Towards the integration of data-centric distribution technology into partitioned embedded systems

REACTION 2013. 2nd International Workshop on Real-time and distributed computing in emerging applications. December 3rd, 2013, Vancouver, Canada.This work proposes an architecture to enable the use of data-centric real-time distribution middleware in partitioned embedded systems based on a hypervisor. Partitioning is a technique that provides strong temporal and spatial isolation, thus allowing mixed-criticality applications to be executed in the same hardware. The proposed architecture not only enables transparent communication among partitions, but it also facilitates the interconnection between partitioned and nonpartitioned systems through distribution middleware. Preliminary results show that hypervisor technology provides low overhead and a reasonable trade-off between temporal isolation and performance.This work has been funded in part by the Spanish Government and FEDER funds under grant number TIN2011-28567-C03-02 (HIPARTES

LUNES: Agent-based Simulation of P2P Systems (Extended Version)

We present LUNES, an agent-based Large Unstructured NEtwork Simulator, which allows to simulate complex networks composed of a high number of nodes. LUNES is modular, since it splits the three phases of network topology creation, protocol simulation and performance evaluation. This permits to easily integrate external software tools into the main software architecture. The simulation of the interaction protocols among network nodes is performed via a simulation middleware that supports both the sequential and the parallel/distributed simulation approaches. In the latter case, a specific mechanism for the communication overhead-reduction is used; this guarantees high levels of performance and scalability. To demonstrate the efficiency of LUNES, we test the simulator with gossip protocols executed on top of networks (representing peer-to-peer overlays), generated with different topologies. Results demonstrate the effectiveness of the proposed approach.Comment: Proceedings of the International Workshop on Modeling and Simulation of Peer-to-Peer Architectures and Systems (MOSPAS 2011). As part of the 2011 International Conference on High Performance Computing and Simulation (HPCS 2011

Data-centric distribution technology in ARINC-653 systems

REACTION 2014. 3rd International Workshop on Real-time and Distributed Computing in Emerging Applications. Rome, Italy. December 2nd, 2014.Standard distribution middleware has recently emerged as a potential solution to interconnect distributed systems in the avionics domain, as it would bring important benefits throughout the software development process. A remaining challenge, however, is reducing the complexity associated with current distribution standards which leads to prohibitive certification costs. To overcome this complexity, this work explores the use of the DDS distribution standard on top of a software platform based on the ARINC-653 specification. Furthermore, it discusses how both technologies can be integrated in order to apply them in mission and safety-critical scenarios.This work has been funded in part by the Spanish Government and FEDER funds under grant number TIN2011-28567-C03-02 (HIPARTES).Publicad

Reliable Messaging to Millions of Users with MigratoryData

Web-based notification services are used by a large range of businesses to selectively distribute live updates to customers, following the publish/subscribe (pub/sub) model. Typical deployments can involve millions of subscribers expecting ordering and delivery guarantees together with low latencies. Notification services must be vertically and horizontally scalable, and adopt replication to provide a reliable service. We report our experience building and operating MigratoryData, a highly-scalable notification service. We discuss the typical requirements of MigratoryData customers, and describe the architecture and design of the service, focusing on scalability and fault tolerance. Our evaluation demonstrates the ability of MigratoryData to handle millions of concurrent connections and support a reliable notification service despite server failures and network disconnections

MultiLibOS: an OS architecture for cloud computing

Cloud computing is resulting in fundamental changes to computing infrastructure, yet these changes have not resulted in corresponding changes to operating systems. In this paper we discuss some key changes we see in the computing infrastructure and applications of IaaS systems. We argue that these changes enable and demand a very different model of operating system. We then describe the MulitLibOS architecture we are exploring and how it helps exploit the scale and elasticity of integrated systems while still allowing for legacy software run on traditional OSes

Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management

As users of big data applications expect fresh results, we witness a new breed of stream processing systems (SPS) that are designed to scale to large numbers of cloud-hosted machines. Such systems face new challenges: (i) to benefit from the pay-as-you-go model of cloud computing, they must scale out on demand, acquiring additional virtual machines (VMs) and parallelising operators when the workload increases; (ii) failures are common with deployments on hundreds of VMs - systems must be fault-tolerant with fast recovery times, yet low per-machine overheads. An open question is how to achieve these two goals when stream queries include stateful operators, which must be scaled out and recovered without affecting query results. Our key idea is to expose internal operator state explicitly to the SPS through a set of state management primitives. Based on them, we describe an integrated approach for dynamic scale out and recovery of stateful operators. Externalised operator state is checkpointed periodically by the SPS and backed up to upstream VMs. The SPS identifies individual operator bottlenecks and automatically scales them out by allocating new VMs and partitioning the check-pointed state. At any point, failed operators are recovered by restoring checkpointed state on a new VM and replaying unprocessed tuples. We evaluate this approach with the Linear Road Benchmark on the Amazon EC2 cloud platform and show that it can scale automatically to a load factor of L=350 with 50 VMs, while recovering quickly from failures. Copyright © 2013 ACM