FABIOLA: Defining the Components for Constraint Optimization Problems in Big Data Environment

The optimization problems can be found in several examples within companies, such as the minimization of the production costs, the faults produced, or the maximization of customer loyalty. The resolution of them is a challenge that entails an extra effort. In addition, many of today’s enterprises are encountering the Big Data problems added to these optimization problems. Unfortunately, to tackle this challenge by medium and small companies is extremely difficult or even impossible. In this paper, we propose a framework that isolates companies from how the optimization problems are solved. More specifically, we solve optimization problems where the data is heterogeneous, distributed and of a huge volume. FABIOLA (FAst BIg cOstraint LAb) framework enables to describe the distributed and structured data used in optimization problems that can be parallelized (the variables are not shared between the various optimization problems), and obtains a solution using Constraint Programming Techniques

Just-in-time Data Distribution for Analytical Query Processing

Distributed processing commonly requires data spread across machines using a priori static or hash-based data allocation. In this paper, we explore an alternative approach that starts from a master node in control of the complete database, and a variable number of worker nodes for delegated query processing. Data is shipped just-in-time to the worker nodes using a need to know policy, and is being reused, if possible, in subsequent queries. A bidding mechanism among the workers yields a scheduling with the most efficient reuse of previously shipped data, minimizing the data transfer costs. Just-in-time data shipment allows our system to benefit from locally available idle resources to boost overall performance. The system is maintenance-free and allocation is fully transparent to users. Our experiments show that the proposed adaptive distributed architecture is a viable and flexible alternative for small scale MapReduce-type of settings

Just-In-Time Data Distribution for Analytical Query Processing

Distributed processing commonly requires data spread across machines using a priori static or hash-based data allocation. In this paper, we explore an alternative approach that starts from a master node in control of the complete database, and a variable number of worker nodes for delegated query processing. Data is shipped just-in-time to the worker nodes using a need to know policy, and is being reused, if possible, in subsequent queries. A bidding mechanism among the workers yields a scheduling with the most efficient reuse of previously shipped data, minimizing the data transfer costs. Just-in-time data shipment allows our system to benefit from locally available idle resources to boost overall performance. The system is maintenance-free and allocation is fully transparent to users. Our experiments show that the proposed adaptive distributed architecture is a viable and flexible alternative for small scale MapReduce-type of settings

Stubby: A Transformation-based Optimizer for MapReduce Workflows

There is a growing trend of performing analysis on large datasets using workflows composed of MapReduce jobs connected through producer-consumer relationships based on data. This trend has spurred the development of a number of interfaces--ranging from program-based to query-based interfaces--for generating MapReduce workflows. Studies have shown that the gap in performance can be quite large between optimized and unoptimized workflows. However, automatic cost-based optimization of MapReduce workflows remains a challenge due to the multitude of interfaces, large size of the execution plan space, and the frequent unavailability of all types of information needed for optimization. We introduce a comprehensive plan space for MapReduce workflows generated by popular workflow generators. We then propose Stubby, a cost-based optimizer that searches selectively through the subspace of the full plan space that can be enumerated correctly and costed based on the information available in any given setting. Stubby enumerates the plan space based on plan-to-plan transformations and an efficient search algorithm. Stubby is designed to be extensible to new interfaces and new types of optimizations, which is a desirable feature given how rapidly MapReduce systems are evolving. Stubby's efficiency and effectiveness have been evaluated using representative workflows from many domains.Comment: VLDB201

Scalable and Adaptive Online Joins

Scalable join processing in a parallel shared-nothing environment requires a partitioning policy that evenly distributes the processing load while minimizing the size of state maintained and number of messages communicated. Previous research proposes static partitioning schemes that require statistics beforehand. In an online or streaming environment in which no statistics about the workload are known, traditional static approaches perform poorly. This paper presents a novel parallel online dataflow join operator that supports arbitrary join predicates. The proposed operator continuously adjusts itself to the data dynamics through adaptive dataflow routing and state repartitioning. The operator is resilient to data skew, maintains high throughput rates, avoids blocking behavior during state repartitioning, takes an eventual consistency approach for maintaining its local state, and behaves strongly consistently as a black-box dataflow operator. We prove that the operator ensures a constant competitive ratio 3.75 in data distribution optimality and that the cost of processing an input tuple is amortized constant, taking into account adaptivity costs. Our evaluation demonstrates that our operator outperforms the state-of-the-art static partitioning schemes in resource utilization, throughput, and execution time

Llama: Leveraging columnar storage for scalable join processing in mapreduce.

