Energy Efficient Data-Intensive Computing With Mapreduce

Power and energy consumption are critical constraints in data center design and operation. In data centers, MapReduce data-intensive applications demand significant resources and energy. Recognizing the importance and urgency of optimizing energy usage of MapReduce applications, this work aims to provide instrumental tools to measure and evaluate MapReduce energy efficiency and techniques to conserve energy without impacting performance. Energy conservation for data-intensive computing requires enabling technology to provide detailed and systemic energy information and to identify in the underlying system hardware and software. To address this need, we present eTune, a fine-grained, scalable energy profiling framework for data-intensive computing on large-scale distributed systems. eTune leverages performance monitoring counters (PMCs) on modern computer components and statistically builds power-performance correlation models. Using learned models, eTune augments direct measurement with a software-based power estimator that runs on compute nodes and reports power at multiple levels including node, core, memory, and disks with high accuracy. Data-intensive computing differs from traditional high performance computing as most execution time is spent in moving data between storage devices, nodes, and components. Since data movements are potential performance and energy bottlenecks, we propose an analysis framework with methods and metrics for evaluating and characterizing costly built-in MapReduce data movements. The revealed data movement energy characteristics can be exploited in system design and resource allocation to improve data-intensive computing energy efficiency. Finally, we present an optimization technique that targets inefficient built-in MapReduce data movements to conserve energy without impacting performance. The optimization technique allocates the optimal number of compute nodes to applications and dynamically schedules processor frequency during its execution based on data movement characteristics. Experimental results show significant energy savings, though improvements depend on both workload characteristics and policies of resource and dynamic voltage and frequency scheduling. As data volume doubles every two years and more data centers are put into production, energy consumption is expected to grow further. We expect these studies provide direction and insight in building more energy efficient data-intensive systems and applications, and the tools and techniques are adopted by other researchers for their energy efficient studies

Energy Efficient Scheduling of MapReduce over Big Data

The majority of large-scale data intensive applications carried out by information centers are based on MapReduce or its open-source implementation, Hadoop. Such applications are carried out on rich clusters requiring ample amounts of energy, helping the energy costs an appreciable fraction of the data centers overall costs. Therefore, reducing the energy consumption when carrying out each MapReduce task is a critical worry for data centers. In this paper, we advise a framework for mending the energy ef?ciency of MapReduce applications, while satisfying the (SLA) Service Level Agreement. We ?rst prototype the problem of energy-aware scheduling of a single MapReduce task as an Integer Program. After that we court two algorithms, known as MapReduce scheduling algorithms and load scheduling algorithm, that ?nd the assignments of map and reduce tasks to the machines plenty in order to reduce the energy consumed when carrying out the application. The energy aware con?guration and scheduling will improve the energy e?ciency of MapReduce clusters thus help in reduction of the service costs of the data-centers

Resource Management In Cloud And Big Data Systems

Cloud computing is a paradigm shift in computing, where services are offered and acquired on demand in a cost-effective way. These services are often virtualized, and they can handle the computing needs of big data analytics. The ever-growing demand for cloud services arises in many areas including healthcare, transportation, energy systems, and manufacturing. However, cloud resources such as computing power, storage, energy, dollars for infrastructure, and dollars for operations, are limited. Effective use of the existing resources raises several fundamental challenges that place the cloud resource management at the heart of the cloud providers\u27 decision-making process. One of these challenges faced by the cloud providers is to provision, allocate, and price the resources such that their profit is maximized and the resources are utilized efficiently. In addition, executing large-scale applications in clouds may require resources from several cloud providers. Another challenge when processing data intensive applications is minimizing their energy costs. Electricity used in US data centers in 2010 accounted for about 2% of total electricity used nationwide. In addition, the energy consumed by the data centers is growing at over 15% annually, and the energy costs make up about 42% of the data centers\u27 operating costs. Therefore, it is critical for the data centers to minimize their energy consumption when offering services to customers. In this Ph.D. dissertation, we address these challenges by designing, developing, and analyzing mechanisms for resource management in cloud computing systems and data centers. The goal is to allocate resources efficiently while optimizing a global performance objective of the system (e.g., maximizing revenue, maximizing social welfare, or minimizing energy). We improve the state-of-the-art in both methodologies and applications. As for methodologies, we introduce novel resource management mechanisms based on mechanism design, approximation algorithms, cooperative game theory, and hedonic games. These mechanisms can be applied in cloud virtual machine (VM) allocation and pricing, cloud federation formation, and energy-efficient computing. In this dissertation, we outline our contributions and possible directions for future research in this field

