Over the last few years, the information technology industry has witnessed revolutions in multiple dimensions. Increasing ubiquitous sources of data have posed two connected challenges to data management solutions -- processing unprecedented volumes of data, and providing ad-hoc real-time analysis in mainstream production data stores without compromising regular transactional workload performance. In parallel, computer hardware systems are scaling out elastically, scaling up in the number of processors and cores, and increasing main memory capacity extensively. The data processing challenges combined with the rapid advancement of hardware systems has necessitated the evolution of a new breed of main-memory databases optimized for mixed OLTAP environments and designed to scale. The Oracle RDBMS In-memory Option (DBIM) is an industry-first distributed dual format architecture that allows a database object to be stored in columnar format in main memory highly optimized to break performance barriers in analytic query workloads, simultaneously maintaining transactional consistency with the corresponding OLTP optimized row-major format persisted in storage and accessed through database buffer cache. In this paper, we present the distributed, highly-available, and fault-tolerant architecture of the Oracle DBIM that enables the RDBMS to transparently scale out in a database cluster, both in terms of memory capacity and query processing throughput. We believe that the architecture is unique among all mainstream in-memory databases. It allows complete application-transparent, extremely scalable and automated distribution of Oracle RDBMS objects in-memory across a cluster, as well as across multiple NUMA nodes within a single server. It seamlessly provides distribution awareness to the Oracle SQL execution framework through affinitized fault-tolerant parallel execution within and across servers without explicit optimizer plan changes or query rewrites.

Two camps of file systems exist: parallel file systems designed for conventional high performance computing (HPC) and distributed file systems designed for newly emerged data-intensive applications. Addressing the big data challenge requires an approach that utilizes both high performance computing and data-intensive computing power. Thus, HPC applications may need to interact with distributed file systems, such as HDFS. The N-1 (N-to-1) parallel file write is a critical technical challenge, because it is very common for HPC applications but HDFS does not allow it. This study introduces a system solution, named SCALER, which allows MPI based applications to directly access HDFS without extra data movement. SCALER supports N-1 file write at both the inter-block level and intra-block level. Experimental results confirm that SCALER achieves the design goal efficiently.

Check pointing is widely used in technical computing. However, the overhead of check pointing is a subject of increasing in concern in recent years, especially for large-scale parallel computer systems. In these systems, check pointing generates a huge number of concurrent I/O writes. The burst of writes plus the worsening I/O-wall problem often leads to network and I/O congestion, and makes the overall system performance painfully slow. Recognizing contention as a dominant performance factor, in this paper we propose a systematic approach named check pointing orchestration to reduce write contention, which combines the marshaling of concurrent checkpoint requests and the adopting of vertical data access in coordination. A prototype of the proposed check pointing orchestration approach has been implemented at the system-level under Open MPI over the PVFS2 file system. Extensive experiments based on NPB benchmarks have been conducted to verify the design and implementation. Experimental results show that check pointing orchestration reduced the check pointing cost at a degree of more than 30%. Check pointing cost was halved for 4 out of 5 the C class NPB benchmarks.

The MapReduce programming paradigm is gaining more and more popularity recently due to its merits of ease of programming, data distribution and fault tolerance. The low barrier of adoption of MapReduce makes it a promising framework for non-dedicated distributed computing environments. However, the variability of hosts resources and availability could substantially degrade the performance of MapReduce applications. The replication-based fault tolerance mechanism helps to alleviate some problems at the cost of inefficient storage space utilization. Intelligent solutions that guarantee the performance of MapReduce applications with low data replication degree are needed to promote the idea of running MapReduce applications in non-dedicated environment at lower costs. In this research, we propose an Availability-aware Data Placement (ADAPT) strategy to improve the application performance without extra storage cost. The basic idea of ADAPT is to dispatch data based on the availability of each node, reduce network traffic, improve data locality, and optimize the application performance. We implement the prototype of ADAPT within the Hadoop framework, an open-source implementation of MapReduce. The performance of ADAPT is evaluated in an emulated non-dedicated distributed environment. The experimental results show that ADAPT can improve the performance by more than 30%. ADAPT achieves high reliability without the need for additional data replication. ADAPT has also been evaluated for large-scale computing environment through simulations, with promising results.

The computing paradigm of "HPC in the Cloud" has gained a surging interest in recent years, due to its merits of cost-efficiency, flexibility, and scalability. Cloud is designed on top of distributed file systems such as Google file system (GFS). The capability of running HPC applications on top of data-intensive file systems is a critical catalyst in promoting Clouds for HPC. However, the semantic gap between data-intensive file systems and HPC imposes numerous challenges. For example, N-1 (N to 1) is a widely used data access pattern for HPC applications such as check pointing, but cannot perform well on data-intensive file systems. In this study, we propose the CHunk-Aware I/O (CHAIO) strategy to enable efficient N-1 data access on data-intensive distributed file systems. CHAIO reorganizes I/O requests to favor data-intensive file systems and avoid possible access contention. It balances the workload distribution and promotes data locality. We have tested the CHAIO design over the Kosmos file system (KFS). Experimental results show that CHAIO achieves a more than two-fold improvement in I/O bandwidth for both write and read operations. Experiments in large-scale environment confirm the potential of CHAIO for small and irregular requests. The aggregator selection algorithm works well to balance the workload distribution. CHAIO is a critical and necessary step to enable HPC in the Cloud.

The MapReduce programming paradigm is gaining more and more popularity in recent years due to its ability in supporting easy programming, data distribution, as well as fault tolerance. Failure is an unwanted but inevitable fact that all large-scale parallel computing systems have to face with. MapReduce introduces a novel data replication and task reexecution strategy for fault tolerance. This study intends to lead a better understanding of such fault tolerance mechanisms. In particular, we build a stochastic performance model to quantify the impact of failures on MapReduce applications and to investigate its effectiveness under different computing environments. Simulations also have been carried out to verify the accuracy of the proposed model. Our results show that data replication is an effective approach even when failure rate is high, and the task migration mechanism of MapReduce works well in balancing the reliability difference among individual nodes. This work provides a theoretical foundation for optimizing large-scale MapReduce applications, especially when fault tolerance is the concern.

Check pointing is a widely used mechanism for supporting fault tolerance, but notorious in its high-cost disk access. The idea of memory-based check pointing has been extensively studied in research but made little success in practice due to its complexity and potential reliability concerns. In this study we present the design and implementation of REMEM, a Remote Memory check pointing system to extend the check pointing storage from disk to remote memory. A unique feature of REMEM is that it can be integrated into existing disk-based check pointing systems seamlessly. A user can flexibly switch between REMEM and disk as check pointing storage to balance the efficiency and reliability. The implementation of REMEM on Open MPI is also introduced. The experimental results confirm that REMEM and the proposed adaptive check pointing storage selection are promising in both performance, reliability and scalability.

Parallel applications can benefit greatly from massive computational capability, but their performance suffers from large latency of I/O accesses. The poor I/O performance has been attributed as a critical cause of the low sustained performance of parallel computing systems. In this study, we propose a data layout-aware optimization strategy to promote a better integration of the parallel I/O middleware and parallel file systems, two major components of the current parallel I/O systems, and to improve the data access performance. We explore the layout-aware optimization in both independent I/O and collective I/O, two primary forms of I/O in parallel applications. We illustrate that the layout-aware I/O optimization could improve the performance of current parallel I/O strategy effectively. The experimental results verify that the proposed strategy could improve parallel I/O performance by nearly 40% on average. The proposed layout-aware parallel I/O has a promising potential in improving the I/O performance of parallel systems.

The increasingly large ensemble size of modern High-Performance Computing (HPC) systems has drastically increased the possibility of failures. Performance under failures and its optimization become timely important issues facing the HPC community. In this study, we propose an analytical model to predict the application performance. The model characterizes the impact of coordinated checkpointing and system failures on application performance, considering all the factors including workload, the number of nodes, failure arrival rate, recovery cost, and checkpointing interval and overhead. Based on the model, we gauge three parameters, the number of compute nodes, checkpointing interval, and the number of spare nodes to conduct a comprehensive study of performance optimization under failures. Performance scalability under failures is also studied to explore the performance improvement space for different parameters. Experimental results from both synthetic and actual system failure logs confirm that the proposed model and optimization methodologies are effective and feasible.

As the scale and complexity of parallel systems continue to grow, failures become more and more an inevitable fact for solving large-scale applications. In this research, we present an analytical study to estimate execution time in the presence of failures of directed acyclic graph (DAG) based scientific applications and provide a guideline for performance optimization. The study is four fold. We first introduce a performance model to predict individual subtask computation time under failures. Next, a layered, iterative approach is adopted to transform a DAG into a layered DAG, which reflects full dependencies among all the subtasks. Then, the expected execution time under failures of the DAG is derived based on stochastic analysis. Unlike existing models, this newly proposed performance model provides both the variance and distribution. It is practical and can be put to real use. Finally, based on the model, performance optimization, weak point identification and enhancement are proposed. Intensive simulations with real system traces are conducted to verify the analytical findings. They show that the newly proposed model and weak point enhancement mechanism work well.

Performance issues of multi-tier Web services have been studied extensively in recent years. Performance modeling and prediction under failure of multi-tier architectures, however, is not well addressed yet. We propose a novel model named Performance under failure of multi-tier architecture, or PerFAMA in short, to address this issue. We first show that the multi-tier architecture with failure considerations is a product-form network, and then analyze and model the failure impact. By applying the PerFAMA model, we are able to predict the end-to-end response time of multi-tier Web services under failures. We have simulated two representative Web services architectures and various failure scenarios to verify the proposed PerFAMA model. The experimental results show that the proposed model works well and the prediction accuracy is up to 98%.

This paper discusses the application of existing workflow management systems to a real world science application (LQCD). Typical workflows and execution environment used in production are described. Requirements for the LQCD production system are discussed. The workflow management systems Askalon and Swift were tested by implementing the LQCD workflows and evaluated against the requirements. We report our findings and future work.

Modern high-end computers are unprecedentedly complex. Occurrence of faults is an inevitable fact in solving large-scale applications on future Petaflop machines. Many methods have been proposed in recent years to mask faults. These methods, however, impose various performance and production costs. A better understanding of faults' influence on application performance is necessary to use existing fault tolerant methods wisely. In this study, we first introduce some practical and effective performance models to predict the application completion time under system failures. These models separate the influence of failure rate, failure repair, checkpointing period, checkpointing cost, and parallel task allocation on parallel and sequential execution times. To benefit the end users of a given computing platform, we then develop effective fault-aware task scheduling algorithms to optimize application performance under system failures. Finally, extensive simulations and experiments are conducted to evaluate our prediction models and scheduling strategies with actual failure trace.