Modular software architecture for flexible reservation mechanisms on heterogeneous resources

Management, allocation and scheduling of heterogeneous resources for complex distributed real-time applications is a chal- lenging problem. Timing constraints of applications may be fulfilled by a proper use of real-time scheduling policies, admission control and enforcement of timing constraints. However, it is not easy to design basic infrastructure services that allow for an easy access to the allocation of multiple heterogeneous resources in a distributed environment. In this paper, we present a middleware for providing distributed soft real-time applications with a uniform API for reserving heterogeneous resources with real-time scheduling capabilities in a distributed environment. The architecture relies on standard POSIX OS facilities, such as time management and standard TCP/IP networking services, and it is designed around CORBA, in order to facilitate modularity, flexibility and portability of the applications using it. However, real-time scheduling is supported by proper extensions at the kernel-level, plugged within the framework by means of dedicated resource managers. Our current implementation on Linux supports reservation of CPU, disk and network bandwidth. However, additional resource managers supporting alternative real-time schedulers for these resources, as well as additional types of resources, may be easily added. We present experimental results gathered on both synthetic applications and a real multimedia video streaming case study, showing advantages deriving from the use of the proposed middleware. Finally, overhead figures are reported, showing sustainability of the approach for a wide class of complex, distributed, soft real-time applications

Optimizing Memory Usage in L4-Based Microkernel

Memory allocation is a critical aspect of any modern operating system kernel because it must run continuously for long periods of time, therefore memory leaks and inefficiency must be eliminated. This paper presents different memory management algorithms and their aplicability to an L4-based microkernel. We aim to reduce memory usage and increase the performance of allocation and deallocation of memory

A library for developing real-time and embedded applications in C

Next generation applications will demand more cost-effective programming abstractions to reduce increasing maintenance and development costs. In this context, the article explores the integration of an efficient programming language and high-level real-time programming abstractions. The resulting abstraction is called Embedded Cyber Physical C (ECP-C) and it is useful for designing real-time applications directly on C. The abstraction has its roots on the real-time Java: one of the most modern programming languages, which benefited from mature programming patterns previously developed for other languages. It also targets embedded processors running on limited hardware. ECP-C takes the programming abstractions described in real-time Java and reflects them into a C application system, providing extensions for multi-threading, resource sharing, memory management, external event, signaling, and memory access. It also reports on the performance results obtained in a set of infrastructures used to check ECP-C, providing clues on the overhead introduced by these mechanisms on limited infrastructures. (C) 2015 Elsevier B.V. All rights reserved.This work has been partially funded by Distributed Java Infrastructure for Real-Time Big-Data (CAS14/00118) and by eMadrid: Investigación y Desarrollo de tecnologías educativas en la Comunidad de Madrid (S2013/ICE-2715). This research was supported by the national project REM4VSS (TIN-2011-28339) and by European Union’s 7th Framework Programme Under Grant Agreement FP7-IC6-318763

Power-efficient data management for dynamic applications

In recent years, the semiconductor industry has turned its focus towards heterogeneous multi-processor platforms. They are an economically viable solution for coping with the growing setup and manufacturing cost of silicon systems. Furthermore, their inherent flexibility also perfectly supports the emerging market of interactive, mobile data and content services. The platform's performance and energy depend largely on how well the data-dominated services are mapped on the memory subsystem. A crucial aspect thereby is how efficient data is transferred between the different memory layers. Several compilation techniques have been developed to optimally use the available bandwidth. Unfortunately, they do not take the interaction between multiple threads running on the different processors into account, only locally optimize the bandwidth nor deal with the dynamic behavior of these applications. The contributions of this chapter are to outline the main limitations of current techniques and to introduce an approach for dealing with the dynamic multi-threaded of our application domain

A Survey of Operating Systems Infrastructure for Embedded Systems

Since early applications in the 1960s, embedded systems have come down in price and there has been a dramatic rise in processing power and functionality. In addition, embedded systems are becoming increasingly complex. High-end devices, such as mobile phones, PDAs, entertainment devices, and set-top boxes, feature millions of lines of code with varying degrees of assurance of correctness. Nowadays, more and more embedded systems are implemented in a distributed way, a wide range of high-performance distributed embedded systems have been designed and deployed. As a lot of aspects of embedded system design become increasingly dependent on the effective interaction of distributed processors, it is clear that as much effort needs to be focused on software infrastructure, such as operating systems, with respect to how to provide functionality in order to fulfill these requirements. This technical report presents some of the approaches associated to operating systems that have been used in order to fulfill these needs.CAPES/MEC - Brasil, Project BEX3342/08-

Optimization-based design of fault-tolerant avionics

This dissertation considers the problem of improving the self-consciousness for avionic systems using numerical optimization techniques, emphasizing UAV applications. This self-consciousness implies a sense of awareness for oneself to make a reliable decision on some crucial aspects. In the context of the avionics or aerospace industry, those aspects are SWaP-C as well as safety and reliability. The decision-making processes to optimize these aspects, which are the main contributions of this work, are presented. In addition, implementation on various types of applications related to avionics and UAV are also provided. The first half of this thesis lays out the background of avionics development ranging from a mechanical gyroscope to a current state-of-the-art electronics system. The relevant mathematics regarding convex optimization and its algorithms, which will be used for formulating this self-consciousness problem, are also provided. The latter half presents two problem formulations for redundancy design automation and reconfigurable middleware. The first formulation focuses on the minimization of SWaP-C while satisfying safety and reliability requirements. The other one aims to maximize the system safety and reliability by introducing a fault-tolerant capability via the task scheduler of middleware or RTOS. The usage of these two formulations is shown by four aerospace applications---reconfigurable multicore avionics, a SITL simulation of a UAV GNC system, a modular drone, and a HITL simulation of a fault-tolerant distributed engine control architecture.Ph.D

