HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Scalable And Secure Provenance Querying For Scientific Workflows And Its Application In Autism Study

In the era of big data, scientific workflows have become essential to automate scientific experiments and guarantee repeatability. As both data and workflow increase in their scale, requirements for having a data lineage management system commensurate with the complexity of the workflow also become necessary, calling for new scalable storage, query, and analytics infrastructure. This system that manages and preserves the derivation history and morphosis of data, known as provenance system, is essential for maintaining quality and trustworthiness of data products and ensuring reproducibility of scientific discoveries. With a flurry of research and increased adoption of scientific workflows in processing sensitive data, i.e., health and medication domain, securing information flow and instrumenting access privileges in the system have become a fundamental precursor to deploying large-scale scientific workflows. That has become more important now since today team of scientists around the world can collaborate on experiments using globally distributed sensitive data sources. Hence, it has become imperative to augment scientific workflow systems as well as the underlying provenance management systems with data security protocols. Provenance systems, void of data security protocol, are susceptible to vulnerability. In this dissertation research, we delineate how scientific workflows can improve therapeutic practices in autism spectrum disorders. The data-intensive computation inherent in these workflows and sensitive nature of the data, necessitate support for scalable, parallel and robust provenance queries and secured view of data. With that in perspective, we propose $OPQL^{Pig}$, a parallel, robust, reliable and scalable provenance query language and introduce the concept of access privilege inheritance in the provenance systems. We characterize desirable properties of role-based access control protocol in scientific workflows and demonstrate how the qualities are integrated into the workflow provenance systems as well. Finally, we describe how these concepts fit within the DATAVIEW workflow management system

Performance and quality of service of data and video movement over a 100 Gbps testbed

AbstractDigital instruments and simulations are creating an ever-increasing amount of data. The need for institutions to acquire these data and transfer them for analysis, visualization, and archiving is growing as well. In parallel, networking technology is evolving, but at a much slower rate than our ability to create and store data. Single fiber 100 Gbps networking solutions have recently been deployed as national infrastructure. This article describes our experiences with data movement and video conferencing across a networking testbed, using the first commercially available single fiber 100 Gbps technology. The testbed is unique in its ability to be configured for a total length of 60, 200, or 400 km, allowing for tests with varying network latency. We performed low-level TCP tests and were able to use more than 99.9% of the theoretical available bandwidth with minimal tuning efforts. We used the Lustre file system to simulate how end users would interact with a remote file system over such a high performance link. We were able to use 94.4% of the theoretical available bandwidth with a standard file system benchmark, essentially saturating the wide area network. Finally, we performed tests with H.323 video conferencing hardware and quality of service (QoS) settings, showing that the link can reliably carry a full high-definition stream. Overall, we demonstrated the practicality of 100 Gbps networking and Lustre as excellent tools for data management

Master of Science

thesisEfficient movement of massive amounts of data over high-speed networks at high throughput is essential for a modern-day in-memory storage system. In response to the growing needs of throughput and latency demands at scale, a new class of database systems was developed in recent years. The development of these systems was guided by increased access to high throughput, low latency network fabrics, and declining cost of Dynamic Random Access Memory (DRAM). These systems were designed with On-Line Transactional Processing (OLTP) workloads in mind, and, as a result, are optimized for fast dispatch and perform well under small request-response scenarios. However, massive server responses such as those for range queries and data migration for load balancing poses challenges for this design. This thesis analyzes the effects of large transfers on scale-out systems through the lens of a modern Network Interface Card (NIC). The present-day NIC offers new and exciting opportunities and challenges for large transfers, but using them efficiently requires smart data layout and concurrency control. We evaluated the impact of modern NICs in designing data layout by measuring transmit performance and full system impact by observing the effects of Direct Memory Access (DMA), Remote Direct Memory Access (RDMA), and caching improvements such as Intel® Data Direct I/O (DDIO). We discovered that use of techniques such as Zero Copy yield around 25% savings in CPU cycles and a 50% reduction in the memory bandwidth utilization on a server by using a client-assisted design with records that are not updated in place. We also set up experiments that underlined the bottlenecks in the current approach to data migration in RAMCloud and propose guidelines for a fast and efficient migration protocol for RAMCloud

Towards Collaborative Scientific Workflow Management System

The big data explosion phenomenon has impacted several domains, starting from research areas to divergent of business models in recent years. As this intensive amount of data opens up the possibilities of several interesting knowledge discoveries, over the past few years divergent of research domains have undergone the shift of trend towards analyzing those massive amount data. Scientific Workflow Management System (SWfMS) has gained much popularity in recent years in accelerating those data-intensive analyses, visualization, and discoveries of important information. Data-intensive tasks are often significantly time-consuming and complex in nature and hence SWfMSs are designed to efficiently support the specification, modification, execution, failure handling, and monitoring of the tasks in a scientific workflow. As far as the complexity, dimension, and volume of data are concerned, their effective analysis or management often become challenging for an individual and requires collaboration of multiple scientists instead. Hence, the notion of 'Collaborative SWfMS' was coined - which gained significant interest among researchers in recent years as none of the existing SWfMSs directly support real-time collaboration among scientists. In terms of collaborative SWfMSs, consistency management in the face of conflicting concurrent operations of the collaborators is a major challenge for its highly interconnected document structure among the computational modules - where any minor change in a part of the workflow can highly impact the other part of the collaborative workflow for the datalink relation among them. In addition to the consistency management, studies show several other challenges that need to be addressed towards a successful design of collaborative SWfMSs, such as sub-workflow composition and execution by different sub-groups, relationship between scientific workflows and collaboration models, sub-workflow monitoring, seamless integration and access control of the workflow components among collaborators and so on. In this thesis, we propose a locking scheme to facilitate consistency management in collaborative SWfMSs. The proposed method works by locking workflow components at a granular attribute level in addition to supporting locks on a targeted part of the collaborative workflow. We conducted several experiments to analyze the performance of the proposed method in comparison to related existing methods. Our studies show that the proposed method can reduce the average waiting time of a collaborator by up to 36% while increasing the average workflow update rate by up to 15% in comparison to existing descendent modular level locking techniques for collaborative SWfMSs. We also propose a role-based access control technique for the management of collaborative SWfMSs. We leverage the Collaborative Interactive Application Methodology (CIAM) for the investigation of role-based access control in the context of collaborative SWfMSs. We present our proposed method with a use-case of Plant Phenotyping and Genotyping research domain. Recent study shows that the collaborative SWfMSs often different sets of opportunities and challenges. From our investigations on existing research works towards collaborative SWfMSs and findings of our prior two studies, we propose an architecture of collaborative SWfMSs. We propose - SciWorCS - a Collaborative Scientific Workflow Management System as a proof of concept of the proposed architecture; which is the first of its kind to the best of our knowledge. We present several real-world use-cases of scientific workflows using SciWorCS. Finally, we conduct several user studies using SciWorCS comprising different real-world scientific workflows (i.e., from myExperiment) to understand the user behavior and styles of work in the context of collaborative SWfMSs. In addition to evaluating SciWorCS, the user studies reveal several interesting facts which can significantly contribute in the research domain, as none of the existing methods considered such empirical studies, and rather relied only on computer generated simulated studies for evaluation

Improving Key-Value Database Scalability with Lazy State Determination

Applications keep demanding higher and higher throughput and lower response times from Database systems. Databases leverage concurrency, by using both multiple computer systems (nodes) and the multiple cores available in each node, to execute multiple requests (transactions) concurrently. Executing multiple transactions concurrently requires coordination, which is ensured by the database concurrency control (CC) module. However, excessive control/limitation of concurrency by the CC module negatively impacts the overall performance (latency and throughput) of the database system. The performance limitations imposed by the database CC module can be addressed by exploring new hardware, or by leveraging software-based techniques such as futures and lazy evaluation of transactions. This is where Lazy State Determination (LSD) shines [43, 42]. LSD proposes a new transactional API that decreases the conflicts between concurrent transactions by enabling the use of futures in both SQL and Key-Value database systems. The use of futures allows LSD to better capture the application semantics and to make more informed decisions on what really constitutes a conflict. These two key insights get together to create a system that provides high throughput in high contention scenarios. Our work builds on top of a previous LSD prototype. We identified and diagnosed its shortcomings, and devised and implemented a new prototype that addressed them. We validated our new LSD system and evaluated its behaviour and performance by comparing and contrasting with the original prototype. Our evaluation showed that the throughput of the new LSD prototype is from 3.7× to 4.9× higher, in centralized and distributed settings respectively, while also reducing the latency up to 10 times. With this work, we provide an LSD-based Key-Value Database System that has better vertical and horizontal scalability, and can take advantage of systems with higher core count or high number of nodes, in centralized and distributed settings, respectively.As aplicações continuam a exigir aos sistemas de base de dados (BD) débitos cada vez maiores e tempos de resposta cada vez menores. As BD respondem explorando a concorrência, usando múltiplos sistemas computacionais (nós) e os vários cores disponíveis em cada um desses nós, para executar vários pedidos (transações) simultaneamente. A execução de múltiplas transações simultaneamente requer coordenação, assegurada pelo módulo de controlo de concorrência (CC) da BD. No entanto, o controlo/limitação excessiva de concorrência pelo módulo de CC impacta negativamente o desempenho geral (latência e débito) do sistema de BD. As limitações de desempenho impostas pelo módulo CC da BD podem ser abordadas tanto explorando novo hardware como recorrendo a técnicas baseadas em software, como futuros e avaliação diferida de transações. É aqui que o Lazy State Determination (LSD) brilha [43, 42]. O LSD propõe uma nova API transacional que permite o uso de futuros em sistemas de BD SQL e Chave-Valor, diminuindo os conflitos entre transações concorrentes. O uso de futuros permite também que o LSD capture melhor a semântica da aplicação e tome decisões mais informadas sobre o que realmente constitui um conflito. Estes dois aspetos combinam-se para criar um sistema transacional que fornece elevado débito em cenários de alta contenção. O nosso trabalho foi desenvolvido sobe um protótipo anterior de LSD. Identificamos e diagnosticamos as suas deficiências e limitações, e concebemos e implementamos um novo protótipo que as endereçou. Validamos o novo sistema LSD e avaliamos o seu comportamento e desempenho comparando e contrastando com o protótipo original. A nossa avaliação mostrou que o débito do novo protótipo LSD é de 3,7× a 4,9× maior, em configurações centralizadas e distribuídas, respetivamente, além de reduzir a latência até 10 vezes. Com este trabalho, disponibilizamos um sistema de base de dados de Chave-Valor baseado em LSD que possui melhor escalabilidade vertical e horizontal, fazendo melhor uso de sistemas com múltiplos cores ou com elevado número de nós

2HOT: An Improved Parallel Hashed Oct-Tree N-Body Algorithm for Cosmological Simulation

We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, with performance and scalability measured up to 256k ($2^{18}$) processors. We present error analysis and scientific application results from a series of more than ten 69 billion ($4096^3$) particle cosmological simulations, accounting for $4 \times 10^{20}$ floating point operations. These results include the first simulations using the new constraints on the standard model of cosmology from the Planck satellite. Our simulations set a new standard for accuracy and scientific throughput, while meeting or exceeding the computational efficiency of the latest generation of hybrid TreePM N-body methods.Comment: 12 pages, 8 figures, 77 references; To appear in Proceedings of SC '1

Low-latency, query-driven analytics over voluminous multidimensional, spatiotemporal datasets

2017 Summer.Includes bibliographical references.Ubiquitous data collection from sources such as remote sensing equipment, networked observational devices, location-based services, and sales tracking has led to the accumulation of voluminous datasets; IDC projects that by 2020 we will generate 40 zettabytes of data per year, while Gartner and ABI estimate 20-35 billion new devices will be connected to the Internet in the same time frame. The storage and processing requirements of these datasets far exceed the capabilities of modern computing hardware, which has led to the development of distributed storage frameworks that can scale out by assimilating more computing resources as necessary. While challenging in its own right, storing and managing voluminous datasets is only the precursor to a broader field of study: extracting knowledge, insights, and relationships from the underlying datasets. The basic building block of this knowledge discovery process is analytic queries, encompassing both query instrumentation and evaluation. This dissertation is centered around query-driven exploratory and predictive analytics over voluminous, multidimensional datasets. Both of these types of analysis represent a higher-level abstraction over classical query models; rather than indexing every discrete value for subsequent retrieval, our framework autonomously learns the relationships and interactions between dimensions in the dataset (including time series and geospatial aspects), and makes the information readily available to users. This functionality includes statistical synopses, correlation analysis, hypothesis testing, probabilistic structures, and predictive models that not only enable the discovery of nuanced relationships between dimensions, but also allow future events and trends to be predicted. This requires specialized data structures and partitioning algorithms, along with adaptive reductions in the search space and management of the inherent trade-off between timeliness and accuracy. The algorithms presented in this dissertation were evaluated empirically on real-world geospatial time-series datasets in a production environment, and are broadly applicable across other storage frameworks

CRDTs for truly concurrent file systems

International audienceBuilding scalable and highly available geo-replicated file systems is hard. These systems need to resolve conflicts that emerge in concurrent operations in a way that maintains file system invariants, is meaningful to the user, and does not depart from the traditional file system interface. Conflict resolution in existing systems often leads to unexpected or inconsistent results. This paper introduces ElmerFS, a geo-replicated, truly concurrent file system designed with the aim of addressing these challenges. ElmerFS is based on two key ideas: (1) the use of Conflict-Free Replicated Data Types (CRDTs) for representing file system structures, which ensures that replicas converge to a correct state, and (2) conflict resolution rules, which are determined by the choice of CRDT types and their composition, designed with the principle of being intuitive to the user. We argue that if the state of the file system after resolving a conflict conveys to the user the resolved conflict in an intuitive way, the user can complement or reverse it using traditional file system operations. We discuss the challenges in the design of geo-replicated weakly consistent file systems, and present the design of ElmerFS