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Notice that each CPU processor has its own set of registers, as well as a private —or local— cache. However, all processors share physical memory over the system bus. The benefit of this model is that many processes can run simultaneously — N processes can run if there are N CPUs—without causing performance to deteriorate significantly.

However, since the CPUs are separate, one may be sitting idle while another is overloaded, resulting in inefficiencies. These inefficiencies can be avoided if the processors share certain data structures. A multiprocessor system of this form will allow processes and resources—such as memory—to be shared dynamically among the various processors and can lower the workload variance among the processors.

Such a system must be written carefully, as we shall see in Chapter 5 and Chapter 6. The definition of multiprocessor has evolved over time and now includes multicore systems, in which multiple computing cores reside on a single chip.

Multicore systems can be more efficient than multiple chips with single cores because on-chip communication is faster than between-chip communication. In addition, one chip with multiple cores uses significantly less power than multiple single-core chips, an important issue for mobile devices as well as laptops.

In Figure 1. In this design, each core has its own register set, as well as its own local cache, often known as a level 1, or L1, cache. Notice, too, that a level 2 L2 cache is local to the chip but is shared by the two processing cores.

Most architectures adopt this approach, combining local and shared caches, where local, lower-level caches are generally smaller and faster than higher-level shared Figure 1. Processor — A physical chip that contains one or more CPUs. Core — The basic computation unit of the CPU. Multicore — Including multiple computing cores on the same CPU. Multiprocessor — Including multiple processors. Although virtually all systems are now multicore, we use the general term CPU when referring to a single computational unit of a computer system and core as well as multicore when specifically referring to one or more cores on a CPU.

Aside from architectural considerations, such as cache, memory, and bus contention, a multicore processor with N cores appears to the operating system as N standard CPUs. This characteristic puts pressure on operating-system designers—and application programmers—to make efficient use of these processing cores, an issue we pursue in Chapter 4.

Adding additional CPUs to a multiprocessor system will increase computing power; however, as suggested earlier, the concept does not scale very well, and once we add too many CPUs, contention for the system bus becomes a bottleneck and performance begins to degrade.

An alternative approach is instead to provide each CPU or group of CPUs with its own local memory that is accessed via a small, fast local bus. The advantage is that, when a CPU accesses its local memory, not only is it fast, but there is also no contention over the system interconnect.

Thus, NUMA systems can scale more effectively as more processors are added. A potential drawback with a NUMA system is increased latency when a CPU must access remote memory across the system interconnect, creating a possible performance penalty.

In other words, for example, CPU0 cannot access the local memory of CPU3 as quickly as it can access its own local memory, slowing down performance. Because NUMA systems can scale to accommodate a large number of processors, they are becoming increasingly popular on servers as well as high-performance computing systems. The difference between these and traditional multiprocessor systems is that each bladeprocessor board boots independently and runs its own operating system.

Some blade-server boards are multiprocessor as well, which blurs the lines between 1. In essence, these servers consist of multiple independent multiprocessor systems.

Clustered systems differ from the multiprocessor systems described in Section 1. Such systems are considered loosely coupled. We should note that the definition of clustered is not concrete; many commercial and opensource packages wrestle to define what a clustered system is and why one form is better than another.

The generally accepted definition is that clustered computers share storage and are closely linked via a local-area network LAN as described in Chapter 19 or a faster interconnect, such as InfiniBand. Clustering is usually used to provide high-availability service—that is, service that will continue even if one or more systems in the cluster fail. Generally, we obtain high availability by adding a level of redundancy in the system.

A layer of cluster software runs on the cluster nodes. Each node can monitor one or more of the others over the network. If the monitored machine fails, the monitoring machine can take ownership of its storage and restart the applications that were running on the failed machine. The users and clients of the applications see only a brief interruption of service. High availability provides increased reliability, which is crucial in many applications.

The ability to continue providing service proportional to the level of surviving hardware is called graceful degradation. Some systems go beyond graceful degradation and are called fault tolerant, because they can suffer a failure of any single component and still continue operation.

Fault tolerance requires a mechanism to allow the failure to be detected, diagnosed, and, if possible, corrected. Clustering can be structured asymmetrically or symmetrically. In asymmetric clustering, one machine is in hot-standby mode while the other is running the applications.

The hot-standby host machine does nothing but monitor the active server. Even the lowest-cost generalpurpose CPU contains multiple cores. Some motherboards contain multiple processor sockets. More advanced computers allow more than one system board, creating NUMA systems.

In symmetric clustering, two or more hosts are running applications and are monitoring each other. This structure is obviously more efficient, as it uses all of the available hardware.

However, it does require that more than one application be available to run. Since a cluster consists of several computer systems connected via a network, clusters can also be used to provide high-performance computing environments. Such systems can supply significantly greater computational power than single-processor or even SMP systems because they can run an application concurrently on all computers in the cluster.

The application must have been written specifically to take advantage of the cluster, however. This involves a technique known as parallelization, which divides a program into separate components that run in parallel on individual cores in a computer or computers in a cluster.

Typically, these applications are designed so that once each computing node in the cluster has solved its portion of the problem, the results from all the nodes are combined into a final solution.

Other forms of clusters include parallel clusters and clustering over a wide-area network WAN as described in Chapter Parallel clusters allow multiple hosts to access the same data on shared storage. Because most oper- 1. Each machine runs Oracle, and a layer of software tracks access to the shared disk. Each machine has full access to all data in the database. To provide this shared access, the system must also supply access control and locking to ensure that no conflicting operations occur.

This function, commonly known as a distributed lock manager DLM , is included in some cluster technology. Cluster technology is changing rapidly. Some cluster products support thousands of systems in a cluster, as well as clustered nodes that are separated by miles. Many of these improvements are made possible by storage-area networks SANs , as described in Section If the applications and their data are stored on the SAN, then the cluster software can assign the application to run on any host that is attached to the SAN.

If the host fails, then any other host can take over. In a database cluster, dozens of hosts can share the same database, greatly increasing performance and reliability. An operating system provides the environment within which programs are executed. Internally, operating systems vary greatly, since they are organized along many different lines.

There are, however, many commonalities, which we consider in this section. For a computer to start running—for instance, when it is powered up or rebooted —it needs to have an initial program to run. As noted earlier, this initial program, or bootstrap program, tends to be simple. Typically, it is stored within the computer hardware in firmware. It initializes all aspects of the system, from CPU registers to device controllers to memory contents.

The bootstrap program must know how to load the operating system and how to 22 Chapter 1 Introduction HADOOP Hadoop is an open-source software framework that is used for distributed processing of large data sets known as big data in a clustered system containing simple, low-cost hardware components.

Hadoop is designed to scale from a single system to a cluster containing thousands of computing nodes. Tasks are assigned to a node in the cluster, and Hadoop arranges communication between nodes to manage parallel computations to process and coalesce results. Hadoop also detects and manages failures in nodes, providing an efficient and highly reliable distributed computing service.

Hadoop is organized around the following three components: 1. A distributed file system that manages data and files across distributed computing nodes.

The MapReduce system, which allows parallel processing of data across nodes in the cluster. Hadoop is designed to run on Linux systems, and Hadoop applications can be written using several programming languages, including scripting languages such as PHP, Perl, and Python.

Java is a popular choice for developing Hadoop applications, as Hadoop has several Java libraries that support MapReduce. To accomplish this goal, the bootstrap program must locate the operating-system kernel and load it into memory. Once the kernel is loaded and executing, it can start providing services to the system and its users. Some services are provided outside of the kernel by system programs that are loaded into memory at boot time to become system daemons, which run the entire time the kernel is running.

Once this phase is complete, the system is fully booted, and the system waits for some event to occur. Events are almost always signaled by the occurrence of an interrupt. In Section 1. Another form of interrupt is a trap or an exception , which is a software-generated interrupt caused either by an error for example, division by zero or invalid memory access or by a specific request from a user program that an operating-system service be performed by executing a special operation called a system call.

Furthermore, users typically want to run more than one program at a time as well. Multiprogramming increases CPU utilization, as well as keeping users satisfied, by organizing programs so that the CPU always has one to execute. In a multiprogrammed system, a program in execution is termed a process.

The idea is as follows: The operating system keeps several processes in memory simultaneously Figure 1. The operating system picks and begins to execute one of these processes. In a non-multiprogrammed system, the CPU would sit idle. In a multiprogrammed system, the operating system simply switches to, and executes, another process. When that process needs to wait, the CPU switches to another process, and so on. Eventually, the first process finishes waiting and gets the CPU back.

As long as at least one process needs to execute, the CPU is never idle. This idea is common in other life situations. A lawyer does not work for only one client at a time, for example. While one case is waiting to go to trial or have papers typed, the lawyer can work on another case.

If she has enough clients, the lawyer will never be idle for lack of work. Idle lawyers tend to become politicians, so there is a certain social value in keeping lawyers busy. Multitasking is a logical extension of multiprogramming. In multitasking systems, the CPU executes multiple processes by switching among them, but the switches occur frequently, providing the user with a fast response time. Input, for example, may be max operating system process 1 process 2 process 3 process 4 0 Figure 1.

Rather than let the CPU sit idle as this interactive input takes place, the operating system will rapidly switch the CPU to another process. Having several processes in memory at the same time requires some form of memory management, which we cover in Chapter 9 and Chapter In addition, if several processes are ready to run at the same time, the system must choose which process will run next.

Making this decision is CPU scheduling, which is discussed in Chapter 5. Finally, running multiple processes concurrently requires that their ability to affect one another be limited in all phases of the operating system, including process scheduling, disk storage, and memory management. We discuss these considerations throughout the text. In a multitasking system, the operating system must ensure reasonable response time.

A common method for doing so is virtual memory, a technique that allows the execution of a process that is not completely in memory Chapter The main advantage of this scheme is that it enables users to run programs that are larger than actual physical memory. Further, it abstracts main memory into a large, uniform array of storage, separating logical memory as viewed by the user from physical memory. This arrangement frees programmers from concern over memory-storage limitations. Multiprogramming and multitasking systems must also provide a file system Chapter 13, Chapter 14, and Chapter The file system resides on a secondary storage; hence, storage management must be provided Chapter In addition, a system must protect resources from inappropriate use Chapter To ensure orderly execution, the system must also provide mechanisms for process synchronization and communication Chapter 6 and Chapter 7 , and it may ensure that processes do not get stuck in a deadlock, forever waiting for one another Chapter 8.

In order to ensure the proper execution of the system, we must be able to distinguish between the execution of operating-system code and user-defined code. The approach taken by most computer systems is to provide hardware support that allows differentiation among various modes of execution.

At the very least, we need two separate modes of operation: user mode and kernel mode also called supervisor mode, system mode, or privileged mode. A bit, called the mode bit, is added to the hardware of the computer to indicate the current mode: kernel 0 or user 1. With the mode bit, we can distinguish between a task that is executed on behalf of the operating system and one that is executed on behalf of the user.

When the computer system is executing on behalf of a user application, the system is in user mode. However, when a user application requests a service from the operating system via a system call , the system must transition from user to kernel mode to fulfill 1.

This is shown in Figure 1. As we shall see, this architectural enhancement is useful for many other aspects of system operation as well. At system boot time, the hardware starts in kernel mode. The operating system is then loaded and starts user applications in user mode.

Whenever a trap or interrupt occurs, the hardware switches from user mode to kernel mode that is, changes the state of the mode bit to 0. Thus, whenever the operating system gains control of the computer, it is in kernel mode. The system always switches to user mode by setting the mode bit to 1 before passing control to a user program. The dual mode of operation provides us with the means for protecting the operating system from errant users—and errant users from one another.

We accomplish this protection by designating some of the machine instructions that may cause harm as privileged instructions. The hardware allows privileged instructions to be executed only in kernel mode. If an attempt is made to execute a privileged instruction in user mode, the hardware does not execute the instruction but rather treats it as illegal and traps it to the operating system.

The instruction to switch to kernel mode is an example of a privileged instruction. Many additional privileged instructions are discussed throughout the text. The concept of modes can be extended beyond two modes. For example, Intel processors have four separate protection rings, where ring 0 is kernel mode and ring 3 is user mode. Although rings 1 and 2 could be used for various operating-system services, in practice they are rarely used.

