QoS and security-aware task assignment and scheduling in real-time systems

Security issues in mission-critical real-time systems (e.g., command and control systems) are becoming increasingly important as there are growing needs for satisfying information assurance in these systems. In such systems, it is important to guarantee real-time deadlines along with the security requirements (e.g., confidentiality, integrity, and availability) of the applications. Traditionally, resource management in real-time systems has focused on meeting deadlines along with satisfying fault-tolerance and/or resource constraints. Such an approach is inadequate to accommodate security requirements into resource management algorithms. Based on the imprecise computation paradigm, a task can have several Quality of Service (QoS) levels, higher QoS result incurs higher computational cost. Similarly, achieving a higher level of confidentially requires stronger encryption, which incurs higher computational cost. Therefore, there exists a tradeoff between schedulability of the tasks on the one hand, and the accuracy (QoS) and security of the results produced on the other hand. This tradeoff must be carefully accounted in the resource management algorithms. In this context, this dissertation makes the following contributions: (i) formulation of scheduling problems accounting both deadline and security requirements of workloads in real-time systems, (ii) development of novel task allocation and scheduling algorithms for such workloads, (iii) and evaluation of the results through simulation studies and a limited test evaluations in one case. In particular, the following are the three key contributions. Firstly, the problem of scheduling a set of non-preemptable real-time tasks with security and QoS requirements with the goal of maximizing integrated QoS and security of the system is addressed. This problem is formulated as MILP, and then its complexity is proved to be NP-hard. An online efficient heuristic algorithm is developed as the problem is NP-hard. Simulation studies for a wide range of workload scenarios showed that the proposed algorithm outperforms a set of baseline algorithms. Further, the proposed algorithm\u27s performance is close to the optimal solution in a specific special case of the problem. Secondly, a static assignment and scheduling of a set of dependent real-time tasks, modeled as Directed Acyclic Graph (DAG), with security and QoS requirements in heterogeneous real-time system with the objective of maximizing Total Quality Value (TQV) of the system is studied. This problem is formulated as MINLP. Since this problem is NP-hard, a heuristic algorithm to maximize TQV while satisfying the security constraint of the system is developed. The proposed algorithm was evaluated through extensive simulation studies and compared to a set of baseline algorithms for variations of synthetic workloads. The proposed algorithm outperforms the baseline algorithms in all the simulated conditions for fully-connected and shared bus network topologies. Finally, the problem of dynamic assignment and scheduling of a set of dependent tasks with QoS and security requirements in heterogeneous distributed system to maximize the system TQV is addressed. Two heuristic algorithms to maximize TQV of the system are proposed because the problem is NP-hard. The proposed algorithms were evaluated by extensive simulation studies and by a test experiment in InfoSpher platform. The proposed algorithms outperform the baseline algorithms in most of the simulated conditions for fully-connected and shared bus network topologies

Different aspects of workflow scheduling in large-scale distributed systems

As large-scale distributed systems gain momentum, the scheduling of workflow applications with multiple requirements in such computing platforms has become a crucial area of research. In this paper, we investigate the workflow scheduling problem in large-scale distributed systems, from the Quality of Service (QoS) and data locality perspectives. We present a scheduling approach, considering two models of synchronization for the tasks in a workflow application: (a) communication through the network and (b) communication through temporary files. Specifically, we investigate via simulation the performance of a heterogeneous distributed system, where multiple soft real-time workflow applications arrive dynamically. The applications are scheduled under various tardiness bounds, taking into account the communication cost in the first case study and the I/O cost and data locality in the second.The work presented in this paper has been partially supported by EU, under the COST program Action IC1305, “Network for Sustainable Ultrascale Computing (NESUS)”, and by the Ministerio de Economía y Competitividad, Spain, under the project TIN2013-41350-P, “Scalable Data Management Techniques for High-End Computing Systems”

MapReduce is Good Enough? If All You Have is a Hammer, Throw Away Everything That's Not a Nail!

