From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

Conejero, Javier; Caminero, Blanca; Carrión, Carmen; Tomás, Luis

doi:10.1016/j.future.2013.12.005

Cited by 7 publications

(3 citation statements)

References 39 publications

(59 reference statements)

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“…In SLA trust negotiation, the CSC puts more important service properties in front consultation according to their own preferences [13,14], and the price is get in the final negotiations after the service level is determined, so the priority sequence of negotiation in this paper is , , , , , the CSC releases the request of the service level of the same properties to different cloud service providers at the same time. After the CSP side receives the request, it discloses to CSC the corresponding negotiation strategies of service properties.…”

Section: Figure 7 the Sub-diagram Of The Hcpn Model On Qos Negotiationmentioning

confidence: 99%

An SLA-oriented Multiparty Trust Negotiation Model based on HCPN in Cloud Environment

Wang¹,

Chen²,

Chen³

et al. 2015

IJUNESST

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Section: Figure 7 the Sub-diagram Of The Hcpn Model On Qos Negotiationmentioning

confidence: 99%

An SLA-oriented Multiparty Trust Negotiation Model based on HCPN in Cloud Environment

Wang¹,

Chen²,

Chen³

et al. 2015

IJUNESST

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“…This enables a better network usage and in turns low-latency applications present a better performance. A similar approach in the Grid Computing field was presented by Conejero et al [14], where three QoS levels are provided by choosing more trustable resources (those which allow advance reservations) for applications that require higher QoS levels, always fulfilling the service level agreements (SLAs) for the high level QoS applications.…”

Section: Related Workmentioning

confidence: 99%

Cloud Service Differentiation in Overbooked Data Centers

Tomás

Tordsson

2014

2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing

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Abstract-Low resource utilization in cloud data centers can be mitigated by overbooking but this increases the risk of performance degradation. We propose a three level Quality of Service (QoS) scheme for overbooked cloud data centers to assure high performance QoS for applications that need it. We design a controller that dynamically maps virtual cores to physical cores and whenever feasible shares physical cores among applications. Our evaluation based on real cloud applications and workloads demonstrates that performance isolation can be achieved for critical applications while overall utilization is increased thanks to overbooking.

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“…Another interesting proposal is the SA-Layer [55] which proposes a set of components for introducing scheduling in advance capabilities to Grids. With these capabilities, users can know how an application will be scheduled on the Grid which is fundamental to know if the application execution will fulfill the quality required by the user as proposed in [56].…”

Section: Resource Allocation In Distributed Platformsmentioning

confidence: 99%

Semantic resource management and interoperability between distributed computing platforms