Jitsu: Just-in-time summoning of unikernel

Network latency is a problem for all cloud services. It can be mitigated by moving computation out of remote datacenters by rapidly instantiating local services near the user. This requires an embedded cloud platform on which to deploy multiple applications securely and quickly. We present Jitsu, a new Xen toolstack that satisfies the demands of secure multi-tenant isolation on resource-constrained embedded ARM devices. It does this by using unikernels: lightweight, compact, single address space, memory-safe virtual machines (VMs) written in a high-level language. Using fast shared memory channels, Jitsu provides a directory service that launches unikernels in response to network traffic and masks boot latency. Our evaluation shows Jitsu to be a power-efficient and responsive platform for hosting cloud services in the edge network while preserving the strong isolation guarantees of a type-1 hypervisor.The research leading to these results received funding from the European Union’s Seventh Framework Programme FP7/2007–2013 under the Trilogy 2 project (grant agreement no. 317756), and the User Centric Networking project, (grant agreement no. 611001), and the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL), under contract FA8750-11-C-0249.This is the author accepted manuscript. The final version is available from USENIX via https://www.usenix.org/conference/nsdi15/technical-sessions/presentation/madhavapedd

Virtualization techniques for memory resource exploitation

Cloud infrastructures have become indispensable in our daily lives with the rise of cloud-based services offered by companies like Facebook, Google, Amazon and many others. These cloud infrastructures use a large numbers of servers provisioned with their own computing resources. Each of these servers use a piece of software, called the Hypervisor (``HV''), that allows them to create multiple virtual instances of the server's physical computing resources and abstract them into "Virtual Machines'' (VMs). A VM runs an Operating System, which in turn runs the applications. The VMs within the servers generate varying memory demand behavior. When the demand increases, costly operations such as (virtual) disk accesses and/or VM migrations can occur. As a result, it is necessary to optimize the utilization of the local memory resources within a single computing server. However, pressure on the memory resources can still increase, making it necessary to migrate the VM to a different server with larger memory or add more memory to the same server. At this point, it is important to consider that some of the servers in the cloud infrastructure might have memory resources that they are not using. Considering the possibility to make memory available to the server, new architectures have been introduced that provide hardware support to enable servers to share their memory capacity. This thesis presents multiple contributions to the memory management problem. First, it addresses the problem of optimizing memory resources in a virtualized server through different types of memory abstractions. Two full contributions are presented for managing memory within a single server called SmarTmem and CARLEMM. In this respect, a third contribution is also presented, called CAVMem, that works as the foundation for CARLEMM. Second, this thesis presents two contributions for memory capacity aggregation across multiple servers, offering two mechanisms called GV-Tmem and vMCA, this latter being based on GV-Tmem but with significant enhancements. These mechanisms distribute the server's total memory within a single-server and globally across computing servers using a user-space process with high-level memory management policies.Las infraestructuras para la nube se han vuelto indispensables en nuestras vidas diarias con la proliferación de los servicios ofrecidos por compañías como Facebook, Google, Amazon entre otras. Estas infraestructuras utilizan una gran cantidad de servidores proveídos con sus propios recursos computacionales. Cada unos de estos servidores utilizan un software, llamado el Hipervisor (“HV”), que les permite crear múltiples instancias virtuales de los recursos físicos de computación del servidor y abstraerlos en “Máquinas Virtuales” (VMs). Una VM ejecuta un Sistema Operativo (OS), el cual a su vez ejecuta aplicaciones. Las VMs dentro de los servidores generan un comportamiento variable de demanda de memoria. Cuando la demanda de memoria aumenta, operaciones costosas como accesos al disco (virtual) y/o migraciones de VMs pueden ocurrir. Como resultado, es necesario optimizar la utilización de los recursos de memoria locales dentro del servidor. Sin embargo, la demanda por memoria puede seguir aumentando, haciendo necesario que la VM migre a otro servidor o que se añada más memoria al servidor. En este punto, es importante considerar que algunos servidores podrían tener recursos de memoria que no están utilizando. Considerando la posibilidad de hacer más memoria disponible a los servidores que lo necesitan, nuevas arquitecturas de servidores han sido introducidos que brindan el soporte de hardware necesario para habilitar que los servidores puedan compartir su capacidad de memoria. Esta tesis presenta múltiples contribuciones para el problema de manejo de memoria. Primero, se enfoca en el problema de optimizar los recursos de memoria en un servidor virtualizado a través de distintos tipos de abstracciones de memoria. Dos contribuciones son presentadas para administrar memoria de manera automática dentro de un servidor virtualizado, llamadas SmarTmem y CARLEMM. En este contexto, una tercera contribución es presentada, llamada CAVMem, que proporciona los fundamentos para el desarrollo de CARLEMM. Segundo, la tesis presenta dos contribuciones enfocadas en la agregación de capacidad de memoria a través de múltiples servidores, ofreciendo dos mecanismos llamados GV-Tmem y vMCA, siendo este último basado en GV-Tmem pero con mejoras significativas. Estos mecanismos administran la memoria total de un servidor a nivel local y de manera global a lo largo de los servidores de la infraestructura de nube utilizando un proceso de usuario que implementa políticas de manejo de ...Postprint (published version