Near-optimal scheduling and decision-making models for reactive and proactive fault tolerance mechanisms

As High Performance Computing (HPC) systems increase in size to fulfill computational power demand, the chance of failure occurrences dramatically increases, resulting in potentially large amounts of lost computing time. Fault Tolerance (FT) mechanisms aim to mitigate the impact of failure occurrences to the running applications. However, the overhead of FT mechanisms increases proportionally to the HPC systems\u27 size. Therefore, challenges arise in handling the expensive overhead of FT mechanisms while minimizing the large amount of lost computing time due to failure occurrences. In this dissertation, a near-optimal scheduling model is built to determine when to invoke a hybrid checkpoint mechanism, by means of stochastic processes and calculus of variations. The obtained schedule minimizes the waste time caused by checkpoint mechanism and failure occurrences. Generally, the checkpoint/restart mechanisms periodically save application states and load the saved state, upon failure occurrences. Furthermore, to handle various FT mechanisms, an adaptive decision-making model has been developed to determine the best FT strategy to invoke at each decision point. The best mechanism at each decision point is selected among considered FT mechanisms to globally minimize the total waste time for an application execution by means of a dynamic programming approach. In addition, the model is adaptive to deal with changes in failure rate over time

A Pattern Language for High-Performance Computing Resilience

High-performance computing systems (HPC) provide powerful capabilities for modeling, simulation, and data analytics for a broad class of computational problems. They enable extreme performance of the order of quadrillion floating-point arithmetic calculations per second by aggregating the power of millions of compute, memory, networking and storage components. With the rapidly growing scale and complexity of HPC systems for achieving even greater performance, ensuring their reliable operation in the face of system degradations and failures is a critical challenge. System fault events often lead the scientific applications to produce incorrect results, or may even cause their untimely termination. The sheer number of components in modern extreme-scale HPC systems and the complex interactions and dependencies among the hardware and software components, the applications, and the physical environment makes the design of practical solutions that support fault resilience a complex undertaking. To manage this complexity, we developed a methodology for designing HPC resilience solutions using design patterns. We codified the well-known techniques for handling faults, errors and failures that have been devised, applied and improved upon over the past three decades in the form of design patterns. In this paper, we present a pattern language to enable a structured approach to the development of HPC resilience solutions. The pattern language reveals the relations among the resilience patterns and provides the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack.Comment: Proceedings of the 22nd European Conference on Pattern Languages of Program

Proactive cloud service assurance framework for fault remediation in cloud environment

Cloud resiliency is an important issue in successful implementation of cloud computing systems. Handling cloud faults proactively, with a suitable remediation technique having minimum cost is an important requirement for a fault management system. The selection of best applicable remediation technique is a decision making problem and considers parameters such as i) Impact of remediation technique ii) Overhead of remediation technique ii) Severity of fault and iv) Priority of the application. This manuscript proposes an analytical model to measure the effectiveness of a remediation technique for various categories of faults, further it demonstrates the implementation of an efficient fault remediation system using a rule-based expert system. The expert system is designed to compute an utility value for each remediation technique in a novel way and select the best remediation technique from its knowledgebase. A prototype is developed for experimentation purpose and the results shows improved availability with less overhead as compared to a reactive fault management system

Scalable and Reliable Sparse Data Computation on Emergent High Performance Computing Systems

Heterogeneous systems with both CPUs and GPUs have become important system architectures in emergent High Performance Computing (HPC) systems. Heterogeneous systems must address both performance-scalability and power-scalability in the presence of failures. Aggressive power reduction pushes hardware to its operating limit and increases the failure rate. Resilience allows programs to progress when subjected to faults and is an integral component of large-scale systems, but incurs significant time and energy overhead. The future exascale systems are expected to have higher power consumption with higher fault rates. Sparse data computation is the fundamental kernel in many scientific applications. It is suitable for the studies of scalability and resilience on heterogeneous systems due to its computational characteristics. To deliver the promised performance within the given power budget, heterogeneous computing mandates a deep understanding of the interplay between scalability and resilience. Managing scalability and resilience is challenging in heterogeneous systems, due to the heterogeneous compute capability, power consumption, and varying failure rates between CPUs and GPUs. Scalability and resilience have been traditionally studied in isolation, and optimizing one typically detrimentally impacts the other. While prior works have been proved successful in optimizing scalability and resilience on CPU-based homogeneous systems, simply extending current approaches to heterogeneous systems results in suboptimal performance-scalability and/or power-scalability. To address the above multiple research challenges, we propose novel resilience and energy-efficiency technologies to optimize scalability and resilience for sparse data computation on heterogeneous systems with CPUs and GPUs. First, we present generalized analytical and experimental methods to analyze and quantify the time and energy costs of various recovery schemes, and develop and prototype performance optimization and power management strategies to improve scalability for sparse linear solvers. Our results quantitatively reveal that each resilience scheme has its own advantages depending on the fault rate, system size, and power budget, and the forward recovery can further benefit from our performance and power optimizations for large-scale computing. Second, we design a novel resilience technique that relaxes the requirement of synchronization and identicalness for processes, and allows them to run in heterogeneous resources with power reduction. Our results show a significant reduction in energy for unmodified programs in various fault situations compared to exact replication techniques. Third, we propose a novel distributed sparse tensor decomposition that utilizes an asynchronous RDMA-based approach with OpenSHMEM to improve scalability on large-scale systems and prove that our method works well in heterogeneous systems. Our results show our irregularity-aware workload partition and balanced-asynchronous algorithms are scalable and outperform the state-of-the-art distributed implementations. We demonstrate that understanding different bottlenecks for various types of tensors plays critical roles in improving scalability

