In this paper, we address the problem of adaptively reserving the CPU to concurrent soft real-time tasks, in order to meet target Quality of Service requirements. First, we present two new techniques inspired to the idea of stochastic control. Then, we present a flexible and modular software architecture suitable for adaptive scheduling, realised as a minimally invasive set of modifications to the Linux Kernel. Finally, we show experimental results that validate our approach and prove its effectiveness in the context of multimedia applications.

Today’s Web depends on a particular pact between sites and users: sites invest capital and labor to create and market a set of features, and users gain access to these features by giving up control of their data (photos, personal information, creative musings, etc.). This paper imagines a very different Web ecosystem, in which users retain control of their data and developers can justify their existence without hoarding that data. 1

An important class of soft real-time applications require dynamic allocation of computational resources in order to comply with their quality of service (QoS) requirements. These applications are characterised by large fluctuations in their computation time requirements. One of the biggest problems in such systems is how to assign the bandwidths to the software tasks so that every task meets its QoS requirements and computational resources are not wasted. In this paper, we present a novel feedback scheduling controller based on a scheduling strategy called resource reservation. First, we model the scheduler as a discrete time switching system; then, we present hybrid control techniques for the design of the feedback scheduler; finally, we report simulation results that show the effectiveness of our approach.

Reservation based (RB) scheduling is a class of scheduling algorithms that is well-suited for a large class of soft real-time applications. They are based on a “bandwidth ” abstraction, meaning that a task is given the illusion of executing on a dedicated slower processor. In this context, a crucial design issue is deciding the bandwidth that each task should receive. The point we advocate is that, in presence of large fluctuations on the computation requirements of the tasks, it can be a beneficial choice to dynamically adapt the bandwidth based on QoS measurements and on the subsequent application of feedback control (adaptive reservations). In this paper, we present two novel contributions to this research area. First, we propose three new control algorithms inspired to the ideas of stochastic control. Second, we present a flexible and modular software architecture for adaptive reservations. An important feature of this architecture is that it is realised by means of a minimally invasive set of modifications to the Linux kernel. 1.

It is common to run multimedia and other periodic, soft real-time applications on general-purpose computer systems. These systems use best-effort scheduling algorithms that cannot guarantee applications will receive responsive scheduling to meet deadline or timing requirements. We present a simple mechanism called Missed Deadline Notification (MDN) that allows applications to notify the system when they do not receive their desired level of responsiveness. Consisting of a single system call with no arguments, this simple interface allows the operating system to provide better support for soft real-time applications without any a priori information about their timing or resource needs. We implemented MDN in three different schedulers: Linux, BEST, and BeRate. We describe these implementations and their performance when running real-time applications and discuss policies to prevent applications from abusing MDN to gain extra resources.

Advances in hardware capacity, especially I/O devices such as cameras and displays, are driving the development of applications like high-definition video conferencing that have tight timing and CPU requirements. Unfortunately, current operating systems do not adequately provide the timing response needed by these applications. In this paper, we present a hierarchical scheduling model that aims to provide these applications with tight timing response, while at the same time preserve the strengths of current schedulers, namely fairness and efficiency. Our approach, called cooperative polling, consists of an application-level event scheduler and a kernel thread scheduler that cooperate to dispatch time-constrained application events accurately and with minimal kernel preemption, while still ensuring rigorously that all applications share resources fairly. Fairness is enforced in a flexible manner, allowing sharing according to a mixture of both traditional resource-centric metrics and new application-centric metrics, the latter being critical to support graceful application-level adaptation in overload. Unlike traditional real-time systems, our model does not require specification or estimation of resource requirements, simplifying its usage dramatically. Our evaluation, using an adaptive video application and a graphics server, shows that our system has event dispatch accuracies that are one to two orders of magnitude smaller than are achieved by existing schedulers. At the same time, our scheduler still maintains fairness and has low overhead.

