A review of performance and energy aware improvement methods for future green cloud computing

With the advent of increased use of computers and computing power, state of the art of cloud computing has become imperative in the present-day global scenario. It has managed to remove the constraints in many organizations in terms of physical internetworking devices and human resources, leaving room for better growth of many organizations. With all these benefits, cloud computing is still facing a number of impediments in terms of energy consumption within data centers and performance degradation to end users. This has led many industries and researchers to find feasible solutions to the current problems. In the context of realizing the problems faced by cloud data centers and end users, this paper presents a summary of the work done, experimentation setup and the need for a greener cloud computing technique/algorithm which satisfies minimum energy consumption, minimum carbon emission and maximum quality of service

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

Uma abordagem de seleção de recursos consciente de consumo de energia baseada em topologia de rede, tamanho de arquivos e potência de equipamentos

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2015.Recentes avanços na área da Computação de Alto Desempenho (HPC) tem gerado uma grande variedade de possibilidades para pesquisas na área. Arquiteturas paralelas e distribuídas modernas apresentam um aumento considerável em sua capacidade de processamento. Entretanto, esse crescimento de desempenho é acompanhado por um aumento de consumo de energia. Neste cenário, a comunidade científica tem estudado técnicas voltadas à redução de consumo de energia em tais plataformas. Arquiteturas de alto desempenho são amplamente utilizadas em ambientes empresarial e acadêmico quando há a necessidade de grande poder computacional. Recentemente, infraestruturas legadas têm sido adaptadas ao modelo de nuvem computacional, o qual fornece recursos sob demanda e permite a usuários contratar serviços de infraestrutura, plataforma e software. Neste trabalho propomos uma abordagem genérica de alocação de recursos energeticamente eficiente que melhora a eficiência energética de ambientes de alto desempenho heterogêneos selecionando recursos menos custosos. A abordagem proposta considera o custo para transferência de dados, assim como o estado e eficiência energética dos nodos computacionais. Após realizados diversos experimentos em um ambiente simulado de nuvem, concluiu-se que, em alguns casos, a abordagem proposta reduz consideravelmente o consumo de energia em comparação com abordagens existentes na literatura.Abstract : Recent advances in High Performance Computing (HPC) have led to a wide range of new possibilities for research. In this context, modern parallel and distributed architectures have presented a steady increase in their processing capabilities. However, such growth is usually followed by an increase in energy consumption. Because of that, the research community has been focusing on techniques to reduce energy consumption on such platforms. HPC architectures are now widely used in business and academic environments when high computing power is crucial. Recently, legacy structures have been adapted to the cloud computing model, which provides resources on demand such as infrastructure, software or platform. In this work we propose a generic energy-efficient scheduling approach that improves the energy efficiency of high performance heterogeneous environments by selecting the least costly resources. The proposed approach takes into consideration the cost of data transfers as well as the state and energy efficiency of computing nodes. After carrying out several experiments in a cloud simulated environment we concluded that, in some cases, the proposed approach achieves considerably better energy efficiency than other existing approaches in the literature

Quantile Function-based Models for Resource Utilization and Power Consumption of Applications

Server consolidation is currently widely employed in order to improve the energy efficiency of data centers. While being a promising technique, server consolidation may lead to resource interference between applications and thus, reduced performance of applications. Current approaches to account for possible resource interference are not well suited to respect the variation in the workloads for the applications. As a consequence, these approaches cannot prevent resource interference if workload for applications vary. It is assumed that having models for the resource utilization and power consumption of applications as functions of the workload to the applications can improve decision making and help to prevent resource interference in scenarios with varying workload. This thesis aims to develop such models for selected applications. To produce varying workload that resembles statistical properties of real-world workload a workload generator is developed in a first step. Usually, the measurement data for such models origins from different sensors and equipment, all producing data at different frequencies. In order to account for these different frequencies, in a second step this thesis particularly investigates the feasibility to employ quantile functions as model inputs. Complementary, since conventional goodness-of-fit tests are not appropriate for this approach, an alternative to assess the estimation error is presented.:1 Introduction 2 Thesis Overview 2.1 Testbed 2.2 Contributions and Thesis Structure 2.3 Scope, Assumptions, and Limitations 3 Generation of Realistic Workload 3.1 Statistical Properties of Internet Traffic 3.2 Statistical Properties of Video Server Traffic 3.3 Implementation of Workload Generation 3.4 Summary 4 Models for Resource Utilization and for Power Consumption 4.1 Introduction 4.2 Prior Work 4.3 Test Cases 4.4 Applying Regression To Samples Of Different Length 4.5 Models for Resource Utilization as Function of Request Size 4.6 Models for Power Consumption as Function of Resource Utilization 4.7 Summary 5 Conclusion & Future Work 5.1 Summary 5.2 Future Work AppendicesServerkonsolidierung wird derzeit weithin zur Verbesserung der Energieeffizienz von Rechenzentren eingesetzt. Während diese Technik vielversprechende Ergebnisse zeitigt, kann sie zu Ressourceninterferenz und somit zu verringerter Performanz von Anwendungen führen. Derzeitige Ansätze, um dieses Problem zu adressieren, sind nicht gut für Szenarien geeignet, in denen die Workload für die Anwendungen variiert. Als Konsequenz daraus folgt, dass diese Ansätze Ressourceninterferenz in solchen Szenarien nicht verhindern können. Es wird angenommen, dass Modelle für Anwendungen, die deren Ressourenauslastung und die Leistungsaufnahme als Funktion der Workload beschreiben, die Entscheidungsfindung bei der Konsolidierung verbessern und Ressourceninterferenz verhindern können. Diese Arbeit zielt darauf ab, solche Modelle für ausgewählte Anwendungen zu entwickeln. Um variierende Workload zu erzeugen, welche den statistischen Eigenschaften realer Workload folgt, wird zunächst ein Workload-Generator entwickelt. Gewöhnlicherweise stammen Messdaten für die Modelle aus verschienenen Sensoren und Messgeräten, welche jeweils mit unterschiedlichen Frequenzen Daten erzeugen. Um diesen verschiedenen Frequenzen Rechnung zu tragen, untersucht diese Arbeit insbesondere die Möglichkeit, Quantilfunktionen als Eingabeparameter für die Modelle zu verwenden. Da konventionelle Anpassungsgütetests bei diesem Ansatz ungeeignet sind, wird ergänzend eine Alternative vorgestellt, um den durch die Modellierung entstehenden Schätzfehler zu bemessen.:1 Introduction 2 Thesis Overview 2.1 Testbed 2.2 Contributions and Thesis Structure 2.3 Scope, Assumptions, and Limitations 3 Generation of Realistic Workload 3.1 Statistical Properties of Internet Traffic 3.2 Statistical Properties of Video Server Traffic 3.3 Implementation of Workload Generation 3.4 Summary 4 Models for Resource Utilization and for Power Consumption 4.1 Introduction 4.2 Prior Work 4.3 Test Cases 4.4 Applying Regression To Samples Of Different Length 4.5 Models for Resource Utilization as Function of Request Size 4.6 Models for Power Consumption as Function of Resource Utilization 4.7 Summary 5 Conclusion & Future Work 5.1 Summary 5.2 Future Work Appendice

