DeSyRe: on-Demand System Reliability

The DeSyRe project builds on-demand adaptive and reliable Systems-on-Chips (SoCs). As fabrication technology scales down, chips are becoming less reliable, thereby incurring increased power and performance costs for fault tolerance. To make matters worse, power density is becoming a significant limiting factor in SoC design, in general. In the face of such changes in the technological landscape, current solutions for fault tolerance are expected to introduce excessive overheads in future systems. Moreover, attempting to design and manufacture a totally defect and fault-free system, would impact heavily, even prohibitively, the design, manufacturing, and testing costs, as well as the system performance and power consumption. In this context, DeSyRe delivers a new generation of systems that are reliable by design at well-balanced power, performance, and design costs. In our attempt to reduce the overheads of fault-tolerance, only a small fraction of the chip is built to be fault-free. This fault-free part is then employed to manage the remaining fault-prone resources of the SoC. The DeSyRe framework is applied to two medical systems with high safety requirements (measured using the IEC 61508 functional safety standard) and tight power and performance constraints

Autonomic log/restore for advanced optimistic simulation systems

In this paper we address state recoverability in optimistic simulation systems by presenting an autonomic log/restore architecture. Our proposal is unique in that it jointly provides the following features: (i) log/restore operations are carried out in a completely transparent manner to the application programmer, (ii) the simulation-object state can be scattered across dynamically allocated non-contiguous memory chunks, (iii) two differentiated operating modes, incremental vs non-incremental, coexist via transparent, optimized run-time management of dual versions of the same application layer, with dynamic selection of the best suited operating mode in different phases of the optimistic simulation run, and (iv) determinationof the best suited mode for any time frame is carried out on the basis of an innovative modeling/optimization approach that takes into account stability of each operating mode vs variations of the model execution parameters. © 2010 IEEE

Keeping checkpoint/restart viable for exascale systems

Next-generation exascale systems, those capable of performing a quintillion operations per second, are expected to be delivered in the next 8-10 years. These systems, which will be 1,000 times faster than current systems, will be of unprecedented scale. As these systems continue to grow in size, faults will become increasingly common, even over the course of small calculations. Therefore, issues such as fault tolerance and reliability will limit application scalability. Current techniques to ensure progress across faults like checkpoint/restart, the dominant fault tolerance mechanism for the last 25 years, are increasingly problematic at the scales of future systems due to their excessive overheads. In this work, we evaluate a number of techniques to decrease the overhead of checkpoint/restart and keep this method viable for future exascale systems. More specifically, this work evaluates state-machine replication to dramatically increase the checkpoint interval (the time between successive checkpoints) and hash-based, probabilistic incremental checkpointing using graphics processing units to decrease the checkpoint commit time (the time to save one checkpoint). Using a combination of empirical analysis, modeling, and simulation, we study the costs and benefits of these approaches on a wide range of parameters. These results, which cover of number of high-performance computing capability workloads, different failure distributions, hardware mean time to failures, and I/O bandwidths, show the potential benefits of these techniques for meeting the reliability demands of future exascale platforms

