIFO算法作为缺页置换算法时,分配的缺页增多,但缺页率反而提高,这样的异常现象称为belady Anomaly. 虽然这种现象说明的场景是缺页置换,但在运用FIFO算法作为缓存算法时,同 ...
- android上的缓存、缓存算法和缓存框架
1.使用缓存的目的 缓存是存取数据的临时地,因为取原始数据代价太大了,加了缓存,可以取得快些.缓存可以认为是原始数据的子集,它是从原始数据里复制出来的,并且为了能被取回,被加上了标志. 在andr ...
- java缓存算法【转】
http://my.oschina.net/u/866190/blog/188712 提到缓存,不得不提就是缓存算法(淘汰算法),常见算法有LRU.LFU和FIFO等算法,每种算法各有各的优势和缺点及 ...
- 面试挂在了 LRU 缓存算法设计上
好吧,有人可能觉得我标题党了,但我想告诉你们的是,前阵子面试确实挂在了 RLU 缓存算法的设计上了.当时做题的时候,自己想的太多了,感觉设计一个 LRU(Least recently used) 缓存 ...
- -实现 LFU 缓存算法
-实现 LFU 缓存算法, 设计一个类 LFUCache,实现下面三个函数 + 构造函数: 传入 Cache 内最多能存储的 key 的数量 + get(key):如果 Cache 中存在该 key, ...
- 缓存算法(FIFO 、LRU、LFU三种算法的区别)
FIFO算法 FIFO 算法是一种比较容易实现的算法.它的思想是先进先出(FIFO,队列),这是最简单.最公平的一种思想,即如果一个数据是最先进入的,那么可以认为在将来它被访问的可能性很小.空间满的时 ...
- 算法进阶面试题06——实现LFU缓存算法、计算带括号的公式、介绍和实现跳表结构
接着第四课的内容,主要讲LFU.表达式计算和跳表 第一题 上一题实现了LRU缓存算法,LFU也是一个著名的缓存算法 自行了解之后实现LFU中的set 和 get 要求:两个方法的时间复杂度都为O(1) ...
- 【转】Memcached之缓存雪崩,缓存穿透,缓存预热,缓存算法
缓存雪崩 缓存雪崩可能是因为数据未加载到缓存中,或者缓存同一时间大面积的失效,从而导致所有请求都去查数据库,导致数据库CPU和内存负载过高,甚至宕机. 解决思路: 1,采用加锁计数,或者使用合理的队列 ...
随机推荐
- java获取当前星期几
//获取当前星期几Calendar calendar;calendar = Calendar.getInstance();System.out.println(calendar);System.out ...
- CMPP错误码说明
与中国移动代码的对应关系. MI::zzzzSMSC返回状态报告的状态值为EXPIREDMJ:zzzzSMSC返回状态报告的状态值为DELETEDMK:zzzzSMSC返回状态报告的状态值为UNDEL ...
- 走进云背后:微软Azure web 项目通过web service部署web site
探索云那不为人知的故事(一):Web Services部署web site 前奏:Windows Azure是微软基于云计算的操作系统,现在更名为“Microsoft Azure”,和Azure Se ...
- msysGit管理GitHub代码
msysGit管理GitHub代码 代码的管理,在日常开发中是很重要的环节,程序员的修炼三部曲——版本控制,单元测试,项目自动化. 本篇就简单的说说通过msysGit来管理GitHub中的代码,实 ...
- java的访问权限
Java语言中有4中访问修饰符:friendly(默认).private.public和protected. public :能被所有的类(接口.成员)访问. protected:只能被本类.同一个包 ...
- DedeCms文档关键词替换,优先替换长尾关键词
本文教大家:dedecms文档关键词维护之关键词出现多次,只给出现的第一个加链接的 举例:当文章中出现了一百次台历时,按官方的原理,他会给一百个台历都加上链接的.dedecms这如何是好? 解决方法( ...
- CentOS7 (64位) 下QT5.5 连接MySQL数据库(driver not loaded)
用qt连接MySQL需要共享库 libqsqlmysql.so的驱动,路径在plugin/sqldrivers目录下,乍看已经可用了,其实不然. 用ldd命令分析一下,libmysqlclient_r ...
- [转]net中哈希表的使用 Hashtable
本文转自:http://www.cnblogs.com/gsk99/archive/2011/08/28/2155988.html 以下是PetShop中DBHelper中的使用过程: //创建哈希表 ...
- 周五了啦啦啦啦-LAMP+PHP‘s OOP
hi 周五咯~~ 1.LAMP配置完结篇 五.LAMP配置环境优化 5.4 虚拟主机工作原理 apache的虚拟主机.virtual-host 用不同的域名访问不同的目录——手动模拟dns 修改hos ...
- Unity打包同一文件Hash不一样
问题起因 游戏开发基本都会涉及到资源版本管理及更新,本文记录我在打包过程中遇到的一小问题: 开过中常用于标记资源版本的方法有计算文件Hash.VCS的版本等. 在Unity中对同一个资源文件进行多次打 ...