Holistic Performance Analysis and Optimization of Unified Virtual Memory

The programming difficulty of creating GPU-accelerated high performance computing (HPC) codes has been greatly reduced by the advent of Unified Memory technologies that abstract the management of physical memory away from the developer. However, these systems incur substantial overhead that paradoxically grows for codes where these technologies are most useful. While these technologies are increasingly adopted for use in modern HPC frameworks and applications, the performance cost reduces the efficiency of these systems and turns away some developers from adoption entirely. These systems are naturally difficult to optimize due to the large number of interconnected hardware and software components that must be untangled to perform thorough analysis. In this thesis, we take the first deep dive into a functional implementation of a Unified Memory system, NVIDIA UVM, to evaluate the performance and characteristics of these systems. We show specific hardware and software interactions that cause serialization between host and devices. We further provide a quantitative evaluation of fault handling for various applications under different scenarios, including prefetching and oversubscription. Through lower-level analysis, we find that the driver workload is dependent on the interactions among application access patterns, GPU hardware constraints, and Host OS components. These findings indicate that the cost of host OS components is significant and present across UM implementations. We also provide a proof-of-concept asynchronous approach to memory management in UVM that allows for reduced system overhead and improved application performance. This study provides constructive insight into future implementations and systems, such as Heterogeneous Memory Management

Improving Computer Network Operations Through Automated Interpretation of State

Networked systems today are hyper-scaled entities that provide core functionality for distributed services and applications spanning personal, business, and government use. It is critical to maintain correct operation of these networks to avoid adverse business outcomes. The advent of programmable networks has provided much needed fine-grained network control, enabling providers and operators alike to build some innovative networking architectures and solutions. At the same time, they have given rise to new challenges in network management. These architectures, coupled with a multitude of devices, protocols, virtual overlays on top of physical data-plane etc. make network management a highly challenging task. Existing network management methodologies have not evolved at the same pace as the technologies and architectures. Current network management practices do not provide adequate solutions for highly dynamic, programmable environments. We have a long way to go in developing management methodologies that can meaningfully contribute to networks becoming self-healing entities. The goal of my research is to contribute to the design and development of networks towards transforming them into self-healing entities. Network management includes a multitude of tasks, not limited to diagnosis and troubleshooting, but also performance engineering and tuning, security analysis etc. This research explores novel methods of utilizing network state to enhance networking capabilities. It is constructed around hypotheses based on careful analysis of practical deficiencies in the field. I try to generate real-world impact with my research by tackling problems that are prevalent in deployed networks, and that bear practical relevance to the current state of networking. The overarching goal of this body of work is to examine various approaches that could help enhance network management paradigms, providing administrators with a better understanding of the underlying state of the network, thus leading to more informed decision-making. The research looks into two distinct areas of network management, troubleshooting and routing, presenting novel approaches to accomplishing certain goals in each of these areas, demonstrating that they can indeed enhance the network management experience

On the Importance of Infrastructure-Awareness in Large-Scale Distributed Storage Systems

Big data applications put significant latency and throughput demands on distributed storage systems. Meeting these demands requires storage systems to use a significant amount of infrastructure resources, such as network capacity and storage devices. Resource demands largely depend on the workloads and can vary significantly over time. Moreover, demand hotspots can move rapidly between different infrastructure locations. Existing storage systems are largely infrastructure-oblivious as they are designed to support a broad range of hardware and deployment scenarios. Most only use basic configuration information about the infrastructure to make important placement and routing decisions. In the case of cloud-based storage systems, cloud services have their own infrastructure-specific limitations, such as minimum request sizes and maximum number of concurrent requests. By ignoring infrastructure-specific details, these storage systems are unable to react to resource demand changes and may have additional inefficiencies from performing redundant network operations. As a result, provisioning enough resources for these systems to address all possible workloads and scenarios would be cost prohibitive. This thesis studies the performance problems in commonly used distributed storage systems and introduces novel infrastructure-aware design methods to improve their performance. First, it addresses the problem of slow reads due to network congestion that is induced by disjoint replica and path selection. Selecting a read replica separately from the network path can perform poorly if all paths to the pre-selected endpoints are congested. Second, this thesis looks at scalability limitations of consensus protocols that are commonly used in geo-distributed key value stores and distributed ledgers. Due to their network-oblivious designs, existing protocols redundantly communicate over highly oversubscribed WAN links, which poorly utilize network resources and limits consistent replication at large scale. Finally, this thesis addresses the need for a cloud-specific realtime storage system for capital market use cases. Public cloud infrastructures provide feature-rich and cost-effective storage services. However, existing realtime timeseries databases are not built to take advantage of cloud storage services. Therefore, they do not effectively utilize cloud services to provide high performance while minimizing deployment cost. This thesis presents three systems that address these problems by using infrastructure-aware design methods. Our performance evaluation of these systems shows that infrastructure-aware design is highly effective in improving the performance of large scale distributed storage systems

