Un protocole contrôle de réplique d'une structure d'arborescence arbitraire. An arbitrary tree-structured replica control protocol

La réplication des données, qui est un problème de calcul distribué, est utilisée dans les grands systèmes distribués, en vue de parvenir à la tolérance aux pannes ainsi que d'améliorer les performances du système. Cependant, des sousjacents protocoles de synchronisation, également connus sous le nom de protocoles contrôle de réplique, sont nécessaires afin de maintenir la cohérence des données entre les répliques. De nombreux protocoles contrôle de réplique existent, chacun ayant ses avantages et inconvénients. Ceux-ci sont mesurés par le coût de communication, la disponibilité ainsi que la charge de système induite par les opérations de lecture ou d'écriture de ces protocoles. En général, ces protocoles contrôle de réplique sont répartis en deux familles: ceux qui supposent que les répliques du système sont organisées logiquement dans une structure et ceux qui ne nécessitent pas d'imposer une structure spécifique aux répliques. Dans cette thèse, à l'équipe ASTRE de l'IRIT (Institut de Recherche en Informatique de Toulouse) et sous la direction du Professeur Jean Paul Bahsoun, nous nous intéressons à l'étudier les protocoles de réplication qui organisent logiquement les répliques dans une structure d'un arbre et étudier la façon de contourner les inconvénients de la racine que ces protocoles en arbre souffrent de son goulot d'étranglement.In large distributed systems, replication is the most widely used approach to offer high data availability, low bandwidth consumption, increased faulttolerance and improved scalability of the overall system. Replication-based systems implement replica control (consistency) protocols that enforce a specified semantics of accessing data. Also, the performance depends on a host of factors chief of which is the protocol used to maintain consistency among the replicas. Several replica control protocols have been described in the literature. They differ according to various parameters such as their communication costs, their ability to tolerate replica failures (also termed as their availability), as well as the load they impose on the system when performing read and write operations. Moreover these replica control protocols can be classified into two families: some protocols assume that replicas of the system are arranged logically into a specific structure (Finite Projective Plane, Grid or Tree) whereas others do not require any specific structure to be imposed on the replicas. In this thesis, at group ASTRE of IRIT and under the supervision of professor Jean-Paul Bashoun, we are interested in studying the replication protocols that arrange logically the replicas into a tree structure and investigate how to circumvent the drawbacks of the root replica as the existing treestructured protocols suffer from the root replica's bottleneck

An Arbitrary 2D Structured Replica Control Protocol

Traditional replication protocols that logically arrange the replicas into a specific structure have reasonable availability, lower communication cost as well as system load than those that do not require any logical organisation of replicas. We propose in this paper the A2DS protocol: a single protocol that, unlike the existing proposed protocols, can be adapted to any 2D structure. Its read operation is carried out on any replica of every level of the structure whereas write operations are performed on all replicas of a single level of the structure. We present several basic 2D structures and introduce the new idea of obtaining other 2D structures by the composition of several basic ones. Two structures are proposed that have near optimal performance in terms of the communication cost, availability and system load of their read and write operations. Also, we introduce a new protocol that provides better performance for its write operations than those of ROWA protocol while preserving similar read performance

On the realistic worst case analysis of quantum arithmetic circuits

We provide evidence that commonly held intuitions when designing quantum circuits can be misleading. In particular we show that: a) reducing the T-count can increase the total depth; b) it may be beneficial to trade CNOTs for measurements in NISQ circuits; c) measurement-based uncomputation of relative phase Toffoli ancillae can make up to 30\% of a circuit's depth; d) area and volume cost metrics can misreport the resource analysis. Our findings assume that qubits are and will remain a very scarce resource. The results are applicable for both NISQ and QECC protected circuits. Our method uses multiple ways of decomposing Toffoli gates into Clifford+T gates. We illustrate our method on addition and multiplication circuits using ripple-carry. As a byproduct result we show systematically that for a practically significant range of circuit widths, ripple-carry addition circuits are more resource efficient than the carry-lookahead addition ones. The methods and circuits were implemented in the open-source QUANTIFY software

Making data centres fit for demand response: introducing GreenSDA and GreenSLA contracts

The power grid has become a critical infrastructure, which modern society cannot do without. It has always been a challenge to keep power supply and demand in balance; the more so with the recent rise of intermittent renewable energy sources. Demand response schemes are one of the counter measures, traditionally employed with large industrial plants. This paper suggests to consider data centres as candidates for demand response as they are large energy consumers and as they are able to adapt their power profile sufficiently well. To unlock this potential, we suggest a system of contracts that regulate collaboration and economic incentives between the data centre and its energy supplier (GreenSDA) as well as between the data centre and its customers (GreenSLA). Several presented use cases serve to validate the suitability of data centers for demand response schemes.Peer ReviewedPostprint (author's final draft

A Generic Architecture For Demand Response: The ALL4Green Approach

Demand Response is a mechanism used in power grids to manage customers’ power consumption during critical situations (e.g. power shortage). Data centres are good candidates to participate in Demand Response programs due to their high energy use. In this paper, we present a generic architecture to enable Demand Response between Energy Provider and Data Centres realised in All4Green. To this end, we show our three-level concept and then illustrate the building blocks of All4Green’s architectural design. Furthermore, we introduce the novel aspects of GreenSDA and GreenSLA for Energy Provider–Data centre sub-ecosystem as well as Data centre–IT Client sub-ecosystem respectively. In order to further reduce energy consumption and CO2 emission, the notion of data centre federation is introduced: savings can be expected if data centres start to collaborate by exchanging workload. Also, we specify the technological solutions necessary to implement our proposed architectural approach. Finally, we present preliminary proof-of-concept experiments, conducted both on traditional and cloud computing data centres, which show relatively encouraging results

Reporting of Model Performance and Statistical Methods in Studies That Use Machine Learning to Develop Clinical Prediction Models: Protocol for a Systematic Review

BACKGROUND: With the growing excitement of the potential benefits of using machine learning and artificial intelligence in medicine, the number of published clinical prediction models that use these approaches has increased. However, there is evidence (albeit limited) that suggests that the reporting of machine learning-specific aspects in these studies is poor. Further, there are no reviews assessing the reporting quality or broadly accepted reporting guidelines for these aspects. OBJECTIVE: This paper presents the protocol for a systematic review that will assess the reporting quality of machine learning-specific aspects in studies that use machine learning to develop clinical prediction models. METHODS: We will include studies that use a supervised machine learning algorithm to develop a prediction model for use in clinical practice (ie, for diagnosis or prognosis of a condition or identification of candidates for health care interventions). We will search MEDLINE for studies published in 2019, pseudorandomly sort the records, and screen until we obtain 100 studies that meet our inclusion criteria. We will assess reporting quality with a novel checklist developed in parallel with this review, which includes content derived from existing reporting guidelines, textbooks, and consultations with experts. The checklist will cover 4 key areas where the reporting of machine learning studies is unique: modelling steps (order and data used for each step), model performance (eg, reporting the performance of each model compared), statistical methods (eg, describing the tuning approach), and presentation of models (eg, specifying the predictors that contributed to the final model). RESULTS: We completed data analysis in August 2021 and are writing the manuscript. We expect to submit the results to a peer-reviewed journal in early 2022. CONCLUSIONS: This review will contribute to more standardized and complete reporting in the field by identifying areas where reporting is poor and can be improved. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020206167; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=206167. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/30956