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Register a new userClassification Of Heterogeneous Operating System
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Added by
Kamlesh Sharma
Publication date
2012
fields
Informatics Engineering
and research's language is
English
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Operating system is a bridge between system and user. An operating system (OS) is a software program that manages the hardware and software resources of a computer. The OS performs basic tasks, such as controlling and allocating memory, prioritizing the processing of instructions, controlling input and output devices, facilitating networking, and managing files. It is difficult to present a complete as well as deep account of operating systems developed till date. So, this paper tries to overview only a subset of the available operating systems and its different categories. OS are being developed by a large number of academic and commercial organizations for the last several decades. This paper, therefore, concentrates on the different categories of OS with special emphasis to those that had deep impact on the evolution process. The aim of this paper is to provide a brief timely commentary on the different categories important operating systems available today.
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Rapid growth of datacenter (DC) scale, urgency of cost control, increasing workload diversity, and huge software investment protection place unprecedented demands on the operating system (OS) efficiency, scalability, performance isolation, and backward-compatibility. The traditional OSes are not built to work with deep-hierarchy software stacks, large numbers of cores, tail latency guarantee, and increasingly rich variety of applications seen in modern DCs, and thus they struggle to meet the demands of such workloads. This paper presents XOS, an application-defined OS for modern DC servers. Our design moves resource management out of the OS kernel, supports customizable kernel subsystems in user space, and enables elastic partitioning of hardware resources. Specifically, XOS leverages modern hardware support for virtualization to move resource management functionality out of the conventional kernel and into user space, which lets applications achieve near bare-metal performance. We implement XOS on top of Linux to provide backward compatibility. XOS speeds up a set of DC workloads by up to 1.6X over our baseline Linux on a 24-core server, and outperforms the state-of-the-art Dune by up to 3.3X in terms of virtual memory management. In addition, XOS demonstrates good scalability and strong performance isolation.
Steal time is a key performance metric for applications executed in a virtualized environment. Steal time measures the amount of time the processor is preempted by code outside the virtualized environment. This, in turn, allows to compute accurately the execution time of an application inside a virtual machine (i.e. it eliminates the time the virtual machine is suspended). Unfortunately, this metric is only available in particular scenarios in which the host and the guest OS are tightly coupled. Typical examples are the Xen hypervisor and Linux-based guest OSes. In contrast, in scenarios where the steal time is not available inside the virtualized environment, performance measurements are, most often, incorrect. In this paper, we introduce a novel and platform agnostic approach to calculate this steal time within the virtualized environment and without the cooperation of the host OS. The theoretical execution time of a deterministic microbenchmark is compared to its execution time in a virtualized environment. When factoring in the virtual machine load, this solution -as simple as it is- can compute the steal time. The preliminary results show that we are able to compute the load of the physical processor within the virtual machine with high accuracy.
This note concerns a search for publications in which one can find statements that explain the concept of an operating system, reasons for introducing operating systems, a formalization of the concept of an operating system or theory about operating systems based on such a formalization. It reports on the way in which the search has been carried out and the outcome of the search. The outcome includes not only what the search was meant for, but also some added bonuses.
This paper is a comprehensive survey of the various operating systems available for the Internet of Things environment. At first the paper introduces the various aspects of the operating systems designed for the IoT environment where resource constraint poses a huge problem for the operation of the general OS designed for the various computing devices. The latter part of the paper describes the various OS available for the resource constraint IoT environment along with the various platforms each OS supports, the software development kits available for the development of applications in the respective OS along with the various protocols implemented in these OS for the purpose of communication and networking.
Recent commercial hardware platforms for embedded real-time systems feature heterogeneous processing units and computing accelerators on the same System-on-Chip. When designing complex real-time application for such architectures, the designer needs to make a number of difficult choices: on which processor should a certain task be implemented? Should a component be implemented in parallel or sequentially? These choices may have a great impact on feasibility, as the difference in the processor internal architectures impact on the tasks execution time and preemption cost. To help the designer explore the wide space of design choices and tune the scheduling parameters, in this paper we propose a novel real-time application model, called C-DAG, specifically conceived for heterogeneous platforms. A C-DAG allows to specify alternative implementations of the same component of an application for different processing engines to be selected off-line, as well as conditional branches to model if-then-else statements to be selected at run-time. We also propose a schedulability analysis for the C-DAG model and a heuristic allocation algorithm so that all deadlines are respected. Our analysis takes into account the cost of preempting a task, which can be non-negligible on certain processors. We demonstrate the effectiveness of our approach on a large set of synthetic experiments by comparing with state of the art algorithms in the literature.
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