Allocation Strategies for Data-Oriented Architectures

Data orientation is a common design principle in distributed data management systems. In contrast to process-oriented or transaction-oriented system designs, data-oriented architectures are based on data locality and function shipping. The tight coupling of data and processing thereon is implemented in different systems in a variety of application scenarios such as data analysis, database-as-a-service, and data management on multiprocessor systems. Data-oriented systems, i.e., systems that implement a data-oriented architecture, bundle data and operations together in tasks which are processed locally on the nodes of the distributed system. Allocation strategies, i.e., methods that decide the mapping from tasks to nodes, are core components in data-oriented systems. Good allocation strategies can lead to balanced systems while bad allocation strategies cause skew in the load and therefore suboptimal application performance and infrastructure utilization. Optimal allocation strategies are hard to find given the complexity of the systems, the complicated interactions of tasks, and the huge solution space. To ensure the scalability of data-oriented systems and to keep them manageable with hundreds of thousands of tasks, thousands of nodes, and dynamic workloads, fast and reliable allocation strategies are mandatory. In this thesis, we develop novel allocation strategies for data-oriented systems based on graph partitioning algorithms. Therefore, we show that systems from different application scenarios with different abstraction levels can be generalized to generic infrastructure and workload descriptions. We use weighted graph representations to model infrastructures with bounded and unbounded, i.e., overcommited, resources and possibly non-linear performance characteristics. Based on our generalized infrastructure and workload model, we formalize the allocation problem, which seeks valid and balanced allocations that minimize communication. Our allocation strategies partition the workload graph using solution heuristics that work with single and multiple vertex weights. Novel extensions to these solution heuristics can be used to balance penalized and secondary graph partition weights. These extensions enable the allocation strategies to handle infrastructures with non-linear performance behavior. On top of the basic algorithms, we propose methods to incorporate heterogeneous infrastructures and to react to changing workloads and infrastructures by incrementally updating the partitioning. We evaluate all components of our allocation strategy algorithms and show their applicability and scalability with synthetic workload graphs. In end-to-end--performance experiments in two actual data-oriented systems, a database-as-a-service system and a database management system for multiprocessor systems, we prove that our allocation strategies outperform alternative state-of-the-art methods

The 1990 Goddard Conference on Space Applications of Artificial Intelligence

The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition

Fourth NASA Goddard Conference on Mass Storage Systems and Technologies

This report contains copies of all those technical papers received in time for publication just prior to the Fourth Goddard Conference on Mass Storage and Technologies, held March 28-30, 1995, at the University of Maryland, University College Conference Center, in College Park, Maryland. This series of conferences continues to serve as a unique medium for the exchange of information on topics relating to the ingestion and management of substantial amounts of data and the attendant problems involved. This year's discussion topics include new storage technology, stability of recorded media, performance studies, storage system solutions, the National Information infrastructure (Infobahn), the future for storage technology, and lessons learned from various projects. There also will be an update on the IEEE Mass Storage System Reference Model Version 5, on which the final vote was taken in July 1994

