Automatic Software Tuning of Parallel Programs for Energy-Aware Executions

in Proc. of 13th Intl. Conf. on Parallel Processing and Applied Mathematics (PPAM 2019) (2019, December)

For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to ... [more ▼]

For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to the type and characteristics of workload that the device is running. For this reason, this paper presents an automatic software tuning method for parallel program generation able to adapt and exploit the hardware features available on a target computing system such as an HPC facility or a cloud system in a better way than traditional compiler infrastructures. We propose a search based approach combining both exact methods and approximated heuristics evolving programs in order to find optimized configurations relying on an ever-increasing number of tunable knobs i.e., code transformation and execution options (such as the num- ber of OpenMP threads and/or the CPU frequency settings). The main objective is to outperform the configurations generated by traditional compiling infrastructures for selected KPIs i.e., performance, energy and power usage (for both for the CPU and DRAM), as well as the runtime. First experimental results tied to the local optimization phase of the proposed framework are encouraging, demonstrating between 8% and 41% improvement for all considered metrics on a reference benchmark- ing application (i.e., Linpack). This brings novel perspectives for the global optimization step currently under investigation within the presented framework, with the ambition to pave the way toward automatic tuning of energy-aware applications beyond the performance of the current state-of-the-art compiler infrastructures. [less ▲]

A Multilayer Low-Altitude Airspace Model for UAV Traffic Management

in Samir Labib, Nader; Danoy, Grégoire; Musial, Jedrzej (Eds.) et al 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (DIVANet '19) (2019, November)

Over the recent years, Unmanned Aerial Vehicles' (UAVs) technology developed rapidly. In turn shedding light on a wide range of potential civil and commercial applications ranging from mapping and ... [more ▼]

Over the recent years, Unmanned Aerial Vehicles' (UAVs) technology developed rapidly. In turn shedding light on a wide range of potential civil and commercial applications ranging from mapping and surveillance, parcel delivery to more demanding ones that require UAVs to operate in heterogeneous swarms. However, with the great advantages UAVs bring, they are expected to soon dominate the shared, low-altitude airspace over populated cities, introducing multiple new research challenges in safely managing the unprecedented traffic demands. The main contribution of this work is addressing the complex problem of UAV traffic management at an abstract level by proposing a structure for the uncontrolled low-altitude airspace. The paper proposes a model of the airspace as a weighted multilayer network of nodes and airways and presents a set of experimental simulations of UAV traffic for the verification and validation of the model. Finally, the paper outlines our intended future work. [less ▲]

Link Definition ameliorating Community Detection in Collaboration Networks

in Frontiers in Big Data (2019), 2

Tackling Large-Scale and Combinatorial Bi-level Problems with a Genetic Programming Hyper-heuristic

in IEEE Transactions on Evolutionary Computation (2019)

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is a NP-hard problem. In this work, we tackle large-scale and combinatorial bi-level problems using GP ... [more ▼]

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is a NP-hard problem. In this work, we tackle large-scale and combinatorial bi-level problems using GP Hyper-heuristics, i.e., an approach that permits to train heuristics like a machine learning model. Our contribution aims at targeting the intensive and complex lower-level optimizations that occur when solving a large-scale and combinatorial bi-level problem. For this purpose, we consider hyper-heuristics through heuristic generation. Using a GP hyper-heuristic approach, we train greedy heuristics in order to make them more reliable when encountering unseen lower-level instances that could be generated during bi-level optimization. To validate our approach referred to as GA+AGH, we tackle instances from the Bi-level Cloud Pricing Optimization Problem (BCPOP) that model the trading interactions between a cloud service provider and cloud service customers. Numerical results demonstrate the abilities of the trained heuristics to cope with the inherent nested structure that makes bi-level optimization problems so hard. Furthermore, it has been shown that training heuristics for lower-level optimization permits to outperform human-based heuristics and metaheuristics which constitute an excellent outcome for bi-level optimization. [less ▲]

A Memory-Based Label Propagation Algorithm for Community Detection

in Complex Networks and Their Applications VII (2019)

The objective of a community detection algorithm is to group similar nodes in a network into communities, while increasing the dissimilarity between them. Several methods have been proposed but many of ... [more ▼]