Machine Learning with Sensitivity Analysis to Determine Key Factors Contributing to Energy Consumption in Cloud Data Centers

Machine learning (ML) approach to modeling and predicting real-world dynamic system behaviours has received widespread research interest. While ML capability in approximating any nonlinear or complex system is promising, it is often a black-box approach, which lacks the physical meanings of the actual system structure and its parameters, as well as their impacts on the system. This paper establishes a model to provide explanation on how system parameters affect its output(s), as such knowledge would lead to potential useful, interesting and novel information. The paper builds on our previous work in machine learning, and also combines an evolutionary artificial neural networks with sensitivity analysis to extract and validate key factors affecting the cloud data center energy performance. This provides an opportunity for software analyst to design and develop energy-aware applications and for Hadoop administrator to optimize the Hadoop infrastructure by having Big Data partitioned in bigger chunks and shortening the time to complete MapReduce jobs

Performance optimization and energy efficiency of big-data computing workflows

Next-generation e-science is producing colossal amounts of data, now frequently termed as Big Data, on the order of terabyte at present and petabyte or even exabyte in the predictable future. These scientific applications typically feature data-intensive workflows comprised of moldable parallel computing jobs, such as MapReduce, with intricate inter-job dependencies. The granularity of task partitioning in each moldable job of such big data workflows has a significant impact on workflow completion time, energy consumption, and financial cost if executed in clouds, which remains largely unexplored. This dissertation conducts an in-depth investigation into the properties of moldable jobs and provides an experiment-based validation of the performance model where the total workload of a moldable job increases along with the degree of parallelism. Furthermore, this dissertation conducts rigorous research on workflow execution dynamics in resource sharing environments and explores the interactions between workflow mapping and task scheduling on various computing platforms. A workflow optimization architecture is developed to seamlessly integrate three interrelated technical components, i.e., resource allocation, job mapping, and task scheduling. Cloud computing provides a cost-effective computing platform for big data workflows where moldable parallel computing models are widely applied to meet stringent performance requirements. Based on the moldable parallel computing performance model, a big-data workflow mapping model is constructed and a workflow mapping problem is formulated to minimize workflow makespan under a budget constraint in public clouds. This dissertation shows this problem to be strongly NP-complete and designs i) a fully polynomial-time approximation scheme for a special case with a pipeline-structured workflow executed on virtual machines of a single class, and ii) a heuristic for a generalized problem with an arbitrary directed acyclic graph-structured workflow executed on virtual machines of multiple classes. The performance superiority of the proposed solution is illustrated by extensive simulation-based results in Hadoop/YARN in comparison with existing workflow mapping models and algorithms. Considering that large-scale workflows for big data analytics have become a main consumer of energy in data centers, this dissertation also delves into the problem of static workflow mapping to minimize the dynamic energy consumption of a workflow request under a deadline constraint in Hadoop clusters, which is shown to be strongly NP-hard. A fully polynomial-time approximation scheme is designed for a special case with a pipeline-structured workflow on a homogeneous cluster and a heuristic is designed for the generalized problem with an arbitrary directed acyclic graph-structured workflow on a heterogeneous cluster. This problem is further extended to a dynamic version with deadline-constrained MapReduce workflows to minimize dynamic energy consumption in Hadoop clusters. This dissertation proposes a semi-dynamic online scheduling algorithm based on adaptive task partitioning to reduce dynamic energy consumption while meeting performance requirements from a global perspective, and also develops corresponding system modules for algorithm implementation in the Hadoop ecosystem. The performance superiority of the proposed solutions in terms of dynamic energy saving and deadline missing rate is illustrated by extensive simulation results in comparison with existing algorithms, and further validated through real-life workflow implementation and experiments using the Oozie workflow engine in Hadoop/YARN systems

