Many real-life problems involve multiple conflicting objectives; hence, the decision maker is provided with a set of trade-off solutions, the Pareto front. While many methods to compute Pareto fronts have been proposed in the mathematical programming literature, comparatively few approaches are available for constraint programming (CP). One of the main state-of-the-art algorithms in CP is a branch-and-bound method that uses a Pareto global constraint, denoted here as MOBAB-CP. In this work, we adapt the SAUGMECON algorithm, a well-known and efficient ε-constraint method, in a CP solver. We also propose a new algorithm that combines SAUGMECON with the Pareto global constraint. Experimental results show that the proposed algorithm consistently achieves better results than our CP implementation of SAUGMECON and is competitive with MOBAB-CP, outperforming it on several of the studied problems.

ABSTRACT The Branch‐and‐Bound (B&B) technique plays a key role in solving many combinatorial optimization problems, enabling efficient problem‐solving and decision‐making in a wide range of applications. It incrementally constructs a tree by building candidates to the solutions and abandoning a candidate as soon as it determines that it cannot lead to an optimal solution. With modern problems growing increasingly large, accelerating B&B algorithms through parallelization has become a critical challenge for handling large solution spaces. At the same time, modern parallel computing systems themselves are becoming larger, more heterogeneous, and more diverse, requiring programming approaches capable of effectively exploiting such complexity. To address these challenges, this work presents a GPU‐accelerated B&B algorithm based on the Partitioned Global Address Space (PGAS) programming model, implemented using the Chapel language. The PGAS‐based design is motivated by the high‐level abstraction provided by this programming model, which favors programmability, whereas vendor‐neutral GPU features of the Chapel language favor GPU portability. The algorithm uses a pool‐based approach for generality and exploits a dynamic load balancing mechanism for performance scalability. Extensive experimentation on the N‐Queens and permutation flowshop scheduling problems demonstrated both code performance and code portability of the proposed algorithm on several GPU architectures compared to optimized CUDA‐based implementations. Moreover, the strong scaling efficiency of the proposed algorithm is investigated on a TOP500 pre‐exascale supercomputer up to 1024 GPUs.

International audience

The substantial increase in AI model training has considerable environmental implications, requiring energy-efficient and sustainable AI practices. On one hand, data-centric approaches show great potential towards training energy-efficient AI models. On the other hand, instance selection methods demonstrate the capability of training AI models with minimised training sets and negligible performance degradation. Despite the growing interest in both topics, the impact of data-centric training set selection on energy efficiency remains to date unexplored. This paper presents an evolutionary-based sampling framework aimed at (i) identifying elite training samples tailored for datasets and model pairs, (ii) comparing model performance and energy efficiency gains against typical model training practice, and (iii) investigating the feasibility of this framework for fostering sustainable model training practices. To evaluate the proposed framework, we conducted an empirical experiment including 8 commonly used AI classification models and 25 publicly available datasets. The results showcase that by considering 10% elite training samples, the models’ performance can show a 50% improvement and remarkable energy savings of 98% compared to the common training practice. In essence, this study establishes a new benchmark for AI researchers and practitioners interested in improving the environmental sustainability of AI model training via data-centric approaches.

In the field of constraint programming, selecting the most effective search strategy for a new problem is a complex task. Despite the existence of numerous autonomous search strategies, the effectiveness of a strategy is highly problem-specific and no single strategy can universally excel. Therefore, for the solver's developers, it is difficult to find a good default strategy working across many problems. For the end-user, it is a daunting task to select the best search strategy, and they will usually rely on the solver's default, missing out better strategies. In this paper, we introduce the probe and solve algorithm which explores different search strategies in a probing phase, using a portion of the global timeout, and uses the best strategy found to solve the problem. By viewing the search strategy as hyperparameters, we leverage Bayesian optimization, a hyperparameter optimization technique well-known in machine learning but, to the best of our knowledge, not used in constraint programming. A key strength of our approach is to be generic and non-invasive: it can be used on top of any MiniZinc or XCSP3-compatible solvers, without modifying those. Further, probe and solve consistently achieved better results in the XCSP3 and MiniZinc competitions than the solver's default search and modern dynamic search strategies: DomWDeg/CACD, FrbaOnDom and PickOnDom, with the ACE and Choco constraint solvers.

The computing continuum can enable new, novel big data use cases across the edge-cloud-supercomputer spectrum. Fast and high-volume data movement workflows rely on state-of-the-art architectures built on top of stream ingestion and file transfer open-source tools. Unfortunately, users struggle when faced with dealing with such diverse architectures: stream ingestion was designed for small-size datasets and low latency, while file transfer was designed for large-size datasets and high throughput. In this paper, we propose to unify ingestion and transfer, while introducing architectural design principles and discussing future implementation challenges.

Federated learning is a particular type of distributed machine learning, designed to permit the joint training of a single machine learning model by multiple participants that each possess a local dataset.A characteristic feature of federated learning strategies is the avoidance of any disclosure of client data to other participants of the learning scheme.While a wealth of well-performing solutions for different scenarios exists for Horizontal Federated Learning (HFL), to date little attention has been devoted to Vertical Federated Learning (VFL).Existing approaches are limited to narrow application scenarios where few clients participate, privacy is a main concern and the vertical distribution of client data is well-understood.In this article, we first argue that VFL is naturally applicable to another, much broader application context where sharing of data is mainly limited by technological instead of privacy constraints, such as in sensor networks or satellite swarms.A VFL scheme applied to such a setting could unlock previously inaccessible on-device machine learning potential.We then propose the Joint-embedding Vertical Federated Learning framework (JoVe-FL), a first VFL framework designed for such settings.JoVe-FL is based on the idea of transforming the vertical federated learning problem to a horizontal one by learning a joint embedding space, allowing us to leverage existing HFL solutions.Finally, we empirically demonstrate the feasibility of the approach on instances consisting of different partitionings of the CIFAR10 dataset.

Abstract With the recent arrival of the exascale era, modern supercomputers are increasingly big making their programming much more complex. In addition to performance, software productivity is a major concern to choose a programming language, such as Chapel, designed for exascale computing. In this paper, we investigate the design of a parallel distributed tree‐search algorithm, namely P3D‐DFS, and its implementation using Chapel. The design is based on the Chapel's DistBag data structure, revisited by: (1) redefining the data structure for Depth‐First tree‐Search (DFS), henceforth renamed DistBag‐DFS; (2) redesigning the underlying load balancing mechanism. In addition, we propose two instantiations of P3D‐DFS considering the Branch‐and‐Bound (B&B) and Unbalanced Tree Search (UTS) algorithms. In order to evaluate how much performance is traded for productivity, we compare the Chapel‐based implementations of B&B and UTS to their best‐known counterparts based on traditional OpenMP (intra‐node) and MPI+X (inter‐node). For experimental validation using 4096 processing cores, we consider the permutation flow‐shop scheduling problem for B&B and synthetic literature benchmarks for UTS. The reported results show that P3D‐DFS competes with its OpenMP baselines for coarser‐grained shared‐memory scenarios, and with its MPI+X counterparts for distributed‐memory settings, considering both performance and productivity‐awareness. In the context of this work, this makes Chapel an alternative to OpenMP/MPI+X for exascale programming.

The interest in Unmanned Aerial Vehicles (UAVs) for civilian applications has seen a drastic increase in the past few years. Indeed, UAVs feature unique properties such as three-dimensional mobility and payload flexibility which provide unprecedented advantages when conducting missions like infrastructure inspection or search and rescue. However their current usage is mainly limited to a single operated or autonomous device which brings several limitations like its range of action and resilience. Using several UAVs as a swarm is one promising approach to address those limitations. However, manually designing globally efficient swarming approaches that solely rely on distributed behaviours is a complex task. The goal of this work is thus to automate the design of UAV swarming behaviours to tackle an area coverage problem. The first contribution of this work consists in modelling this problem as a multi-objective optimisation problem. The second contribution is a hyper-heuristic based on multi-objective reinforcement learning for generating distributed heuristics for that problem. Experimental results demonstrate the good stability of the generated heuristic on instances with different sizes and its capacity to well balance the multiple objectives of the optimisation problem.

International audience

In this work, we propose an optimal UAV selection scheme (Opti-U) for the UAV-as-a-Service platforms to offer persistent services to the end-users. In such a platform, multiple UAVs work synchronously and collaboratively to serve different applications. On the other hand, the end-users spend a certain amount to receive uninterrupted and seamless services from the platform. Typically, the UAVs are incapable to serve an application continuously for a long duration due to their energy-constraint nature. Therefore, when the energy level of a UAV drops to a certain threshold value, an alternative suitable UAV is necessary to be selected for continuing uninterrupted and seamless services to the end-users. In a UAV-as-a-Service platform, a UAV hosts multiple sensor nodes for serving different applications. All the UAVs present in a platform may not be suitable to serve all types of applications due to the lack of appropriate sensors in them. Moreover, different UAVs have diverse capabilities of hovering, processing, and communicating. Consequently, the UAV selection mechanism becomes a challenging and important issue, which is unaddressed in the current literature, in a UAV-as-a-Service platform. The proposed scheme, Opti-U, selects an optimal UAV, among the available ones, considering their capabilities and utilization factor in a UAV-as-a-Service platform.

The protection of data processing is emerging as an essential aspect of data analytics, machine learning, delegation of computation, Internet of Things, medical and financial analysis, smart cities, genomics, non-disclosure searching, among others. Often, they use sensitive information that cannot be protected by traditional cryptosystems. Homomorphic Encryption (HE) schemes and secure Multi-Party Computation (MPC) are considered suitable solutions for privacy protection. In this paper, we propose and analyze the performance of three homomorphic Logistic Regression (LR) models with Gradient Descent (GD) algorithms based on the Residue Number System (RNS). We compare their performance with four traditional non-homomorphic versions, one homomorphic algorithm based on RNS with Batch GD, and two state-of-the-art homomorphic algorithms. To validate our approach, we consider six public datasets of different medicine domains (diabetes, cancer, drugs, etc.) and genomics. We use a 5-fold cross-validation technique for a fair comparison in terms of the solution quality and training time. The results show that propose homomorphic solutions have similar accuracy with non-homomorphic algorithms, increased classification performance, and decreased training time compared with the state-of-the-art HE algorithms.

This paper introduces a scheme for autonomous service exchange among multiple sensor-cloud service providers (SCSPs) in a sensor-cloud (SC) platform for Internet of Things (IoT) applications. Typically, SC offers Sensors-as-a-Service (SeaaS) using the concept of sensor virtualization for serving different IoT applications seamlessly in real-time. On the other hand, an SC platform reduces the tasks of sensor deployment and management on the user by employing SCSP. In an SC platform, single SCSP may be incapable of serving an entire IoT application requested by an end-user due to the lack of sufficient sensor nodes (SNs) present in the region of interested of an application. However, the presence of multiple SCSPs in an SC platform plays a complementary role in serving an IoT application entirely by implementing the idea of service exchange among them. The proposed scheme, ServEx, enables service exchange among multiple SCSPs in an SC platform. In ServEx, we apply a 2-phase approach. In the first phase, we introduce the use of a data structure to store the service profile of end-users and SCSPs. On the other hand, in the second phase, we design a profile matching mechanism for enabling a SCSP to find desired SNs registered to other SCSPs. Through extensive experiments, we observe that ServEx reduces the average delay by 57% and the average energy consumption by 74% and increases the capability of complete service provisioning for each SCSPs.

peer reviewed

With renewed global interest for Artificial Intelligence (AI) methods, the past decade has seen a myriad of new programming models and tools that enable better and faster Machine Learning (ML). More recently, a subset of ML known as Deep Learning (DL) raised an increased interest due to its inherent ability to tackle efficiently novel cognitive computing applications. DL allows computational models that are composed of multiple processing layers to learn in an automated way representations of data with multiple levels of abstraction, and can deliver higher predictive accuracy when trained on larger data sets. Based on Artificial Neural Networks (ANN), DL is now at the core of state of the art voice recognition systems (which enable easy control over e.g. Internet-of- Things (IoT) smart home appliances for instance), self-driving car engine, online recommendation systems. The ecosystem of DL frameworks is fast evolving, as well as the DL architectures that are shown to perform well on specialized tasks and to exploit GPU accelerators. For this reason, the frequent performance evaluation of the DL ecosystem is required, especially since the advent of novel distributed training frameworks such as Horovod allowing for scalable training across multiple computing resources.In this paper, the scalability evaluation of the reference DL frameworks (Tensorflow, Keras, MXNet, and PyTorch) is performed over up-to-date High Performance Computing (HPC) resources to compare the efficiency of different implementations across several hardware architectures (CPU and GPU). Experimental results demonstrate that the DistributedDataParallel features in the Pytorch library seem to be the most efficient framework for distributing the training process across many devices, allowing to reach a throughput speedup of 10.11 when using 12 NVidia Tesla V100 GPUs when training Resnet44 on the CIFAR10 dataset.

This paper presents the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model used by an Unmanned Aerial Vehicle (UAV) swarm to perform surveillance tasks. CACOC uses chaotic solutions of a dynamical system and pheromones for optimising area coverage. Consequently, several parameters of CACOC are to be optimised with the aim of improving its coverage performance. We propose a Genetic Algorithm (GA) and two Cooperative Coevolutionary Genetic Algorithms (CCGA) to tackle this problem. After testing our proposals on four case studies we performed a comparative analysis to conclude that the cooperative approaches allow a better exploration of the search space by optimising each UAV parameters independently.

Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is an NP-hard problem. In this paper, 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.

The huge volume, variety, and velocity of big data have empowered Machine Learning (ML) techniques and Artificial Intelligence (AI) systems. However, the vast portion of data used to train AI systems is sensitive information. Hence, any vulnerability has a potentially disastrous impact on privacy aspects and security issues. Nevertheless, the increased demands for high-quality AI from governments and companies require the utilization of big data in the systems. Several studies have highlighted the threats of big data on different platforms and the countermeasures to reduce the risks caused by attacks. In this paper, we provide an overview of the existing threats which violate privacy aspects and security issues inflicted by big data as a primary driving force within the AI/ML workflow. We define an adversarial model to investigate the attacks. Additionally, we analyze and summarize the defense strategies and countermeasures of these attacks. Furthermore, due to the impact of AI systems in the market and the vast majority of business sectors, we also investigate Standards Developing Organizations (SDOs) that are actively involved in providing guidelines to protect the privacy and ensure the security of big data and AI systems. Our far-reaching goal is to bridge the research and standardization frame to increase the consistency and efficiency of AI systems developments guaranteeing customer satisfaction while transferring a high degree of trustworthiness.

Carsharing services have successfully established their presence and are now growing steadily in many cities around the globe. Carsharing helps to ease traffic congestion and reduce city pollution. To be efficient, carsharing fleet vehicles need to be located on city streets in high population density areas and considering demographics, parking restrictions, traffic and other relevant information in the area to satisfy travel demand. This work proposes to formulate the initial placement of a fleet of cars for a round-trip carsharing service as a multi-objective optimization problem. The performance of state-of-the-art metaheuristic algorithms, namely, SPEA2, NSGA-II, and NSGA-III, on this problem is evaluated on a novel benchmark composed of synthetic and real-world instances built from real demographic data and street network. Inverted generational distance (IGD), spread and hypervolume metrics are used to compare the algorithms. Our findings demonstrate that NSGA-II yields significantly lower IGD and higher hypervolume than the rest and SPEA2 has a significantly better diversity if compared with NSGA-II and NSGA-III.

Urban planning typically relies on experience-based solutions and traditional methodologies to face urbanization issues and investigate the complex dynamics of cities. Recently, novel data-driven approaches in urban computing have emerged for researchers and companies. They aim to address historical urbanization issues by exploiting sensing data gathered by mobile devices under the so-called mobile crowdsensing (MCS) paradigm. This work shows how to exploit sensing data to improve traditionally experience-based approaches for urban decisions. In particular, we apply widely known Machine Learning (ML) techniques to achieve highly accurate results in predicting categories of local businesses (LBs) (e.g., bars, restaurants), and their attractiveness in terms of classes of temporal demands (e.g., nightlife, business hours). The performance evaluation is conducted in Luxembourg city and the city of Munich with publicly available crowdsensed datasets. The results highlight that our approach does not only achieve high accuracy, but it also unveils important hidden features of the interaction of citizens and LBs.

While High Performance Computing (HPC) centers continuously evolve to provide more computing power to their users, we observe a wish for the convergence between Cloud Computing (CC) and High Performance Computing (HPC) platforms, with the commercial hope to see Cloud Computing (CC) infrastructures to eventually replace in-house facilities. If we exclude the performance point of view where many previous studies highlight a non-negligible overhead induced by the virtualization layer at the heart of every Cloud middleware when running a HPC workload, the question of the real cost-effectiveness is often left aside with the intuition that, most probably, the instances offered by the Cloud providers are competitive from a cost point of view. In this article, we wanted to assert (or infirm) this intuition by analyzing what composes the Total Cost of Ownership (TCO) of an in-house HPC facility operated internally since 2007. This Total Cost of Ownership (TCO) model is then used to compare with the induced cost that would have been required to run the same platform (and the same workload) over a competitive Cloud IaaS offer. Our approach to address this price comparison is three-fold. First we propose a theoretical price-performance model based on the study of the actual Cloud instances proposed by one of the major Cloud IaaS actors: Amazon Elastic Compute Cloud (EC2). Then, based on the HPC facility TCO analysis we propose a hourly price comparison between our in-house cluster and the equivalent EC2 instances. Finally, based on the experimental benchmarking on the local cluster and on the Cloud instances we propose an update of the former theoretical price model to reflect the real system performance. The results obtained advocate in general for the acquisition of an in-house HPC facility, which balances the common intuition in favor of Cloud Computing platforms, would they be provided by the reference Cloud provider worldwide.

Cloud services are delivered to cloud customers on a pay-per-use model by the Cloud Services Providers (CSPs). CSP is using Service Level Agreements (SLAs) to define the quality of the provided services. Unfortunately, a standard mechanism does not exist to verify and assure that delivered services satisfy the signed SLA agreement. In this context, an automatic framework named PRESENCE was introduced to define a set of Common performance metrics handled by a set of agents within a customized client (called the Auditor) for measuring the behaviour of cloud applications on top of a given CSP. In this paper, one of the components of the PRESENCE framework dedicated to the provision of stochastic models for selected performance metrics is presented. More precisely, 11 generated models are depicted and analysed, out of which 90, 91% accurately represent Web Service (WS) performance metrics for four representatives SaaS components used for the validation of the PRESENCE approach.

The huge increase of population living in cities calls for a sustainable urban development. Mobile crowdsensing (MCS) leverages participation of active citizens to improve performance of existing sensing infrastructures. In typical MCS systems, sensing tasks are allocated and reported on individual-basis. In this paper, we investigate on collaboration among users for data delivery as it brings a number of benefits for both users and sensing campaign organizers and leads to better coordination and use of resources. By taking advantage from proximity, users can employ device-to-device (D2D) communications like Wi-Fi Direct that are more energy efficient than 3G/4G technology. In such scenario, once a group is set, one of its member is elected to be the owner and perform data forwarding to the collector. The efficiency of forming groups and electing suitable owners defines the efficiency of the whole collaborative-based system. This paper proposes three policies optimized for MCS that are compliant with current Android implementation of Wi-Fi Direct. The evaluation results, obtained using CrowdSenSim simulator, demonstrate that collaborative-based approaches outperform significantly individual-based approaches.

The unprecedented growth of the population living in urban environments calls for a rational and sustainable urban development. Smart cities can fill this gap by providing the citizens with high-quality services through efficient use of Information and Communication Technology (ICT). To this end, active citizen participation with mobile crowdsensing (MCS) techniques is a becoming common practice. As MCS systems require wide participation, the development of large scale real testbeds is often not feasible and simulations are the only alternative solution. Modeling the urban environment with high precision is a key ingredient to obtain effective results. However, currently existing tools like OpenStreetMap (OSM) fail to provide sufficient levels of details. In this paper, we apply a procedure to augment the precision (AOP) of the graph describing the street network provided by OSM. Additionally, we compare different mobility models that are synthetic and based on a realistic dataset originated from a well known MCS data collection campaign (ParticipAct). For the dataset, we propose two arrival models that determine the users' arrivals and match the experimental contact distribution. Finally, we assess the scalability of AOP for different cities, verify popular metrics for human mobility and the precision of different arrival models.

The execution of scientific workflows on dynamic environments such as cloud computing has become multi-objective scheduling in order to satisfy user demands from several perspectives. Among these objectives, Cost and Makespan are probably the most common. This research also includes Data Movement as an additional objective as it has significant effect to network utilization and energy consumption in network equipment in cloud data center. This paper proposes a multi-objective scheduling, Extreme Nondominated Sorting Genetic Algorithm (E-NSGA-III). It is an extension of the Nondominated Sorting Genetic Algorithm (NSGA-III). E-NSGA-III utilizes extreme solutions in the population generation module in order improve quality of solutions. Five well-known scientific workflows are selected as testbeds. Hypervolume and the Pareto front are chosen as the performance metrics. E-NSGA-III is evaluated by comparing its performance against the two previous versions (NSGA-II and NSGA-III). The comparison reveals that E-NSGA-III yields the best performance among them in multi-objective scheduling of the five scientific workflows.

Motivation: The root mean square deviation (RMSD) is one of the most used similarity criteria in structural biology and bioinformatics. Standard computation of the RMSD has a linear complexity with respect to the number of atoms in a molecule, making RMSD calculations time-consuming for the large-scale modeling applications, such as assessment of molecular docking predictions or clustering of spatially proximate molecular conformations. Previously, we introduced the RigidRMSD algorithm to compute the RMSD corresponding to the rigid-body motion of a molecule. In this study, we go beyond the limits of the rigid-body approximation by taking into account conformational flexibility of the molecule. We model the flexibility with a reduced set of collective motions computed with e.g. normal modes or principal component analysis. Results: The initialization of our algorithm is linear in the number of atoms and all the subsequent evaluations of RMSD values between flexible molecular conformations depend only on the number of collective motions that are selected to model the flexibility. Therefore, our algorithm is much faster compared to the standard RMSD computation for large-scale modeling applications. We demonstrate the efficiency of our method on several clustering examples, including clustering of flexible docking results and molecular dynamics (MD) trajectories. We also demonstrate how to use the presented formalism to generate pseudo-random constant-RMSD structural molecular ensembles and how to use these in cross-docking. Availability and implementation: We provide the algorithm written in C++ as the open-source RapidRMSD library governed by the BSD-compatible license, which is available at http://team.inria.fr/nano-d/software/RapidRMSD/. The constant-RMSD structural ensemble application and clustering of MD trajectories is available at http://team.inria.fr/nano-d/software/nolb-normal-modes/. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Biomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually. RESULTS: We investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach together with an evolutionary algorithm for informative combination of distance metrics. We compare the enrichment of the obtained clusters between the solutions and with expert knowledge. We calculate the number of Gene and Disease Ontology terms discovered by different solutions as a measure of cluster quality. Our results show that combining distance metrics can improve clustering accuracy, based on the comparison with expert-provided clusters. Also, the performance of specific combinations of distance functions depends on the clustering depth (number of clusters). By employing bi-level optimization approach we evaluated relative importance of distance functions and we found that indeed the order by which they are combined affects clustering performance. Next, with the enrichment analysis of clustering results we found that both hierarchical and bi-level clustering schemes discovered more Gene and Disease Ontology terms than expert-provided clusters for the same knowledge repository. Moreover, bi-level clustering found more enriched terms than the best hierarchical clustering solution for three distinct distance metric combinations in three different instances of disease maps. CONCLUSIONS: In this work we examined the impact of different distance functions on clustering of a visual biomedical knowledge repository. We found that combining distance functions may be beneficial for clustering, and improve exploration of such repositories. We proposed bi-level optimization to evaluate the importance of order by which the distance functions are combined. Both combination and order of these functions affected clustering quality and knowledge recognition in the considered benchmarks. We propose that multiple dimensions can be utilized simultaneously for visual knowledge exploration.

Network Function Virtualization (NFV) is an emerging network architecture to increase flexibility and agility within operator's networks by placing virtualized services on demand in Cloud data centers (CDCs). One of the main challenges for the NFV environment is how to minimize network latency in the rapidly changing network environments. Although many researchers have already studied in the field of Virtual Machine (VM) migration and Virtual Network Function (VNF) placement for efficient resource management in CDCs, VNF migration problem for low network latency among VNFs has not been studied yet to the best of our knowledge. To address this issue in this article, we i) formulate the VNF migration problem and ii) develop a novel VNF migration algorithm called VNF Real-time Migration (VNF-RM) for lower network latency in dynamically changing resource availability. As a result of experiments, the effectiveness of our algorithm is demonstrated by reducing network latency by up to 70.90% after latency-aware VNF migrations.

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-regulatedMCDA, 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.

Street lighting is an essential community service, but current implementations are not energy efficient and and require municipalities to spend up to 40% of their allocated budget. In this paper, we propose heuristics and devise a comparison methodology for new smart lighting solutions in next generation smart cities. The proposed smart lighting techniques make use of Internet of Things (IoT) augmented lampposts, which save energy by turning off or dimming the light in the absence of citizens nearby. Assessing costs and benefits in adopting the new smart lighting solutions is a pillar step for municipalities to foster real implementation. For evaluation purposes, we have developed a custom simulator which allows the deployment of lampposts in realistic urban environments. The citizens travel on foot along the streets and trigger activation of the lampposts according to the proposed heuristics. For the city of Luxembourg, the results highlight that replacing all existing lamps with LEDs and dimming light intensity in the absence of users in the vicinity of the lampposts is convenient and provides an economical return already after the first year of deployment.

Cloud computing has become a de facto approach for service provisioning over the Internet. It operates relying on a pool of shared computing resources available on demand and usually hosted in data centers. Assessing performance and energy efficiency of data centers becomes fundamental. Industries use a number of metrics to assess efficiency and energy consumption of cloud computing systems, focusing mainly on the efficiency of IT equipment, cooling and power distribution systems. However, none of the existing metrics is precise enough to distinguish and analyze the performance of data center communication systems from IT equipment. This paper proposes a framework of new metrics able to assess performance and energy efficiency of cloud computing communication systems, processes and protocols. The proposed metrics have been evaluated for the most common data center architectures including fat tree three-tier, BCube, DCell and Hypercube.

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 and pile up on 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 has a critical attractor in the point in which 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.

Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets, and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis of the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns.

Smart cities exploit the most advanced information technologies to improve and add value to existing public services. Having citizens involved in the process through mobile crowdsensing (MCS) augments the capabilities of the platform without enquiring additional costs. In this paper, we propose a novel framework for data acquisition in MCS deployed over a fog computing platform which facilitates a number of key operations including user recruitment and task completion. Proper data acquisition minimizes the monetary expenditure the platform sustains to recruit and compensate users as well as the energy they spend to sense and deliver data. We propose a new user recruitment policy called Distance, Sociability, Energy (DSE). This policy exploits three criteria: (i) spatial distance between users and tasks, (ii) user sociability, which is an estimate of the willingness of users to contribute to sensing tasks, and (iii) remaining battery charge of the devices. Performance evaluation is conducted in a real urban environment for a large number of participants with new metrics assessing the efficiency of recruitment and the accuracy of task completion. Results reveal that the average number of recruited users improves by nearly 20 percent if compared to policies using only spatial distance as selection criterion.

Data centers provision industry and end users with the necessary computing and communication resources to access the vast majority of services online and on a pay-as-you-go basis. In this paper, we study the problem of energy proportionality in data center networks (DCNs). Devices are energy proportional when any increase of the load corresponds to a proportional increase of energy consumption. In data centers, energy consumption is concern as it considerably impacts on the operational expenses (OPEX) of the operators. In our analysis, we investigate the impact of three different allocation policies on the energy proportionality of computing and networking equipment for different DCNs, including 2-Tier, 3-Tier, and Jupiter topologies. For evaluation, the size of the DCNs varies to accommodate up to several thousands of computing servers. Validation of the analysis is conducted through simulations. We propose new metrics with the objective to characterize in a holistic manner the energy proportionality in data centers. The experiments unveil that, when consolidation policies are in place and regardless of the type of architecture, the size of the DCN plays a key role, i.e., larger DCNs containing thousands of servers are more energy proportional than small DCNs.

Since its advent in the middle of the 2000's, the Cloud Computing (CC) paradigm is increasingly advertised as THE solution to most IT problems. While High Performance Computing (HPC) centers continuously evolve to provide more computing power to their users, several voices (most probably commercial ones) emit the wish that CC platforms could also serve HPC needs and eventually replace in-house HPC platforms. If we exclude the pure performance point of view where many previous studies highlight a non-negligible overhead induced by the virtualization layer at the heart of every Cloud middleware when submitted to an High Performance Computing (HPC) workload, the question of the real cost-effectiveness is often left aside with the intuition that, most probably, the instances offered by the Cloud providers are competitive from a cost point of view. In this article, we wanted to assert (or infirm) this intuition by evaluating the Total Cost of Ownership (TCO) of the in-house HPC facility we operate since 2007 within the University of Luxembourg (UL), and compare it with the investment that would have been required to run the same platform (and the same workload) over a competitive Cloud IaaS offer. Our approach to address this price comparison is two-fold. First we propose a theoretical price - performance model based on the study of the actual Cloud instances proposed by one of the major Cloud IaaS actors: Amazon Elastic Compute Cloud (EC2). Then, based on our own cluster TCO and taking into account all the Operating Expense (OPEX), we propose a hourly price comparison between our in-house cluster and the equivalent EC2 instances. The results obtained advocate in general for the acquisition of an in-house HPC facility, which balance the common intuition in favor of Cloud Computing (CC) platforms, would they be provided by the reference Cloud provider worldwide.

Cloud computing uses Internet data centers to host applications and data storage. Cloud computing resources and services are offered to customers on pay-per-use model while the quality of the offered resources and services are defined using service level agreements also known as SLAs. Unfortunately, there is no standard mechanism to verify and assure that services delivered by the cloud provider satisfy the SLA agreement in an automatic way. To fill this gap we propose a framework for SLA assurance, which can be used by both cloud providers and cloud users. The proposed framework assesses performance of cloud applications with and without introducing system and component failures and then helps to resolve or mitigate failures to assure the required quality of cloud applications. The evaluation results obtained through simulations and using testbed experiments demonstrate good agreement with the design objectives.

Cloud service providers (CSPs) offer different Service Level Agreements (SLAs) to the cloud users. Cloud Service Brokers (CSBs) provide multiple sets of alternatives to the cloud users according to users requirements. Generally, a CSB considers the service commitments of CSPs rather than the actual quality of CSPs services. To overcome this issue, the broker should verify the service performances while recommending cloud services to the cloud users, using all available data. In this paper, we compare our two approaches to do so: a min-max-min decomposition based on Intuitionistic Fuzzy Logic (IFL) and a Performance Heat Map technique, to evaluate the performance of commercially available cloud providers. While the IFL technique provides simple, total order of the evaluated CSPs, Performance Heat Map provides transparent and explanatory, yet consistent evaluation of service performance of commercially available CSPs. The identified drawbacks of the IFL technique are: 1) It does not return the accurate performance evaluation over multiple decision alternatives due to highly influenced by critical feedback of the evaluators, 2) Overall ranking of the CSPs is not as expected according to the performance measurement. As a result, we recommend to use performance Heat Map for this problem.