Programming model abstractions for optimizing I/O intensive applications

This thesis contributes from the perspective of task-based programming models to the efforts of optimizing I/O intensive applications. Throughout this thesis, we propose programming model abstractions and mechanisms that target a twofold objective: from the one hand, improve the I/O and total performance of applications on nowadays complex storage infrastructures. From the other hand, achieve such performance improvement without increasing the complexity of applications programming. The following paragraphs briefly summarize each of our contributions. First, towards exploiting compute-I/O patterns of I/O intensive applications and transparently improving I/O and total performance, we propose a number of abstractions that we refer to as I/O Awareness abstractions. An I/O aware task-based programming model is able to separate the handling of I/O and computations by supporting I/O Tasks. The execution of such tasks can overlap with compute tasks execution. Moreover, we provide programming model support to improve I/O performance by addressing the issue of I/O congestion. This is achieved by using Storage Bandwidth Constraints to control the level of task parallelism. We support two types of such constraints: (i) Static storage bandwidth constraints that are manually set by application developers. (ii) Auto-tunable constraints that are automatically set and tuned throughout the execution of application. Second, in order to exploit the heterogeneity of modern storage systems to improve performance in a transparent manner, we propose a set of capabilities that we refer to as Storage heterogeneity Awareness. A storage-heterogeneity aware task-based programming model builds on the concepts and abstractions that are introduced in the first contribution to improve the I/O performance of applications on heterogeneous storage systems. More specifically, such programming models support the following features: (i) abstracting the heterogeneity of the storage devices and exposing them as one hierarchical storage resource. (ii) supporting dedicated I/O scheduling. (iii) Finally, we introduce a mechanism that automatically and periodically flushes obsolete data from higher storage layers to lower storage layers. Third, targeting increasing parallelism levels of applications, we propose a Hybrid Programming Model that combines task-based programming models and MPI. In this programming model, tasks are used to achieve coarse-grained parallelism on large-scale distributed infrastructures, whereas MPI is used to gain fine-grained parallelism by parallelizing tasks execution. Such a hybrid programming model offers the possibility to enable parallel I/O and high-level I/O libraries in tasks. We enable such a hybrid programming model by supporting Native MPI Tasks. These tasks are native to the programming model for two reasons: they execute task code as opposed to calling external MPI binaries or scripts. Also, the data transfers and input/output handling is done in a completely transparent manner to application developers. Therefore, increasing parallelism levels while easing the design and programming of applications. Finally, to exploit the inherent parallelism opportunities in applications and overlap computation with I/O, we propose an Eager mechanism for releasing data dependencies. Unlike the traditional approach for releasing dependencies, eagerly releasing data dependencies allows successor tasks to be released for execution as soon as their data dependencies are ready, without having to wait for predecessor task(s) to completely finish execution. In order to support the eager-release of data dependencies, we describe the following core modifications to the design of task-based programming models: (i) defining and managing data dependency relationships as parameter-aware dependencies (ii) a mechanism for notifying the programming model that an output data has been generated before the execution of the producer task ends.Aquesta tesi contribueix des de la perspectiva dels models de programació basats en tasques als esforços d’optimitzar les aplicacions intensives de I/O. Al llarg d'aquesta tesi, proposem abstraccions i mecanismes del model de programació que persegueixen un doble objectiu: per una banda, millorar la I/O i el rendiment total de les aplicacions a les complexes infraestructures d'emmagatzematge de l'actualitat. D'altra banda, aconsegueixi aquesta millora del rendiment sense augmentar la complexitat de la programació d'aplicacions. Els paràgrafs següents resumeixen cadascuna de les nostres contribucions. En primer lloc, proposem una sèrie d'abstraccions a què ens referim com a abstraccions de consciència de I/O. Un model de programació basat en tasques amb reconeixement d'I/O pot separar el maneig d'I/O i els càlculs en admetre Tasques d'I/O. L'execució d'aquestes tasques es pot superposar amb l'execució de tasques de càlcul. A més, proporcionem suport de model de programació per millorar el rendiment d'I/O en abordar el problema de la congestió d'I/O. Això s'aconsegueix mitjançant l'ús de restriccions d'amplada de banda d'emmagatzematge per controlar el nivell de paral·lelisme de tasques. Admetem dos tipus d'aquestes restriccions: estàtic i autoajustable. En segon lloc, proposem un conjunt de capacitats a què ens referim com a Consciència d'heterogeneïtat d'emmagatzematge. Un model de programació basat en tasques conscient de l'heterogeneïtat de l'emmagatzematge es basa en els conceptes i les abstraccions que s'introdueixen en la primera contribució per millorar el rendiment d'I/O de les aplicacions en sistemes d'emmagatzematge heterogenis. Més específicament, aquests models de programació admeten les característiques següents: (i) abstreure l'heterogeneïtat dels dispositius d'emmagatzematge i exposar-los com a recurs d'emmagatzematge jeràrquic. (ii) admetre la programació d'I/O dedicada. (iii) Finalment, presentem un mecanisme que descarrega automàticament i periòdicament les dades obsoletes de les capes d'emmagatzematge superiors a les capes d'emmagatzematge inferiors. En tercer lloc, proposem un model de programació híbrid que combina models de programació basats en tasques i MPI. En aquest model de programació, les tasques s'utilitzen per aconseguir un paral·lelisme de gra gruixut en infraestructures distribuïdes a gran escala, mentre que MPI es fa servir per obtenir un paral·lelisme de gra fi en paral·lelitzar l'execució de tasques. Un model d'aquest tipus de programació híbrid ofereix la possibilitat d'habilitar I/O paral·leles i biblioteques d'I/O d'alt nivell en tasques. Habilitem un model de programació híbrid d'aquest tipus en admetre tasques MPI natives que executen codi de tasca en lloc de trucar a binaris o scripts MPI externs. A més, la transferència de dades i el maneig d’entrada / sortida es realitza d’una manera completament transparent per als desenvolupadors d’aplicacions. Per tant, augmenta els nivells de paral·lelisme alhora que se'n facilita el disseny i la programació d'aplicacions. Finalment proposem un mecanisme Eager per alliberar dependències de dades. A diferència de l'enfocament tradicional per alliberar dependències, alliberar amb entusiasme les dependències de dades permet que les tasques successores s'alliberin per a la seva execució tan aviat com les dependències de dades estiguin llestes, sense haver d'esperar que les tasques predecessores acabin completament l'execució. Per tal de donar suport a l'alliberament ansiós de les dependències de dades, descrivim les següents modificacions centrals al disseny de models de programació basats en tasques: (i) definir i administrar les relacions de dependència de dades com a dependències conscients de paràmetres (ii ) un mecanisme per notificar la model de programació que s'ha generat una dada de sortida abans que finalitzi l'execució de la tasca de productor.Postprint (published version

