568 research outputs found

    An Extensive Exploration of Techniques for Resource and Cost Management in Contemporary Cloud Computing Environments

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    Resource and cost optimization techniques in cloud computing environments target minimizing expenditure while ensuring efficient resource utilization. This study categorizes these techniques into three primary groups: Cloud and VM-focused strategies, Workflow techniques, and Resource Utilization and Efficiency techniques. Cloud and VM-focused strategies predominantly concentrate on the allocation, scheduling, and optimization of resources within cloud environments, particularly virtual machines. These strategies aim at a balance between cost reduction and adhering to specified deadlines, while ensuring scalability and adaptability to different cloud models. However, they may introduce complexities due to their dynamic nature and continuous optimization requirements. Workflow techniques emphasize the optimal execution of tasks in distributed systems. They address inconsistencies in Quality of Service (QoS) and seek to enhance the reservation process and task scheduling. By employing models, such as Integer Linear Programming, these techniques offer precision. But they might be computationally demanding, especially for extensive problems. Techniques focusing on Resource Utilization and Efficiency attempts to maximize the use of available resources in an energy-efficient and cost-effective manner. Considering factors like current energy levels and application requirements, these models aim to optimize performance without overshooting budgets. However, a continuous monitoring mechanism might be necessary, which can introduce additional complexities

    Improving the Performance and Energy Efficiency for Mobile Cloud Computing

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    Based on the worldwide high-speed networks and advanced hardware (e.g., multiple cores mobile processor, and various sensors), mobile software industries enthusiastically release advanced mobile applications. These phenomena cause mobile devices to break down the limitation of time and place. Mobile cloud computing provides the most convenient communication and effective working environment to humans. However, the fundamental hardware has technical difficulties to keep up advanced technologies and applications in mobile devices, which means that there is a gap between available hardware resource and the demand of complex applications in mobile devices. The limited hardware decreases the quality of service. Mobile Cloud computing with computation offloading algorithms can alleviate current concern in mobile device industries. This paper proposes a Dynamic Threshold Algorithm (DTA), which is an formulated algorithm to offload tasks in workflow to either the cloud environment or a local mobile device. Experimental results will prove that DTA is able to maximize the performance and minimize the energy consumption for mobile devices

    Dynamic workflow management for large scale scientific applications

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    The increasing computational and data requirements of scientific applications have made the usage of large clustered systems as well as distributed resources inevitable. Although executing large applications in these environments brings increased performance, the automation of the process becomes more and more challenging. The use of complex workflow management systems has been a viable solution for this automation process. In this thesis, we study a broad range of workflow management tools and compare their capabilities especially in terms of dynamic and conditional structures they support, which are crucial for the automation of complex applications. We then apply some of these tools to two real-life scientific applications: i) simulation of DNA folding, and ii) reservoir uncertainty analysis. Our implementation is based on Pegasus workflow planning tool, DAGMan workflow execution system, Condor-G computational scheduler, and Stork data scheduler. The designed abstract workflows are converted to concrete workflows using Pegasus where jobs are matched to resources; DAGMan makes sure these jobs execute reliably and in the correct order on the remote resources; Condor-G performs the scheduling for the computational tasks and Stork optimizes the data movement between different components. Integrated solution with these tools allows automation of large scale applications, as well as providing complete reliability and efficiency in executing complex workflows. We have also developed a new site selection mechanism on top of these systems, which can choose the most available computing resources for the submission of the tasks. The details of our design and implementation, as well as experimental results are presented

    Multisite adaptive computation offloading for mobile cloud applications

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    The sheer amount of mobile devices and their fast adaptability have contributed to the proliferation of modern advanced mobile applications. These applications have characteristics such as latency-critical and demand high availability. Also, these kinds of applications often require intensive computation resources and excessive energy consumption for processing, a mobile device has limited computation and energy capacity because of the physical size constraints. The heterogeneous mobile cloud environment consists of different computing resources such as remote cloud servers in faraway data centres, cloudlets whose goal is to bring the cloud closer to the users, and nearby mobile devices that can be utilised to offload mobile tasks. Heterogeneity in mobile devices and the different sites include software, hardware, and technology variations. Resource-constrained mobile devices can leverage the shared resource environment to offload their intensive tasks to conserve battery life and improve the overall application performance. However, with such a loosely coupled and mobile device dominating network, new challenges and problems such as how to seamlessly leverage mobile devices with all the offloading sites, how to simplify deploying runtime environment for serving offloading requests from mobile devices, how to identify which parts of the mobile application to offload and how to decide whether to offload them and how to select the most optimal candidate offloading site among others. To overcome the aforementioned challenges, this research work contributes the design and implementation of MAMoC, a loosely coupled end-to-end mobile computation offloading framework. Mobile applications can be adapted to the client library of the framework while the server components are deployed to the offloading sites for serving offloading requests. The evaluation of the offloading decision engine demonstrates the viability of the proposed solution for managing seamless and transparent offloading in distributed and dynamic mobile cloud environments. All the implemented components of this work are publicly available at the following URL: https://github.com/mamoc-repo

