Agustín C. Caminero

iCanCloud: A Flexible and Scalable Cloud Infrastructure Simulator

Simulation techniques have become a powerful tool for deciding the best starting conditions on pay-as-you-go scenarios. This is the case of public cloud infrastructures, where a given number and type of virtual machines (in short VMs) are instantiated during a specified time, being this reflected in the final budget. With this in mind, this paper introduces and validates iCanCloud, a novel simulator of cloud infrastructures with remarkable features such as flexibility, scalability, performance and usability. Furthermore, the iCanCloud simulator has been built on the following design principles: (1) it’s targeted to conduct large experiments, as opposed to others simulators from literature; (2) it provides a flexible and fully customizable global hypervisor for integrating any cloud brokering policy; (3) it reproduces the instance types provided by a given cloud infrastructure; and finally, (4) it contains a user-friendly GUI for configuring and launching simulations, that goes from a single VM to large cloud computing systems composed of thousands of machines.

Extending GridSim with an architecture for failure detection

Grid technologies are emerging as the next generation of distributed computing, allowing the aggregation of resources that are geographically distributed across different locations. However, these resources are independent and managed separately by various organizations with different policies. This will have a major impact to users who submit their jobs to the Grid, as they have to deal with issues such as policy heterogeneity, security and fault tolerance. Moreover, the changes of Grid conditions, such as resources that may become unavailable for a period of time due to maintenance and/or suffer failures, would significantly affect the quality of service (QoS) requirements of users. Therefore, it is essential for users to take into account the effects of resource failures during jobs execution. In this paper, we present our work on introducing resource failures and failure detection into the GridSim simulation toolkit. As we need to conduct repeatable and controlled experiments, it is easier to use simulation as a means of studying complex scenarios. We also give a detailed description of the overall design and a use case scenario demonstrating the conditions of resources varied over time.

Network-aware meta-scheduling in advance with autonomous self-tuning system

The provision of Quality of Service (QoS) in Grid environments is still an open issue that needs attention from the research community. One way of contributing to the provision of QoS in Grids is by performing meta-scheduling of jobs in advance, that is, jobs are scheduled some time before they are actually executed. In this way, it becomes more likely that the appropriate resources are available to run the job when needed, so that QoS requirements of jobs are met (i.e. jobs are finished within a deadline). This paper presents a framework built on top of Globus and the GridWay meta-scheduler to provide QoS by means of performing meta-scheduling in advance. Thanks to this, QoS requirements of jobs are met. This framework manages idle/busy periods of resources in order to choose the most suitable resource for each job, and uses red-black trees for this task. Besides, no prior knowledge on the duration of jobs is required, as opposed to other works using similar techniques. This framework uses heuristics that consider the network as a first level resource. Furthermore, this framework presents an autonomous behaviour so that it adapts to the dynamic changes of the Grid resources. The autonomous behaviour is obtained by means of computing a trust for each resource and performing job rescheduling. All this set of features make this framework suitable for real Grids. Finally, a performance evaluation using a real testbed is presented that illustrates the efficiency of this approach to meet the QoS requirements of users.

Network-aware heuristics for inter-domain meta-scheduling in Grids

Grid computing generally involves the aggregation of geographically distributed resources in the context of a particular application. As such resources can exist within different administrative domains, requirements on the communication network must also be taken into account when performing meta-scheduling, migration or monitoring of jobs. Similarly, coordinating efficient interaction between different domains should also be considered when performing such meta-scheduling of jobs. A strategy to perform peer-to-peer-inspired meta-scheduling in Grids is presented. This strategy has three main goals: (1) it takes the network characteristics into account when performing meta-scheduling; (2) communication and query referral between domains is considered, so that efficient meta-scheduling can be performed; and (3) the strategy demonstrates scalability, making it suitable for many scientific applications that require resources on a large scale. Simulation results are presented that demonstrate the usefulness of this approach, and it is compared with other proposals from literature.

