When the process enters into the system, then this process is put into a job queue. This queue consists of all processes in the system. The operating system also maintains other queues such as device queue. Each device has its own device queue. This figure shows the queuing diagram of process scheduling. Processes waits in ready queue for allocating the CPU.
Once the CPU is assigned to a process, then that process will execute. While executing the process, any one of the following events can occur. Two state process model refers to running and non-running states which are described below. Non-Running Processes that are not running are kept in queue, waiting for their turn to execute. Each entry in the queue is a pointer to a particular process. Queue is implemented by using linked list.
Use of dispatcher is as follows. When 2 a process is interrupted, that process is transferred in the waiting queue. If the process has completed or aborted, the process is discarded. In either case, the dispatcher then selects a process from the queue to execute. Schedulers Schedulers are special system software which handles process scheduling in various ways.
Their main task is to select the jobs to be submitted into the system and to decide which process to run. Long term scheduler determines which programs are admitted to the system for processing. Job scheduler selects processes from the queue and loads them into memory for execution. Process loads into the memory for CPU scheduling.
It also controls the degree of multiprogramming. If the degree of multiprogramming is stable, then the average rate of process creation must be equal to the average departure rate of processes leaving the system. On some systems, the long term scheduler may not be available or minimal. Time-sharing operating systems have no long term scheduler. When process changes the state from new to ready, then there is use of long term scheduler.
Main objective is increasing system performance in accordance with the chosen set of criteria. It is the change of ready state to running state of the process. CPU scheduler selects process among the processes that are ready to execute and allocates CPU to one of them. Short term scheduler also known as dispatcher, execute most frequently and makes the fine grained decision of which process to execute next. Short term scheduler is faster than long term scheduler.
Medium Term Scheduler Medium term scheduling is part of the swapping. It removes the processes from the memory. It reduces the degree of multiprogramming. The medium term scheduler is in-charge of handling the swapped out-processes.
Suspended processes cannot make any progress towards completion. In this condition, to remove the process from memory and make space for other process, the suspended process is moved to the secondary storage. This process is called swapping, and the process is said to be swapped out or rolled out. Swapping may be necessary to improve the process mix. It controls the degree of It provides lesser control multiprogramming over degree of It reduces the degree of 3 multiprogramming multiprogramming.
It is almost absent or It is also minimal in time It is a part of Time sharing 4 minimal in time sharing sharing system systems. A context switch is the mechanism to store and restore the state or context of a CPU in Process Control block so that a process execution can be resumed from the same point at a later time.
Using this technique a context switcher enables multiple processes to share a single CPU. Context switching is an essential part of a multitasking operating system features.
When the scheduler switches the CPU from executing one process to execute another, the context switcher saves the content of all processor registers for the process being removed from the CPU, in its process descriptor.
The context of a process is represented in the process control block of a process. Context switch time is pure overhead. Context switching can significantly affect performance as modern computers have a lot of general and status registers to be saved. Content switching times are highly dependent on hardware support.
Context switching Some hardware systems employ two or more sets of processor registers to reduce the amount of context switching time. When the process is switched, the following information is stored. Process with highest priority is to be executed first and so on. Process is preempted and other process executes for given time period. Dining Philosophers Problem The scenario involves five philosophers sitting at a round table with a bowl of food and five chopsticks.
Each chopstick sits between two adjacent philosophers. The philosophers are allowed to think and eat. Since two chopsticks are required for each philosopher to eat, and only five chopsticks exist at the table, no two adjacent philosophers may be eating at the same time.
A scheduling problem arises as to who gets to eat at what time. This problem is similar to the problem of scheduling processes that require a limited number of resources Problems The problem was designed to illustrate the challenges of avoiding deadlock, a system state in which no progress is possible.
This attempted solution fails because it allows the system to reach a deadlock state, in which no progress is possible. This is a state in which each philosopher has picked up the fork to the left, and is waiting for the fork to the right to become available. What is Thread? A thread is a flow of execution through the process code, with its own program counter, system registers and stack.
A thread is also called a light weight process. Threads provide a way to improve application performance through parallelism. Threads represent a software approach to improving performance of operating system by reducing the overhead thread is equivalent to a classical process.
Each thread belongs to exactly one process and no thread can exist outside a process. Each thread represents a separate flow of control.
Threads have been successfully used in implementing network servers and web server. They also provide a suitable foundation for parallel execution of applications on shared memory multiprocessors. Following figure shows the working of the single and multithreaded processes. Process Thread Process is heavy weight or Thread is light weight taking lesser 1 resource intensive. Process switching needs Thread switching does not need to 1 interaction with operating system.
In multiple processing environments each process All threads can share same set of open 1 executes the same code but has its files, child processes. If one process is blocked then no While one thread is blocked and other process can execute until the waiting, second thread in the same task 1 first process is unblocked. Multiple processes without using Multiple threaded processes use 1 threads use more resources.
In multiple processes each process One thread can read, write or change 1 operates independently of the another thread's data. The thread library contains code for creating and destroying threads, for passing message and data between threads, for scheduling thread execution and for saving and restoring thread contexts. The application begins with a single thread and begins running in that thread.
Kernel Level Threads In this case, thread management done by the Kernel. There is no thread management code in the application area. Kernel threads are supported directly by the operating system. Any application can be programmed to be multithreaded.
All of the threads within an application are supported within a single process. Scheduling by the Kernel is done on a thread basis. The Kernel performs thread creation, scheduling and management in Kernel space. Kernel threads are generally slower to create and manage than the user threads.
Some operating system provides a combined user level thread and Kernel level thread facility. Solaris is a good example of this combined approach.
Many to Many Model In this model, many user level threads multiplexes to the Kernel thread of smaller or equal numbers. The number of Kernel threads may be specific to either a particular application or a particular machine. Many to One Model Many to one model maps many user level threads to one Kernel level thread.
Thread management is done in user space. When thread makes a blocking system call, the entire process will be blocks. Only one thread can access the Kernel at a time, so multiple threads are unable to run in parallel on multiprocessors. If the user level thread libraries are implemented in the operating system in such a way that system does not support them then Kernel threads use the many to one relationship modes.
One to One Model There is one to one relationship of user level thread to the kernel level thread. This model provides more concurrency than the many to one model.
It also another thread to run when a thread makes a blocking system call. It support multiple thread to execute in parallel on microprocessors. Disadvantage of this model is that creating user thread requires the corresponding Kernel thread.
Implementation is by a thread Operating system supports creation of 2 library at the user level. Kernel threads. User level thread is generic and Kernel level thread is specific to the 3 can run on any operating system. Multi-threaded application cannot Kernel routines themselves can 4 take advantage of multiprocessing. Race Condition? A race condition is an undesirable situation that occurs when a device or system attempts to perform two or more operations at the same time, but because of the nature of the device or system, the operations must be done in the proper sequence to be done correctly.
A race condition occurs when two threads access a shared variable at the same time. The first thread reads the variable, and the second thread reads the same value from the variable. Then the first thread and second thread perform their operations on the value, and they race to see which thread can write the value last to the shared variable. The value of the thread that writes its value last is preserved, because the thread is writing over the value that the previous thread wrote.
Memory management is the functionality of an operating system which handles or manages primary memory. Memory management keeps track of each and every memory location either it is allocated to some process or it is free. It checks how much memory is to be allocated to processes. It decides which process will get memory at what time. It tracks whenever some memory gets freed or unallocated and correspondingly it updates the status.
Memory management provides protection by using two registers, a base register and a limit register. The base register holds the smallest legal physical memory address and the limit register specifies the size of the range.
For example, if the base register holds and the limit register is , then the program can legally access all addresses from through All routines are kept on disk in a re-locatable load format. The main program is loaded into memory and is executed.
