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State-of-the-art applications are typically deployed on top of cloud services which offer the illusion of infinite resources, elastic scalability, and a simple pay-per-use billing model. While this is very convenient for developers, it also comes with relatively high access latency for end users. Future application domains such as the Internet of Things, autonomous driving, or future 5G mobile apps, however, require low latency access which is typically achieved by moving computation towards the edge of the network. This natural extension of the cloud towards the edge is typically referred to as Fog Computing and has lately found a lot of attention. However, Fog Computing as a deployment platform has not yet found widespread adoption; this, we believe, could be helped through a consistent use of the service-oriented computing paradigm for fog infrastructure services. Based on this motivation, this paper describes the concept of Fog Computing in detail, discusses the main obstacles for Fog Computing adoption, and derives open research challenges.

International audience; With the increasing popularity of the cloud computing model and rapid proliferation of cloud infrastructures there are increasing concerns about energy consumption and consequent impact of cloud computing as a contributor to global CO2 emissions. To date, little is known about how to incorporate energy consumption and CO2 concerns into cloud application development and deployment decision models. In this respect, this paper describes an eco-aware approach that relies on the definition, monitoring and utilization of energy and CO2 metrics combined with the use of innovative application scheduling and runtime adaptation techniques to optimize energy consumption and CO2 footprint of cloud applications as well as the underlying infrastructure. The eco-aware approach involves measuring or quantifying the energy consumption and CO2 at different levels of cloud computing, using that information to create scheduling and adaptation techniques that contribute towards reducing the energy consumption and CO2 emissions, and finally testing and validating the developed solutions in a multi-site cloud environment with the help of challenging case study applications. The experimental and validation results show the potential of the eco-aware approach to significantly reduce the CO2 footprint and consequent environmental impact of cloud applications.

Reducing energy consumption is increasingly gaining attention in the area of Cloud computing, as means to reduce costs and improve corporate sustainability image. ASCETiC is focused on providing novel methods and tools to support software developers to optimise energy efficiency and minimise the carbon footprint resulting from developing, deploying and running software in Clouds. At the same time, quality of service, experience and perception are still taken into account, so energy efficiency will complement them and boost Cloud efficiency at several dimensions. ASCETiC primary focus is to relate software design and energy use, which will depend on the deployment conditions and the correct operation of the software by means of an adaptive environment. This paper presents specific objectives for the project, as well as requirements, business goals and architecture for the resultant Open Source Cloud stack providing energy efficiency at software, platform and infrastructure Cloud layers.

Accurate and fine-grained monitoring of dynamic and heterogeneous cloud resources is essential to the overall operation of the cloud. In this paper, we review the principles of pervasive cloud monitoring, and discuss the requirements of a pervasive monitoring solution needed to support proactive and autonomous management of cloud resources. This paper reviews existing monitoring solutions used by the industry and assesses their suitability to support pervasive monitoring. We find that the collectd daemon is a good candidate to form the basis of a lightweight monitoring agent that supports high resolution probing, but it needs to be supplemented by high-level interaction capabilities for pervasive monitoring.

Current praxis of software licensing has been identified as major obstacle for Grid computing a couple of years ago already. Recent surveys of Clouds indicate that the same holds true for Cloud computing. As a consequence, using commercial applications that require access to a license server for authorisation at run-time has been quite limited until recently in distributed computing environments. Due to the mandatory centralised control of license usage during application run-time traditional software licensing practices are not suitable. In this paper we present a novel approach for managing software licenses as web service resources in distributed service oriented environments. Licenses become mobile objects, which may move to the environment where required to authorise the execution of a license protected application. The SmartLM solution, which has been recently implemented as a prototype decouples authorisation for license usage from authorisation for application execution.

A recent survey of the 451group on Cloud usage highlights software licensing as one of the top five obstacles for Cloud computing, quite similar to what has been observed in the Grid already a couple of years. The reasons are the same: the current praxis of software licensing, both in terms of business models and licensing technology. As a consequence, using commercial applications that require access to a license server for authorisation at run-time has been quite limited until recently in distributed computing environments, especially when the environment stretches across administrative domains like it is the case for public Clouds. In this paper we present a novel approach for managing software licenses as web service resources in distributed service oriented environments. Licenses become mobile objects, which may move to the environment where required to authorise the execution of a license protected application.

Platform-as-a-Service (PaaS) is the Cloud area concentrating the major growth in the last years in terms of adoption and market share. At the same time Cloud models are evolving towards Hybrid Clouds through the consideration of service expansion across a diversity of Cloud offerings. So far, the majority of multi-cloud approaches have explored IaaS layer, neglecting the rest of the Cloud stack. This limits enterprises to enjoy the benefits of an agile, automated and adaptable infrastructure though flexibility provided by PaaS multi-cloud. This paper presents and compares results of two research projects addressing PaaS multi-cloud architectures: ASCETiC and SeaClouds.

The Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation (TANGO) project’s goal is to characterise factors which affect power consumption in software development and operation for Heterogeneous Parallel Hardware (HPA) environments. Its main contribution is the combination of requirements engineering and design modelling for self-adaptive software systems, with power consumption awareness in relation to these environments. The energy efficiency and application quality factors are integrated into the application lifecycle (design, implementation and operation). To support this, the key novelty of the project is a reference architecture and its implementation. Moreover, a programming model with built-in support for various hardware architectures including heterogeneous clusters, heterogeneous chips and programmable logic devices is provided. This leads to a new cross-layer programming approach for heterogeneous parallel hardware architectures featuring software and hardware modelling. Application power consumption and performance, data location and time-criticality optimization, as well as security and dependability requirements on the target hardware architecture are supported by the architecture. This work has been supported by the European Commission through the Horizon 2020 Research and Innovation program under contract 687584 (TANGO project) by the Spanish Government under contract TIN2015-65316 and grant SEV-2015-0493 (Severo Ochoa Program) and by Generalitat de Catalunya under contracts 2014- SGR-1051 and 2014-SGR-1272.

Fuente TESIUNAM

Fuente TESIUNAM