1.4 Operating-System Operations

 Now that we have discussed basic information about computer-system organization and architecture, we are ready to talk about operating systems. 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 start executing that system. To accomplish this goal, the bootstrap program must locate the operating-system kernel and load it into memory.

Once the kernel is loaded an executing, it can start providing services to the system and its users. Some service 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. In Linux, the first system program is "systemd," and it starts many other daemons. Once this phase is complete, the system if fully booted, and the system waits for some event to occur.

If there are no processes to execute, no I/O devices to service, and no users to whom to respond, an operating system will sit quietly, waiting for something to happen. Events are almost always signaled by the occurrence of an interrupt. In Section 1.2.1 we described hardware interrupts. 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 operating called a system call.

1.3.3 Clustered Systems

 Another type of multiprocessor system is a clustered system, which gathers together multiple CPUs. Clustered systems differ from the multiprocessor systems described in Section 1.3.2 in that they are composed of two or more individual systems-or nodes-joined together; each node is typically a multicore system. Such systems are considerd loosely coupled. We should note that the definition of clustered is not concrete; many commercial and open source 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 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 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 noting but monitor the active server. IOf that server fails, the hot-standby host becomes the active server. 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 application 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 19). Parallel clusters allow multiple hosts to access the same data on shared storage. Because most operating systems lack support for simultaneous data access by multiple hosts, parallel clusters usually require the use of special versions of software and special releases of applications. For example, Oracle Real Application Cluster is a version of Oracle's database that has been designed to run on a parallel cluster. 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 clusted nodes that are separated by miles. Many of these improvements are made posiible by storage-area networks(SANs), as described in Section 11.7.4, which allow many systems to attach to a pool of storage. 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 databasse cluster, dozens of hosts can share the same database, greatly increasing performance and reliability. Figure 1.11 depicts the general structure of a clustered system.

1.3.2 Multiprocessor Systems

 On modern computers, from mobile devices to servers, multiprocessor systems now dominate the landscape of computing. Traditionally, such systems have two (or more) processors, each with a single-core CPU, The processors share the computer bus and sometimes the clock, memory, and peripheral devices. The primary advantage of multiprocessor systems is increased throughput. That is, by increasing the number of processors, we expect to get more work done in less time. The speed-up ratio with N processors is not N, however; it is less than N. When multiple processors cooperate on a task, a certain amount of overhead is incurred in keeping all the parts working correctly. This overhead, plus contention for shared resources, lowers the expected gain from additional processors.

The most common multiprocessor systems use symmetric multiprocessing(SMP), in which each peer CPU processor performs all tasks, including operating-system functions and user processes. Figure 1.8 illustrates a typical SMP architecture with two processors, each with its ovwn CPU. 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 core uses significantly less power than multiple single-core chips, an important issue for mobile device as well as laptops.

In Figure 1.9, we show a dual-core design with two cores on the same processor chip. In this design, each core ahs its own register set, as well as its own local cache, often known as a level 1, or L1, cache. Notice, too, that a level2(L2) cache is local to the chip but is shared byu 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 caches. Aside from architectureal considerations, such as cache, memory, and bus contention, a multicore processor with N cores apprears 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. Virtually all modern operating systems-including Windows, macOS, and Linux, as well as Android and iOS mobile systems-support multicore SMP systems.

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 CPUs are connected by a shared system interconnect, so that all CPUs share one physical address space. This approach-known as non-uniform memory access, or NUMA-is illustrated in Figure 1.10. 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.

Apotential 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, CPU cannot access the local memory of CPU as quickly as it can access its own local memory, slowing down performance. Operating system can minimize the NUMA penalty through careful CPU scheduling and memory management, as discussed in Section 5.5.2 and Section 10.5.4. Because NUMA system can scale to accommodate a large number of processors, they are becoming increasingly popular on servers as well as high-performance computing systems.

