Improvement of Data-Intensive Applications Running on Cloud Computing Clusters

MapReduce, designed by Google, is widely used as the most popular distributed programming model in cloud environments. Hadoop, an open-source implementation of MapReduce, is a data management framework on large cluster of commodity machines to handle data-intensive applications. Many famous enterprises including Facebook, Twitter, and Adobe have been using Hadoop for their data-intensive processing needs. Task stragglers in MapReduce jobs dramatically impede job execution on massive datasets in cloud computing systems. This impedance is due to the uneven distribution of input data and computation load among cluster nodes, heterogeneous data nodes, data skew in reduce phase, resource contention situations, and network configurations. All these reasons may cause delay failure and the violation of job completion time. One of the key issues that can significantly affect the performance of cloud computing is the computation load balancing among cluster nodes. Replica placement in Hadoop distributed file system plays a significant role in data availability and the balanced utilization of clusters. In the current replica placement policy (RPP) of Hadoop distributed file system (HDFS), the replicas of data blocks cannot be evenly distributed across cluster\u27s nodes. The current HDFS must rely on a load balancing utility for balancing the distribution of replicas, which results in extra overhead for time and resources. This dissertation addresses data load balancing problem and presents an innovative replica placement policy for HDFS. It can perfectly balance the data load among cluster\u27s nodes. The heterogeneity of cluster nodes exacerbates the issue of computational load balancing; therefore, another replica placement algorithm has been proposed in this dissertation for heterogeneous cluster environments. The timing of identifying the straggler map task is very important for straggler mitigation in data-intensive cloud computing. To mitigate the straggler map task, Present progress and Feedback based Speculative Execution (PFSE) algorithm has been proposed in this dissertation. PFSE is a new straggler identification scheme to identify the straggler map tasks based on the feedback information received from completed tasks beside the progress of the current running task. Straggler reduce task aggravates the violation of MapReduce job completion time. Straggler reduce task is typically the result of bad data partitioning during the reduce phase. The Hash partitioner employed by Hadoop may cause intermediate data skew, which results in straggler reduce task. In this dissertation a new partitioning scheme, named Balanced Data Clusters Partitioner (BDCP), is proposed to mitigate straggler reduce tasks. BDCP is based on sampling of input data and feedback information about the current processing task. BDCP can assist in straggler mitigation during the reduce phase and minimize the job completion time in MapReduce jobs. The results of extensive experiments corroborate that the algorithms and policies proposed in this dissertation can improve the performance of data-intensive applications running on cloud platforms

Resource Management and Scheduling for Big Data Applications in Cloud Computing Environments

This chapter presents software architectures of the big data processing platforms. It will provide an in-depth knowledge on resource management techniques involved while deploying big data processing systems on cloud environment. It starts from the very basics and gradually introduce the core components of resource management which we have divided in multiple layers. It covers the state-of-art practices and researches done in SLA-based resource management with a specific focus on the job scheduling mechanisms.Comment: 27 pages, 9 figure

Scheduling in Mapreduce Clusters

MapReduce is a framework proposed by Google for processing huge amounts of data in a distributed environment. The simplicity of the programming model and the fault-tolerance feature of the framework make it very popular in Big Data processing. As MapReduce clusters get popular, their scheduling becomes increasingly important. On one hand, many MapReduce applications have high performance requirements, for example, on response time and/or throughput. On the other hand, with the increasing size of MapReduce clusters, the energy-efficient scheduling of MapReduce clusters becomes inevitable. These scheduling challenges, however, have not been systematically studied. The objective of this dissertation is to provide MapReduce applications with low cost and energy consumption through the development of scheduling theory and algorithms, energy models, and energy-aware resource management. In particular, we will investigate energy-efficient scheduling in hybrid CPU-GPU MapReduce clusters. This research work is expected to have a breakthrough in Big Data processing, particularly in providing green computing to Big Data applications such as social network analysis, medical care data mining, and financial fraud detection. The tools we propose to develop are expected to increase utilization and reduce energy consumption for MapReduce clusters. In this PhD dissertation, we propose to address the aforementioned challenges by investigating and developing 1) a match-making scheduling algorithm for improving the data locality of Map- Reduce applications, 2) a real-time scheduling algorithm for heterogeneous Map- Reduce clusters, and 3) an energy-efficient scheduler for hybrid CPU-GPU Map- Reduce cluster. Advisers: Ying Lu and David Swanso

Near-Data Prediction Based Speculative Optimization in a Distribution Environment

