Grids offer a dramatic increase in the number of available processing and storing resources that can be delivered to applications. However, efficient job submission and management continue being far from accessible to ordinary scientists and engineers due to their dynamic and complex nature. This paper describes a new Globus based framework that allows an easier and more efficient execution of jobs in a ‘submit and forget’ fashion. The framework automatically performs the steps involved in job submission and also watches over its efficient execution. In order to obtain a reasonable degree of performance, job execution is adapted to dynamic resource conditions and application demands. Adaptation is achieved by supporting automatic application migration following performance degradation, ‘better’ resource discovery, requirement change, owner decision or remote resource failure. The framework is currently functional on any Grid testbed based on Globus because it does not require new system software to be installed in the resources. The paper also includes practical experiences of the behavior of our framework on the TRGP and UCM‐CAB testbeds. Copyright © 2004 John Wiley & Sons, Ltd.

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SUMMARYMany production Grid and e-Science infrastructures have begun to offer services to end-users during the past several years with an increasing number of scientific applications that require access to a wide variety of resources and services in multiple Grids. Therefore, the Grid Interoperation Now-Community Group of the Open Grid Forum-organizes and manages interoperation efforts among those production Grid infrastructures to reach the goal of a world-wide Grid vision on a technical level in the near future. This contribution highlights fundamental approaches of the group and discusses open standards in the context of production e-Science infrastructures.

Automated resource provisioning techniques enable the implementation of elastic services, by adapting the available resources to the service demand. This is essential for reducing power consumption and guaranteeing QoS and SLA fulfillment, especially for those services with strict QoS requirements in terms of latency or response time, such as web servers with high traffic load, data stream processing, or real-time big data analytics. Elasticity is often implemented in cloud platforms and virtualized data-centers by means of auto-scaling mechanisms. These make automated resource provisioning decisions based on the value of specific infrastructure and/or service performance metrics. This paper presents and evaluates a novel predictive auto-scaling mechanism based on machine learning techniques for time series forecasting and queuing theory. The new mechanism aims to accurately predict the processing load of a distributed server and estimate the appropriate number of resources that must be provisioned in order to optimize the service response time and fulfill the SLA contracted by the user, while attenuating resource over-provisioning in order to reduce energy consumption and infrastructure costs. The results show that the proposed model obtains a better forecasting accuracy than other classical models, and makes a resource allocation closer to the optimal case.

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The interconnection of the different geographically dispersed cloud and fog infrastructures is a key issue for the development of the fog technology. Although most existing cloud providers and platforms offer some kind of connectivity services to allow the interconnection with external networks, these services exhibit many limitations and they are not suitable for fog computing environments. In this work we present a hybrid fog and cloud interconnection framework, which allows the automatic provision of cross-site virtual networks to interconnect geographically distributed cloud and fog infrastructures. This framework provides a scalable and multi-tenant solution, and a simple and generic interface for instantiating, configuring and deploying Layer 2 and Layer 3 overlay networks across heterogeneous fog and cloud platforms, with abstraction from the underlying cloud/fog technologies and network virtualization technologies. Keywords: Heterogeneous Fog and Cloud Interconnection; Virtual Network Provisioning; Layer 2 and Layer 3 Overlay Networks; Scalability and Multi-Tenancy Support.Fog computing paradigm [1] is emerging as a key enabling technology for the Internet of Things (IoT), by broadening the scope of cloud platforms and services to the edge of the network, and allowing the efficient and agile deployment of mobile applications with strict geo-distribution, location awareness, and low latency requirements. Other similar paradigms to fog computing are ETSI Mobile Edge Computing [2] and Berkeley Cloudlets [3]. As shown in Figure 1, fog computing extends the cloud computing model by including an additional layer between the cloud and the mobile devices. In this three layer architecture (device-fog-cloud), a fog computing node is a small to medium size computing infrastructure that includes compute, storage and networking elements and is usually located at the premises of the end mobile users (e.g. shopping centers, airports, tourist attractions, etc.), and fog instances are physical or virtualized resources, deployed on top of the fog node infrastructure, that run the applications consumed by these end users, and can be accessed by mobile devices at one-hop distance over the wireless network. In addition, various fog nodes can also be connected to a central cloud, that could provide coordination between the different fog infrastructures, and some other additional services, such as extra computing capacity, large database management, off-line data processing for business intelligence, etc. This central cloud can be implemented as a commercial cloud provider (e.g. Amazon EC2, Microsoft Azure, Google Cloud, etc.) or as a public or private cloud managed by a cloud management platform [4] (e.g. OpenNebula, OpenStack, etc.) Some important features that must be considered in the design of a fog computing architecture are the following: a) multi-tenancy and isolation, since fog nodes must support the coexistence of several applications belonging to different tenants, and guarantee isolation among them; c) scalability ...

Grids offer a dramatic increase in the number

Current systems based on pilot jobs are not exploiting all the scheduling advantages that the technique offers, or they lack compatibility or adaptability. To overcome the limitations or drawbacks in existing approaches, this study presents a different general-purpose pilot system, GWpilot. This system provides individual users or institutions with a more easy-to-use, easy-toinstall, scalable, extendable, flexible and adjustable framework to efficiently run legacy applications. The framework is based on the GridWay meta-scheduler and incorporates the powerful features of this system, such as standard interfaces, fair-share policies, ranking, migration, accounting and compatibility with diverse infrastructures. GWpilot goes beyond establishing simple network overlays to overcome the waiting times in remote queues or to improve the reliability in task production. It properly tackles the characterisation problem in current infrastructures, allowing users to arbitrarily incorporate customised monitoring of resources and their running applications into the system. This functionality allows the new framework to implement innovative scheduling algorithms that accomplish the computational needs of a wide range of calculations faster and more efficiently. The system can also be easily stacked under other software layers, such as self-schedulers. The advanced techniques included by default in the framework result in significant performance improvements even when very short tasks are scheduled.