![]() Then some historically important systems and OS functions are described. We begin with a statement of the objectives and functions of an operating system. Chapter 2 provides an overview to which the reader can return at any point in the book for context. The topic of operating system (OS) design covers a huge territory, and it is easy to get lost in the details and lose the context of a discussion of a particular issue. Chapter 1 provides a brief survey of the processor, memory, and Input/Output (I/O) elements of a computer system. ![]() ![]() To appreciate the functionality of the operating system and the design issues involved, one must have some appreciation for computer organization and architecture. We present empirical evaluation of our approach in a maze-world navigation and their implications on "adaptive timeslice parameter" show efficient throughput time.Īn operating system mediates among application programs, utilities, and users, on the one hand, and the computer system hardware on the other. We measure the importance of states, in option framework, by evaluating the impact of their absence and propose an algorithm to identify such checkpoint states. We propose a machine-learning module, based on temporally extended reinforcement-learning agent, to predict a better performing timeslice. In this work we try to utilize the historical performances of a scheduler and try to reduce the number of redundant context switches. A process is allowed to utilize CPU resources for a fixed quantum of time (timeslice) and subsequent context switch leads to considerable overhead. In this work we try to adapt options framework to model an operating system scheduler, which is expected not to allow processor stay idle if there is any process ready or waiting for its execution. The broader framework of 'Options' gives us a flexible way of representing such extended course of action in Markov decision processes. Temporally extended actions have been proved to enhance the performance of reinforcement learning agents. Results for an "adaptive timeslice parameter" for preemption show good saving on CPU cycles and efficient throughput time. We propose a machine-learning module to predict a better performing timeslice which is calculated based on static knowledge base and adaptive reinforcement learning based suggestive module. In this work we try to utilize the historical performances of a scheduler and predict the nature of current running process, thereby trying to reduce the number of preemptions. Each of these 'process preemption' leads to considerable overhead of CPU cycles which are valuable resource for runtime execution. A process which is allowed to utilize CPU resources for a fixed quantum of time (termed as timeslice for preemption) and is then preempted for another waiting process. It is in this regard that schedulers are provided with the ability to preempt a running process, by following any scheduling algorithm, and give us an illusion of simultaneous running of several processes. This problem gains more importance as the numbers of processes always outnumber the processors by large margins. An operating system scheduler is expected to not allow processor stay idle if there is any process ready or waiting for its execution.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |