Budget-aware scheduling algorithm for scientific workflow applications across multiple clouds. A Mathematical Optimization-Based Approach

Scientific workflows have become a prevailing means of achieving significant scientific advances at an ever-increasing rate. Scheduling mechanisms and approaches are vital to automating these large-scale scientific workflows efficiently. On the other hand, with the advent of cloud computing and its easier availability and lower cost of use, more attention has been paid to the execution and scheduling of scientific workflows in this new paradigm environment. For scheduling large-scale workflows, a multi-cloud environment will typically have a more significant advantage in various computing resources than a single cloud provider. Also, the scheduling makespan and cost can be reduced if the computing resources are used optimally in a multi-cloud environment. Accordingly, this thesis addressed the problem of scientific workflow scheduling in the multi-cloud environment under budget constraints to minimize associated makespan. Furthermore, this study tries to minimize costs, including fees for running VMs and data transfer, minimize the data transfer time, and fulfill budget and resource constraints in the multi-clouds scenario. To this end, we proposed Mixed-Integer Linear Programming (MILP) models that can be solved in a reasonable time by available solvers. We divided the workflow tasks into small segments, distributed them among VMs with multi-vCPU, and formulated them in mathematical programming. In the proposed mathematical model, the objective of a problem and real and physical constraints or restrictions are formulated using exact mathematical functions. We analyzed the treatment of optimal makespan under variations in budget, workflow size, and different segment sizes. The evaluation's results signify that our proposed approach has achieved logical and expected results in meeting the set objectives

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

Data Placement And Task Mapping Optimization For Big Data Workflows In The Cloud

Data-centric workflows naturally process and analyze a huge volume of datasets. In this new era of Big Data there is a growing need to enable data-centric workflows to perform computations at a scale far exceeding a single workstation\u27s capabilities. Therefore, this type of applications can benefit from distributed high performance computing (HPC) infrastructures like cluster, grid or cloud computing. Although data-centric workflows have been applied extensively to structure complex scientific data analysis processes, they fail to address the big data challenges as well as leverage the capability of dynamic resource provisioning in the Cloud. The concept of “big data workflows” is proposed by our research group as the next generation of data-centric workflow technologies to address the limitations of exist-ing workflows technologies in addressing big data challenges. Executing big data workflows in the Cloud is a challenging problem as work-flow tasks and data are required to be partitioned, distributed and assigned to the cloud execution sites (multiple virtual machines). In running such big data work-flows in the cloud distributed across several physical locations, the workflow execution time and the cloud resource utilization efficiency highly depends on the initial placement and distribution of the workflow tasks and datasets across the multiple virtual machines in the Cloud. Several workflow management systems have been developed for scientists to facilitate the use of workflows; however, data and work-flow task placement issue has not been sufficiently addressed yet. In this dissertation, I propose BDAP strategy (Big Data Placement strategy) for data placement and TPS (Task Placement Strategy) for task placement, which improve workflow performance by minimizing data movement across multiple virtual machines in the Cloud during the workflow execution. In addition, I propose CATS (Cultural Algorithm Task Scheduling) for workflow scheduling, which improve workflow performance by minimizing workflow execution cost. In this dissertation, I 1) formalize data and task placement problems in workflows, 2) propose a data placement algorithm that considers both initial input dataset and intermediate datasets obtained during workflow run, 3) propose a task placement algorithm that considers placement of workflow tasks before workflow run, 4) propose a workflow scheduling strategy to minimize the workflow execution cost once the deadline is provided by user and 5)perform extensive experiments in the distributed environment to validate that our proposed strategies provide an effective data and task placement solution to distribute and place big datasets and tasks into the appropriate virtual machines in the Cloud within reasonable time

A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds

[EN] Bag-of-Tasks (BoT) workflows are widespread in many big data analysis fields. However, there are very few cloud resource provisioning and scheduling algorithms tailored for BoT workflows. Furthermore, existing algorithms fail to consider the stochastic task execution times of BoT workflows which leads to deadline violations and increased resource renting costs. In this paper, we propose a dynamic cloud resource provisioning and scheduling algorithm which aims to fulfill the workflow deadline by using the sum of task execution time expectation and standard deviation to estimate real task execution times. A bag-based delay scheduling strategy and a single-type based virtual machine interval renting method are presented to decrease the resource renting cost. The proposed algorithm is evaluated using a cloud simulator ElasticSim which is extended from CloudSim. The results show that the dynamic algorithm decreases the resource renting cost while guaranteeing the workflow deadline compared to the existing algorithms. (C) 2017 Elsevier B.V. All rights reserved.The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No. 61602243 and 61572127), the Natural Science Foundation ofJiangsu Province (Grant No. BK20160846), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology, Grant No. 30916014107), the Fundamental Research Funds for the Central University (Grant No. 30916015104). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD" (No. DP12015-65895-R) co-financed by FEDER funds.Cai, Z.; Li, X.; Ruiz García, R.; Li, Q. (2017). A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds. Future Generation Computer Systems. 71:57-72. https://doi.org/10.1016/j.future.2017.01.020S57727