Semantic Caching Framework: An FPGA-Based Application for IoT Security Monitoring

Security monitoring is one subdomain of cybersecurity which aims to guarantee the safety of systems, continuously monitoring unusual events. The development of Internet Of Things leads to huge amounts of information, being heterogeneous and requiring to be efficiently managed. Cloud Computing provides software and hardware resources for large scale data management. However, performances for sequences of on-line queries on long term historical data may be not compatible with the emergency security monitoring. This work aims to address this problem by proposing a semantic caching framework and its application to acceleration hardware with FPGA for fast- and accurate-enough logs processing for various data stores and execution engines

A novel MapReduce-based approach for distributed frequent subgraph mining

National audienceRecently, graph mining approaches have become very popular, especially in certain domains such as bioinformatics, chemoinformatics and social networks. One of the most challenging tasks is frequent subgraph discovery. This task has been highly motivated by the tremendously increasing size of existing graph databases. Due to this fact, there is an urgent need of efficient and scaling approaches for frequent subgraph discovery. In this paper, we propose a novel approach to approximate large-scale subgraph mining by means of a density-based partitioning technique, using the MapReduce framework. Our partitioning aims to balance computational load on a collection of machines. We experimentally show that our approach decreases significantly the execution time and scales the subgraph discovery process to large graph databases

Modèles de coût pour la sélection de vues matérialisées dans le nuage, application aux services Amazon EC2 et S3

National audienceDans les bases et entrepôts de données, la performance des requêtes est classiquement assurée grâce à des structures comme les caches, les index et les vues matérialisées. Dans ce contexte, des modèles de coût permettent de sélectionner un ensemble efficace de ce type de structures. Toutefois, cette tâche de sélection devient plus complexe dans le nuage, car en plus des temps de réponse, il faut simultanément optimiser le coût monétaire d'utilisation du nuage. En conséquence, nous proposons dans cet article de nouveaux modèles de coût qui intègrent le paradigme de paiement à la demande en vigueur dans les nuages informatiques. Sur la base de ces modèles, nous définissons un problème d'optimisation consistant à sélectionner, parmi un ensemble de vues candidates, celles qu'il faut matérialiser pour minimiser le coût d'interrogation et de maintenance de la base de données, ainsi que le temps de réponse à une charge de requêtes donnée. Dans un premier temps, nous optimisons les deux critères précédents séparément: le temps de réponse est optimisé sous contrainte de coût et vice versa. Les expériences que nous avons menées pour valider cette proposition montrent que la matérialisation de vues dans le nuage est toujours avantageuse

SLA-Aware Cloud Query Processing with Reinforcement Learning-based Multi-Objective Re-Optimization

International audienceQuery processing on cloud database systems is a challenging problem due to the dynamic cloud environment. In cloud database systems, besides query execution time, users also consider the monetary cost to be paid to the cloud provider for executing queries. Moreover, a Service Level Agreement (SLA) is signed between users and cloud providers before any service is provided. Thus, from the profit-oriented perspective for the cloud providers, query re-optimization is multi-objective optimization that minimizes not only query execution time and monetary cost but also SLA violations. In this paper, we introduce ReOptRL and SLAReOptRL, two novel query re-optimization algorithms based on deep reinforcement learning. Experiments show that both algorithms improve query execution time and query execution monetary cost by 50% over existing algorithms, and SLAReOptRL has the lowest SLA violation rate among all the algorithms

MASCARA (ModulAr Semantic CAching fRAmework) towards FPGA Acceleration for IoT Security Monitoring

With the explosive growth of the Internet Of Things (IOTs), emergency security monitoring becomes essential to efficiently manage an enormous amount of information from heterogeneous systems. In concern of increasing the performance for the sequence of online queries on long-term historical data, query caching with semantic organization, called Semantic Query Caching or Semantic Caching (SC), can play a vital role. SC is implemented mostly in software perspective without providing a generic description of modules or cache services in the given context. Hardware acceleration with FPGA opens new research directions to achieve better performance for SC. Hence, our work aims to propose a flexible, adaptable, and tunable ModulAr Semantic CAching fRAmework (MASCARA) towards FPGA acceleration for fast and accurate massive logs processing applications

