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Return to Eric Rutten. Adaptive and reconfigurable computing systems are becoming widespread, in different areas such as Cloud computing, High-Performance Computing, or Smart Environments in the Internet of Things. Their particularity is to be able to change their computing structure dynamically, in response to changes in their environment or execution platform. Motivations for the adaptivity are found in resource management, energy efficiency, or functionality. Autonomic Computing in an approach to automate the management of adaptation with feedback loops, with a better handling of complexity and responsiveness.
The design of the reconfiguration controllers requires methods, models and tools, in order to have safe and predictable behaviors of the automated systems, for which Control Theory provides an effective background. This series of workshops provides opportunities for specialists of different aspects of the problem to meet and exchange approaches and experience, with presentations from different points of view on the topic, from software infrastructures to Control Theory.
This workshop follows three previous editions on october 7, , may 27, and november 4, , with copies of the slides, here https: Register here! Examples includes anomaly detection in large scale distributed systems, such as data center, mobile network, and power grid. Even with the powerful data processing engines, e. To solve such a conundrum, approximate big data computing advocates to explicitly tradeoffs the analysis accuracy by reducing or altering data input via techniques like subsampling or lowering data precisions.
In this talk, I will first introduce AccStream, a latency-aware approximate framework based on Spark Streaming. AccStream dynamically learns the latency model of analysis jobs via on-line probing technique and employs sampling theory to fulfill given accuracy targets. AccStream features on the design of hybrid windowing that trades off data freshness via a combination of tumbling and rolling windows, when encountering conflicts of latency and accuracy targets.
In the second part of talk, I will present DApprox, a Differential Approximate framework that expands beyond AccStream and supports multi-priority workloads by using differentiated approximation ratios. Different from typical priority scheduling that evicts low-priority jobs, DApprox deflates their workload, abiding by the minimum accuracy requirement, thereby making resources available for high-priority jobs. I will preset both theoretical results and empirical validations on real systems.
The key outcome of this project is a software framework that covers both design-time and runtime support for integrating self-adaptive behaviours into potentially complex legacy systems. Future case studies of this approach will cover green computing, crowd- sensing, and big data systems. The widespread use of mobile devices and location-based services has generated massive amounts of mobility databases. While processing these data is highly valuable improvement of public transportation facilities, collaborative mapping, etc.
Dedicated literature has investigated ways to protect mobility data by providing a large range of Location Privacy Protection Mechanisms. However, the privacy level of the protected data significantly varies depending on the protection mechanism used, its configuration and on the characteristics of the mobility data. Meanwhile, the protected data still needs to enable some useful post-processing. In order to tackle these issues, we present PULP, a framework that finds the suitable protection mechanism and configure it for each mobility database user in order to achieve user-defined objectives in terms of both privacy and utility.
Implementing self-adaptive embedded systems, such as UV, involves an offline provisioning of the several implementations of the embedded functionalities with different characteristics in resource usage and performance in order for the system to dynamically adapt itself under uncertainties. FPGA-based architectures offer for support for high flexibility with dynamic reconfiguration features. We propose an autonomic control architecture for self-adaptive and self-reconfigurable FPGA-based embedded systems.
The control architecture is structured in three layers: In this paper we focus on the design of the reconfiguration manager. We propose a design approach using automata-based discrete control. It involves reactive programming that provides formal semantics, and discrete controller synthesis from declarative objectives. Autonomic Computing has recently contributed to the development of self-manageable Cloud services. It provides means to free Cloud administrators of the burden of manually managing varying-demand services while enforcing Service-Level Agreements SLAs.
All Cloud artifacts, regardless of the layer carrying them, share many common characteristics e. Thus, it should be possible to specify, re configure and monitor any XaaS Anything-as-a-Service layer in an homogeneous way. To this end, the CoMe4ACloud approach proposes a generic model-based architecture for autonomic management of Cloud systems. We derive an unique and generic Autonomic Manager AM capable of managing any Cloud service, regardless of the layer.
The generic AM is based on a generic constraint solver which tries to find the optimal configuration for the modeled XaaS and the best balance between costs and revenues while meeting constraints regarding the established SLA. Recently, smart usage of renewable energy has been a hot topic in the Cloud community. Batch jobs arrives with deadline, hence can scheduled depending on the energy profile. On the contrary, interactive applications possess lesser flexibility, i. To cope up with these limitations, we have proposed a green energy adaptive solution to create green energy awareness inside the application that inherits the capability to smartly use the available green energy having static amount of underlying resources.
This work adds to previous ones as it considers elastic underlying infrastructure, that is, we propose a PaaS solution which efficiently utilize the elasticity nature at both infrastructure and application levels, by leveraging adaptation in facing to changing condition i. Both adaptive behaviors are implemented in separated modules and are coordinated in a sequential manner.
Large scale distributed systems are expected to consume huge amounts of energy. To solve this issue, shutdown policies constitute an appealing approach able to dynamically adapt the resource set to the actual workload. However, multiple constraints have to be taken into account for such policies to be applied on real infrastructures, in partic- ular the time and energy cost of shutting down and waking up nodes, and power capping to avoid disruption of the system.