Sandboxed, Online Debugging of Production Bugs for SOA Systems

Short time-to-bug localization is extremely important for any 24x7 service-oriented application. To this end, we introduce a new debugging paradigm called live debugging. There are two goals that any live debugging infrastructure must meet: Firstly, it must offer real-time insight for bug diagnosis and localization, which is paramount when errors happen in user-facing applications. Secondly, live debugging should not impact user-facing performance for normal events. In large distributed applications, bugs which impact only a small percentage of users are common. In such scenarios, debugging a small part of the application should not impact the entire system. With the above-stated goals in mind, this thesis presents a framework called Parikshan, which leverages user-space containers (OpenVZ) to launch application instances for the express purpose of live debugging. Parikshan is driven by a live-cloning process, which generates a replica (called debug container) of production services, cloned from a production container which continues to provide the real output to the user. The debug container provides a sandbox environment, for safe execution of monitoring/debugging done by the users without any perturbation to the execution environment. As a part of this framework, we have designed customized-network proxies, which replicate inputs from clients to both the production and test-container, as well safely discard all outputs. Together the network duplicator, and the debug container ensure both compute and network isolation of the debugging environment. We believe that this piece of work provides the first of its kind practical real-time debugging of large multi-tier and cloud applications, without requiring any application downtime, and minimal performance impact

Autonomous grid scheduling using probabilistic job runtime scheduling

Computational Grids are evolving into a global, service-oriented architecture – a universal platform for delivering future computational services to a range of applications of varying complexity and resource requirements. The thesis focuses on developing a new scheduling model for general-purpose, utility clusters based on the concept of user requested job completion deadlines. In such a system, a user would be able to request each job to finish by a certain deadline, and possibly to a certain monetary cost. Implementing deadline scheduling is dependent on the ability to predict the execution time of each queued job, and on an adaptive scheduling algorithm able to use those predictions to maximise deadline adherence. The thesis proposes novel solutions to these two problems and documents their implementation in a largely autonomous and self-managing way. The starting point of the work is an extensive analysis of a representative Grid workload revealing consistent workflow patterns, usage cycles and correlations between the execution times of jobs and its properties commonly collected by the Grid middleware for accounting purposes. An automated approach is proposed to identify these dependencies and use them to partition the highly variable workload into subsets of more consistent and predictable behaviour. A range of time-series forecasting models, applied in this context for the first time, were used to model the job execution times as a function of their historical behaviour and associated properties. Based on the resulting predictions of job runtimes a novel scheduling algorithm is able to estimate the latest job start time necessary to meet the requested deadline and sort the queue accordingly to minimise the amount of deadline overrun. The testing of the proposed approach was done using the actual job trace collected from a production Grid facility. The best performing execution time predictor (the auto-regressive moving average method) coupled to workload partitioning based on three simultaneous job properties returned the median absolute percentage error centroid of only 4.75%. This level of prediction accuracy enabled the proposed deadline scheduling method to reduce the average deadline overrun time ten-fold compared to the benchmark batch scheduler. Overall, the thesis demonstrates that deadline scheduling of computational jobs on the Grid is achievable using statistical forecasting of job execution times based on historical information. The proposed approach is easily implementable, substantially self-managing and better matched to the human workflow making it well suited for implementation in the utility Grids of the future

참신성에 따른 스타트업 크라우드펀딩 창업 자금 조달 전략

학위논문(박사) -- 서울대학교대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2023. 2. 황준석.This thesis explores startups' fundraising and development strategies that started from novel ideas to sustainable companies through crowdfunding. From the stage of persuasion by presenting novel ideas to subsequent business development, the study analyzes the factors that enable startups to grow successfully through crowdfunding and accordingly reveals what an effective action strategy from the entrepreneur's point of view is. The purpose of the thesis is to understand the cycle of the campaign, value delivery, and subsequent development while focusing on the strategic perspective of entrepreneurs using crowdfunding as an entrepreneurial fundraising tool. First, at the fundraising point, which is the beginning of crowdfunding startups, the study focuses on indicators that can measure an idea's novelty and explore the behavioral strategies of founders during crowdfunding campaigns according to the degree of novelty. This study proposes a machine learning-based methodological measurement to understand the novelty and presents a behavioral strategy using the method. The study demonstrates that the novelty of an idea is a crucial element in changing the direction project founders must act for successful fundraising in reward-based crowdfunding. The second study proposes a framework for a satisfactory crowdfunding experience for reward-based crowdfunding participants. Through the framework of utilitarian-hedonic value delivery borrowed from consumer research, the study finds the determinants of how founders deliver value to crowdfunding participants after realizing business ideas. This study explores the post-campaign idea implementation and satisfaction delivery process, taking preliminary steps to broadly understand the subsequent business processes after fundraising. The third study examines the differences in characteristics of crowdfunding startups that have attracted follow-up venture funds. In particular, the study analyzes how the timing and valuation of follow-up venture financing are affected by the characteristics of the crowdfunding campaign process. This study in-depth finds the relationship between the process of crowdfunding and long-term sustainable startups.Chapter 1. Introduction 1 1.1. Research Background 1 1.2 Research Objectives 4 1.3 Research Outline 5 Chapter 2. Literature Review 8 2.1 Entrepreneurial Financing 8 2.1.1 Venture Capital 8 2.1.2 Crowdfunding 11 2.1.2.1 Crowdfunding in entrepreneur perspectives 12 2.1.2.2 Crowdfunding in investor perspectives 14 2.2 Idea Realization 16 2.2.1 Signaling theory 18 2.3 Contribution of the study 19 Chapter 3. Effective Strategies to Attract Crowdfunding Investment Based on the Novelty of Business Ideas 23 3.1 Introduction 24 3.2 Literature Review 27 3.2.1 Crowdfunding as entrepreneurial financing and signaling theory 27 3.2.2 Novelty of an idea and crowdfunding success 29 3.2.3 Measuring novelty and innovation performance 31 3.3 Theoretical framework and hypotheses development 33 3.3.1 Ideas novelty 34 3.3.2 Target diversification and an ideas novelty 36 3.3.3 Information updates and two-sided communication 39 3.3.4 Method 45 3.3.4.1 Data sources 45 3.3.4.2 Descriptive statistics 46 3.3.4.3 Dependent and explanatory variables 47 3.3.4.4 Control variables 49 3.3.4.5 Empirical model 51 3.3.5 Results 54 3.3.6 Discussion 66 Chapter 4. Delivering Satisfaction after Crowdfunding through Utilitarian and Hedonic Value Structure 74 4.1 Introduction 75 4.2 Theoretical Background 77 4.2.1 Idea realization in crowdfunding 77 4.2.2 Market feedback from funder satisfaction after fundraising 78 4.2.3 Idea implementing capacity: delivering the utilitarian value 81 4.2.4 Emotional satisfaction of participating innovation: improving the hedonic value 84 4.3 Research objective, Methodology, and Data 88 4.3.1 Research objective and data source 88 4.3.2 Dependent variable 89 4.3.3 Explanatory variables 90 4.3.4 Control variables 91 4.3.5 Descriptive statistics 92 4.3.6 Empirical model 93 4.4 Results and Discussion 96 4.4.1 Empirical results 96 4.4.2 Discussion 99 4.5 Conclusion 102 4.5.1 Limitations and further studies 103 Chapter 5. Subsequent funding of crowdfunded startups: Focusing on factors affecting follow-up funding amount and timing 105 5.1 Introduction 106 5.2 Theoretical framework and hypotheses 109 5.2.1 Crowdfunding as entrepreneurial financing 109 5.2.2 Venture financing performance: amount and timing 112 5.2.3 Research framework 113 5.2.4 Feedback aspect and follow-up financing 114 5.2.4.1 Securing market expectation 114 5.2.4.2 Securing market satisfaction/dissatisfaction 117 5.2.5 Relationships with investors and follow-up funding 119 5.3 Data and method 122 5.3.1 Data sources 122 5.3.2 Descriptive statistics 123 5.3.3 Dependent and explanatory variables 124 5.3.4 Control variables 127 5.3.5 Empirical model 128 5.4 Results 130 5.5 Discussion with case studies 135 5.6. Limitations and further research 141 Chapter 6. Conclusion 144 6.1 Overall Summary 144 6.2 Implications and Contributions 148박

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp