PerfXplain: Debugging MapReduce Job Performance

While users today have access to many tools that assist in performing large scale data analysis tasks, understanding the performance characteristics of their parallel computations, such as MapReduce jobs, remains difficult. We present PerfXplain, a system that enables users to ask questions about the relative performances (i.e., runtimes) of pairs of MapReduce jobs. PerfXplain provides a new query language for articulating performance queries and an algorithm for generating explanations from a log of past MapReduce job executions. We formally define the notion of an explanation together with three metrics, relevance, precision, and generality, that measure explanation quality. We present the explanation-generation algorithm based on techniques related to decision-tree building. We evaluate the approach on a log of past executions on Amazon EC2, and show that our approach can generate quality explanations, outperforming two naive explanation-generation methods.Comment: VLDB201

Performance Model of MapReduce Iterative Applications for Hybrid Cloud Bursting

Hybrid cloud bursting (i.e., leasing temporary off-premise cloud resources to boost the overall capacity during peak utilization) can be a cost-effective way to deal with the increasing complexity of big data analytics, especially for iterative applications. However, the low throughput, high latency network link between the on-premise and off-premise resources (“weak link”) makes maintaining scalability difficult. While several data locality techniques have been designed for big data bursting on hybrid clouds, their effectiveness is difficult to estimate in advance. Yet such estimations are critical, because they help users decide whether the extra pay-as-you-go cost incurred by using the off-premise resources justifies the runtime speed-up. To this end, the current paper presents a performance model and methodology to estimate the runtime of iterative MapReduce applications in a hybrid cloud-bursting scenario. The paper focuses on the overhead incurred by the weak link at fine granularity, for both the map and the reduce phases. This approach enables high estimation accuracy, as demonstrated by extensive experiments at scale using a mix of real-world iterative MapReduce applications from standard big data benchmarking suites that cover a broad spectrum of data patterns. Not only are the produced estimations accurate in absolute terms compared with experimental results, but they are also up to an order of magnitude more accurate than applying state-of-art estimation approaches originally designed for single-site MapReduce deployments

MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments

An improved task assignment scheme for Hadoop running in the clouds

Nowadays, data-intensive problems are so prevalent that numerous organizations in various industries have to face them in their business operation. It is often crucial for enterprises to have the capability of analyzing large volumes of data in an effective and timely manner. MapReduce and its open-source implementation Hadoop dramatically simplified the development of parallel data-intensive computing applications for ordinary users, and the combination of Hadoop and cloud computing made large-scale parallel data-intensive computing much more accessible to all potential users than ever before. Although Hadoop has become the most popular data management framework for parallel data-intensive computing in the clouds, the Hadoop scheduler is not a perfect match for the cloud environments. In this paper, we discuss the issues with the Hadoop task assignment scheme, and present an improved scheme for heterogeneous computing environments, such as the public clouds. The proposed scheme is based on an optimal minimum makespan algorithm. It projects and compares the completion times of all task slots\u27 next data block, and explicitly strives to shorten the completion time of the map phase of MapReduce jobs. We conducted extensive simulation to evaluate the performance of the proposed scheme compared with the Hadoop scheme in two types of heterogeneous computing environments that are typical on the public cloud platforms. The simulation results showed that the proposed scheme could remarkably reduce the map phase completion time, and it could reduce the amount of remote processing employed to a more significant extent which makes the data processing less vulnerable to both network congestion and disk contention. © 2013 Dai and Bassiouni