ARMv8 systems have seven modes. CPUs that support virtualization Section In this mode, the VMM has more privileges than user processes but fewer than the kernel. It needs that level of privilege so it can create and manage virtual machines, changing the CPU state to do so. We can now better understand the life cycle of instruction execution in a computer system. Initial control resides in the operating system, where instructions are executed in kernel mode.

When control is given to a user application, the mode is set to user mode. Eventually, control is switched back to the operating system via an interrupt, a trap, or a system call. Most contemporary operating systems—such as Microsoft Windows, Unix, and Linux— 26 Chapter 1 Introduction take advantage of this dual-mode feature and provide greater protection for the operating system.

A system call is invoked in a variety of ways, depending on the functionality provided by the underlying processor. In all forms, it is the method used by a process to request action by the operating system. A system call usually takes the form of a trap to a specific location in the interrupt vector. This trap can be executed by a generic trap instruction, although some systems have a specific syscall instruction to invoke a system call.

When a system call is executed, it is typically treated by the hardware as a software interrupt. Control passes through the interrupt vector to a service routine in the operating system, and the mode bit is set to kernel mode. The system-call service routine is a part of the operating system. The kernel examines the interrupting instruction to determine what system call has occurred; a parameter indicates what type of service the user program is requesting.

Additional information needed for the request may be passed in registers, on the stack, or in memory with pointers to the memory locations passed in registers. The kernel verifies that the parameters are correct and legal, executes the request, and returns control to the instruction following the system call. We describe system calls more fully in Section 2. Once hardware protection is in place, it detects errors that violate modes. These errors are normally handled by the operating system.

The trap transfers control through the interrupt vector to the operating system, just as an interrupt does. When a program error occurs, the operating system must terminate the program abnormally. This situation is handled by the same code as a user-requested abnormal termination.

An appropriate error message is given, and the memory of the program may be dumped. The memory dump is usually written to a file so that the user or programmer can examine it and perhaps correct it and restart the program.

We cannot allow a user program to get stuck in an infinite loop or to fail to call system services and never return control to the operating system. To accomplish this goal, we can use a timer.

A timer can be set to interrupt the computer after a specified period. A variable timer is generally implemented by a fixed-rate clock and a counter. The operating system sets the counter. Every time the clock ticks, the counter is decremented. When the counter reaches 0, an interrupt occurs. For instance, a bit counter with a 1-millisecond clock allows interrupts at intervals from 1 millisecond to 1, milliseconds, in steps of 1 millisecond.

Before turning over control to the user, the operating system ensures that the timer is set to interrupt. If the timer interrupts, control transfers automatically to the operating system, which may treat the interrupt as a fatal error or 1.

An HZ value of means that the timer generates interrupts per second, or one interrupt every 4 milliseconds. The value of HZ depends upon how the kernel is configured, as well the machine type and architecture on which it is running. A related kernel variable is jiffies, which represent the number of timer interrupts that have occurred since the system was booted. A programming project in Chapter 2 further explores timing in the Linux kernel.

Clearly, instructions that modify the content of the timer are privileged. A program in execution, as mentioned, is a process. A program such as a compiler is a process, and a word-processing program being run by an individual user on a PC is a process.

Similarly, a social media app on a mobile device is a process. For now, you can consider a process to be an instance of a program in execution, but later you will see that the concept is more general. As described in Chapter 3, it is possible to provide system calls that allow processes to create subprocesses to execute concurrently. These resources are typically allocated to the process while it is running.

In addition to the various physical and logical resources that a process obtains when it is created, various initialization data input may be passed along. For example, consider a process running a web browser whose function is to display the contents of a web page on a screen.

The process will be given the URL as an input and will execute the appropriate instructions and system calls to obtain and display the desired information on the screen. When the process terminates, the operating system will reclaim any reusable resources. We emphasize that a program by itself is not a process.

A program is a passive entity, like the contents of a file stored on disk, whereas a process is an active entity. A single-threaded process has one program counter specifying the next instruction to execute.