Other routines methods or modules are loaded on request. Dynamic loading makes better memory space utilization and unused routines are never loaded. Operating system can link system level libraries to a program. When it combines the libraries at load time, the linking is called static linking and when this linking is done at the time of execution, it is called as dynamic linking. In static linking, libraries linked at compile time, so program code size becomes bigger whereas in dynamic linking libraries linked at execution time so program code size remains smaller.
Swapping Swapping is a mechanism in which a process can be swapped temporarily out of main memory to a backing store, and then brought back into memory for continued execution.
Backing store is a usually a hard disk drive or any other secondary storage which fast in access and large enough to accommodate copies of all memory images for all users. It must be capable of providing direct access to these memory images. Operating system uses the following memory allocation mechanism. N Memory Description. Allocation In this type of allocation, relocation-register scheme is used to protect Single-partition user processes from each other, and from changing operating-system allocation code and data.
Relocation register contains value of smallest physical 1 address whereas limit register contains range of logical addresses. Each logical address must be less than the limit register. In this type of allocation, main memory is divided into a number of Multiple- fixed-sized partitions where each partition should contain only one partition process.
When a partition is free, a process is selected from the input 2 allocation queue and is loaded into the free partition. When the process terminates, the partition becomes available for another process. Fragmentation As processes are loaded and removed from memory, the free memory space is broken into little pieces. It happens after sometimes that processes cannot be allocated to memory blocks considering their small size and memory blocks remains unused.
This problem is known as Fragmentation. Fragmentation is of two types S. Fragmentation Description External Total memory space is enough to satisfy a request or to reside a 1 fragmentation process in it, but it is not contiguous so it cannot be used. Some portion of fragmentation memory is left unused as it cannot be used by another process. External fragmentation can be reduced by compaction or shuffle memory contents to place all free memory together in one large block.
External fragmentation is avoided by using paging technique. Paging is a technique in which physical memory is broken into blocks of the same size called pages size is power of 2, between bytes and bytes.
When a process is to be executed, it's corresponding pages are loaded into any available memory frames. Logical address space of a process can be non-contiguous and a process is allocated physical memory whenever the free memory frame is available. Operating system keeps track of all free frames. Operating system needs n free frames to run a program of size n pages. Segmentation Segmentation is a technique to break memory into logical pieces where each piece represents a group of related information.
For example, data segments or code segment for each process, data segment for operating system and so on. Segmentation can be implemented using or without using paging. Speed differences between two devices. A slow device may write data into a buffer, and when the buffer is full, the entire buffer is sent to the fast device all at once.
So that the slow device still has somewhere to write while this is going on, a second buffer is used, and the two buffers alternate as each becomes full. This is known asdouble buffering. Double buffering is often used in animated graphics, so that one screen image can be generated in a buffer while the other completed buffer is displayed on the screen.
This prevents the user from ever seeing any half-finished screen images. Data transfer size differences. Buffers are used in particular in networking systems to break messages up into smaller packets for transfer, and then for re-assembly at the receiving side. To support copy semantics. For example, when an application makes a request for a disk write, the data is copied from the user's memory area into a kernel buffer.
Now the application can change their copy of the data, but the data which eventually gets written out to disk is the version of the data at the time the write request was made. VirtualMemory This section describes concepts of virtual memory, demand paging and various page replacement algorithms. Virtual memory is a technique that allows the execution of processes which are not completely available in memory.
The main visible advantage of this scheme is that programs can be larger than physical memory. Virtual memory is the separation of user logical memory from physical memory.
This separation allows an extremely large virtual memory to be provided for programmers when only a smaller physical memory is available. Following are the situations, when entire program is not required to be loaded fully in main memory. Virtual memory is commonly implemented by demand paging. It can also be implemented in a segmentation system. Demand segmentation can also be used to provide virtual memory. Virtual memory algorithms Page replacement algorithms Page replacement algorithms are the techniques using which Operating System decides which memory pages to swap out, write to disk when a page of memory needs to be allocated.