Transiency-driven Resource Management for Cloud Computing Platforms

Modern distributed server applications are hosted on enterprise or cloud data centers that provide computing, storage, and networking capabilities to these applications. These applications are built using the implicit assumption that the underlying servers will be stable and normally available, barring for occasional faults. In many emerging scenarios, however, data centers and clouds only provide transient, rather than continuous, availability of their servers. Transiency in modern distributed systems arises in many contexts, such as green data centers powered using renewable intermittent sources, and cloud platforms that provide lower-cost transient servers which can be unilaterally revoked by the cloud operator. Transient computing resources are increasingly important, and existing fault-tolerance and resource management techniques are inadequate for transient servers because applications typically assume continuous resource availability. This thesis presents research in distributed systems design that treats transiency as a first-class design principle. I show that combining transiency-specific fault-tolerance mechanisms with resource management policies to suit application characteristics and requirements, can yield significant cost and performance benefits. These mechanisms and policies have been implemented and prototyped as part of software systems, which allow a wide range of applications, such as interactive services and distributed data processing, to be deployed on transient servers, and can reduce cloud computing costs by up to 90\%. This thesis makes contributions to four areas of computer systems research: transiency-specific fault-tolerance, resource allocation, abstractions, and resource reclamation. For reducing the impact of transient server revocations, I develop two fault-tolerance techniques that are tailored to transient server characteristics and application requirements. For interactive applications, I build a derivative cloud platform that masks revocations by transparently moving application-state between servers of different types. Similarly, for distributed data processing applications, I investigate the use of application level periodic checkpointing to reduce the performance impact of server revocations. For managing and reducing the risk of server revocations, I investigate the use of server portfolios that allow transient resource allocation to be tailored to application requirements. Finally, I investigate how resource providers (such as cloud platforms) can provide transient resource availability without revocation, by looking into alternative resource reclamation techniques. I develop resource deflation, wherein a server\u27s resources are fractionally reclaimed, allowing the application to continue execution albeit with fewer resources. Resource deflation generalizes revocation, and the deflation mechanisms and cluster-wide policies can yield both high cluster utilization and low application performance degradation

Mathematics in Software Reliability and Quality Assurance

This monograph concerns the mathematical aspects of software reliability and quality assurance and consists of 11 technical papers in this emerging area. Included are the latest research results related to formal methods and design, automatic software testing, software verification and validation, coalgebra theory, automata theory, hybrid system and software reliability modeling and assessment