MILP-Based Short-Term Thermal Unit Commitment and Hydrothermal Scheduling Including Cascaded Reservoirs and Fuel Constraints

Reservoirs are often built in cascade on the same river system, introducing inexorable constraints. It is therefore strategically important to scheme out an efficient commitment of thermal generation units along with the scheduling of hydro generation units for better operational efficiency, considering practical system conditions. This paper develops a comprehensive, unit-wise hydraulic model with reservoir and river system constraints, as well as gas constraints, with head effects, to commit thermal generation units and schedule hydro ones in the short-term. A mixed integer linear programming (MILP) methodology, using the branch and bound & cut (BB&C) algorithm, is employed to solve the resultant problem. Due to the detailed modelling of individual hydro units and cascaded dependent reservoirs, the problem size is substantially swollen. Multithread computing is invoked to accelerate the solution process. Simulation results, conducted on various test systems, reiterate that the developed MILP-based hydrothermal scheduling approach outperforms other techniques in terms of cost efficiency

Energy-aware scheduling in heterogeneous computing systems

In the last decade, the grid computing systems emerged as useful provider of the computing power required for solving complex problems. The classic formulation of the scheduling problem in heterogeneous computing systems is NP-hard, thus approximation techniques are required for solving real-world scenarios of this problem. This thesis tackles the problem of scheduling tasks in a heterogeneous computing environment in reduced execution times, considering the schedule length and the total energy consumption as the optimization objectives. An efficient multithreading local search algorithm for solving the multi-objective scheduling problem in heterogeneous computing systems, named MEMLS, is presented. The proposed method follows a fully multi-objective approach, applying a Pareto-based dominance search that is executed in parallel by using several threads. The experimental analysis demonstrates that the new multithreading algorithm outperforms a set of fast and accurate two-phase deterministic heuristics based on the traditional MinMin. The new ME-MLS method is able to achieve significant improvements in both makespan and energy consumption objectives in reduced execution times for a large set of testbed instances, while exhibiting very good scalability. The ME-MLS was evaluated solving instances comprised of up to 2048 tasks and 64 machines. In order to scale the dimension of the problem instances even further and tackle large-sized problem instances, the Graphical Processing Unit (GPU) architecture is considered. This line of future work has been initially tackled with the gPALS: a hybrid CPU/GPU local search algorithm for efficiently tackling a single-objective heterogeneous computing scheduling problem. The gPALS shows very promising results, being able to tackle instances of up to 32768 tasks and 1024 machines in reasonable execution times.En la última década, los sistemas de computación grid se han convertido en útiles proveedores de la capacidad de cálculo necesaria para la resolución de problemas complejos. En su formulación clásica, el problema de la planificación de tareas en sistemas heterogéneos es un problema NP difícil, por lo que se requieren técnicas de resolución aproximadas para atacar instancias de tamaño realista de este problema. Esta tesis aborda el problema de la planificación de tareas en sistemas heterogéneos, considerando el largo de la planificación y el consumo energético como objetivos a optimizar. Para la resolución de este problema se propone un algoritmo de búsqueda local eficiente y multihilo. El método propuesto se trata de un enfoque plenamente multiobjetivo que consiste en la aplicación de una búsqueda basada en dominancia de Pareto que se ejecuta en paralelo mediante el uso de varios hilos de ejecución. El análisis experimental demuestra que el algoritmo multithilado propuesto supera a un conjunto de heurísticas deterministas rápidas y e caces basadas en el algoritmo MinMin tradicional. El nuevo método, ME-MLS, es capaz de lograr mejoras significativas tanto en el largo de la planificación y como en consumo energético, en tiempos de ejecución reducidos para un gran número de casos de prueba, mientras que exhibe una escalabilidad muy promisoria. El ME-MLS fue evaluado abordando instancias de hasta 2048 tareas y 64 máquinas. Con el n de aumentar la dimensión de las instancias abordadas y hacer frente a instancias de gran tamaño, se consideró la utilización de la arquitectura provista por las unidades de procesamiento gráfico (GPU). Esta línea de trabajo futuro ha sido abordada inicialmente con el algoritmo gPALS: un algoritmo híbrido CPU/GPU de búsqueda local para la planificación de tareas en en sistemas heterogéneos considerando el largo de la planificación como único objetivo. La evaluación del algoritmo gPALS ha mostrado resultados muy prometedores, siendo capaz de abordar instancias de hasta 32768 tareas y 1024 máquinas en tiempos de ejecución razonables