Master'sMASTER OF SCIENC

Actionable Program Analyses for Improving Software Performance

Nowadays, we have greater expectations of software than ever before. This is followed by constant pressure to run the same program on smaller and cheaper machines. To meet this demand, the application’s performance has become the essential concern in software development. Unfortunately, many applications still suffer from performance issues: coding or design errors that lead to performance degradation. However, finding performance issues is a challenging task: there is limited knowledge on how performance issues are discovered and fixed in practice, and current performance profilers report only where resources are spent, but not where resources are wasted. The goal of this dissertation is to investigate actionable performance analyses that help developers optimize their software by applying relatively simple code changes. To understand causes and fixes of performance issues in real-world software, we first present an empirical study of 98 issues in popular JavaScript projects. The study illustrates the prevalence of simple and recurring optimization patterns that lead to significant performance improvements. Then, to help developers optimize their code, we propose two actionable performance analyses that suggest optimizations based on reordering opportunities and method inlining. In this work, we focus on optimizations with four key properties. First, the optimizations are effective, that is, the changes suggested by the analysis lead to statistically significant performance improvements. Second, the optimizations are exploitable, that is, they are easy to understand and apply. Third, the optimizations are recurring, that is, they are applicable across multiple projects. Fourth, the optimizations are out-of-reach for compilers, that is, compilers can not guarantee that a code transformation preserves the original semantics. To reliably detect optimization opportunities and measure their performance benefits, the code must be executed with sufficient test inputs. The last contribution complements state-of-the-art test generation techniques by proposing a novel automated approach for generating effective tests for higher-order functions. We implement our techniques in practical tools and evaluate their effectiveness on a set of popular software systems. The empirical evaluation demonstrates the potential of actionable analyses in improving software performance through relatively simple optimization opportunities

M2DF: a macro data flow interpreter targeting multi-cores

The thesis describes the implementation of a Macro Data Flow run-time support for multi-core architectures. The interpreter is based on the multi-threading paradigm with shared memory communication mechanism. The interpreter is assumed to be the target back-end for the compilation of high level, structured parallel programs. Experimental results are shown on state-of-the-art Intel multi-cores. Furthermore the interpreter is compared with a standard industrial tool, such as OpenMP

Multi-Tenant Geo-Distributed Data Analytics

University of Minnesota Ph.D. dissertation. July 2019. Major: Computer Science. Advisors: Abhishek Chandra, Jon Weissman. 1 computer file (PDF); x, 132 pages.Geo-distributed data analytics has gained much interest in recent years due to the need for extracting insights from geo-distributed data. Traditionally, data analytics has been done within a cluster/data center environment. However, analyzing geo-distributed data using existing cluster-based systems typically cannot satisfy the timeliness requirement of most applications and result in wasteful resource consumption due to the fundamental differences of the environments, especially due to the scarce, highly heterogeneous, and dynamic nature of the wide-area resources: compute power and network bandwidth. This thesis addresses the challenges faced by geo-distributed data analytics systems in ensuring high-performance and reliable execution of multiple data analytics applications/queries. Specifically, the focus is on sharing resources across multiple users, applications, and computing frameworks. Sharing resources is attractive as it increases resource utilization and reduces operational cost. However, ensuring high-performance execution of multiple applications in a shared environment is challenging as they may compete for the same resources, especially in a wide-area environment with scarce resources. Furthermore, dynamics such as workload variation, resource variation, stragglers, and failures are inevitable in large-scale distributed systems. These can cause large resource perturbation that significantly affect the performance of query executions. This thesis makes the following contributions. First, we present a resource sharing technique across multiple geo-distributed data analytics frameworks. The main challenge here is how to elastically partition resources while allowing high locality scheduling to each individual framework, which is critical to the execution performance of geo-distributed analytics queries. We then address the problem of how to identify and exploit common executions across multiple queries to mitigate wasteful resource consumption. We demonstrate that traditional multi-query optimization may degrade the overall query execution performance due to its lack of support for network awareness. Finally, we highlight the importance of adaptability in ensuring reliable query execution in the presence of dynamics, both for single and multiple query executions. We propose a systematic approach that can selectively determine which queries to adapt and how to adapt them based on the types of queries, dynamics, and optimization goals