ReLeaSER: A Reinforcement Learning Strategy for Optimizing Utilization Of Ephemeral Cloud Resources

International audienceCloud data center capacities are over-provisioned to handle demand peaks and hardware failures which leads to low resources' utilization. One way to improve resource utilization and thus reduce the total cost of ownership is to offer unused resources (referred to as ephemeral resources) at a lower price. However, reselling resources needs to meet the expectations of its customers in terms of Quality of Service. The goal is so to maximize the amount of reclaimed resources while avoiding SLA penalties. To achieve that, cloud providers have to estimate their future utilization to provide availability guarantees. The prediction should consider a safety margin for resources to react to unpredictable workloads. The challenge is to find the safety margin that provides the best trade-off between the amount of resources to reclaim and the risk of SLA violations. Most state-of-the-art solutions consider a fixed safety margin for all types of metrics (e.g., CPU, RAM). However, a unique fixed margin does not consider various workloads variations over time which may lead to SLA violations or/and poor utilization. In order to tackle these challenges, we propose ReLeaSER, a Reinforcement Learning strategy for optimizing the ephemeral resources' utilization in the cloud. ReLeaSER dynamically tunes the safety margin at the host-level for each resource metric. The strategy learns from past prediction errors (that caused SLA violations). Our solution reduces significantly the SLA violation penalties on average by 2.7x and up to 3.4x. It also improves considerably the CPs' potential savings by 27.6% on average and up to 43.6%

A Scored Semantic Cache Replacement Strategy for Mobile Cloud Database Systems

International audienceCurrent mobile cloud database systems are widespread and require special considerations for mobile devices. Although many systems rely on numerous metrics for use and optimization, few systems leverage metrics for decisional cache replacement on the mobile device. In this paper we introduce the Lowest Scored Replacement (LSR) policy-a novel cache replacement policy based on a predefined score which leverages contextual mobile data and user preferences for decisional replacement. We show an implementation of the policy using our previously proposed MOCCAD-Cache as our decisional semantic cache and our Normalized Weighted Sum Algorithm (NWSA) as a score basis. Our score normalization is based on the factors of query response time, energy spent on mobile device, and monetary cost to be paid to a cloud provider. We then demonstrate a relevant scenario for LSR, where it excels in comparison to the Least Recently Used (LRU) and Least Frequently Used (LFU) cache replacement policies

ASSIST: Outil pour l'extraction et l'analyse statistique d'articles

National audienceIl y a moins d'auteures que d'auteurs dans la recherche scientifique. Cependant, il n'existe pas encore, à notre connaissance, de système permettant différentes analyses sur les données disponibles et de confirmer cette hypothese. Ce travail propose une extension d'un outil précédemment réalisé, afin de le rendre plus performant et d'ajouter de nouvelles fonctionnalités. Ces nouvelles fonctionnalités sont le nuage de mots-clés ou la nouvelle fonctionnalité statistique. Les sources, références et autres informations sur l'article seront affichées pour chaque article récupéré. Les sexes des auteurs seront déterminés à l'aide d'une base de données reliant les prénoms aux sexes, afin de pouvoir obtenir des statistiques sur un grand nombre d'articles collectés

The Lannion report on Big Data and Security Monitoring Research

International audienceDuring the last decade, big data management has attracted increasing interest from both the industrial and academic communities. In parallel, Cyber Security has become mandatory due to various and more intensive threats. In June 2022, a group of researchers has met to reflect on their community's impacts on current research challenges. In particular, they have considered four dimensions: (1) dedicated systems being data processing and analytic platforms or time series management systems; (2) graphs analytics and distributed computation; (3) privacy; and (4) new hardware

Semantic caching framework, an application to FPGA-based application for IoT security monitoring

International audienceSecurity monitoring is one subdomain of cybersecurity which aims to guarantee the safety of systems, continuously monitoring unusual events. The development of Internet Of Things leads to huge amounts of information, being heterogeneous and requiring to be efficiently managed. Cloud Computing provides software and hardware resources for large scale data management. However, performances for sequences of on-line queries on long term historical data may be not compatible with the emergency security monitoring. This work aims to address this problem by proposing a semantic caching framework and its application to acceleration hardware with FPGA for fast-and accurate-enough logs processing for various data stores and execution engines