In this paper, we present a software architecture to support soft real-time applications, such as multimedia streaming and telecommunication systems, in open embedded systems. Examples of such systems are consumer electronic devices (as cellular phones, PDAs, etc.), as well as multimedia servers (video servers, VoIP servers, etc.) and telecommunication infrastructure devices. For such applications, it is important to keep under control the resource utilization of every task, otherwise the Quality of Service experienced by the users may be degraded. Our proposal is to combine a resource reservation scheduler (that allows us to partition the CPU time in a reliable way) and a feedback based mechanism for dynamically adjusting the CPU fraction (bandwidth) allocated to a tasks. In particular, our controller enables specified Quality of Service (QoS) levels for the application while keeping the allocated bandwidth close to its actual needs. The adaptation mechanism consists of the combination of a prediction and of a feedback correction that operates locally on each task. The consistency of the system is preserved by a supervisor component that manages overload conditions and enacts security policies. We implemented the framework in AQuOSA, a software architecture that runs on top of the Linux kernel. We provide extensive experimental validation of our results and offer evaluation of the introduced overhead, which is remarkably lower than the one introduced by other different solutions. I.

At the heart of a secure software system is a small, trustworthy component, called the Trusted Computing Base (TCB). However, developers persist in building monolithic systems that force their users to trust the entire system. We posit that this is due to the lack of a straightforward mechanism of partitioning – or disaggregating – systems into trusted and untrusted components. We propose to use dynamic libraries as the unit of disaggregation, because these are a familiar abstraction, which is commonly used in mainstream software development. In this paper, we present our early ideas on the disaggregated library approach, which can be applied to existing applications that run on commodity operating systems. We first make the case for a new approach to disaggregation, and then describe how we are implementing it. We also draw comparisons with the wide range of related work in this area.

“Time-sharing ” is discussed generally in this article to cover,any application of a computer system that has simultaneous users. The discussion defines general purpose time-sharing so as to include special purpose time-sharing, “real time”, and “on line ” systems as a subset. “Graceful Creation”, or the “boot strapping ” of a system, is described in which newly ‘created in.dividual user procedures are immedi-ately available to the whole community of users, an, & the system expands-in a:n open-ended fashion because many users contribute to the formation. Although the discussion is separated into hardware, operating system software, and user components, a sharp delineation does not exist in reality. After the basic system. is specified, it is the phi1osoph.y of the author that the system should be formed in a time-shared environment (including the construction of the operating system software). Few resrictive features or functions should be “built-in”, but instead, be optionally available through the library or common files.

view of a XenoServer's design . . . . . . . . . . . . . . . 74 3.3 Registration of XenoServers and clients . . . . . . . . . . . . . . . 77 3.4 Advertisement and discovery of resources . . . . . . . . . . . . . . 80 3.5 Service deployment operations . . . . . . . . . . . . . . . . . . . . 83 3.6 Environment management operations . . . . . . . . . . . . . . . . 87 4.1 Hierarchical resource naming . . . . . . . . . . . . . . . . . . . . . 106 4.2 Coordinated resource descriptions . . . . . . . . . . . . . . . . . . 110 4.3 Server advertisement . . . . . . . . . . . . . . . . . . . . . . . . . 111 4.4 Resource and pricing description coordination maps . . . . . . . . 112 4.5 High-level view of the RBRM architecture . . . . . . . . . . . . . 114 4.6 Authentication and filtering of deployed policies . . . . . . . . . . 116 4.7 Policy evaluation process . . . . . . . . . . . . . . . . . . . . . . . 126 4.8 Policy deployment in the XenoServer Platform . . . . . . . . . . . 132 4.9 Policy deployment in Condor . . . . . . . . . . . . . . . . . . . . 138 5.1 Architecture of a Xen-based XenoServer . . . . . . . . . . . . . . 146 5.2 Control-plane architecture of a XenoServer . . . . . . . . . . . . . 148 5.3 Architecture of XenoClient . . . . . . . . . . . . . . . . . . . . . . 152 5.4 Interface for user registration . . . . . . . . . . . . . . . . . . . . 153 5.5 Interface for purchase order creation and management . . . . . . . 154 5.6 Interface for XenoServer discovery and selection . . . . . . . . . . 155 5.7 Interface for purchasing resources on a XenoServer . . . . . . . . . 156 5.8 Interface for building deployment specifications . . . . . . . . . . 157 5.9 Interface for service and session management . . . . . . . . . . . . 158 5.10 Architecture of XenoCorp . . . . . . . ....