Cloud computing: Case study

The interest of applications in adopting cloud computing has grown steadily. Requirements for users to plan for provisioning turned more flexible, and allows to increase them when there is a rise in service demand. To understand the different services that the "cloud" can offer to companies and users, we wrote the State-of-the-Art. Studies over already deployed cloud-based infrastructures consume too many resources. Simulation tools have become key to the evolution of cloud computing, and one of the most valued for this task it is CloudSim. The practical part of the Bachelor degree thesis is based on a web service model from this toolkit, and we have modelled its workload to process with different probability distributions. The default model provided by the program presented some limitations when the workload has large differences, so we have proposed a new model.El interés de aplicaciones para adoptar el cloud computing no ha parado de crecer. Se flexibilizan los requisitos de los usuarios a la hora de planificar el suministro de recursos, y permite incrementarlos cuando hay un aumento de la demanda del servicio. Para entender el funcionamiento de los diferentes servicios que puede ofrecer la "nube" tanto a empresas como a usuarios, hemos escrito el State-of-the-Art. Hacer estudios sobre estructuras desplegadas en la "nube" consume demasiados recursos. Las herramientas de simulación se han vuelto clave para la evolución del cloud computing y una de las más valoradas para esta tarea es el CloudSim. La parte práctica del trabajo final de grado se basa en un modelo de servicio web de esta herramienta, y hemos modelado su carga de trabajo a procesar con diferentes distribuciones de probabilidad. El modelo que ofrece el programa por defecto presenta ciertas limitaciones cuando la carga de trabajo varía demasiado, y hemos propuesto un nuevo modelo.L’interès d’aplicacions per adoptar el cloud computing no ha parat de créixer. Es flexibilitzen els requisits dels usuaris a l’hora de planificar l’aprovisionament de recursos, i permet incrementar-los quan hi ha un augment de la demanda del servei. Per tal d’entendre el funcionament dels diferents serveis que pot oferir el “núvol” tant a empreses com a usuaris, hem escrit l’State-of-the-Art. Fer estudis sobre estructures desplegades al “núvol” consumeix masses recursos. Les eines de simulació s’han tornat clau per a la evolució del cloud computing i una de les més valorades per aquesta tasca és el CloudSim. La part pràctica del treball final de grau es basa en un model de servei web d’aquesta eina, i hem modelat la seva càrrega de treball a processar amb diferents distribucions de probabilitat. El model que ofereix el programa per defecte presenta certes limitacions quan la carrega de treball varia massa, i hem proposat un nou model

Multilayer Architecture Model for Mobile Cloud Computing Paradigm

Mobile Cloud Computing is one of today's more disruptive paradigms of computation due to its effects on the performance of mobile computing and the development of Internet of Things. It is able to enhance the capabilities of devices by outsourcing the workload to external computing platforms deployed along the network, such as cloud servers, cloudlets, or other edge platforms. The research described in this work presents a computational model of a multilayer architecture for increasing the performance of devices using the Mobile Cloud Computing paradigm. The main novelty of this work lies in defining a comprehensive model where all the available computing platforms along the network layers are involved to perform the outsourcing of the application workload. This proposal provides a generalization of the Mobile Cloud Computing paradigm which allows handling the complexity of scheduling tasks in such complex scenarios. The behaviour of the model and its ability of generalization of the paradigm are exemplified through simulations. The results show higher flexibility for making offloading decisions.This work was supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF), under Project CloudDriver4Industry TIN2017-89266-R, and by the Conselleria de Educación, Investigación, Cultura y Deporte, of the Community of Valencia, Spain, within the program of support for research under Project AICO/2017/134

Proposing Optimus Scheduler Algorithm for Virtual Machine Placement Within a Data Center

With the evolution of the Internet, we are witnessing the birth of an increasing number of applications that rely on the network; what was previously executed on the user's computers as stand-alone programs has been redesigned to be executed on servers with permanent connections to the Internet, making the information available from any device that has network access. Instead of buying a copy of a program, users can now pay to obtain access to it through the network, which is one of the models of cloud computing, Software as a Service (SaaS). The continuous growth of Internet bandwidth has also given rise to new multimedia applications, such as social networks and video over the Internet; and to complete this new paradigm, mobile platforms provide the ubiquity of information that allows people to stay connected. Service providers may own servers and data centers or, alternatively, may contract infrastructure providers that use economies of scale to offer access to servers as a service in the cloud computing model, i.e., Infrastructure as a Service (IaaS). As users become more dependent on cloud services and mobile platforms increase the ubiquity of the cloud, the quality of service becomes increasingly important. A fundamental metric that defines the quality of service is the delay of the information as it travels between the user computers and the servers, and between the servers themselves. Along with the quality of service and the costs, the energy consumption and the CO2 emissions are fundamental considerations in regard to planning cloud computing networks. In this research work, an Optimus Scheduler algorithm to be proposed for Add, Remove or Resize an application by using Tabu Search Algorithm

PENGARUH PEMANFAATAN CLOUD COMPUTING SEBAGAI KNOWLEDGE MANAGEMENT TERHADAP PRESTASI BELAJAR MATEMATIKA KELAS VIII DI SEKOLAH MENENGAH PERTAMA NEGERI 1 PARONGPONG

Pemanfaatan cloud computing sebagai knowledge management dalam pembelajaran merupakan salah satu implementasi dari program digitalisasi sekolah, yang memanfaatkan teknologi informasi dan komunikasi (TIK) dalam kegiatan pembelajaran. Sehingga, teknologi cloud computing memiliki peluang untuk diterapkan dalam proses pembelajaran dan diharapkan mampu meningkatkan prestasi belajar siswa. Adapun tujuan penelitian yang dilakukan ialah guna meneliti pengaruh pemanfaatan cloud computing sebagai knowledge management terhadap prestasi belajar matematika siswa kelas VIII di SMPN 1 Parongpong. Penelitian dilakukan untuk menjawab permasalahan penelitian yaitu terkait ada tidaknya pengaruh pemanfaatan cloud computing sebagai knowledge management terhadap prestasi belajar matematika kelas VIII di SMPN 1 Parongpong. Penelitian dilakukan menggunakan pendekatan kuantitatif dengan metode kuasi eksperimen. Desain eksperimen yang digunakan adalah control-group design dengan model control-group pretest and posttest design. Populasi pada penelitian ini yaitu seluruh kelas VIII di SMPN 1 Parongpong sejumlah 11 kelas. Sementara itu, untuk teknik pengambilan sampel yang digunakan menggunakan non-probability sampling melalui teknik purposive sampling, sehingga diperoleh sampel penelitian adalah kelas VIII-A dengan jumlah 37 siswa dan VIII-B dengan jumlah 38 siswa. Hasil analisis data penelitian menyatakan adanya pengaruh positif terhadap peningkatan prestasi belajar siswa setelah memanfaatkan cloud computing sebagai knowledge management, hal tersebut ditunjukkan dengan prestasi belajar kelas yang memanfaatkan cloud computing sebagai knowledge management lebih tinggi dibandingkan kelas yang menggunakan metode konvensional. The utilization of cloud computing as knowledge management in learning is one of the implementations of the school's digitization program, which uses information and communication technology (ICT) in learning activities. Thus, cloud computing technology has the opportunity to be applied in the learning process and is expected to be able to improve student learning outcomes. This research aims to examine the effect of using cloud computing as knowledge management on the mathematics learning achievement of class VIII students at SMPN 1 Parongpong. The research was conducted to answer research problems, whether an effect of using cloud computing as knowledge management on the results of mathematics learning achievement for class VIII at SMPN 1 Parongpong. This research used a quantitative approach with a quasi-experimental method. The experimental design was a control-group design with a control-group pretest and posttest design model. This study population’s were all class of VIII at SMPN 1 Parongpong with a total 11 classes. Meanwhile, the research sample acquired is class VIII-A with 37 students and VIII-B with 38 students, using non-probability sampling through purposive sampling technique. The results of research data analysis stated there was a positive influence on increasing student achievement after using cloud computing as knowledge management, it was indicated by the results of the class learning achievement using cloud computing as knowledge management was higher than the class using conventional methods

Resource Renting for Periodical Cloud Workflow Applications

[EN] Cloud computing is a new resource provisioning mechanism, which represents a convenient way for users to access different computing resources. Periodical workflow applications commonly exist in scientific and business analysis, among many other fields. One of the most challenging problems is to determine the right amount of resources for multiple periodical workflow applications. In this paper, the periodical workflow applications scheduling problem with total renting cost minimization is considered. The novelty of this work relies precisely on this objective function, which is more realistic in practice than the more commonly considered makespan minimization. An integer programming model is constructed for the problem under study. A Precedence Tree based Heuristic (PTH) is developed which considers three types of initial schedule construction methods. Based on the initial schedule, two improvement procedures are presented. The proposed methods are compared with existing algorithms for the related makespan based multiple workflow scheduling problem. Experimental and statistical results demonstrate the effectiveness and efficiency of the proposed algorithm.This work is supported by the National Natural Science Foundation of China (No. 61572127, 61272377), the Key Research & Development program in Jiangsu Province (No. BE2015728) and Collaborative Innovation Center of Wireless Communications Technology. Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD-Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) financed by FEDER funds.Chen, L.; Li, X.; Ruiz García, R. (2020). Resource Renting for Periodical Cloud Workflow Applications. IEEE Transactions on Services Computing. 13(1):130-143. https://doi.org/10.1109/TSC.2017.2677450S13014313