Ejarque Artigas

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Distributed Computing is the paradigm where the application execution is distributed across different computers connected by a communication network. Distributed Computing platforms have evolved very fast during the las decades: starting from Clusters, where a set of computers were working together in a single location; then evolving to the Grids, where computing resources are shared by different entities, creating a global computing infrastructure which is available to different user communities; and finally becoming in what is currently known as the Cloud, where computing and data resources are provided, on demand, in a very dynamic fashion, and following the Utility Computing model where you pay only for what you consume. Different types of companies and institutions are exploring the potential benefits of moving their IT services and applications to Cloud infrastructures, in order to decouple the management of computing resources from their core business process to become more productive. Nevertheless, migrating software to Clouds is not an easy task, since it requires a deep knowledge of the technology to decompose the application and the capabilities offered by providers and how to use them. Besides this complex deployment process, the current cloud market place has several providers offering resources with different capabilities, prices and quality, and each provider uses their own properties and APIs for describing and accessing their resources. Therefore, when customers want to execute an application in the providers' resources, they must understand the different providers' description, compare them and select the most suitable resources for their interests. Once the provider and resources have been selected, developers have to inter-operate with the different providers' interfaces to perform the application execution steps. To do all the mentioned steps, application developers have to deal with the design and implementation of complex integration procedures. This thesis presents several contributions to overcome the aforementioned problems by providing a platform that facilitates and automates the integration of applications in different providers' infrastructures lowering the barrier of adopting new distributed computing infrastructure such as Clouds. The achievement of this objective has been split in several parts. In the first part, we have studied how semantic web technologies are helping to describe applications and to automatically infer a model for deploying them in a distributed platform. Once the application deployment model has been inferred, the second step is finding the resources to deploy and execute the different application components. Regarding this topic, we have studied how semantic web technologies can be applied in the resource allocation problem. Once the different components have been allocated in the providers' resources, it is time to deploy and execute the application components on these resources by invoking a workflow of provider API calls. However, every provider defines their own management interfaces, so the workflow to perform the same actions is different depending on the selected provider. In this thesis, we propose a framework to automatically infer the workflow of provider interface calls required to perform any resource management tasks. In the last part of the thesis, we have studied how to introduce the benefits of software agents for coordinating the application management in distributed platforms. We propose a multi-agent system which is in charge of coordinating the different steps of the application deployment in a distributed way as well as monitoring the correct execution of the application in the computing resources. The different contributions have been validated with a prototype implementation and a set of use cases. La Computación Distribuida es un paradigma donde la ejecución de aplicaciones se distribuye entre diferentes computadores contados a través de una red de comunicación. Las plataformas de computación distribuida han evolucionado rápidamente durante las últimas décadas, empezando por los "Clusters", donde varios computadores están conectados por una red local; pasando por los "Grids", donde los recursos computacionales son compartidos por varias instituciones creando un red de computación global; llegando finalmente a lo que actualmente conocemos como "Clouds", donde nos podemos proveer de recursos de manera dinámica, bajo demanda y pagando solo por lo que consumimos. Actualmente, varias compañías están descubriendo los beneficios de mover sus aplicaciones a las infraestructuras Cloud, desacoplando la administración de los recursos computacionales de su "core business" para ser más productivos. Sin embargo migrar el software al Cloud no es una tarea fácil porque se requiere un conocimiento exhaustivo de la tecnología y como usar los servicios ofrecidos por los diferentes proveedores. Además cada proveedor ofrece recursos con diferentes capacidades, precios y calidades, con su propia interfaz para acceder a ellos. Por consiguiente, cuando un usuario quiere ejecutar una aplicación en el Cloud, debe entender que ofrece cada proveedor y como usarlo y una vez que ha elegido debe programar los diferentes pasos del despliegue de su aplicación. Si además se quieren usar varios proveedores o cambiar a otro, este proceso debe repetirse varias veces. Esta tesis presenta varias contribuciones para mitigar estos problemas diseñando una plataforma para facilitar y automatizar la integración de aplicaciones en los diferentes proveedores. Estas contribuciones se dividen en varias partes: Primero, el estudio de como las tecnologías semánticas pueden ayudar para describir aplicaciones y automáticamente inferir como se puede desplegar en un plataforma distribuida. Una vez obtenemos este modelo de despliegue, la segunda contribución nos presenta como estas mismas tecnologías pueden usarse para asignar las diferentes partes del despliegue de la aplicación a los recursos de los proveedores. Una vez sabemos la asignación, la siguiente contribución nos resuelve como se puede usar "AI planning" para encontrar la secuencia de servicios que se deben ejecutar para realizar el despliegue deseado. Finalmente, la última parte de la tesis, nos presenta como el despliegue y ejecuciones de las aplicaciones puede coordinarse por un sistema multi-agentes de una manera escalable y distribuida. Las diferentes contribuciones de la tesis han sido validadas mediante la implementación de prototipos y casos de uso.

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From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

Cited by 7 publications

References 39 publications

An SLA-oriented Multiparty Trust Negotiation Model based on HCPN in Cloud Environment

An SLA-oriented Multiparty Trust Negotiation Model based on HCPN in Cloud Environment

Cloud Service Differentiation in Overbooked Data Centers

Semantic resource management and interoperability between distributed computing platforms

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