Hadoop is an open source from Apache with a distributed file system and MapReduce distributed computing framework. The current Apache 2.0 license agreement supports on-demand payment by consumers for cloud platform services, helping users leverage their respective different hardware to provides cloud services. In cloud-based environment, there is a need to balance the resource requirements of workloads, optimize load performance, and the cloud compute costs to manage. When the processing power of clustered machines varies widely, such as when hardware is aging or overloaded, Hadoop offers a speculative execution (SE) optimization strategy, by monitoring task progress in real time, in the starting identical backup tasks on different nodes when multiple tasks under a job are not running at the same speed, providing the first to go. The completed calculations maintain the overall progress of the job. At present, the SE strategy’s incorrect selection of backup nodes and resource constraints may result in poor Hadoop performance, and subsequent tasks cannot be completed execution and other problems. This paper proposes an SE optimization strategy based on near data prediction, which analyzes the prediction of real-time task execution information to predict the required running time, select backup nodes based on actual requirements and approximate data to make the SE strategy achieve the best performance. Experiments prove that in a heterogeneous Hadoop environment, the optimization strategy can effectively improve the effectiveness and accuracy of various tasks and enhance the performance of cloud computing. Platform performance can benefits consumers better than before

MapReduce based RDF assisted distributed SVM for high throughput spam filtering

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityElectronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filterin

Deep Data Locality on Apache Hadoop

The amount of data being collected in various areas such as social media, network, scientific instrument, mobile devices, and sensors is growing continuously, and the technology to process them is also advancing rapidly. One of the fundamental technologies to process big data is Apache Hadoop that has been adopted by many commercial products, such as InfoSphere by IBM, or Spark by Cloudera. MapReduce on Hadoop has been widely used in many data science applications. As a dominant big data processing platform, the performance of MapReduce on Hadoop system has a significant impact on the big data processing capability across multiple industries. Most of the research for improving the speed of big data analysis has been on Hadoop modules such as Hadoop common, Hadoop Distribute File System (HDFS), Hadoop Yet Another Resource Negotiator (YARN) and Hadoop MapReduce. In this research, we focused on data locality on HDFS to improve the performance of MapReduce. To reduce the amount of data transfer, MapReduce has been utilizing data locality. However, even though the majority of the processing cost occurs in the later stages, data locality has been utilized only in the early stages, which we call Shallow Data Locality (SDL). As a result, the benefit of data locality has not been fully realized. We have explored a new concept called Deep Data Locality (DDL) where the data is pre-arranged to maximize the locality in the later stages. Specifically, we introduce two implementation methods of the DDL, i.e., block-based DDL and key-based DDL. In block-based DDL, the data blocks are pre-arranged to reduce the block copying time in two ways. First the RLM blocks are eliminated. Under the conventional default block placement policy (DBPP), data blocks are randomly placed on any available slave nodes, requiring a copy of RLM (Rack-Local Map) blocks. In block-based DDL, blocks are placed to avoid RLMs to reduce the block copy time. Second, block-based DDL concentrates the blocks in a smaller number of nodes and reduces the data transfer time among them. We analyzed the block distribution status with the customer review data from TripAdvisor and measured the performances with Terasort Benchmark. Our test result shows that the execution times of Map and Shuffle have been improved by up to 25% and 31% respectively. In key-based DDL, the input data is divided into several blocks and stored in HDFS before going into the Map stage. In comparison with conventional blocks that have random keys, our blocks have a unique key. This requires a pre-sorting of the key-value pairs, which can be done during ETL process. This eliminates some data movements in map, shuffle, and reduce stages, and thereby improves the performance. In our experiments, MapReduce with key-based DDL performed 21.9% faster than default MapReduce and 13.3% faster than MapReduce with block-based DDL. Additionally, key-based DDL can be combined with other methods to further improve the performance. When key-based DDL and block-based DDL are combined, the Hadoop performance went up by 34.4%. In this research, we developed the MapReduce workflow models with a novel computational model. We developed a numerical simulator that integrates the computational models. The model faithfully predicts the Hadoop performance under various conditions

A Survey on Automatic Parameter Tuning for Big Data Processing Systems

Big data processing systems (e.g., Hadoop, Spark, Storm) contain a vast number of configuration parameters controlling parallelism, I/O behavior, memory settings, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators grapple with understanding and tuning them to achieve good performance. We investigate existing approaches on parameter tuning for both batch and stream data processing systems and classify them into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We summarize the pros and cons of each approach and raise some open research problems for automatic parameter tuning.Peer reviewe