Finally, blad servers are systems in which multiple processor boards, I/O boards, and networking boards are placed in the same chassis. The difference between these and traditional multiprocessor systems is that each bladeprocessor board boots independently and runs its own operating system. Some blade-server boasrds are multiprocessor as well, which blurs the lines between types of computers. In essence, these servers consist of multiple independent multiprocessor systems.

1.3.1 Single-Processor Systems

 Many years ago, most computers systems used a single processor containing one CPU with a single processing core. The core is the component the executes instructions and registers for storing data locally. The one main CPU with its core is capable of executing a general-purpose instruction set, including instructions from processes. These systems have other special-purpose processors as well. They may come in the form of device-specific processors, such as disk, keyboard, and graphics controllers.

All of these special-purpose processors run a limited instruction set and do not urn processes, they are managed by the operating system, in that the operating system sends them information about their next task and monitors their status. For example, a disk-controller microprocessor receives a sequence of requests from the main CPU core and implements its own disk queue and scheduling algorithm. This arrangement relieves the main CPU of the overhead of disk scheduling. PCs contain a microprocessor in the keyboard to convert the keystrokes into codes to be sen5t to the CPU. In other systems or circumstances, special-purpose processors are low-level components built into the hardware. The operating system cannot communicate with these processors; they do their jobs autonomously. The use of special-purpose microprocessor. If there is only one general-purpose CPU with a single processing core, then the system is a single-processor system. According the this definition, however, ver few contemporary computer system are single-processor systems.

1.3 Computer-System Architecture

 In Section 1.2, we introduced the general structure of a typical computer system. A computer system can be organized in a number of different ways, which we can categorize toughly according to the number of general-purpose processors used.

1.2.3 I/O Structure

 A large portion of operation system code is dedicated to managing I/O, both because of its importance to the reliability and performance of a system and because of the varying nature of the devices.

Recall from the begining of this section that a general-purpose computer system consists of multiple devices, all of which exchange data via a common bus. The form of interrupt-driven I/O described in Section 1.2.1 is fine for moving small amounts of data but can produce high overhead when used for bulk data movement such as NVS I/O. To solve this problem, direct memory access(DMA) is used. After setting up buffers, pointers, and counters for the I/O device, the device controller transfers an entire block of data directly to or from the device and main memory, with no intervention by the CPU. Only one interrupt is generated per block, to tell the device driver that the operation has completed, rather than the one interrupt per byte generated for low-speed devices. While the device controller is performing these operations, the CPU is available to accomplish other work.

Some high-end systems use switch rather than bus architecture. On these systems, multiple components can talk to other components concurrently, rather than ompeting for cycles on a shared bus, In this case, DMA is even more effective. Figure 1.7 shows the interplay of all components of a computer system.

STORAGE DEFINITIONS AND NOTATION

 The basic u nit of computer storage is the bit. A bit can contain one of two values, 0 and 1. All other storage in a computer is based on collections of bits. Given enough bits, it is amazing how many things a computer can represent: numbers, letters, images, movies, sounds, documents, and programs, to name a few. A byte is 8 bits, and on most computers it is the smallest convenient chunk of storage. For example, most computers don't have an instruction to move a bit but do have one to move a byte.  A less common term is word, which is a given computer architecture's native unit of data. A word is made up of one or more bytes. For example, a computer that has 64-bit registers and 64-bit memory addressing typically has 64-bit (8-byte) words. A computer executes many operations in its native word size rather than a byte at a time.

Computer storage, along with most computer throughput, is generally measured and manipulated in byutes and collections of bytes. A kilobyte, or KB, is 1,024bytes; a megabyte, or MB, is 1,024(2) bytes; a gigabyte, or GB, is 1,024(3) bytes; a terabyte, or TB, is 1,024(4) bytes; a  petabyte, or PB, is 1,024(5) bytes. Computer manufactures often round off these numbers and say that a megabyte is 1 million bytes and a gigabyte is 1 billion bytes. Networking measurements are an exceptioni to this general rule; they are given in bits(Because networks move data a bit at a time).