Optimization and Management of Large-scale Scientific Workflows in Heterogeneous Network Environments: From Theory to Practice

Next-generation computation-intensive scientific applications feature large-scale computing workflows of various structures, which can be modeled as simple as linear pipelines or as complex as Directed Acyclic Graphs (DAGs). Supporting such computing workflows and optimizing their end-to-end network performance are crucial to the success of scientific collaborations that require fast system response, smooth data flow, and reliable distributed operation.We construct analytical cost models and formulate a class of workflow mapping problems with different mapping objectives and network constraints. The difficulty of these mapping problems essentially arises from the topological matching nature in the spatial domain, which is further compounded by the resource sharing complicacy in the temporal dimension. We provide detailed computational complexity analysis and design optimal or heuristic algorithms with rigorous correctness proof or performance analysis. We decentralize the proposed mapping algorithms and also investigate these optimization problems in unreliable network environments for fault tolerance.To examine and evaluate the performance of the workflow mapping algorithms before actual deployment and implementation, we implement a simulation program that simulates the execution dynamics of distributed computing workflows. We also develop a scientific workflow automation and management platform based on an existing workflow engine for experimentations in real environments. The performance superiority of the proposed mapping solutions are illustrated by extensive simulation-based comparisons with existing algorithms and further verified by large-scale experiments on real-life scientific workflow applications through effective system implementation and deployment in real networks

Evaluating and Enabling Scalable High Performance Computing Workloads on Commercial Clouds

Performance, usability, and accessibility are critical components of high performance computing (HPC). Usability and performance are especially important to academic researchers as they generally have little time to learn a new technology and demand a certain type of performance in order to ensure the quality and quantity of their research results. We have observed that while not all workloads run well in the cloud, some workloads perform well. We have also observed that although commercial cloud adoption by industry has been growing at a rapid pace, its use by academic researchers has not grown as quickly. We aim to help close this gap and enable researchers to utilize the commercial cloud more efficiently and effectively. We present our results on architecting and benchmarking an HPC environment on Amazon Web Services (AWS) where we observe that there are particular types of applications that are and are not suited for the commercial cloud. Then, we present our results on architecting and building a provisioning and workflow management tool (PAW), where we developed an application that enables a user to launch an HPC environment in the cloud, execute a customizable workflow, and after the workflow has completed delete the HPC environment automatically. We then present our results on the scalability of PAW and the commercial cloud for compute intensive workloads by deploying a 1.1 million vCPU cluster. We then discuss our research into the feasibility of utilizing commercial cloud infrastructure to help tackle the large spikes and data-intensive characteristics of Transportation Cyberphysical Systems (TCPS) workloads. Then, we present our research in utilizing the commercial cloud for urgent HPC applications by deploying a 1.5 million vCPU cluster to process 211TB of traffic video data to be utilized by first responders during an evacuation situation. Lastly, we present the contributions and conclusions drawn from this work

Performance optimization of big data computing workflows for batch and stream data processing in multi-clouds

Workflow techniques have been widely used as a major computing solution in many science domains. With the rapid deployment of cloud infrastructures around the globe and the economic benefits of cloud-based computing and storage services, an increasing number of scientific workflows have migrated or are in active transition to clouds. As the scale of scientific applications continues to grow, it is now common to deploy various data- and network-intensive computing workflows such as serial computing workflows, MapReduce/Spark-based workflows, and Storm-based stream data processing workflows in multi-cloud environments, where inter-cloud data transfer oftentimes plays a significant role in both workflow performance and financial cost. Rigorous mathematical models are constructed to analyze the intra- and inter-cloud execution process of scientific workflows and a class of budget-constrained workflow mapping problems are formulated to optimize the network performance of big data workflows in multi-cloud environments. Research shows that these problems are all NP-complete and a heuristic solution is designed for each that takes into consideration module execution, data transfer, and I/O operations. The performance superiority of the proposed solutions over existing methods are illustrated through extensive simulations and further verified by real-life workflow experiments deployed in public clouds

