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

An Extensive Exploration of Techniques for Resource and Cost Management in Contemporary Cloud Computing Environments

Resource and cost optimization techniques in cloud computing environments target minimizing expenditure while ensuring efficient resource utilization. This study categorizes these techniques into three primary groups: Cloud and VM-focused strategies, Workflow techniques, and Resource Utilization and Efficiency techniques. Cloud and VM-focused strategies predominantly concentrate on the allocation, scheduling, and optimization of resources within cloud environments, particularly virtual machines. These strategies aim at a balance between cost reduction and adhering to specified deadlines, while ensuring scalability and adaptability to different cloud models. However, they may introduce complexities due to their dynamic nature and continuous optimization requirements. Workflow techniques emphasize the optimal execution of tasks in distributed systems. They address inconsistencies in Quality of Service (QoS) and seek to enhance the reservation process and task scheduling. By employing models, such as Integer Linear Programming, these techniques offer precision. But they might be computationally demanding, especially for extensive problems. Techniques focusing on Resource Utilization and Efficiency attempts to maximize the use of available resources in an energy-efficient and cost-effective manner. Considering factors like current energy levels and application requirements, these models aim to optimize performance without overshooting budgets. However, a continuous monitoring mechanism might be necessary, which can introduce additional complexities

A Framework for Approximate Optimization of BoT Application Deployment in Hybrid Cloud Environment

We adopt a systematic approach to investigate the efficiency of near-optimal deployment of large-scale CPU-intensive Bag-of-Task applications running on cloud resources with the non-proportional cost to performance ratios. Our analytical solutions perform in both known and unknown running time of the given application. It tries to optimize users' utility by choosing the most desirable tradeoff between the make-span and the total incurred expense. We propose a schema to provide a near-optimal deployment of BoT application regarding users' preferences. Our approach is to provide user with a set of Pareto-optimal solutions, and then she may select one of the possible scheduling points based on her internal utility function. Our framework can cope with uncertainty in the tasks' execution time using two methods, too. First, an estimation method based on a Monte Carlo sampling called AA algorithm is presented. It uses the minimum possible number of sampling to predict the average task running time. Second, assuming that we have access to some code analyzer, code profiling or estimation tools, a hybrid method to evaluate the accuracy of each estimation tool in certain interval times for improving resource allocation decision has been presented. We propose approximate deployment strategies that run on hybrid cloud. In essence, proposed strategies first determine either an estimated or an exact optimal schema based on the information provided from users' side and environmental parameters. Then, we exploit dynamic methods to assign tasks to resources to reach an optimal schema as close as possible by using two methods. A fast yet simple method based on First Fit Decreasing algorithm, and a more complex approach based on the approximation solution of the transformed problem into a subset sum problem. Extensive experiment results conducted on a hybrid cloud platform confirm that our framework can deliver a near optimal solution respecting user's utility function

Energy-aware scheduling in distributed computing systems

Distributed computing systems, such as data centers, are key for supporting modern computing demands. However, the energy consumption of data centers has become a major concern over the last decade. Worldwide energy consumption in 2012 was estimated to be around 270 TWh, and grim forecasts predict it will quadruple by 2030. Maximizing energy efficiency while also maximizing computing efficiency is a major challenge for modern data centers. This work addresses this challenge by scheduling the operation of modern data centers, considering a multi-objective approach for simultaneously optimizing both efficiency objectives. Multiple data center scenarios are studied, such as scheduling a single data center and scheduling a federation of several geographically-distributed data centers. Mathematical models are formulated for each scenario, considering the modeling of their most relevant components such as computing resources, computing workload, cooling system, networking, and green energy generators, among others. A set of accurate heuristic and metaheuristic algorithms are designed for addressing the scheduling problem. These scheduling algorithms are comprehensively studied, and compared with each other, using statistical tools to evaluate their efficacy when addressing realistic workloads and scenarios. Experimental results show the designed scheduling algorithms are able to significantly increase the energy efficiency of data centers when compared to traditional scheduling methods, while providing a diverse set of trade-off solutions regarding the computing efficiency of the data center. These results confirm the effectiveness of the proposed algorithmic approaches for data center infrastructures.Los sistemas informáticos distribuidos, como los centros de datos, son clave para satisfacer la demanda informática moderna. Sin embargo, su consumo de energético se ha convertido en una gran preocupación. Se estima que mundialmente su consumo energético rondó los 270 TWh en el año 2012, y algunos prevén que este consumo se cuadruplicará para el año 2030. Maximizar simultáneamente la eficiencia energética y computacional de los centros de datos es un desafío crítico. Esta tesis aborda dicho desafío mediante la planificación de la operativa del centro de datos considerando un enfoque multiobjetivo para optimizar simultáneamente ambos objetivos de eficiencia. En esta tesis se estudian múltiples variantes del problema, desde la planificación de un único centro de datos hasta la de una federación de múltiples centros de datos geográficmentea distribuidos. Para esto, se formulan modelos matemáticos para cada variante del problema, modelado sus componentes más relevantes, como: recursos computacionales, carga de trabajo, refrigeración, redes, energía verde, etc. Para resolver el problema de planificación planteado, se diseñan un conjunto de algoritmos heurísticos y metaheurísticos. Estos son estudiados exhaustivamente y su eficiencia es evaluada utilizando una batería de herramientas estadísticas. Los resultados experimentales muestran que los algoritmos de planificación diseñados son capaces de aumentar significativamente la eficiencia energética de un centros de datos en comparación con métodos tradicionales planificación. A su vez, los métodos propuestos proporcionan un conjunto diverso de soluciones con diferente nivel de compromiso respecto a la eficiencia computacional del centro de datos. Estos resultados confirman la eficacia del enfoque algorítmico propuesto

Scheduling independent stochastic tasks under deadline and budget constraints

International audienceThis paper discusses scheduling strategies for the problem of maximizing the expected number of tasks that can be executed on a cloud platform within a given budget and under a deadline constraint. The execution times of tasks follow IID probability laws. The main questions are how many processors to enroll and whether and when to interrupt tasks that have been executing for some time. We provide complexity results and an asymptotically optimal strategy for the problem instance with discrete probability distributions and without deadline. We extend the latter strategy for the general case with continuous distributions and a deadline and we design an efficient heuristic which is shown to outperform standard approaches when running simulations for a variety of useful distribution laws