Hadoop performance modeling and job optimization for big data analytics

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonBig data has received a momentum from both academia and industry. The MapReduce model has emerged into a major computing model in support of big data analytics. Hadoop, which is an open source implementation of the MapReduce model, has been widely taken up by the community. Cloud service providers such as Amazon EC2 cloud have now supported Hadoop user applications. However, a key challenge is that the cloud service providers do not a have resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user responsibility to estimate the require amount of resources for their job running in a public cloud. This thesis presents a Hadoop performance model that accurately estimates the execution duration of a job and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model employs Locally Weighted Linear Regression (LWLR) model to estimate execution time of a job and Lagrange Multiplier technique for resource provisioning to satisfy user job with a given deadline. The performance of the propose model is extensively evaluated in both in-house Hadoop cluster and Amazon EC2 Cloud. Experimental results show that the proposed model is highly accurate in job execution estimation and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. In addition, the Hadoop framework has over 190 configuration parameters and some of them have significant effects on the performance of a Hadoop job. Manually setting the optimum values for these parameters is a challenging task and also a time consuming process. This thesis presents optimization works that enhances the performance of Hadoop by automatically tuning its parameter values. It employs Gene Expression Programming (GEP) technique to build an objective function that represents the performance of a job and the correlation among the configuration parameters. For the purpose of optimization, Particle Swarm Optimization (PSO) is employed to find automatically an optimal or a near optimal configuration settings. The performance of the proposed work is intensively evaluated on a Hadoop cluster and the experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings.Abdul Wali Khan University Marda

Optimizations for Energy-Aware, High-Performance and Reliable Distributed Storage Systems

With the decreasing cost and wide-spread use of commodity hard drives, it has become possible to create very large-scale storage systems with less expense. However, as we approach exabyte-scale storage systems, maintaining important features such as energy-efficiency, performance, reliability and usability became increasingly difficult. Despite the decreasing cost of storage systems, the energy consumption of these systems still needs to be addressed in order to retain cost-effectiveness. Any improvements in a storage system can be outweighed by high energy costs. On the other hand, large-scale storage systems can benefit more from the object storage features for improved performance and usability. One area of concern is metadata performance bottleneck of applications reading large directories or creating a large number of files. Similarly, computation on big data where data needs to be transferred between compute and storage clusters adversely affects I/O performance. As the storage systems become more complex and larger, transferring data between remote compute and storage tiers becomes impractical. Furthermore, storage systems implement reliability typically at the file system or client level. This approach might not always be practical in terms of performance. Lastly, object storage features are usually tailored to specific use cases that makes it harder to use them in various contexts. In this thesis, we are presenting several approaches to enhance energy-efficiency, performance, reliability and usability of large-scale storage systems. To begin with, we improve the energy-efficiency of storage systems by moving I/O load to a subset of the storage nodes with energy-aware node allocation methods and turn off the unused nodes, while preserving load balance on demand. To address the metadata performance issue associated with large creates and directory reads, we represent directories with object storage collections and implement lazy creation of objects. Similarly, in-situ computation on large-scale data is enabled by using object storage features to integrate a computational framework with the existing object storage layer to eliminate the need to transfer data between compute and storage silos for better performance. We then present parity-based redundancy using object storage features to achieve reliability with less performance impact. Finally, unified storage brings together the object storage features to meet the needs of distinct use cases; such as cloud storage, big data or high-performance computing to alleviate the unnecessary fragmentation of storage resources. We evaluate each proposed approach thoroughly and validate their effectiveness in terms of improving energy-efficiency, performance, reliability and usability of a large-scale storage system