Stable Multithreading: A New Paradigm for Reliable and Secure Threads

Multi threaded programs have become pervasive and critical due to the rise of the multi core hardware and the accelerating computational demand. Unfortunately, despite decades of research and engineering effort, these programs remain notoriously difficult to get right, and they are plagued with harmful concurrency bugs that can cause wrong outputs, program crashes, security breaches, and so on. Our research reveals that a root cause of this difficulty is that multithreaded programs have too many possible thread interleavings (or schedules) at runtime. Even given only a single input, a program may run into a great number of schedules, depending on factors such as hardware timing and OS scheduling. Considering all inputs, the number of schedules is even much greater. It is extremely challenging to understand, test, analyze, or verify this huge number of schedules for a multi threaded program and make sure that all these schedules are free of concurrency bugs. Thus, multi threaded programs are extremely difficult to get right. To reduce the number of possible schedules for all inputs, we looked into the relation between inputs and schedules of real-world programs, and made an exciting discovery: many programs need only a small set of schedules to efficiently process a wide range of inputs! Leveraging this discovery, we have proposed a new idea called Stable Multithreading (or StableMT) that reuses each schedule on a wide range of inputs, greatly reducing the number of possible schedules for all inputs. By addressing the root cause that makes multithreading difficult to get right, StableMT makes understanding, testing, analyzing, and verification of multithreaded programs much easier. To realize StableMT, we have built three StableMT systems, TERN, PEREGRINE, and PARROT, with each addressing a distinct research challenge. Evaluation on a wide range of 108 popular multithreaded programs with our latest StableMT system, PARROT, shows that StableMT is simple, fast, and deployable. All PARROT's source code, entire benchmarks, and raw evaluation results are available at http://github.com/columbia/smt-mc. To encourage deployment, we have applied StableMT to improve several reliability techniques, including: (1) making reproducing real world concurrency bugs much easier; (2) greatly improving the precision of static program analysis, leading to the detection of several new harmful data races in heavily tested programs; and (3) greatly increasing the coverage of model checking, a systematic testing technique, by many orders of magnitudes. StableMT has attracted the research community's interests, and some techniques and ideas in our StableMT systems have been leveraged by other researchers to compute a small set of schedules to cover all or most inputs for multi threaded programs

Concurrency Attacks

Just as errors in sequential programs can lead to security exploits, errors in concurrent programs can lead to concurrency attacks. Questions such as whether these attacks are real and what characteristics they have remain largely unknown. In this paper, we present a preliminary study of concurrency attacks and the security implications of real concurrency errors. Our study yields several interesting findings. For instance, we observe that the exploitability of a concurrency error depends on the duration of the timing window within which the error may occur. We further observe that attackers can increase this window through carefully crafted inputs. We also find that four out of five commonly used sequential defense mechanisms become unsafe when applied to concurrent programs. Based on our findings, we propose new defense directions and fixes to existing defenses

Dynamic Analysis of Embedded Software

abstract: Most embedded applications are constructed with multiple threads to handle concurrent events. For optimization and debugging of the programs, dynamic program analysis is widely used to collect execution information while the program is running. Unfortunately, the non-deterministic behavior of multithreaded embedded software makes the dynamic analysis difficult. In addition, instrumentation overhead for gathering execution information may change the execution of a program, and lead to distorted analysis results, i.e., probe effect. This thesis presents a framework that tackles the non-determinism and probe effect incurred in dynamic analysis of embedded software. The thesis largely consists of three parts. First of all, we discusses a deterministic replay framework to provide reproducible execution. Once a program execution is recorded, software instrumentation can be safely applied during replay without probe effect. Second, a discussion of probe effect is presented and a simulation-based analysis is proposed to detect execution changes of a program caused by instrumentation overhead. The simulation-based analysis examines if the recording instrumentation changes the original program execution. Lastly, the thesis discusses data race detection algorithms that help to remove data races for correctness of the replay and the simulation-based analysis. The focus is to make the detection efficient for C/C++ programs, and to increase scalability of the detection on multi-core machines.Dissertation/ThesisDoctoral Dissertation Computer Science 201