Threads are covered in Chapter 4. The execution of such a process must be sequential. The CPU executes one instruction of the process after another, until the process completes. Further, at any time, one instruction at most is executed on behalf of the process.

Thus, although 28 Chapter 1 Introduction two processes may be associated with the same program, they are nevertheless considered two separate execution sequences. A multithreaded process has multiple program counters, each pointing to the next instruction to execute for a given thread.

A system consists of a collection of processes, some of which are operating-system processes those that execute system code and the rest of which are user processes those that execute user code. All these processes can potentially execute concurrently—by multiplexing on a single CPU core—or in parallel across multiple CPU cores.

Main memory is a large array of bytes, ranging in size from hundreds of thousands to billions. The CPU reads instructions from main memory during the instruction-fetch cycle and both reads and writes data from main memory during the data-fetch cycle on a von Neumann architecture.

As noted earlier, the main memory is generally the only large storage device that the CPU is able to address and access directly. In the same way, instructions must be in memory for the CPU to execute them. For a program to be executed, it must be mapped to absolute addresses and loaded into memory. As the program executes, it accesses program instructions and data from memory by generating these absolute addresses.

Eventually, the program terminates, its memory space is declared available, and the next program can be loaded and executed. Many different memorymanagement schemes are used. These schemes reflect various approaches, and the effectiveness of any given algorithm depends on the situation. In selecting a memory-management scheme for a specific system, we must take into account many factors—especially the hardware design of the system.

Each algorithm requires its own hardware support. The operating system abstracts from the physical properties of its storage devices to define a logical storage unit, the fil. The operating system maps files onto physical media and accesses these files via the storage devices.

File management is one of the most visible components of an operating system. Computers can store information on several different types of physical media. Secondary storage is the most common, but tertiary storage is also possible. Each of these media has its own characteristics and physical organization. Most are controlled by a device, such as a disk drive, that also has its own unique characteristics. These properties include access speed, capacity, data-transfer rate, and access method sequential or random.

A file is a collection of related information defined by its creator. Commonly, files represent programs both source and object forms and data. Data files may be numeric, alphabetic, alphanumeric, or binary. Files may be freeform for example, text files , or they may be formatted rigidly for example, fixed fields such as an mp3 music file. Clearly, the concept of a file is an extremely general one.

The operating system implements the abstract concept of a file by managing mass storage media and the devices that control them. In addition, files are normally organized into directories to make them easier to use. Finally, when multiple users have access to files, it may be desirable to control which user may access a file and how that user may access it for example, read, write, append.

Most modern computer systems use HDDs and NVM devices as the principal on-line storage media for both programs and data. Most programs—including compilers, web browsers, word processors, and games—are stored on these devices until loaded into memory.

The programs then use the devices as both the source and the destination of their processing. Hence, the proper management of secondary storage is of central importance to a computer system. The entire speed of operation of a computer may hinge on the speeds of the secondary storage subsystem and the algorithms that manipulate that subsystem. At the same time, there are many uses for storage that is slower and lower in cost and sometimes higher in capacity than secondary storage.

Backups of disk data, storage of seldom-used data, and long-term archival storage are some examples. Tertiary storage is not crucial to system performance, but it still must be managed. Some operating systems take on this task, while others leave tertiary-storage management to application programs. Some of the functions that operating systems can provide include mounting and unmounting media in devices, allocating and freeing the devices for exclusive use by processes, and migrating data from secondary to tertiary storage.

Techniques for secondary storage and tertiary storage management are discussed in Chapter Information is normally kept in some storage system such as main memory. As it is used, it is copied into a faster storage system—the cache —on a temporary basis. When we need a particular piece of information, we first check whether it is in the cache. If it is, we use the information directly from the cache. If it is not, we use the information from the source, putting a copy in the cache under the assumption that we will need it again soon.

In addition, internal programmable registers provide a high-speed cache for main memory. The programmer or compiler implements the registerallocation and register-replacement algorithms to decide which information to keep in registers and which to keep in main memory. Other caches are implemented totally in hardware. For instance, most systems have an instruction cache to hold the instructions expected to be executed next.

Without this cache, the CPU would have to wait several cycles while an instruction was fetched from main memory.

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