Impact du mécanisme chaotique sur l’optimisation d’un modèle de mobilité pour un essaim de drones devant réaliser une couverture de zone

in Falcon, Eric; Lefranc, Marc; Pétrélis, François (Eds.) et al Comptes-rendus de la 20e Rencontre du Non Linéaire (2017, March)

Solution of differential equations system can be chaotic attractors with various chaotic mechanisms. In this paper we highlight that the use of these chaotic mechanisms permits to enhance the ... [more ▼]

Solution of differential equations system can be chaotic attractors with various chaotic mechanisms. In this paper we highlight that the use of these chaotic mechanisms permits to enhance the diversification of metaheuristics. We applied our approach to the coverage problem using a swarm of UAVs where the diversification of an ant colony algorithm is enhanced by chaos coming from Ma system and Rössler system. [less ▲]

Load Balancing at the Edge of Chaos: How Can Self-Organized Criticality Lead to Energy-Efficient Computing

in IEEE Transactions on Parallel and Distributed Systems (2017), 28

This paper investigates a self-organized critical approach for dynamically load-balancing computational workloads. The proposed model is based on the Bak-Tang-Wiesenfeld sandpile: a cellular automaton ... [more ▼]

This paper investigates a self-organized critical approach for dynamically load-balancing computational workloads. The proposed model is based on the Bak-Tang-Wiesenfeld sandpile: a cellular automaton that works in a critical regime at the edge of chaos. In analogy to grains of sand, tasks arrive, pile up and slip through the different processing elements or sites of the system. When a pile exceeds a certain threshold, it collapses and initiates an avalanche of migrating tasks, i.e. producing load-balancing. We show that the frequency of such avalanches is in power-law relation with their sizes, a scale-invariant fingerprint of self-organized criticality that emerges without any tuning of parameters. Such an emergent pattern has organic properties such as the self-organization of tasks into resources or the self-optimization of the computing performance. The conducted experimentation also reveals that the system is in balanced (i.e. not driving to overloaded or underutilized resources) as long as the arrival rate of tasks equals the processing power of the system. Taking advantage of this fact, we hypothesize that the processing elements can be turned on and off depending on the state of the workload as to maximize the utilization of resources. An interesting side-effect is that the overall energy consumption of the system is minimized without compromising the quality of service. [less ▲]

Self-Regulated Multi-criteria Decision Analysis: An Autonomous Brokerage-Based Approach for Service Provider Ranking in the Cloud

in 9th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2017), December 11-14, Hong Kong China. (2017)

The use of multi-criteria decision analysis (MCDA) by online broker to rank different service providers in the Cloud is based upon criteria provided by a customer. However, such ranking is prone to bias ... [more ▼]

The use of multi-criteria decision analysis (MCDA) by online broker to rank different service providers in the Cloud is based upon criteria provided by a customer. However, such ranking is prone to bias if the customer has insufficient domain knowledge. He/she may exclude relevant or include irrelevant criterion termed as ’misspecification of criterion’. This causes structural uncertainty within the MCDA leading to selection of suboptimal service provider by online broker. To cater such issue, we propose a self-regulated MCDA, which uses notion of factor analysis from the field of statistics. Two QoS based datasets were used for evaluation of proposed model. The prior dataset i.e., feedback from customers, was compiled using leading review websites such as Cloud Hosting Reviews, Best Cloud Computing Providers, and Cloud Storage Reviews and Ratings. The later dataset i.e., feedback from servers, was generated from Cloud brokerage architecture that was emulated using high performance computing (HPC) cluster at University of Luxembourg (HPC @ Uni.lu). The results show better performance of proposed model as compared to its counterparts in the field. The beneficiary of the research would be enterprises that view insufficient domain knowledge as a limiting factor for acquisition of Cloud services. [less ▲]

Law as a Service (LaaS): Enabling Legal Protection over a Blockchain Network

in 14th International Conference on Smart Cities: Improving Quality of Life using ICT & IoT (HONET-ICT 17), October 09-11, Irbid Jordan. (2017)

Breaches in online contracts (Service Level Agreements, SLAs) are usually compensated by gift vouchers at present, however as the online contracts emerge towards smart contracts, the breaches could ... [more ▼]

Breaches in online contracts (Service Level Agreements, SLAs) are usually compensated by gift vouchers at present, however as the online contracts emerge towards smart contracts, the breaches could potentially lead to court injunctions over blockchains. This research proposes Probability based Factor Model (PFM) that can be implemented over the blockchain to automatically identify breaches that can cause substantial damage and have high probability for recurrence. PFM can also issue court injunctions for the breaches. The underlying concept in PFM is built upon the notion of factor analysis and stochastic modeling from the discipline of Data Science. High performance computing (HPC) cluster at University of Luxembourg (HPC @ Uni.lu) and docker (a software container platform) were used to emulate contractual environment of three service providers: Redis, MongoDB, and Memcached Servers. The results showed that court injunction(s) was issued only for Redis and MongoDB Servers. Technically, this difference could be attributed to the fact that Memcached is simply used for caching and therefore, it is less prone to breach of contract. Whereas, Redis and MongoDB as databases and message brokers are performing more complex operations and are more likely to cause a breach. This research will benefit enterprises that view breach of contract as a limiting factor for implementation of smart contract in cyber physical system or internet of things. [less ▲]

Bayesian Optimization Approach of General Bi-level Problems

in Proceedings of the Genetic and Evolutionary Computation Conference Companion (2017)