Using Virtualization and Automatic Evaluation: Adapting Network Services Management Courses to the EHEA

This paper outlines the adaptation of a course on the management of network services in operating systems, called NetServicesOS, to the context of the new European Higher Education Area (EHEA). NetServicesOS is a mandatory course in one of the official graduate programs in the Faculty of Computer Science at the Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain, the Spanish University for Distance Education. Since UNED is a distance university, this course is taught entirely online. The adaptation process includes the implementation of continuous assessment, a key feature in EHEAs methodology. This learning-teaching methodology is driven by a set of student activities that implement the continuous assessment. For this, a virtual machine-based framework was defined, and an automatic evaluation system implemented. The evaluation system can handle the large student numbers characteristic of distance learning. If the set of proposed activities had to be performed in person on site, significant resources would have been necessary since many computer laboratories would have been required to accommodate all the students. Furthermore, the proposed framework ensures that students can perform the practical work simply and flexibly, without the need for large computing platforms.

On the Use of Extended TAM to Assess Students' Acceptance and Intent to Use Third-Generation Learning Management Systems.

Service-oriented e-learning platforms can be considered as a third generation of learning management systems ( LMSs). As opposed to the previous generations, consisting of ad hoc solutions and traditional LMS, this new technology contemplates e-learning systems as services that can be integrated into different learning scenarios. This paper shows that the flexibility of these systems allows building personal learning environments and proposes the use of an extended technology acceptance model ( TAM) to assess the acceptance and intention to use a third generation of LMS. To fit and validate the service-oriented e-learning platforms acceptance model, structural equation modeling and path analysis have been implemented. The results point out that the intention to use this third-generation LMS is determined by the gadgets and container design, both of which are structural elements of this technology. Finally, it is also shown that previous experience does not determine the use intention of this technology. [ABSTRACT FROM AUTHOR]

Network-aware summarisation for resource discovery in P2P-content networks

Many application scenarios have a read-dominated behaviour of information provision, which means that there are few updates, and that users execute frequent queries for information discovery. Discovery of content in such systems could benefit from the use of summary techniques in order to facilitate the discovery process and minimise the size of the data exchanged. This is particularly applicable within Grid computing environments where nodes (representing a group of computational and storage resources), which enable jobs to reach them may need to exchange metadata with each other about their resources. We introduce network-aware summarisation algorithms for the resource discovery in P2P-content networks, which are based on Cobweb clustering. We identify how summarisation can improve the discovery process while also improving the accuracy of the discovered resource(s). Metrics based on precision-recall are used to compare the accuracy for specific types of queries generated over the summarised content.

Exponential Smoothing for Network-Aware Meta-scheduler in Advance in Grids

Grid computing involves the coordinated use of disperse heterogeneous computing resources. This heterogeneity and dispersion makes Quality of Service (QoS) still an open issue requiring attention from the research community. One way of contributing to the provision of QoS in Grids is by performing meta-scheduling of jobs in advance, that is, the computing resource where a job will be executed is decided some time before jobs are actually executed. But this way of scheduling needs to do predictions about the future status of resources, including network. The main aim of this work is to provide QoS in Grid environments through network-aware job scheduling in advance. In our case, QoS means the fulfillment of a deadline for the completion of jobs. For this, predictions about future status of computing and network resources are made by using exponential smoothing functions. This paper presents a performance evaluation using a real testbed that illustrates the efficiency of this approach to meet the QoS requirements of users. This evaluation highlights the effects of using Exponential Smoothing (ES) to tune predictions in order to deliver the requested QoS.

Harnessing clouds for e-learning: New directions followed by UNED

In this paper, we present our work on enhancing the technological infrastructure at Spanish National University for Distance Education (UNED, Universidad Nacional de Educación a Distancia) with cloud computing principles. This includes the development of (1) virtualized environments to allow students to do practical exercises easily in the field of networks and communications; and (2) load forecasting techniques to improve on the usage of the technological infrastructures of UNED, so that the Quality of Service (QoS) experienced by users is kept and power consumption is minimized.

Using network information to perform meta-scheduling in advance in grids

In extremely heterogeneous and distributed systems, like Grid environments, it is quite difficult to provide quality of service (QoS). In addition, the dynamic behaviour of the resources makes the time needed to complete the execution of a job highly variable. So, fulfilling the user QoS requirements in a Grid is still an open issue. The main aim of this work is to provide QoS in Grid environments through network-aware job scheduling in advance. This paper presents a technique to manage idle/busy periods of resources using red-black trees which considers the network as a first level resource. Besides, no a priori knowledge on the duration of jobs is required, as opposed to other works. A performance evaluation using a real testbed is presented which illustrates the efficiency of this approach to meet the QoS requirements of users, and highlights the importance of taking the network into account when predicting the duration of jobs.