Efficient Mapping of Large-scale Data under Heterogeneous Big Data Computing Systems

Hadoop biological systems become progressively significant for professionals of huge scale information examination, they likewise acquire huge energy cost. This pattern is dynamic up the requirement for planning energy-effective Hadoop clusters so as to lessen the operational costs and the carbon emanation related with its energy utilization. Be that as it may, in spite of broad investigations of the issue, existing methodologies for energy proficiency have not completely measured the heterogeneity of both workloads. So that here enhancing the model by find that heterogeneity-unaware task task methodologies are hindering to both execution and energy effectiveness of Hadoop clusters. Our perception demonstrates that even heterogeneity-mindful methods that intend to decrease the job fulfillment time don't ensure a decrease in energy utilization of heterogeneous machines. We propose E-Ant which plans to get better the general energy utilization in a heterogeneous Hadoop group without giving up job execution. It adaptively plans heterogeneous workloads on energy-effective machines. E-Ant utilizes a subterranean insect state improvement approach that creates task assignment arrangements dependent on the input of each jobs energy utilization by Tasktrackers and also we incorporate DVFS method with E-Ant to further improve the energy proficiency

Energy Saving and Scavenging in Stand-alone and Large Scale Distributed Systems.

This thesis focuses on energy management techniques for distributed systems such as hand-held mobile devices, sensor nodes, and data center servers. One of the major design problems in multiple application domains is the mismatch between workloads and resources. Sub-optimal assignment of workloads to resources can cause underloaded or overloaded resources, resulting in performance degradation or energy waste. This work specifically focuses on the heterogeneity in system hardware components and workloads. It includes energy management solutions for unregulated or batteryless embedded systems; and data center servers with heterogeneous workloads, machines, and processor wear states. This thesis describes four major contributions: (1) This thesis describes a battery test and energy delivery system design process to maintain battery life in embedded systems without voltage regulators. (2) In battery-less sensor nodes, this thesis demonstrates a routing protocol to maintain reliable transmission through the sensor network. (3) This thesis has characterized typical workloads and developed two models to capture the heterogeneity of data center tasks and machines: a task performance model and a machine resource utilization model. These models allow users to predict task finish time on individual machines. It then integrates these two models into a task scheduler based on the Hadoop framework for MapReduce tasks, and uses this scheduler for server energy minimization using task concentration. (4) In addition to saving server energy consumption, this thesis describes a method of reducing data center cooling energy by maintaining optimal server processor temperature setpoints through a task assignment algorithm. This algorithm considers the reliability impact of processor wear states. It records processor wear states through automatic timing slack tests on a cluster of machines with varying core temperatures, voltages, and frequencies. These optimal temperature setpoints are used in a task scheduling algorithm that saves both server and cooling energy.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116746/1/xjhe_1.pd

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

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

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

Power Management in Heterogeneous MapReduce Cluster

The growing expenses of power in data centers as compared to the operation costs has been a concern for the past several decades. It has been predicted that without an intervention, the energy cost will soon outgrow the infrastructure and operation cost. Therefore, it is of great importance to make data center clusters more energy efficient which is critical for avoiding system overheating and failures. In addition, energy inefficiency causes not only the loss of capital but also environmental pollution. Various Power Management(PM) strategies have been developed over the years to make system more energy efficient and to counteract the sharply rising cost of electricity. However, it is still a challenge to make the system both power efficient and computation efficient due to many underlying system constraints. In this thesis, we investigate the Power Management technique in heterogeneous MapReduce clusters while also maintaining the required system QoS (Quality of Service). For a cluster that supports MapReduce jobs, it is necessary to develop a PM technique that also considers the data availability. We develop our PM strategy by exploiting the fact that the servers in the system are underutilized most of the time. Hence, we first develop a model of our testbed and study how the server utilization levels affect the power consumption and the system throughput. With the established models, we form and solve the power optimization problem for heterogeneous MadReduce clusters where we control the server utilization levels intelligently to minimize the total power consumption. We have conducted simulations and shown the power savings achieved using our PM technique. Then we validate some of our simulation results by running experiments in a real testbed. Our simulation and experimental data have shown that our PM strategy works well for heterogeneous MapReduce clusters which consists of different power efficient and inefficient servers. Adviser: Ying L

Toward Energy Efficient Systems Design For Data Centers

Surge growth of numerous cloud services, Internet of Things, and edge computing promotes continuous increasing demand for data centers worldwide. Significant electricity consumption of data centers has tremendous implications on both operating and capital expense. The power infrastructure, along with the cooling system cost a multi-million or even billion dollar project to add new data center capacities. Given the high cost of large-scale data centers, it is important to fully utilize the capacity of data centers to reduce the Total Cost of Ownership. The data center is designed with a space budget and power budget. With the adoption of high-density rack designs, the capacity of a modern data center is usually limited by the power budget. So the core of the challenge is scaling up power infrastructure capacity. However, resizing the initial power capacity for an existing data center can be a task as difficult as building a new data center because of a non-scalable centralized power provisioning scheme. Thus, how to maximize the power utilization and optimize the performance per power budget is critical for data centers to deliver enough computation ability. To explore and attack the challenges of improving the power utilization, we have planned to work on different levels of data center, including server level, row level, and data center level. For server level, we take advantage of modern hardware to maximize power efficiency of each server. For rack level, we propose Pelican, a new power scheduling system for large-scale data centers with heterogeneous workloads. For row level, we present Ampere, a new approach to improve throughput per watt by provisioning extra servers. By combining these studies on different levels, we will provide comprehensive energy efficient system designs for data center