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, Ready, but the code is on the disk at the moment because the process was removed from memory in favor of another process), Sleeping in Memory (ie, Waiting), and Sleeping Swapped (ie, Waiting for some event like an I/O completion, and the code is on the disk so that another process can occupy the memory) When a process moves out of the Waiting state, it does not go directly to the Running state Instead, when the event for which it was waiting occurs, a Waiting process becomes Ready The OS then chooses from among the Ready processes one process to run THREADS Modern operating systems often provide the thread model for execution control Threads allow multiple lines of execution within a single process This is much like multiple processes, but without the overhead of.

The output is:

hash(Rad) hash(Uhr) hash(Ohr) hash(Tor) hash(Hut) hash(Tag) = = = = = = 3 4 2 8 5 3

CHAP. 6]

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The hash function values are computed at line 19, where CAPACITY is 11 and MASK is 2147483647, expressed in hexadecimal as 0x7FFFFFFF. The operation n & MASK simply removes the sign from whatever integer n has. This is the right thing to do in Java before using the remainder operator to compute an array index because (unlike C++) Java may give a negative result to m % CAPACITY if m is negative. So the resulting value returned by the hash() function in this example is guaranteed to be in the range of 0 to 10. The first five strings hash into index values 3, 4, 2, 8, and 5, so they would be stored in those locations in the hash table. But the sixth string ("Tag") also hashes to 3, causing a collision with "Rad", which would already be stored in component 3. The most common algorithm to apply when such collisions occur is to simply put the new item in the next available component. That would be component 6 in this example, since "Uhr" would already be in component 4 and "Hut" would already be in component 5. This collision resolution algorithm is called linear probing.

The HashMap class uses a hash function just like the one in Example 8.4 to implement its accessor methods: containsKey(), get(), put(), remove(), and entrySet(). Its sets the hash table size at 101 initially. With that knowledge, we can see why the six strings in the previous examples were stored in the order indicated. EXAMPLE 8.5 Mapping Keys into a Hash Table of Size 101

managing multiple PCBs, and with the built-in facility for sharing memory and other resources among threads of the same process Instead of a PCB, a thread requires a much smaller thread table entry, primarily to provide storage for register and program counter contents when the OS switches among executing threads In an operating system that supports threads ( kernel threads, which we will discuss shortly), the object that gets scheduled, and which gains control of the CPU, is the thread The process is still important, but as a collection of resources Threads are what get executed, and threads are what enter the states of Ready, Running, and Waiting Since threads resemble processes, some operating systems refer to threads as lightweight processes (eg, SunOS).

sion effectively. He usually appears closely wedded to the things he talks about, cares deeply about the subjects, and is eager to share. His enthusiasm energizes people young and old. Perhaps as important as having charisma is the ability to use it to establish an excellent first impression. First impressions last. This well-worn saying is true. In rising as fast as he has, from obscurity to winning the 2008 presidential election, Barack Obama has developed a formidable ability to establish a great first impression.

This program is identical to the one in Example 8.4 except that the hash table CAPACITY is 101 instead of 11:

And just as OSs came to be described as multiprogrammed when they began to support multiple processes simultaneously, OSs that support threads are described as multithreaded Threads are useful because many applications benefit from separating the whole job into semi-independent parts Earlier we mentioned the word-processor/spell-checker example Another example could be a timeand-attendance application where one thread does a quick check of the employee ID as the employee enters or leaves the plant, and another thread updates the payroll data base in the background In this threaded design, the quick gate-check thread can keep people moving through the gates, even if the more complex data-base thread takes a little more time Web servers are almost universally multithreaded The main server thread waits for a request to arrive, and, when one does, it creates a new worker thread which then handles the request and provides the reply.

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