Multi-objective scheduling of Scientific Workflows in multisite clouds

Clouds appear as appropriate infrastructures for executing Scientific Workflows (SWfs). A cloud is typically made of several sites (or data centers), each with its own resources and data. Thus, it becomes important to be able to execute some SWfs at more than one cloud site because of the geographical distribution of data or available resources among different cloud sites. Therefore, a major problem is how to execute a SWf in a multisite cloud, while reducing execution time and monetary costs. In this paper, we propose a general solution based on multi-objective scheduling in order to execute SWfs in a multisite cloud. The solution consists of a multi-objective cost model including execution time and monetary costs, a Single Site Virtual Machine (VM) Provisioning approach (SSVP) and ActGreedy, a multisite scheduling approach. We present an experimental evaluation, based on the execution of the SciEvol SWf in Microsoft Azure cloud. The results reveal that our scheduling approach significantly outperforms two adapted baseline algorithms (which we propose by adapting two existing algorithms) and the scheduling time is reasonable compared with genetic and brute-force algorithms. The results also show that our cost model is accurate and that SSVP can generate better VM provisioning plans compared with an existing approach.Work partially funded by EU H2020 Programme and MCTI/RNP-Brazil (HPC4E grant agreement number 689772), CNPq, FAPERJ, and INRIA (MUSIC project), Microsoft (ZcloudFlow project) and performed in the context of the Computational Biology Institute (www.ibc-montpellier.fr). We would like to thank Kary Ocaña for her help in modeling and executing the SciEvol SWf.Peer ReviewedPostprint (author's final draft

Improving the Performance and Energy Efficiency for Mobile Cloud Computing

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

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

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

Simulation and Modeling for Improving Access to Care for Underserved Populations

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

Towards an Improved Software Project Monitoring Task Model of Agile Kanban Method

Agile Kanban method recently is gaining increasing attention and popularity in software development organizations (SDOs). This method has numerous advantages that make it performs better than other Agile methods in terms of managing software projects. However, different studies revealed that this method has significant challenges that negatively impact the scheduling of the development process. Therefore, late delivery of software projects may occur, thus the rate of projects failures will be increased. In response, this paper aims to explicate the current challenges in progress monitoring task of Agile Kanban method. Accordingly, the results gave insights to bridge that gap by developing an improved software project monitoring task model of Agile Kanban method. To do so, we identified the components and criteria that affect software project monitoring task, and then an initial model has proposed. The initial model consists of three main components, which are (1) extending progress tracking, (2) generating optimum WIP limits, and (3) visualizing useful insights for workflow. Further research can be focused on developing and evaluating the proposed model through discussion with the knowledge and domain experts

Multisite adaptive computation offloading for mobile cloud applications

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

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

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

Intervention to Improve Screening Mammograms in Clinical Practice.

Abstract Breast cancer remains a challenging health issue in the United States, representing the second leading cause of cancer deaths for women. The key approach to tackle this issue is the early detection of breast cancer through annual mammography screening in asymptomatic women. This quality improvement project sought to improve the proportion of breast cancer screening documentation in a private obstetrics and gynecology practice that serves primarily Black women. In addition, the project is thought to improve the current utilization of office mammogram services and its mammogram completion rate. Comprehensive interventions implemented included establishing clear guidelines, staff education, using paper checklists and Electronic Health Records (EHR) tools, enhancing the scheduling workflow process, and providing phone call reminders to patients before mammogram appointments. The project site\u27s EHR and the CMS breast cancer screening report from the electronic clinical quality measure were used to collect data. The interventions were assessed by analyzing data extracted before and after the project. Data indicated that the proportion of eligible patients up-to-date with mammograms or receiving recommendations for breast cancer screening went from 50% pre-implementation to 76% post-implementation (chi-square 97.72, p \u3c .001). There was a 16 percent increase in the CMS breast cancer screening quality measures. The mammogram department saw a 12 percent increase in mammogram performance, and the rate of patient adherence to appointments increased by 19 percent (z = 2.89, p = .03). Project results indicate that an evidence-based, comprehensive process enhances the cancer screening process and improves patient appointment adherence. Recommendations include sustaining the project and improving breast cancer screening referrals and tracking in the EHR. Keywords: breast cancer, mammogram, screening

Performance of distributed multiscale simulations

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