Paging happens whenever a page fault occurs and a free page cannot be used for allocation purpose accounting to reason that pages are not available or the number of free pages is lower than required pages.
This process determines the quality of the page replacement algorithm: the lesser the time waiting for page-ins, the better is the algorithm. A page replacement algorithm looks at the limited information about accessing the pages provided by hardware, and tries to select which pages should be replaced to minimize the total number of page misses, while balancing it with the costs of primary storage and processor time of the algorithm itself.
There are many different page replacement algorithms. We evaluate an algorithm by running it on a particular string of memory reference and computing the number of page faults.
Reference String The string of memory references is called reference string. Reference strings are generated artificially or by tracing a given system and recording the address of each memory reference. The latter choice produces a large number of data, where we note two things.
A translation look-aside buffer TLB : A translation lookaside buffer TLB is a memory cache that stores recent translations of virtual memory to physical addresses for faster retrieval. When a virtual memory address is referenced by a program, the search starts in the CPU. First, instruction caches are checked. At this point, TLB is checked for a quick reference to the location in physical memory. When an address is searched in the TLB and not found, the physical memory must be searched with a memory page crawl operation.
As virtual memory addresses are translated, values referenced are added to TLB. TLBs also add the support required for multi-user computers to keep memory separate, by having a user and a supervisor mode as well as using permissions on read and write bits to enable sharing. TLBs can suffer performance issues from multitasking and code errors. This performance degradation is called a cache thrash. Cache thrash is caused by an ongoing computer activity that fails to progress due to excessive use of resources or conflicts in the caching system.
Use the time when a page is to be used. OperatingSystemSecurity This section describes various security related aspects like authentication, one time password, threats and security classifications. So a computer system must be protected against unauthorized access, malicious access to system memory, viruses, worms etc. We're going to discuss following topics in this article. One time passwords provides additional security along with normal authentication.
In One- Time Password system, a unique password is required every time user tries to login into the system. Once a one-time password is used then it cannot be used again. One time password are implemented in various ways.
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Our Businesses US. Canada Sunbelt Rentals entered the Canadian market in and now has 81 stores. Latest news. It places particular emphasis on modern developments, and their widespread applications in fields such as large-scale resource allocation problems, signal processing, and machine learning. These works are complementary in that they deal primarily with convex, possibly nondifferentiable, optimization problems and rely on convex analysis.
By contrast the nonlinear programming book focuses primarily on analytical and computational methods for possibly nonconvex differentiable problems. It relies primarily on calculus and variational analysis, yet it still contains a detailed presentation of duality theory and its uses for both convex and nonconvex problems.
This on-line edition contains detailed solutions to all the theoretical book exercises. Among its special features, the book: Provides extensive coverage of iterative optimization methods within a unifying framework Covers in depth duality theory from both a variational and a geometric point of view Provides a detailed treatment of interior point methods for linear programming Includes much new material on a number of topics, such as proximal algorithms, alternating direction methods of multipliers, and conic programming Focuses on large-scale optimization topics of much current interest, such as first order methods, incremental methods, and distributed asynchronous computation, and their applications in machine learning, signal processing, neural network training, and big data applications Includes a large number of examples and exercises Was developed through extensive classroom use in first-year graduate courses.
Author : David G. It comprehensively covers modern theoretical insights in this crucial computing area, and will be required reading for analysts and operations researchers in a variety of fields.
Author : H. Eiselt Publisher: Springer Nature ISBN: Category: Mathematics Page: View: Read Now » This book provides a comprehensive introduction to nonlinear programming, featuring a broad range of applications and solution methods in the field of continuous optimization. It begins with a summary of classical results on unconstrained optimization, followed by a wealth of applications from a diverse mix of fields, e.
In turn, the book presents a formal description of optimality conditions, followed by an in-depth discussion of the main solution techniques. Each method is formally described, and then fully solved using a numerical example. Author : John T. The third edition has been thoroughly updated and includes new material on implicit Runge—Kutta discretization techniques, new chapters on partial differential equations and delay equations, and more than 70 test problems and open source FORTRAN code for all of the problems.
This book will be valuable for academic and industrial research and development in optimal control theory and applications. It is appropriate as a primary or supplementary text for advanced undergraduate and graduate students. The text accomplishes two goals. First, it provides readers with an introduction to standard mathematical models and algorithms. Second, it is a thorough examination of practical issues relevant to the development and use of computational methods for problem solving.
Highlights: All chapters contain up-to-date topics and summaries A succinct presentation to fit a one-term course Each chapter has references, readings, and list of key terms Includes illustrative and current applications New exercises are added throughout the text Software tools have been updated with the newest and most popular software Many students of various disciplines such as mathematics, economics, industrial engineering and computer science often take one course in operations research.
This book is written to provide a succinct and efficient introduction to the subject for these students, while offering a sound and fundamental preparation for more advanced courses in linear and nonlinear optimization, and many stochastic models and analyses.
It provides relevant analytical tools for this varied audience and will also serve professionals, corporate managers, and technical consultants.
Author : William P. In design, construction, and maintenance of any engineering system, engineers must make technological and managerial decisions to minimize either the effort or cost required or to maximize benefits.
There is no single method available for solving all optimization problems efficiently. Several optimization methods have been developed for different types of problems.
The optimum-seeking methods are mathematical programming techniques specifically, nonlinear programming techniques. Nonlinear Optimization: Models and Applications presents the concepts in several ways to foster understanding.
Geometric interpretation: is used to re-enforce the concepts and to foster understanding of the mathematical procedures. The student sees that many problems can be analyzed, and approximate solutions found before analytical solutions techniques are applied.
Numerical approximations: early on, the student is exposed to numerical techniques. These numerical procedures are algorithmic and iterative. Algorithms: all algorithms are provided with a step-by-step format.
Examples follow the summary to illustrate its use and application. Nonlinear Optimization: Models and Applications: Emphasizes process and interpretation throughout Presents a general classification of optimization problems Addresses situations that lead to models illustrating many types of optimization problems Emphasizes model formulations Addresses a special class of problems that can be solved using only elementary calculus Emphasizes model solution and model sensitivity analysis About the author: William P.
He received his Ph. He has written many publications, including over 20 books and over journal articles. Currently, he is an adjunct professor in the Department of Mathematics at the College of William and Mary. Author : M. This book presents recent developments of key topics in nonlinear programming NLP using a logical and self-contained format.
The volume is divided into three sections: convex analysis, optimality conditions, and dual computational techniques. Precise statements of algortihms are given along with convergence analysis. Each chapter contains detailed numerical examples, graphical illustrations, and numerous exercises to aid readers in understanding the concepts a. Mixed-integer nonlinear programming MINLP problems combine the numerical difficulties of handling nonlinear functions with the challenge of optimizing in the context of nonconvex functions and discrete variables.
MINLP is one of the most flexible modeling paradigms available for optimization; but because its scope is so broad, in the most general cases it is hopelessly intractable. Nonetheless, an expanding body of researchers and practitioners — including chemical engineers, operations researchers, industrial engineers, mechanical engineers, economists, statisticians, computer scientists, operations managers, and mathematical programmers — are interested in solving large-scale MINLP instances.
Aimed toward scientists and graduate students who utilize optimization methods to model and solve problems in mathematical programming, operations research, business, engineering, and industry, this book enables readers with a background in nonlinear optimization and linear algebra to use GAMS technology to understand and utilize its important capabilities to optimize algorithms for modeling and solving complex, large-scale, continuous nonlinear optimization problems or applications.
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