The objective of a community detection algorithm is to group similar nodes in a network into communities, while increasing the dissimilarity between them. Several methods have been proposed but many of them are not suitable for large-scale networks because they have high complexity and use global knowledge. The Label Propagation Algorithm (LPA) assigns a unique label to every node and propagates the labels locally, while applying the majority rule to reach a consensus. Nodes which share the same label are then grouped into communities. Although LPA excels with near linear execution time, it gets easily stuck in local optima and often returns a single giant community. To overcome these problems we propose MemLPA, a novel LPA where each node implements memory and the decision rule takes past states of the network into account. We demonstrate through extensive experiments on the Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world networks that MemLPA outperforms most of state-of-the-art community detection algorithms. [less ▲]

White Paper: Data Protection and Privacy in Smart ICT - Scientific Research and Technical Standardization

Report (2018)

A Standardized Broker Model in Smart Cities

Poster (2018, May)

As urban residents are expected to represent more than 60 per cent of the world’s population by 2050, the current developments and interests in the “Smart City” concept are essential to enable the ... [more ▼]

As urban residents are expected to represent more than 60 per cent of the world’s population by 2050, the current developments and interests in the “Smart City” concept are essential to enable the successful transition to this new era. This paradigm relies on the integration of emerging Information and Communication Technologies (ICT), such as the Internet of Things (IoT), Cloud Computing, Big Data to manage assets and resources efficiently while facilitating the planning, construction, management and smart services within cities. While smart cities aim to enhance the quality, performance and interactivity of urban services at reduced cost, their realization is faced by many regulatory and technical challenges. Among these challenges, is the integration of renewable energy resources to the utility system of smart cities motivated by the increasingclimate change concerns. Adding further to its complexity, is the challenge of incorporating multiple renewable energy retailers in the same region each with their own pricing strategies due to the lack of a standardized metering indicator and billing system. These challenges create a need for an intelligent and standardized cloud-based energy broker to satisfying the end-user requests, and minimize expenses by efficiently selecting the most suitable energy retailer. In our work, a particular focus is raised towards the optimization of such energy brokering service which is motivated by the orchestration of a brokering role aiming to improve user experience and interaction with smart city services. Hence our main contribution is proposing a standardized intelligent broker model with smart trading strategies to cope with the dynamics and complexity of the energy retail market, while allocating energy resources based on endusers’ demands. This is achieved through the following steps: 1) studying a complete model of the broker service and involved parties within the exposed framework. 2) proposing a multiobjective heuristic to provide a dynamic optimization of the grid operations and resources, with full cyber-security, within the boundaries of the city. 3) analyzing the gaps among industry practices, market requirements and current technical standardization efforts at ISO/IEC JTC 1/ SC 38 (Cloud Computing and distributed platform) in order to pave the way to establishing standards in metering indicators and billing principles for cloud services this while keeping in mind privacy and data protection risks and regulations enforced by ISO JT1/SC 27 and EU General Data Protection Regulation effective May 2018. This research was conducted in collaboration with ILNAS - the Institut Luxem- bourgeois de la Normalisation, de l’Accréditation, de la Sécurité et qualité des produits et services (ILNAS) under the authority of the Minister of Economy, Luxembourg. [less ▲]

Collision Avoidance Effects on the Mobility of a UAV Swarm Using Chaotic Ant Colony with Model Predictive Control

in Journal of Intelligent \& Robotic Systems (2018)

The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a ... [more ▼]

The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a novel mobility model has been presented in previous work, combining an Ant Colony algorithm with chaotic dynamics (CACOC). This work is extending CACOC by a Collision Avoidance (CA) mechanism and testing its efficiency in terms of area coverage by the UAV swarm. For this purpose, CACOC is used to compute UAV target waypoints which are tracked by model predictively controlled UAVs. The UAVs are represented by realistic motion models within the virtual robot experimentation platform (V-Rep). This environment is used to evaluate the performance of the proposed CACOC with CA algorithm in an area exploration scenario with 3 UAVs. Finally, its performance is analyzed using metrics. [less ▲]

RapidRMSD: Rapid determination of RMSDs corresponding to motions of flexible molecules

in Bioinformatics (2018)

Single and Multiobjective Evolutionary Algorithms for Clustering Biomedical Information with Unknown Number of Clusters

in Bioinspired Optimization Methods and Their Applications (2018)

This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the ... [more ▼]

This article presents single and multiobjective evolutionary approaches for solving the clustering problem with unknown number of clusters. Simple and ad-hoc operators are proposed, aiming to keep the evolutionary search as simple as possible in order to scale up for solving large instances. The experimental evaluation is performed considering a set of real problem instances, including a real-life problem of analyzing biomedical information in the Parkinson's disease map project. The main results demonstrate that the proposed evolutionary approaches are able to compute accurate trade-off solutions and efficiently handle the problem instance involving biomedical information. [less ▲]