    Doctor of Philosophy

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    dissertationClinical research plays a vital role in producing knowledge valuable for understanding human disease and improving healthcare quality. Human subject protection is an obligation essential to the clinical research endeavor, much of which is governed by federal regulations and rules. Institutional Review Boards (IRBs) are responsible for overseeing human subject research to protect individuals from harm and to preserve their rights. Researchers are required to submit and maintain an IRB application, which is an important component in the clinical research process that can significantly affect the timeliness and ethical quality of the study. As clinical research has expanded in both volume and scope over recent years, IRBs are facing increasing challenges in providing efficient and effective oversight. The Clinical Research Informatics (CRI) domain has made significant efforts to support various aspects of clinical research through developing information systems and standards. However, information technology use by IRBs has not received much attention from the CRI community. This dissertation project analyzed over 100 IRB application systems currently used at major academic institutions in the United States. The varieties of system types and lack of standardized application forms across institutions are discussed in detail. The need for building an IRB domain analysis model is identified. . iv In this dissertation, I developed an IRB domain analysis model with a special focus on promoting interoperability among CRI systems to streamline the clinical research workflow. The model was evaluated by a comparison with five real-world IRB application systems. Finally, a prototype implementation of the model was demonstrated by the integration of an electronic IRB system with a health data query system. This dissertation project fills a gap in the research of information technology use for the IRB oversight domain. Adoption of the IRB domain analysis model has potential to enhance efficient and high-quality ethics oversight and to streamline the clinical research workflow

    Performance of distributed multiscale simulations

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    Multiscale simulations model phenomena across natural scales using monolithic or component-based code, running on local or distributed resources. In this work, we investigate the performance of distributed multiscale computing of component-based models, guided by six multiscale applications with different characteristics and from several disciplines. Three modes of distributed multiscale computing are identified: supplementing local dependencies with large-scale resources, load distribution over multiple resources, and load balancing of small- and large-scale resources. We find that the first mode has the apparent benefit of increasing simulation speed, and the second mode can increase simulation speed if local resources are limited. Depending on resource reservation and model coupling topology, the third mode may result in a reduction of resource consumption

    Actes de la conférence BDA 2014 : Gestion de données - principes, technologies et applications

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    International audienceActes de la conférence BDA 2014 Conférence soutenue par l'Université Joseph Fourier, Grenoble INP, le CNRS et le laboratoire LIG. Site de la conférence : http://bda2014.imag.fr Actes en ligne : https://hal.inria.fr/BDA201

    Simulation and Modeling for Improving Access to Care for Underserved Populations

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    Indiana University-Purdue University Indianapolis (IUPUI)This research, through partnership with seven Community Health Centers (CHCs) in Indiana, constructed effective outpatient appointment scheduling systems by determining care needs of CHC patients, designing an infrastructure for meaningful use of patient health records and clinic operational data, and developing prediction and simulation models for improving access to care for underserved populations. The aims of this study are 1) redesigning appointment scheduling templates based on patient characteristics, diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive modeling to improve understanding the complexity of appointment adherence in underserved populations; and 3) developing simulation models with complex data to guide operational decision-making in community health centers. This research addresses its aims by applying a multi-method approach from different disciplines, such as statistics, industrial engineering, computer science, health informatics, and social sciences. First, a novel method was developed to use Electronic Health Record (EHR) data for better understanding appointment needs of the target populations based on their characteristics and reasons for seeking health, which helped simplify, improve, and redesign current appointment type and duration models. Second, comprehensive and informative predictive models were developed to better understand appointment non-adherence in community health centers. Logistic Regression, Naïve Bayes Classifier, and Artificial Neural Network found factors contributing to patient no-show. Predictors of appointment non-adherence might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems in CHCs, and necessary steps to extract information for simulation modeling of scheduling systems in CHCs are described. Agent-Based Models were built in AnyLogic to test different scenarios of scheduling methods, and to identify how these scenarios could impact clinic access performance. This research potentially improves well-being of and care quality and timeliness for uninsured, underinsured, and underserved patients, and it helps clinics predict appointment no-shows and ensures scheduling systems are capable of properly meeting the populations’ care needs.2021-12-2

    Survey and Analysis of Production Distributed Computing Infrastructures

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    This report has two objectives. First, we describe a set of the production distributed infrastructures currently available, so that the reader has a basic understanding of them. This includes explaining why each infrastructure was created and made available and how it has succeeded and failed. The set is not complete, but we believe it is representative. Second, we describe the infrastructures in terms of their use, which is a combination of how they were designed to be used and how users have found ways to use them. Applications are often designed and created with specific infrastructures in mind, with both an appreciation of the existing capabilities provided by those infrastructures and an anticipation of their future capabilities. Here, the infrastructures we discuss were often designed and created with specific applications in mind, or at least specific types of applications. The reader should understand how the interplay between the infrastructure providers and the users leads to such usages, which we call usage modalities. These usage modalities are really abstractions that exist between the infrastructures and the applications; they influence the infrastructures by representing the applications, and they influence the ap- plications by representing the infrastructures
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