Target Tracking Optimization of UAV Swarms Based on Dual-Pheromone Clustering

in CYBCONF 2017-12-13 09:39:53 +0000 2017-12-13 09:39:53 +0000 (2017)

Service Performance Pattern Analysis and Prediction of Commercially Available Cloud Providers

in Service Performance Pattern Analysis and Prediction of Commercially Available Cloud Providers (2016, December 12)

The knowledge of service performance of cloud providers is essential for cloud service users to choose the cloud services that meet their requirements. Instantaneous performance readings are accessible ... [more ▼]

The knowledge of service performance of cloud providers is essential for cloud service users to choose the cloud services that meet their requirements. Instantaneous performance readings are accessible, but prolonged observations provide more reliable information. However, due to technical complexities and costs of monitoring services, it may not be possible to access the service performance of cloud provider for longer time durations. The extended observation periods are also a necessity for prediction of future behavior of services. These predictions have very high value for decision making both for private and corporate cloud users, as the uncertainty about the future performance of purchased cloud services is an important risk factor. Predictions can be used by specialized entities, such as cloud service brokers (CSBs) to optimally recommend cloud services to the cloud users. In this paper, we address the challenge of prediction. To achieve this, the current service performance patterns of cloud providers are analyzed and future performance of cloud providers are predicted using to the observed service performance data. It is done using two automatic predicting approaches: ARIMA and ETS. Error measures of entire service performance prediction of cloud providers are evaluated against the actual performance of the cloud providers computed over a period of one month. Results obtained in the performance prediction show that the methodology is applicable for both short- term and long-term performance prediction. [less ▲]

Proceedings of the 8th IEEE International Conference on Cloud Computing Technology and Science (ClouCom 2016)

Book published by IEEE Computer Society (2016)

CloudCom is the premier conference on Cloud Computing worldwide, attracting researchers, developers, users, students and practitioners from the fields of big data, systems architecture, services research ... [more ▼]

CloudCom is the premier conference on Cloud Computing worldwide, attracting researchers, developers, users, students and practitioners from the fields of big data, systems architecture, services research, virtualization, security and privacy, high performance computing, always with an emphasis on how to build cloud computing platforms with real impact. The conference is co-sponsored by the Institute of Electrical and Electronics Engineers (IEEE), is steered by the Cloud Computing Association, and draws on the excellence of its world-class Program Committee and its participants. The 8th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2016) was held in the city of Luxembourg on 12-15 December 2016 [less ▲]

Game-Theoretic Recruitment of Sensing Service Providers for Trustworthy Cloud-Centric Internet-of-Things (IoT) Applications

in IEEE Global Communications Conference (GLOBECOM) Workshops: Fifth International Workshop on Cloud Computing Systems, Networks, and Applications (CCSNA) (2016, December)

Widespread use of connected smart devices that are equipped with various built-in sensors has introduced the mobile crowdsensing concept to the IoT-driven information and communication applications ... [more ▼]

Widespread use of connected smart devices that are equipped with various built-in sensors has introduced the mobile crowdsensing concept to the IoT-driven information and communication applications. Mobile crowdsensing requires implicit collaboration between the crowdsourcer/recruiter platforms and users. Additionally, users need to be incentivized by the crowdsensing platform because each party aims to maximize their utility. Due to the participatory nature of data collection, trustworthiness and truthfulness pose a grand challenge in crowdsensing systems in the presence of malicious users, who either aim to manipulate sensed data or collaborate unfaithfully with the motivation of maximizing their income. In this paper, we propose a game-theoretic approach for trustworthiness-driven user recruitment in mobile crowdsensing systems that consists of three phases: i) user recruitment, ii) collaborative decision making on trust scores, and iii) badge rewarding. Our proposed framework incentivizes the users through a sub-game perfect equilibrium (SPE) and gamification techniques. Through simulations, we show that the platform utility can be improved by up to the order of 50\% while the average user utility can be increased by at least 15\% when compared to fully-distributed and user-centric trustworthy crowdsensing. [less ▲]

From Random Process to Chaotic Behavior in Swarms of UAVs

in DIVANet '16 Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications (2016, November)

Unmanned Aerial Vehicles (UAVs) applications have seen an important increase in the last decade for both military and civilian applications ranging from fire and high seas rescue to military surveillance ... [more ▼]

Unmanned Aerial Vehicles (UAVs) applications have seen an important increase in the last decade for both military and civilian applications ranging from fire and high seas rescue to military surveillance and target detection. While this technology is now mature for a single UAV, new methods are needed to operate UAVs in swarms, also referred to as fleets. This work focuses on the mobility management of one single autonomous swarm of UAVs which mission is to cover a given area in order to collect information. Several constraints are applied to the swarm to solve this problem due to the military context. First, the UAVs mobility must be as unpredictable as possible to prevent any UAV tracking. However the Ground Control Station (GCS) operator(s) still needs to be able to forecast the UAVs paths. Finally, the UAVs are autonomous in order to guarantee the mission continuity in a hostile environment and the method must be distributed to ensure fault-tolerance of the system. To solve this problem, we introduce the Chaotic Ant Colony Optimization to Coverage (CACOC) algorithm that combines an Ant Colony Optimization approach (ACO) with a chaotic dynamical system. CACOC permits to obtain a deterministic but unpredictable system. Its performance is compared to other state-of-the art models from the literature using several coverage-related metrics, i.e. coverage rate, recent coverage and fairness. Numerical results obtained by simulation underline the performance of our CACOC method: a deterministic method with unpredictable UAV trajectories that still ensures a high area coverage. [less ▲]