Our models are validated through simulations on real workload traces and power measurements on real testbeds. This is done by changing their behaviour upon the occurrence of specific events. Building such a system requires to design and implement autonomic loops that collect events and measurements, make decisions and execute the corresponding actions. The design and the implementation of such loops are made difficult by several factors: In this paper, we propose a design framework for reliable and self-adaptive systems, where multiple autonomic loops can be composed into complex managers, and we consider its application to smart environments.
We build upon the proposed framework a generic autonomic loop which combines an automata-based controller that makes correct and coherent decisions, a transactional execution mechanism that avoids inconsistencies, and an abstraction layer that hides the heterogeneity of the devices. We propose patterns for composition of such loops, in parallel, coordinated, and hierarchically, with benefits from the leveraging of automata-based modular constructs, that provides for guarantees on the correct behaviour of the controlled system.
A case study in the field of building automation is presented to illustrate the proposed framework. Free attendance but compulsory inscription here: Time limit is exhausted. Made with by Graphene Themes. Toggle search form. Toggle navigation Ctrl-A Ctrl-A. Grenoble Workshop on Autonomic Computing and Control Adaptive and reconfigurable computing systems are becoming widespread, in different areas such as Cloud computing, High-Performance Computing, or Smart Environments in the Internet of Things.
First day see below for the second day, 23 oct. Achieving Privacy and Utility in Mobility Databases. Danny Weyns Linnaeus University, Sweden: Thanks for the organization team and all the representers. Leave a Reply Cancel reply Your email address will not be published. In this section Eric Rutten infos labex parties peps-api staars. Blogroll HAL tools Inria.
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Return to Eric Rutten. Adaptive and reconfigurable computing systems are becoming widespread, in different areas such as Cloud computing, High-Performance Computing, or Smart Environments in the Internet of Things. Their particularity is to be able to change their computing structure dynamically, in response to changes in their environment or execution platform. Motivations for the adaptivity are found in resource management, energy efficiency, or functionality.
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PhD position - Innovating programming schemes and designs to solve RRAM variability
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RRAM Resistive memories, which are based on the formation of a conductive filament in a resistive layer, are one of the most serious candidate to replace standard Flash memories. They are envisaged for embedded applications, but also for the new opportunities they offer storage class memories, Internet of Things…. RRAM maturity keeps growing, and 1st products start appearing on the market. However, available densities are still small kb-Mb , due to variability issue. Indeed, RRAM filament formation mechanisms are stochastic, leading to an overlap between the memory states. This is particularly critical when a high number of memory cells is involved. In summary, RRAM variability is today the main roadblock to be solved before this technology could widely enter the market. In order to solve variability, new memory stacks were proposed. However, solutions at the circuit level should be added.
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The Biorefinery and Biomaterials program of the Master in Materials Science and Engineering is aimed at students wishing to move towards the professions of valorization of plant biomass into chemicals, energy and biomaterials. The transformation of plant biomass into energy, chemicals and polymers is an important component of the green economy.
PhD position - Innovating programming schemes and designs to solve RRAM variability
The Doctorate of Business Administration DBA aims for graduates to discern the emergent orientations to be applied within a company: This doctorate in Business will assist you in acquiring and mastering the methodology of research to apply to your job. This doctoral business degree is particularly focused on the management of technology, organizational change, and innovation. However, the program may also cover more traditional research topics in fields such as management, finance, marketing, and organizational behavior. The programs are presently offered in the following locations:. Regardless of the location, the content is the same and participants who successfully complete the program are awarded a Grenoble DBA degree. DBA students start by learning methods and techniques for research design, data collection, and data analysis in order to conduct, analyze, author, and publish research that meets doctoral standards. Students complete a series of four to six research training workshops that are held in Grenoble and partner countries. Between workshops, students prepare a detailed research thesis proposal, complete a pilot study, and perform individual research and research fieldwork under the direction of doctoral supervisors. In order to proceed to Stage II, students are required to produce three pieces of written work.
Doctorate of Business Administration (DBA)
RRAM Resistive memories, which are based on the formation of a conductive filament in a resistive layer, are one of the most serious candidate to replace standard Flash memories. They are envisaged for embedded applications, but also for the new opportunities they offer storage class memories, Internet of Things…. RRAM maturity keeps growing, and 1st products start appearing on the market. However, available densities are still small kb-Mb , due to variability issue. Indeed, RRAM filament formation mechanisms are stochastic, leading to an overlap between the memory states. This is particularly critical when a high number of memory cells is involved.
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Job listing. Biological sciences Chemistry Physics Researcher Profile: Temporary Job Status: Full-time Hours Per Week: Bordenave, M Durand. Grenoble offers a high quality of life and lots of opportunities for those who like outdoor activities and mountains.
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The college trains future professionals for careers in industry , services and research. It unites three poles working within one central theme: The three poles are as follows: The main objective of the multidisciplinary project Sedela, is to define combined methods and tools for personal Lifelong learning. Such methods and tools should allow i to formalize experience on tacit knowledge, ii to capitalize knowledge and competences, and iii to foster collaborative interactions and social knowledge building, in a trusted environment. Users will be able to manage their own learning data, to store lifelong learning paths, and to share their experience according to personal preferences.Mon métier conseiller clientèle banque : formation en alternance à l'IMT Grenoble