A collaborative citizen science platform for real-time volunteer computing and games

Volunteer computing (VC) or distributed computing projects are common in the citizen cyberscience (CCS) community and present extensive opportunities for scientists to make use of computing power donated by volunteers to undertake large-scale scientific computing tasks. Volunteer computing is generally a non-interactive process for those contributing computing resources to a project whereas volunteer thinking (VT) or distributed thinking, which allows volunteers to participate interactively in citizen cyberscience projects to solve human computation tasks. In this paper we describe the integration of three tools, the Virtual Atom Smasher (VAS) game developed by CERN, LiveQ, a job distribution middleware, and CitizenGrid, an online platform for hosting and providing computation to CCS projects. This integration demonstrates the combining of volunteer computing and volunteer thinking to help address the scientific and educational goals of games like VAS. The paper introduces the three tools and provides details of the integration process along with further potential usage scenarios for the resulting platform.Comment: 12 pages, 13 figure

A complete simulator for volunteer computing environments

Volunteer computing is a type of distributed computing in which ordinary people donate their idle computer time to science projects like SETI@home, Climateprediction.net and many others. BOINC provides a complete middleware system for volunteer computing, and it became generalized as a platform for distributed applications in areas as diverse as mathematics, medicine, molecular biology, climatology, environmental science, and astrophysics. In this document we present the whole development process of ComBoS, a complete simulator of the BOINC infrastructure. Although there are other BOINC simulators, our intention was to create a complete simulator that, unlike the existing ones, could simulate realistic scenarios taking into account the whole BOINC infrastructure, that other simulators do not consider: projects, servers, network, redundant computing, scheduling, and volunteer nodes. The output of the simulations allows us to analyze a wide range of statistical results, such as the throughput of each project, the number of jobs executed by the clients, the total credit granted and the average occupation of the BOINC servers. This bachelor thesis describes the design of ComBoS and the results of the validation performed. This validation compares the results obtained in ComBoS with the real ones of three different BOINC projects (Einstein@home, SETI@home and LHC@home). Besides, we analyze the performance of the simulator in terms of memory usage and execution time. This document also shows that our simulator can guide the design of BOINC projects, describing some case studies using ComBoS that could help designers verify the feasibility of BOINC projects.Ingeniería Informátic

A heterogeneous mobile cloud computing model for hybrid clouds

Mobile cloud computing is a paradigm that delivers applications to mobile devices by using cloud computing. In this way, mobile cloud computing allows for a rich user experience; since client applications run remotely in the cloud infrastructure, applications use fewer resources in the user's mobile devices. In this paper, we present a new mobile cloud computing model, in which platforms of volunteer devices provide part of the resources of the cloud, inspired by both volunteer computing and mobile edge computing paradigms. These platforms may be hierarchical, based on the capabilities of the volunteer devices and the requirements of the services provided by the clouds. We also describe the orchestration between the volunteer platform and the public, private or hybrid clouds. As we show, this new model can be an inexpensive solution to different application scenarios, highlighting its benefits in cost savings, elasticity, scalability, load balancing, and efficiency. Moreover, with the evaluation performed we also show that our proposed model is a feasible solution for cloud services that have a large number of mobile users. (C) 2018 Elsevier B.V. All rights reserved.This work has been partially supported by the Spanish MINISTERIO DE ECONOMÍA Y COMPETITIVIDAD under the project grant TIN2016-79637-P TOWARDS UNIFICATION OF HPC AND BIG DATA PARADIGMS

Cloud Computing for Climate Modelling: Evaluation, Challenges and Benefits

Cloud computing is a mature technology that has already shown benefits for a wide range of academic research domains that, in turn, utilize a wide range of application design models. In this paper, we discuss the use of cloud computing as a tool to improve the range of resources available for climate science, presenting the evaluation of two different climate models. Each was customized in a different way to run in public cloud computing environments (hereafter cloud computing) provided by three different public vendors: Amazon, Google and Microsoft. The adaptations and procedures necessary to run the models in these environments are described. The computational performance and cost of each model within this new type of environment are discussed, and an assessment is given in qualitative terms. Finally, we discuss how cloud computing can be used for geoscientific modelling, including issues related to the allocation of resources by funding bodies. We also discuss problems related to computing security, reliability and scientific reproducibilityS

Efficient replication of large volumes of data and maintaining data consistency by using P2P techniques in Desktop Grid

Desktop Grid is increasing in popularity because of relatively very low cost and good performance in institutions. Data-intensive applications require data management in scientific experiments conducted by researchers and scientists in Desktop Grid-based Distributed Computing Infrastructure (DCI). Some of these data-intensive applications deal with large volumes of data. Several solutions for data-intensive applications have been proposed for Desktop Grid (DG) but they are not efficient in handling large volumes of data. Data management in this environment deals with data access and integration, maintaining basic properties of databases, architecture for querying data, etc. Data in data-intensive applications has to be replicated in multiple nodes for improving data availability and reducing response time. Peer-to-Peer (P2P) is a well established technique for handling large volumes of data and is widely used on the internet. Its environment is similar to the environment of DG. The performance of existing P2P-based solution dealing with generic architecture for replicating large volumes of data is not efficient in DG-based DCI. Therefore, there is a need for a generic architecture for replicating large volumes of data efficiently by using P2P in BOINC based Desktop Grid. Present solutions for data-intensive applications mainly deal with read only data. New type of applications are emerging which deal large volumes of data and Read/Write of data. In emerging scientific experiments, some nodes of DG generate new snapshot of scientific data after regular intervals. This new snapshot of data is generated by updating some of the values of existing data fields. This updated data has to be synchronised in all DG nodes for maintaining data consistency. The performance of data management in DG can be improved by addressing efficient data replication and consistency. Therefore, there is need for algorithms which deal with data Read/Write consistency along with replication for large volumes of data in BOINC based Desktop Grid. The research is to identify efficient solutions for data replication in handling large volumes of data and maintaining Read/Write data consistency using Peer-to-Peer techniques in BOINC based Desktop Grid. This thesis presents the solutions that have been carried out to complete the research

Enabling BOINC in infrastructure as a service cloud system

Volunteer or crowd computing is becoming increasingly popular for solving complex research problems from an increasingly diverse range of areas. The majority of these have been built using the Berkeley Open Infrastructure for Network Computing (BOINC) platform, which provides a range of different services to manage all computation aspects of a project. The BOINC system is ideal in those cases where not only does the research community involved need low-cost access to massive computing resources but also where there is a significant public interest in the research being done. We discuss the way in which cloud services can help BOINC-based projects to deliver results in a fast, on demand manner. This is difficult to achieve using volunteers, and at the same time, using scalable cloud resources for short on demand projects can optimize the use of the available resources. We show how this design can be used as an efficient distributed computing platform within the cloud, and outline new approaches that could open up new possibilities in this field, using Climateprediction.net (http://www.climateprediction.net/) as a case study

Enabling BOINC in infrastructure as a service cloud system

Volunteer or crowd computing is becoming increasingly popular for solving complex research problems from an increasingly diverse range of areas. The majority of these have been built using the Berkeley Open Infrastructure for Network Computing (BOINC) platform, which provides a range of different services to manage all computation aspects of a project. The BOINC system is ideal in those cases where not only does the research community involved need low-cost access to massive computing resources but also where there is a significant public interest in the research being done. We discuss the way in which cloud services can help BOINC-based projects to deliver results in a fast, on demand manner. This is difficult to achieve using volunteers, and at the same time, using scalable cloud resources for short on demand projects can optimize the use of the available resources. We show how this design can be used as an efficient distributed computing platform within the cloud, and outline new approaches that could open up new possibilities in this field, using Climateprediction.net (http://www.climateprediction.net/) as a case studyS