Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications

MapReduce is a popular programming paradigm for developing large-scale, data-intensive computation. Many frameworks that implement this paradigm have recently been developed. To leverage these frameworks, however, developers must become familiar with their APIs and rewrite existing code. Casper is a new tool that automatically translates sequential Java programs into the MapReduce paradigm. Casper identifies potential code fragments to rewrite and translates them in two steps: (1) Casper uses program synthesis to search for a program summary (i.e., a functional specification) of each code fragment. The summary is expressed using a high-level intermediate language resembling the MapReduce paradigm and verified to be semantically equivalent to the original using a theorem prover. (2) Casper generates executable code from the summary, using either the Hadoop, Spark, or Flink API. We evaluated Casper by automatically converting real-world, sequential Java benchmarks to MapReduce. The resulting benchmarks perform up to 48.2x faster compared to the original.Comment: 12 pages, additional 4 pages of references and appendi

Towards Efficient Resource Provisioning in Hadoop

Considering recent exponential growth in the amount of information processed in Big Data, the high energy consumed by data processing engines in datacenters has become a major issue, underlining the need for efficient resource allocation for better energy-efficient computing. This thesis proposes the Best Trade-off Point (BToP) method which provides a general approach and techniques based on an algorithm with mathematical formulas to find the best trade-off point on an elbow curve of performance vs. resources for efficient resource provisioning in Hadoop MapReduce and Apache Spark. Our novel BToP method is expected to work for any applications and systems which rely on a tradeoff curve with an elbow shape, non-inverted or inverted, for making good decisions. This breakthrough method for optimal resource provisioning was not available before in the scientific, computing, and economic communities. To illustrate the effectiveness of the BToP method on the ubiquitous Hadoop MapReduce, our Terasort experiment shows that the number of task resources recommended by the BToP algorithm is always accurate and optimal when compared to the ones suggested by three popular rules of thumbs. We also test the BToP method on the emerging cluster computing framework Apache Spark running in YARN cluster mode. Despite the effectiveness of Spark’s robust and sophisticated built-in dynamic resource allocation mechanism, which is not available in MapReduce, the BToP method could still consistently outperform it according to our Spark-Bench Terasort test results. The performance efficiency gained from the BToP method not only leads to significant energy saving but also improves overall system throughput and prevents cluster underutilization in a multi-tenancy environment. In General, the BToP method is preferable for workloads with identical resource consumption signatures in production environment where job profiling for behavioral replication will lead to the most efficient resource provisioning

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Towards efficient resource provisioning in MapReduce

The paper presents a novel approach and algorithm with mathematical formula for obtaining the exact optimal number of task resources for any workload running on HadoopMapReduce. In the era of Big Data, energy efficiency has become an important issue for the ubiquitous Hadoop MapReduce framework. However, the question of what is the optimal number of tasks required for a job to get the most efficient performance from MapReduce still has no definite answer. Our algorithm for optimal resource provisioning allows users to identify the best trade-off point between performance and energy efficiency on the runtime elbow curve fitted from sampled executions on the target cluster for subsequent behavioral replication. Our verification and comparison show that the currently well-known rules of thumb for calculating the required number of reduce tasks are inaccurate and could lead to significant waste of computing resources and energy with no further improvement in execution time

Efficient Storage Management over Cloud Using Data Compression without Losing Searching Capacity

Nowadays due to social media, people may communicate with each other, share their thoughts and moments of life in form of texts, images or videos.  We are uploading our private data in terms of photos, videos, and documents on internet websites like Facebook, Whatsapp, Google+ and Youtube etc. In short today world is surrounded with large volume of data in different form. This put a requirement for effective management of these billions of terabytes of electronic data generally called BIG DATA. Handling large data sets is a major challenge for data centers. The only solution for this problem is to add as many hard disk as required. But if the data is kept in unformatted the requirement of hard disk will be very high. Cloud technology in today is becoming popular but efficient storage management for large volume of data on cloud still there is a big question. Many frameworks are available to address this problem. Hadoop is one of them. Hadoop provides an efficient way to store and retrieve large volume of data. But Hadoop is efficient only if the file containing data is large enough. Basically Hadoop uses a big hard disk block to store data. And this makes it inefficient in the area where volume to data is large but individual file is small. To satisfy both challenges to store large volume of data in less space. And to store small unit of file without wasting the space. We require to store data not is usual form but in compressed form so that we can keep the block size small. But if we do so it added one more dimension of problem. Searching the content in a compressed file is very in-efficient. Therefore we require an efficient algorithm which compress the file without disturbing the search capacity of the data center. Here we will provide the way how we can solve these challenges. Keywords:Cloud, Big DATA, Hadoop, Data Compression, MapReduc

MapReduce network enabled algorithms for classification based on association rules

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.There is growing evidence that integrating classification and association rule mining can produce more efficient and accurate classifiers than traditional techniques. This thesis introduces a new MapReduce based association rule miner for extracting strong rules from large datasets. This miner is used later to develop a new large scale classifier. Also new MapReduce simulator was developed to evaluate the scalability of proposed algorithms on MapReduce clusters. The developed associative rule miner inherits the MapReduce scalability to huge datasets and to thousands of processing nodes. For finding frequent itemsets, it uses hybrid approach between miners that uses counting methods on horizontal datasets, and miners that use set intersections on datasets of vertical formats. The new miner generates same rules that usually generated using apriori-like algorithms because it uses the same confidence and support thresholds definitions. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. This thesis also introduces a new MapReduce classifier that based MapReduce associative rule mining. This algorithm employs different approaches in rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. The new classifier works on multi-class datasets and is able to produce multi-label predications with probabilities for each predicted label. To evaluate the classifier 20 different datasets from the UCI data collection were used. Results show that the proposed approach is an accurate and effective classification technique, highly competitive and scalable if compared with other traditional and associative classification approaches. Also a MapReduce simulator was developed to measure the scalability of MapReduce based applications easily and quickly, and to captures the behaviour of algorithms on cluster environments. This also allows optimizing the configurations of MapReduce clusters to get better execution times and hardware utilization