Efficient and Fair Bandwidth Scheduling in Cloud Environments

Hundreds of thousands of servers from data centers are operated to provide users with pay-as-yougo infrastructure as a service, platform as a service, and software as a service. Many different types of virtual machine (VM) instances hosted on these servers oftentimes need to efficiently communicate with data movement under current bandwidth capacity. This motivates providers to seek for a bandwidth scheduler to satisfy objectives, namely assuring the minimum bandwidth per VM for the guaranteed deadline and eliminating network congestion as much as possible. Based on some rigorous mathematical models, we formulated a cloud-based bandwidth scheduling algorithm which enables dynamic and fair bandwidth management by categorizing the total bandwidth into several categories and adjusting the allocated bandwidth limit per VM for both upstream and downstream traffics in real time. The simulation showed that paradigm was able to utilize the total assigned bandwidth more efficiently compared to algorithms such as bandwidth efficiency persistence proportional sharing (PPS), PPS, and PS at the network level

Performance Optimization and Dynamics Control for Large-scale Data Transfer in Wide-area Networks

Transport control plays an important role in the performance of large-scale scientific and media streaming applications involving transfer of large data sets, media streaming, online computational steering, interactive visualization, and remote instrument control. In general, these applications have two distinctive classes of transport requirements: large-scale scientific applications require high bandwidths to move bulk data across wide-area networks, while media streaming applications require stable bandwidths to ensure smooth media playback. Unfortunately, the widely deployed Transmission Control Protocol is inadequate for such tasks due to its performance limitations. The purpose of this dissertation is to conduct rigorous analytical study of the design and performance of transport solutions, and develop an integrated transport solution in a systematical way to overcome the limitations of current transport methods. One of the primary challenges is to explore and compose a set of feasible route options with multiple constraints. Another challenge essentially arises from the randomness inherent in wide-area networks, particularly the Internet. This randomness must be explicitly accounted for to achieve both goodput maximization and stabilization over the constructed routes by suitably adjusting the source rate in response to both network and host dynamics.The superior and robust performance of the proposed transport solution is extensively evaluated in a simulated environment and further verified through real-life implementations and deployments over both Internet and dedicated connections under disparate network conditions in comparison with existing transport methods

Scientific Workflows for Metabolic Flux Analysis

Metabolic engineering is a highly interdisciplinary research domain that interfaces biology, mathematics, computer science, and engineering. Metabolic flux analysis with carbon tracer experiments (13 C-MFA) is a particularly challenging metabolic engineering application that consists of several tightly interwoven building blocks such as modeling, simulation, and experimental design. While several general-purpose workflow solutions have emerged in recent years to support the realization of complex scientific applications, the transferability of these approaches are only partially applicable to 13C-MFA workflows. While problems in other research fields (e.g., bioinformatics) are primarily centered around scientific data processing, 13C-MFA workflows have more in common with business workflows. For instance, many bioinformatics workflows are designed to identify, compare, and annotate genomic sequences by "pipelining" them through standard tools like BLAST. Typically, the next workflow task in the pipeline can be automatically determined by the outcome of the previous step. Five computational challenges have been identified in the endeavor of conducting 13 C-MFA studies: organization of heterogeneous data, standardization of processes and the unification of tools and data, interactive workflow steering, distributed computing, and service orientation. The outcome of this thesis is a scientific workflow framework (SWF) that is custom-tailored for the specific requirements of 13 C-MFA applications. The proposed approach – namely, designing the SWF as a collection of loosely-coupled modules that are glued together with web services – alleviates the realization of 13C-MFA workflows by offering several features. By design, existing tools are integrated into the SWF using web service interfaces and foreign programming language bindings (e.g., Java or Python). Although the attributes "easy-to-use" and "general-purpose" are rarely associated with distributed computing software, the presented use cases show that the proposed Hadoop MapReduce framework eases the deployment of computationally demanding simulations on cloud and cluster computing resources. An important building block for allowing interactive researcher-driven workflows is the ability to track all data that is needed to understand and reproduce a workflow. The standardization of 13 C-MFA studies using a folder structure template and the corresponding services and web interfaces improves the exchange of information for a group of researchers. Finally, several auxiliary tools are developed in the course of this work to complement the SWF modules, i.e., ranging from simple helper scripts to visualization or data conversion programs. This solution distinguishes itself from other scientific workflow approaches by offering a system of loosely-coupled components that are flexibly arranged to match the typical requirements in the metabolic engineering domain. Being a modern and service-oriented software framework, new applications are easily composed by reusing existing components

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor