Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Information practices for sustainability: role of iSchools in achieving the UN sustainable development goals(SDGs)

In September 2015, the United Nations (UN) GeneralAssembly passed a resolution identifying 17 Sustain-able Development Goals (SDGs) and 169 associated tar-gets, and countries around the world agreed to achievethese by 2030. By conducting a thematic analysis offour key UN policy documents related to sustainabledevelopment, this paper argues that alongside financialand other resources, access to, and use of, appropriateinformation are essential for achieving SDGs. The paperalso reviews research on information and sustainabilityundertaken at the iSchools and the computer andhuman–computer interaction HCI communities. Giventhat the mission of iSchools is to connect people andsociety with the required information through the use ofappropriate technologies and tools, this paper arguesthat iSchools can play a key role in helping people, insti-tutions, and businesses, and thus countries around theworld achieve SDGs. The paper identifies 4 broad areasof teaching and research that can help iSchools aroundthe world prepare a trained workforce who can manage,and facilitate access to, information in specific domainsand contexts. It is also argued that cooperation and col-laborations among iSchools can promote a culture ofsustainable information practices among universitygraduates and researchers in different disciplines thatwill pave the way for achieving SDGs in every sector

Enabling Hyperscale Web Services

Modern web services such as social media, online messaging, web search, video streaming, and online banking often support billions of users, requiring data centers that scale to hundreds of thousands of servers, i.e., hyperscale. In fact, the world continues to expect hyperscale computing to drive more futuristic applications such as virtual reality, self-driving cars, conversational AI, and the Internet of Things. This dissertation presents technologies that will enable tomorrow’s web services to meet the world’s expectations. The key challenge in enabling hyperscale web services arises from two important trends. First, over the past few years, there has been a radical shift in hyperscale computing due to an unprecedented growth in data, users, and web service software functionality. Second, modern hardware can no longer support this growth in hyperscale trends due to a decline in hardware performance scaling. To enable this new hyperscale era, hardware architects must become more aware of hyperscale software needs and software researchers can no longer expect unlimited hardware performance scaling. In short, systems researchers can no longer follow the traditional approach of building each layer of the systems stack separately. Instead, they must rethink the synergy between the software and hardware worlds from the ground up. This dissertation establishes such a synergy to enable futuristic hyperscale web services. This dissertation bridges the software and hardware worlds, demonstrating the importance of that bridge in realizing efficient hyperscale web services via solutions that span the systems stack. The specific goal is to design software that is aware of new hardware constraints and architect hardware that efficiently supports new hyperscale software requirements. This dissertation spans two broad thrusts: (1) a software and (2) a hardware thrust to analyze the complex hyperscale design space and use insights from these analyses to design efficient cross-stack solutions for hyperscale computation. In the software thrust, this dissertation contributes uSuite, the first open-source benchmark suite of web services built with a new hyperscale software paradigm, that is used in academia and industry to study hyperscale behaviors. Next, this dissertation uses uSuite to study software threading implications in light of today’s hardware reality, identifying new insights in the age-old research area of software threading. Driven by these insights, this dissertation demonstrates how threading models must be redesigned at hyperscale by presenting an automated approach and tool, uTune, that makes intelligent run-time threading decisions. In the hardware thrust, this dissertation architects both commodity and custom hardware to efficiently support hyperscale software requirements. First, this dissertation characterizes commodity hardware’s shortcomings, revealing insights that influenced commercial CPU designs. Based on these insights, this dissertation presents an approach and tool, SoftSKU, that enables cheap commodity hardware to efficiently support new hyperscale software paradigms, improving the efficiency of real-world web services that serve billions of users, saving millions of dollars, and meaningfully reducing the global carbon footprint. This dissertation also presents a hardware-software co-design, uNotify, that redesigns commodity hardware with minimal modifications by using existing hardware mechanisms more intelligently to overcome new hyperscale overheads. Next, this dissertation characterizes how custom hardware must be designed at hyperscale, resulting in industry-academia benchmarking efforts, commercial hardware changes, and improved software development. Based on this characterization’s insights, this dissertation presents Accelerometer, an analytical model that estimates gains from hardware customization. Multiple hyperscale enterprises and hardware vendors use Accelerometer to make well-informed hardware decisions.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169802/1/akshitha_1.pd

30th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems. The amount and complexity of information itself, the number of abstraction levels of information, and the size of databases and knowledge bases are continuously growing. Conceptual modelling is one of the sub-areas of information modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers

A Model for Scientific Workflows with Parallel and Distributed Computing

In the last decade we witnessed an immense evolution of the computing infrastructures in terms of processing, storage and communication. On one hand, developments in hardware architectures have made it possible to run multiple virtual machines on a single physical machine. On the other hand, the increase of the available network communication bandwidth has enabled the widespread use of distributed computing infrastructures, for example based on clusters, grids and clouds. The above factors enabled different scientific communities to aim for the development and implementation of complex scientific applications possibly involving large amounts of data. However, due to their structural complexity, these applications require decomposition models to allow multiple tasks running in parallel and distributed environments. The scientific workflow concept arises naturally as a way to model applications composed of multiple activities. In fact, in the past decades many initiatives have been undertaken to model application development using the workflow paradigm, both in the business and in scientific domains. However, despite such intensive efforts, current scientific workflow systems and tools still have limitations, which pose difficulties to the development of emerging large-scale, distributed and dynamic applications. This dissertation proposes the AWARD model for scientific workflows with parallel and distributed computing. AWARD is an acronym for Autonomic Workflow Activities Reconfigurable and Dynamic. The AWARD model has the following main characteristics. It is based on a decentralized execution control model where multiple autonomic workflow activities interact by exchanging tokens through input and output ports. The activities can be executed separately in diverse computing environments, such as in a single computer or on multiple virtual machines running on distributed infrastructures, such as clusters and clouds. It provides basic workflow patterns for parallel and distributed application decomposition and other useful patterns supporting feedback loops and load balancing. The model is suitable to express applications based on a finite or infinite number of iterations, thus allowing to model long-running workflows, which are typical in scientific experimention. A distintive contribution of the AWARD model is the support for dynamic reconfiguration of long-running workflows. A dynamic reconfiguration allows to modify the structure of the workflow, for example, to introduce new activities, modify the connections between activity input and output ports. The activity behavior can also be modified, for example, by dynamically replacing the activity algorithm. In addition to the proposal of a new workflow model, this dissertation presents the implementation of a fully functional software architecture that supports the AWARD model. The implemented prototype was used to validate and refine the model across multiple workflow scenarios whose usefulness has been demonstrated in practice clearly, through experimental results, demonstrating the advantages of the major characteristics and contributions of the AWARD model. The implemented prototype was also used to develop application cases, such as a workflow to support the implementation of the MapReduce model and a workflow to support a text mining application developed by an external user. The extensive experimental work confirmed the adequacy of the AWARD model and its implementation for developing applications that exploit parallelism and distribution using the scientific workflows paradigm

MILAS : ModernIzing Legtacy Applications towards Service Oriented Architecture (SOA) and Software as a Service (SaaS)

MILAS er om modernisering av gamle applikasjoner, og løfte dem opp til et SOA, SaaS nivå. MILAS beskriver en metodikk for hvordan dette kan best gjøres. Metodikken baserer seg på standarder og modelleringsmuligheter. MILAS dekker spesielt modellering av tjenester og hvordan en kan modellere tjeneser ut fra gamle komponeter

Software Design Change Artifacts Generation through Software Architectural Change Detection and Categorisation

Software is solely designed, implemented, tested, and inspected by expert people, unlike other engineering projects where they are mostly implemented by workers (non-experts) after designing by engineers. Researchers and practitioners have linked software bugs, security holes, problematic integration of changes, complex-to-understand codebase, unwarranted mental pressure, and so on in software development and maintenance to inconsistent and complex design and a lack of ways to easily understand what is going on and what to plan in a software system. The unavailability of proper information and insights needed by the development teams to make good decisions makes these challenges worse. Therefore, software design documents and other insightful information extraction are essential to reduce the above mentioned anomalies. Moreover, architectural design artifacts extraction is required to create the developer’s profile to be available to the market for many crucial scenarios. To that end, architectural change detection, categorization, and change description generation are crucial because they are the primary artifacts to trace other software artifacts. However, it is not feasible for humans to analyze all the changes for a single release for detecting change and impact because it is time-consuming, laborious, costly, and inconsistent. In this thesis, we conduct six studies considering the mentioned challenges to automate the architectural change information extraction and document generation that could potentially assist the development and maintenance teams. In particular, (1) we detect architectural changes using lightweight techniques leveraging textual and codebase properties, (2) categorize them considering intelligent perspectives, and (3) generate design change documents by exploiting precise contexts of components’ relations and change purposes which were previously unexplored. Our experiment using 4000+ architectural change samples and 200+ design change documents suggests that our proposed approaches are promising in accuracy and scalability to deploy frequently. Our proposed change detection approach can detect up to 100% of the architectural change instances (and is very scalable). On the other hand, our proposed change classifier’s F1 score is 70%, which is promising given the challenges. Finally, our proposed system can produce descriptive design change artifacts with 75% significance. Since most of our studies are foundational, our approaches and prepared datasets can be used as baselines for advancing research in design change information extraction and documentation

A distributed analysis and monitoring framework for the compact Muon solenoid experiment and a pedestrian simulation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The design of a parallel and distributed computing system is a very complicated task. It requires a detailed understanding of the design issues and of the theoretical and practical aspects of their solutions. Firstly, this thesis discusses in detail the major concepts and components required to make parallel and distributed computing a reality. A multithreaded and distributed framework capable of analysing the simulation data produced by a pedestrian simulation software was developed. Secondly, this thesis discusses the origins and fundamentals of Grid computing and the motivations for its use in High Energy Physics. Access to the data produced by the Large Hadron Collider (LHC) has to be provided for more than five thousand scientists all over the world. Users who run analysis jobs on the Grid do not necessarily have expertise in Grid computing. Simple, userfriendly and reliable monitoring of the analysis jobs is one of the key components of the operations of the distributed analysis; reliable monitoring is one of the crucial components of the Worldwide LHC Computing Grid for providing the functionality and performance that is required by the LHC experiments. The CMS Dashboard Task Monitoring and the CMS Dashboard Job Summary monitoring applications were developed to serve the needs of the CMS community