Jean-Marc Pierson

Modeling service-based multimedia content adaptation in pervasive computing

Pervasive computing applications allow users to access information from anywhere while traveling and using variety of devices. Heterogeneity and limitation of resources involved in this application demand adaptation of content according to the current context (device, user, network etc.). The dynamic nature of adaptation mechanisms together with emerging opportunities of Web Service technology provides new approach of adaptation which is service-based. While this approach would provide a valuable service for the end customer, the service provider, and the content provider, it is important to have an architectural framework which is simple, scalable, flexible and interoperable. Moreover, in order to provide a complete service-based content negotiation and adaptation solution, we must have a model, or a tool, that allows defining environmental constraints, mapping them to appropriate adaptation service requirements and finding an optimal service configuration.In this paper, we present service-based content adaptation architecture, enabling the use of third-party adaptation services and a novel content negotiation and adaptation model. The proposed architectural framework is validated through a prototype.

Cloud Computing: Survey on Energy Efficiency

Cloud computing is today’s most emphasized Information and Communications Technology (ICT) paradigm that is directly or indirectly used by almost every online user. However, such great significance comes with the support of a great infrastructure that includes large data centers comprising thousands of server units and other supporting equipment. Their share in power consumption generates between 1.1% and 1.5% of the total electricity use worldwide and is projected to rise even more. Such alarming numbers demand rethinking the energy efficiency of such infrastructures. However, before making any changes to infrastructure, an analysis of the current status is required. In this article, we perform a comprehensive analysis of an infrastructure supporting the cloud computing paradigm with regards to energy efficiency. First, we define a systematic approach for analyzing the energy efficiency of most important data center domains, including server and network equipment, as well as cloud management systems and appliances consisting of a software utilized by end users. Second, we utilize this approach for analyzing available scientific and industrial literature on state-of-the-art practices in data centers and their equipment. Finally, we extract existing challenges and highlight future research directions.

Medical Images Simulation, Storage, and Processing on the European DataGrid Testbed

The European 1ST DataGrid project was a pioneer in identifying the medical imaging field as an application domain that can benefit from Grid technologies. This paper describes how and for which purposes medical imaging applications can be Grid-enabled. Applications that have been deployed on the DataGrid testbed and middleware are described. They relate to medical image manipulation, including image production, secured image storage, and image processing. Results show that Grid technologies are still in their youth to address all issues related to complex medical imaging applications. If the benefit of Grid enabling for some medical applications is clear, there remain opened research and technical issues to develop and integrate all necessary services.

The GREEN-NET framework: Energy efficiency in large scale distributed systems

The question of energy savings has been a matter of concern since a long time in the mobile distributed systems and battery-constrained systems. However, for large-scale non-mobile distributed systems, which nowadays reach impressive sizes, the energy dimension (electrical consumption) just starts to be taken into account. In this paper, we present the GREEN-NET1 framework which is based on 3 main components: an ON/OFF model based on an Energy Aware Resource Infrastructure (EARI), an adapted Resource Management System (OAR) for energy efficiency and a trust delegation component to assume network presence of sleeping nodes.

Energy-aware service allocation

In this paper we study the problem of energy-aware resource allocation for hosting long-term services or on-demand computing jobs in clusters, e.g., deployed as part of computing infrastructures. We formalize the problem as three constrained optimization problems: maximize job performance under power consumption constraints, minimize power consumption under job performance constraints, and optimize a linear combination of power consumption and job performance. These problems are NP-hard but, given an instance, a bound on the optimal solution can be computed via a rational linear program. We propose polynomial heuristics for all three problems. Simulation experiments show that in all three cases some heuristics can achieve results close to optimal, i.e., lead to good job performance while conserving energy.