Hadoop is currently the large-scale data analysis "hammer" of choice, but there exist classes of algorithms that aren't "nails", in the sense that they are not particularly amenable to the MapReduce programming model. To address this, researchers have proposed MapReduce extensions or alternative programming models in which these algorithms can be elegantly expressed. This essay espouses a very different position: that MapReduce is "good enough", and that instead of trying to invent screwdrivers, we should simply get rid of everything that's not a nail. To be more specific, much discussion in the literature surrounds the fact that iterative algorithms are a poor fit for MapReduce: the simple solution is to find alternative non-iterative algorithms that solve the same problem. This essay captures my personal experiences as an academic researcher as well as a software engineer in a "real-world" production analytics environment. From this combined perspective I reflect on the current state and future of "big data" research

Topology-Aware and Dependence-Aware Scheduling and Memory Allocation for Task-Parallel Languages

International audienceWe present a joint scheduling and memory allocation algorithm for efficient execution of task-parallel programs on non-uniform memory architecture (NUMA) systems. Task and data placement decisions are based on a static description of the memory hierarchy and on runtime information about intertask communication. Existing locality-aware scheduling strategies for fine-grained tasks have strong limitations: they are specific to some class of machines or applications, they do not handle task dependences, they require manual program annotations, or they rely on fragile profiling schemes. By contrast, our solution makes no assumption on the structure of programs or on the layout of data in memory. Experimental results, based on the OpenStream language, show that locality of accesses to main memory of scientific applications can be increased significantly on a 64-core machine, resulting in a speedup of up to 1.63× compared to a state-of-the-art work-stealing scheduler

Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

Qualität und Nutzen - Über den Gebrauch von Zeit-Wert-Funktionen zur Integration qualitäts- und zeit-flexibler Aspekte in einer dynamischen Echtzeit-Einplanungsumgebung

Scheduling methodologies for real-time applications have been of keen interest to diverse research communities for several decades. Depending on the application area, algorithms have been developed that are tailored to specific requirements with respect to both the individual components of which an application is made up and the computational platform on which it is to be executed. Many real-time scheduling algorithms base their decisions solely or partly on timing constraints expressed by deadlines which must be met even under worst-case conditions. The increasing complexity of computing hardware means that worst-case execution time analysis becomes increasingly pessimistic. Scheduling hard real-time computations according to their worst-case execution times (which is common practice) will thus result, on average, in an increasing amount of spare capacity. The main goal of flexible real-time scheduling is to exploit this otherwise wasted capacity. Flexible scheduling schemes have been proposed to increase the ability of a real-time system to adapt to changing requirements and nondeterminism in the application behaviour. These models can be categorised as those whose source of flexibility is the quality of computations and those which are flexible regarding their timing constraints. This work describes a novel model which allows to specify both flexible timing constraints and quality profiles for an application. Furthermore, it demonstrates the applicability of this specification method to real-world examples and suggests a set of feasible scheduling algorithms for the proposed problem class.Einplanungsverfahren für Echtzeitanwendungen stehen seit Jahrzehnten im Interesse verschiedener Forschungsgruppen. Abhängig vom Anwendungsgebiet wurden Algorithmen entwickelt, welche an die spezifischen Anforderungen sowohl hinsichtlich der einzelnen Komponenten, aus welchen eine Anwendung besteht, als auch an die Rechnerplattform, auf der diese ausgeführt werden sollen, angepasst sind. Viele Echtzeit-Einplanungsverfahren gründen ihre Entscheidungen ausschließlich oder teilweise auf Zeitbedingungen, welche auch bei Auftreten maximaler Ausführungszeiten eingehalten werden müssen. Die zunehmende Komplexität von Rechner-Hardware bedeutet, dass die Worst-Case-Analyse in steigendem Maße pessimistisch wird. Die Einplanung harter Echtzeit-Berechnungen anhand ihrer maximalen Ausführungszeiten (was die gängige Praxis darstellt) resultiert daher im Regelfall in einer frei verfügbaren Rechenkapazität in steigender Höhe. Das Hauptziel flexibler Echtzeit-Einplanungsverfahren ist es, diese ansonsten verschwendete Kapazität auszunutzen. Flexible Einplanungsverfahren wurden vorgeschlagen, welche die Fähigkeit eines Echtzeitsystems erhöhen, sich an veränderte Anforderungen und Nichtdeterminismus im Verhalten der Anwendung anzupassen. Diese Modelle können unterteilt werden in solche, deren Quelle der Flexibilität die Qualität der Berechnungen ist, und jene, welche flexibel hinsichtlich ihrer Zeitbedingungen sind. Diese Arbeit beschreibt ein neuartiges Modell, welches es erlaubt, sowohl flexible Zeitbedingungen als auch Qualitätsprofile für eine Anwendung anzugeben. Außerdem demonstriert sie die Anwendbarkeit dieser Spezifikationsmethode auf reale Beispiele und schlägt eine Reihe von Einplanungsalgorithmen für die vorgestellte Problemklasse vor