Design and implementation of fuzzy based control system for natural gas pipes system based on LabVIEW

The quality of Natural Gas Piping Systems (NGPS) must be ensured against any manufacturing defects. For this purpose, we develop a special testing machine (STM) constructed at the lab to test (NGPS). The proposed (STM) function is based on testing the weak points at the pipe connections e.g. pipe bends, and intermediate connections. For more than 1500 pieces of (NGPS), crack propagation simultaneously followed up and monitored on the output screen at the critical positions of the pipelines connections. The control system utilizes the LabVIEW tools for various signals acquisition and monitoring also for designing the control system strategy

Checkpointing algorithms and fault prediction

This paper deals with the impact of fault prediction techniques on checkpointing strategies. We extend the classical first-order analysis of Young and Daly in the presence of a fault prediction system, characterized by its recall and its precision. In this framework, we provide an optimal algorithm to decide when to take predictions into account, and we derive the optimal value of the checkpointing period. These results allow to analytically assess the key parameters that impact the performance of fault predictors at very large scale.Comment: Supported in part by ANR Rescue. Published in Journal of Parallel and Distributed Computing. arXiv admin note: text overlap with arXiv:1207.693

Thinking BigData: Motivation, Results and a Few Recipes for a Balanced Growth of High Performance Computing in Academia

Big Data is today both an emerging research area and a real present and future demand. High Performance Computing (HPC) Centers cannot neglect this fact and have to be reshaped to fulfill this need. In this paper we share our experience of upgrading a HPC Center at Politecnico di Torino, originally designed and deployed in 2010. We believe that this issue could be common to some other existing "general purpose" HPC centers where, at least in the short term, the possibility to start from scratch a new Big Data HPC center cannot be afforded but a balanced upgrade of the existing system has to be preferre

Adaptive and Power-aware Fault Tolerance for Future Extreme-scale Computing

Two major trends in large-scale computing are the rapid growth in HPC with in particular an international exascale initiative, and the dramatic expansion of Cloud infrastructures accompanied by the Big Data passion. To satisfy the continuous demands for increasing computing capacity, future extreme-scale systems will embrace a multi-fold increase in the number of computing, storage, and communication components, in order to support an unprecedented level of parallelism. Despite the capacity and economies benefits, making the upward transformation to extreme-scale poses numerous scientific and technological challenges, two of which are the power consumption and fault tolerance. With the increase in system scale, failure would become a norm rather than an exception, driving the system to significantly lower efficiency with unforeseen power consumption. This thesis aims at simultaneously addressing the above two challenges by introducing a novel fault-tolerant computational model, referred to as \textit{Leaping Shadows}. Based on Shadow Replication, Leaping Shadows associates with each main process a suite of coordinated shadow processes, which execute in parallel but at differential rates, to deal with failures and meet the QoS requirements of the underlying application under strict power/energy constraints. In failure-prone extreme-scale computing environments, this new model addresses the limitations of the basic Shadow Replication model, and achieves adaptive and power-aware fault tolerance that is more time and energy efficient than existing techniques. In this thesis, we first present an analytical model based optimization framework that demonstrates Shadow Replication's adaptivity and flexibility in achieving multi-dimensional QoS requirements. Then, we introduce Leaping Shadows as a novel power-aware fault tolerance model, which tolerates multiple types of failures, guarantees forward progress, and maintains a consistent level of resilience. Lastly, the details of a Leaping Shadows implementation in MPI is discussed, along with extensive performance evaluation that includes comparison to checkpoint/restart. Collectively, these efforts advocate an adaptive and power-aware fault tolerance alternative for future extreme-scale computing

On the impact of process replication on executions of large-scale parallel applications with coordinated checkpointing

International audienceProcessor failures in post-petascale parallel computing platforms are common occurrences. The traditional fault-tolerance solution, checkpoint-rollback-recovery, severely limits parallel efficiency. One solution is to replicate application processes so that a processor failure does not necessarily imply an application failure. Process replication, combined with checkpoint-rollback-recovery, has been recently advocated. We first derive novel theoretical results for Exponential failure distributions, namely exact values for the Mean Number of Failures To Interruption and the Mean Time To Interruption. We then extend these results to arbitrary failure distributions, obtaining closed-form solutions for Weibull distributions. Finally, we evaluate process replica-tion in simulation using both synthetic and real-world failure traces so as to quantify average application makespan. One interesting result from these experiments is that, when process repli-cation is used, application performance is not sensitive to the checkpointing period, provided that that period is within a large neighborhood of the optimal period. More generally, our empirical results make it possible to identify regimes in which process replication is beneficial