Key Management for Encrypted Data Storage in Distributed Systems

Confidential data stored on mass storage devices is at risk to be disclosed to persons getting physical or administrator access to the device. Encrypting the data reduces this risk, at the cost of more cumbersome administration. In this publication, we examine the problem of encrypted data storage in a grid computing environment, where storage capacity and data is shared across organizational boundaries. We propose an architecture that allows users to store and share encrypted data in this environment. Access to decryption keys is granted based on the grids data access permissions. The system is therefore usable as an additional security feature together with a classical access control mechanism. Data owners can choose different tradeoffs of security versus efficiency. Storage servers need not to be trusted and common access control models are supported.

Energy Consumption of Residential and Professional Switches

Precise evaluation of network appliance energy consumption is necessary to accurately model or simulate the power consumption of distributed systems. In this paper we evaluate the influence of traffic onto the consumption of electrical power of four switches found in home and professional environments. First we describe our measurement and data analysis approach, and how our results can be used for estimating the power consumption when knowing the average traffic bandwidth.Then we present the measurement results of two residential switches, and two professional switches. For each type we present regression models and parameters describing their quality. Similar to other works we find that for one of the switches the power consumption actually drops for high traffic loads, while for the others the situation is reverse. Measures justify that during most energy consumption evaluation, network appliance energy cost can be approximated as constant. This work gives information on the possible changes of this cost.

Energy-efficient and SLA-aware management of IaaS clouds

Cloud computing utilizes arbitrary mega-scale computing infrastructures and is currently revolutionizing the ICT landscape by allowing remote access to computing power and data over the Internet. Besides the huge economical impact Cloud technology exhibits a high potential to be a cornerstone of a new generation of sustainable and energy-efficient ICT. The challenging issue thereby is the energy-efficient utilization of physical machines (PMs) and the resource-efficient management of virtual machines (VMs) while attaining promised non-functional qualities of service expressed by means of Service Level Agreements (SLAs). Currently, there exist solutions for PM power management, VM migrations, and dynamic reconfiguration of VMs. However, most of the existing approaches consider each of them alone, and only use rudimentary concepts for migration costs or disrespect the nature of the highly volatile workloads. In this paper we present an integrated approach for VM migration and reconfiguration, and PM power management. Thereby, we incorporate an autonomic management loop, where proactive actions are suggested for all three areas in a hierarchically structured way. We evaluate our approach with both, synthetic workload data and real-word monitoring data of a Next Generation Sequencing (NGS) application used for the protein folding in the bioinformatics area. The efficacy of our approach is evaluated by considering classical algorithms like First Fit, Monte Carlo and Vector Packing, adapted for energy-efficient reallocation. The results show energy savings up to 61.6% while keeping acceptably low SLA violation rates.

Semantic Access Control for Medical Applications in Grid Environments

Access control is the field of security which deals with permissions to access resources, where resources may be computing power, storage capacity and data. On the other hand computational grids are systems, where users share those resources in a mostly transparent way. Grid access control poses novel challenges, since the distributed nature of grids make it difficult to manage access control by a central authority. Numerous overlapping domains with different access control policies exist and the sharing of storage resources makes it possible that data leaves the domain of its owner. To enable the owner to enforce his access control policy in such cases, access control solutions adapted to grid environments are needed. In this article we introduce Semantic Access Certificates as an extension to existing access control solutions for grids, to solve some problems that arise when grids are used to process medical data.

A Runtime Framework for Energy Efficient HPC Systems without a Priori Knowledge of Applications

The rising computing demands of scientific endeavors often require the creation and management of High Performance Computing (HPC) systems for running experiments and processing vast amounts of data. These HPC systems generally operate at peak performance, consuming a large quantity of electricity, even though their workload varies over time. Understanding the behavioral patterns (i.e., phases) of HPC systems during their use is key to adjust performance to resource demand and hence improve the energy efficiency. In this paper, we describe (i) a method to detect phases of an HPC system based on its workload, and (ii) a partial phase recognition technique that works cooperatively with on-the-fly dynamic management. We implement a prototype that guides the use of energy saving capabilities to demonstrate the benefits of our approach. Experimental results reveal the effectiveness of the phase detection method under real-life workload and benchmarks. A comparison with baseline unmanaged execution shows that the partial phase recognition technique saves up to 15% of energy with less than 1% performance degradation.