Mobile Cloud Robotics as a Service with OCCIware

Best Paper AwardInternational audienceWe have recently witnessed the emerging of cloud computing on one hand and robotics platforms on the other hand. Naturally, these two visions have been merging to give birth to the Cloud Robotics paradigm in order to offer even more remote services. But such a vision is still in its infancy. Architectures and platforms are still to be defined to efficiently program robots so they can provide different services, in a standardized way masking their heterogeneity. This paper introduces Open Mobile Cloud Robotics Interface (OMCRI), a Robot-as-a-Service vision based platform, which offers a unified easy access to remote heterogeneous mobile robots. OMCRI encompasses an extension of the Open Cloud Computing Interface (OCCI) standard and a gateway hosting mobile robot resources. We then provide an implementation of OMCRI based on the open source model-driven Eclipse-based OCCIware tool chain and illustrates its use for three off-the-shelf mobile robots: Lego Mindstorm NXT, Turtlebot, and Parrot AR.Drone

Challenges in constraining anthropogenic aerosol effects on cloud radiative forcing using present-day spatiotemporal variability

This is the final version. Available from National Academy of Sciences via the DOI in this recordA large number of processes are involved in the chain from emissions of aerosol precursor gases and primary particles to impacts on cloud radiative forcing. Those processes are manifest in a number of relationships that can be expressed as factors dlnX/dlnY driving aerosol effects on cloud radiative forcing. These factors include the relationships between cloud condensation nuclei (CCN) concentration and emissions, droplet number and CCN concentration, cloud fraction and droplet number, cloud optical depth and droplet number, and cloud radiative forcing and cloud optical depth. The relationship between cloud optical depth and droplet number can be further decomposed into the sum of two terms involving the relationship of droplet effective radius and cloud liquid water path with droplet number. These relationships can be constrained using observations of recent spatial and temporal variability of these quantities. However, we are most interested in the radiative forcing since the preindustrial era. Because few relevant measurements are available from that era, relationships from recent variability have been assumed to be applicable to the preindustrial to present-day change. Our analysis of Aerosol Comparisons between Observations and Models (AeroCom) model simulations suggests that estimates of relationships from recent variability are poor constraints on relationships from anthropogenic change for some terms, with even the sign of some relationships differing in many regions. Proxies connecting recent spatial/temporal variability to anthropogenic change, or sustained measurements in regions where emissions have changed, are needed to constrain estimates of anthropogenic aerosol impacts on cloud radiative forcing.The Pacific Northwest National Laboratory (PNNL) is operated for the Department of Energy (DOE) by Battelle Memorial Institute under Contract DE-AC06-76RLO 1830. Work at PNNL was supported by the US DOE Decadal and Regional Climate Prediction using Earth System Models program and by the US DOE Earth System Modeling program. Work of M.W. and S.Z. performed at Nanjing University was supported by the One Thousand Young Talent Program, Jiangsu Province Specially-Appointed Professor Grant, and the National Natural Science Foundation of China (41575073). A portion of this research was performed using PNNL Institutional Computing resources. The ECHAM6-HAM model was developed by a consortium composed of ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford, the Finnish Meteorological Institute, and the Leibniz Institute for Tropospheric Research, and is managed by the Center for Climate Systems Modeling (C2SM) at ETH Zurich. D.N. acknowledges support by the Austrian Science Fund (J 3402-N29, Erwin Schrödinger Fellowship Abroad). C2SM at ETH Zurich is acknowledged for providing technical and scientific support. This work was also supported by a grant from the Swiss National Supercomputing Centre under Project ID s431. D.G.P. and P.S. acknowledge support from the United Kingdom (UK) Natural Environment Research Council Grant NE/I020148/1. P.S. and Z.K. acknowledge funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/2007–2013) ERC project ACCLAIM (Grant Agreement FP7-280025). The development of modal version of the GLObal Model of Aerosol Processes (GLOMAP-mode) within Hadley Center Global Environmental Mode (HadGEM) is part of the United Kingdom Chemistry and Aerosols (UKCA) project, which is supported by both National Environmental Research Council (NERC) and the Joint Department of Energy & Climate Change/Department for Environment, Food & Rural Affairs Meteorology Office Hadley Centre Climate Programme. We acknowledge use of the Met Office and NERC MONSooN high performance computing system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, a strategic partnership between the Met Office and the NERC. Simulations by SPRINTARS were executed with the supercomputer system SX-9/ACE of the National Institute for Environmental Studies, Japan. SPRINTARS is partly supported by the Environment Research and Technology Development Fund (S-12-3) of the Ministry of the Environment, Japan and Japan Society for the Promotion of Science KAKENHI Grants-in-Aid for Scientific Research 15H01728 and 15K12190. Computing resources for CAM5-MG2 simulations were provided by the Climate Simulation Laboratory at National Center for Atmospheric Research (NCAR) Computational and Information Systems Laboratory. NCAR is sponsored by the US National Science Foundation