Fault Tolerant and Energy Efficient One-Sided Matrix Decompositions on Heterogeneous Systems with GPUs

Heterogeneous computing system with both CPUs and GPUs has become a class of widely used hardware architecture in supercomputers. As heterogeneous systems delivering higher computational performance, they are being built with an increasing number of complex components. This is anticipated that these systems will be more susceptible to hardware faults with higher power consumption. Numerical linear algebra libraries are used in a wide spectrum of high-performance scientific applications. Among numerical linear algebra operations, one-sided matrix decompositions can sometimes take a large portion of execution time or even dominate the whole scientific application execution. Due to the computational characteristic of one-sided matrix decompositions, they are very suitable for computation platforms such as heterogeneous systems with CPUs and GPUs. Many works have been done to implement and optimize one-sided matrix decompositions on heterogeneous systems with CPUs and GPUs. However, it is challenging to enable stable and high performance one-sided matrix decompositions running on computing platforms that are unreliable and high energy consumption. So, in this thesis, we aim to develop novel fault tolerance and energy efficiency optimizations for one-sided matrix decompositions on heterogeneous systems with CPUs and GPUs.To improve reliability and energy efficiency, extensive researches have been done on developing and optimizing fault tolerance methods and energy-saving strategies for one-sided matrix decompositions. However, current designs still have several limitations: (1) Little has been done on developing and optimizing fault tolerance method for one-sided matrix decompositions on heterogeneous systems with GPUs; (2) Limited by the protection coverage and strength, existing fault tolerance works provide insufficient protection when applied to one-sided matrix decompositions on heterogeneous systems with GPUs; (3) Lack the knowledge of algorithms, existing system level energy saving solutions cannot achieve the optimal energy savings due to potentially inaccurate and high-cost workload prediction they rely on when they are used in one-sided matrix decompositions; (4) It is challenging to apply both fault tolerance techniques and energy saving strategies to one-side matrix decompositions at the same time given that their current designs are not naturally compatible with each other.To address the first problem, based on the original (Algorithm Based Fault Tolerance) ABFT, we develop the first ABFT for matrix decomposition on heterogeneous systems with GPUs together with the novel storage errors protection and several optimization techniques specifically for GPUs. As for the second problem, we design a novel checksum scheme for ABFT that allows data stored in matrices to be encoded in two dimensions. This stronger checksum encoding mechanism enables much stronger protection including enhanced error propagation protection. In addition, we introduce a more efficient checking scheme. By prioritizing the checksum verification according to the sensitivity of matrix operations to soft errors with optimized checksum verification kernel for GPUs, we can achieve strong protect to matrix decompositions with comparable overhead. For the third problem, to improve energy efficiency for one-sided matrix decompositions, we introduce an algorithm-based energy-saving approach designed to maximize energy savings by utilizing algorithmic characteristics. Our approach can predict program execution behavior much more accurately, which is difficult for system level solutions for applications with variable execution characteristics. Experiments show that our approach can lead to much higher energy saving than existing works. Finally, for the fourth problem, we propose a novel energy saving approach for one-sided matrix decompositions on heterogeneous systems with GPUs. It allows energy saving strategies and fault tolerance techniques to be enabled at the same time without brings performance impact or extra energy cost