Self-organized criticality in the Kardar-Parisi-Zhang-equation

Kardar-Parisi-Zhang interface depinning with quenched noise is studied in an ensemble that leads to self-organized criticality in the quenched Edwards-Wilkinson (QEW) universality class and related sandpile models. An interface is pinned at the boundaries, and a slowly increasing external drive is added to compensate for the pinning. The ensuing interface behavior describes the integrated toppling activity history of a QKPZ cellular automaton. The avalanche picture consists of several phases depending on the relative importance of the terms in the interface equation. The SOC state is more complicated than in the QEW case and it is not related to the properties of the bulk depinning transition.Comment: 5 pages, 3 figures; accepted for publication in Europhysics Letter

An interoperable and self-adaptive approach for SLA-based service virtualization in heterogeneous Cloud environments

Cloud computing is a newly emerged computing infrastructure that builds on the latest achievements of diverse research areas, such as Grid computing, Service-oriented computing, business process management and virtualization. An important characteristic of Cloud-based services is the provision of non-functional guarantees in the form of Service Level Agreements (SLAs), such as guarantees on execution time or price. However, due to system malfunctions, changing workload conditions, hard- and software failures, established SLAs can be violated. In order to avoid costly SLA violations, flexible and adaptive SLA attainment strategies are needed. In this paper we present a self-manageable architecture for SLA-based service virtualization that provides a way to ease interoperable service executions in a diverse, heterogeneous, distributed and virtualized world of services. We demonstrate in this paper that the combination of negotiation, brokering and deployment using SLA-aware extensions and autonomic computing principles are required for achieving reliable and efficient service operation in distributed environments. © 2012 Elsevier B.V. All rights reserved

An SLA-based resource virtualization approach for on-demand service provision

Cloud computing is a newly emerged research infrastructure that builds on the latest achievements of diverse research areas, such as Grid computing, Service-oriented computing, business processes and virtualization. In this paper we present an architecture for SLA-based resource virtualization that provides an extensive solution for executing user applications in Clouds. This work represents the first attempt to combine SLA-based resource negotiations with virtualized resources in terms of on-demand service provision resulting in a holistic virtualization approach. The architecture description focuses on three topics: agreement negotiation, service brokering and deployment using virtualization. The contribution is also demonstrated with a real-world case study

Cloud Workload Prediction by Means of Simulations

Clouds hide the complexity of maintaining a physical infrastructure with a disadvantage: they also hide their internal workings. Should users need to know about these details e.g., to increase the reliability or performance of their applications, they would need to detect slight behavioural changes in the underlying system. Existing solutions for such purposes offer limited capabilities. This paper proposes a technique for predicting background workload by means of simulations that are providing knowledge of the underlying clouds to support activities like cloud orchestration or workflow enactment. We propose these predictions to select more suitable execution environments for scientific workflows. We validate the proposed prediction approach with a biochemical application

Flexible Representation of IoT Sensors for Cloud Simulators

In Internet of Things (IoT), sensors, actuators and smart devices are connected to the Internet. Application providers combine this connectivity with novel scenarios involving cloud computing. Some require in depth analysis of the interaction between IoT devices and clouds. Research focuses on questions like how to govern such large cohort of devices (i.e., often over tens of thousands). Distributed systems simulators help in such analysis, but they are problematic to apply in this newly emerging domain. Most simulators are either too detailed (e.g., need extensive knowledge on networking), or not extensible enough to support the new scenarios. This paper introduces our attempt to show how a state of the art simulator could model generic IoT sensors. We show the fundamental properties of IoT entities represented in the simulator. Based on these properties, we present an XML based, declarative modelling language aiming at: (i) describing the behaviour of sensors and their relation to clouds, and (ii) allowing rapid prototyping of simulations. Finally, we validate the applicability of our IoT extensions in five scenarios in the field of weather forecasting

Cost-efficient Datacentre Consolidation for Cloud Federations

Cloud Computing has become mature enough to enable the virtualized management of multiple datacentres. Datacentre consolidation is an important method for the efficient operation of such distributed infrastructures. Several approaches have been developed to improve the efficiency e.g. in terms of power consumption, but only a few attention has been turned to combining pricing methods with consolidation techniques. In this paper we discuss how we introduced cost models to the DISSECT-CF simulator to foster the development of cost efficient datacentre consolidation solutions. We also exemplify the usage of this extended simulator by performing cost-aware datacentre consolidation. We apply real world traces to simulate cloud load, and propose 7 strategies to address the problem