Cloud services providers deliver cloud services to cloud customers on pay-per-use model while the quality of the provided services are defined using service level agreements also known as SLAs. Unfortunately, there is no standard mechanism which exists to verify and assure that delivered services satisfy the signed SLA agreement in an automatic way. There is no guarantee in terms of quality. Those applications have many performance metrics. In this doctoral thesis, we propose a framework for SLA assurance, which can be used by both cloud providers and cloud users. Inside the proposed framework, we will define the performance metrics for the different applications. We will assess the applications performance in different testing environment to assure good services quality as mentioned in SLA. The proposed framework will be evaluated through simulations and using testbed experiments. After testing the applications performance by measuring the performance metrics, we will review the time correlations between those metrics.

The Internet of Things (IoT) paradigm makes the Internet more pervasive. IoT devices are objects equipped with computing, storage and sensing capabilities and they are interconnected with communication technologies. Smart cities exploit the most advanced information technologies to improve public services. For being effective, smart cities require a massive amount of data, typically gathered from sensors. The application of the IoT paradigm to smart cities is an excellent solution to build sustainable Information and Communication Technology (ICT) platforms and to produce a large amount of data following Sensing as a Service (S2aaS) business models. Having citizens involved in the process through mobile crowdsensing (MCS) techniques unleashes potential benefits as MCS augments the capabilities of existing sensing platforms. To this date, it remains an open challenge to quantify the costs the users sustain to contribute data with IoT devices such as the energy from the batteries and the amount of data generated at city-level. In this paper, we analyze existing solutions, we provide guidelines to design a large-scale urban level simulator and we present preliminary results from a prototype.

Saving power becomes one of the main objectives in information technology industry and research. Companies consume a lot of money in the shape of power consuming. Virtual Desktop Infrastructure (VDI) is a new shape of delivering operating systems remotely. Operating systems are executing in a cloud data center. Users desktops and applications can be accessed by using thin client devices. Thin client device is consisting of screen attached with small CPU. VDI has benefits in terms of cost reduction and energy saving. In this paper, we increase the power saved by Grinfy system. Without VDI, Grinfy can save at least 30% of energy consumption to its users companies. By integrating VDI in computing systems and using Grinfy, the power efficiency and saving can be improved and save more than 30%. The improving and increasing of energy saving features of VDI are also illustrated by experiment and will be integrated to Grinfy system to increase percentage of energy saved.

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.

In molecular biology, the subject of protein structure prediction is of continued interest, not only to chart the molecular map of living cells, but also to design proteins with new functions. The Inverse Folding Problem (IFP) of finding sequences that fold into a defined structure is in itself an important research problem at the heart of rational protein design. In this work the Preference-Based Genetic Algorithm (PBGA) is employed to find many diversified solutions to the IFP. The PBGA algorithm incorporates a weighted sum model in order to combine fitness and diversity into a single objective function scoring a set of individuals as a whole. By adjusting the sum weights, a direct control of the fitness vs. diversity trade-off in the algorithm population is achieved by means of a selection scheme iteratively removing the least contributing individuals. Experimental results demonstrate the superior performance of the PBGA algorithm compared to other state-of-the-art algorithms both in terms of fitness and diversity.

The Internet of Things (IoT) paradigm makes the Internet more pervasive, interconnecting objects of everyday life, and is a promising solution for the development of next-generation services. Smart cities exploit the most advanced information technologies to improve and add value to existing public services. Applying the IoT paradigm to smart cities is fundamental to build sustainable Information and Communication Technology (ICT) platforms. Having citizens involved in the process through mobile crowdsensing (MCS) techniques unleashes potential benefits as MCS augments the capabilities of the platform without additional costs. Recruitment of participants is a key challenge when MCS systems assign sensing tasks to the users. Proper recruitment both minimizes the cost and maximizes the return, such as the number and the accuracy of accomplished tasks. In this paper, we propose a novel user recruitment policy for data acquisition in mobile crowdsensing systems. The policy can be employed in two modes, namely sociability-driven mode and distance-based mode. Sociability stands for the willingness of users in contributing to sensing tasks. %Furthermore, we propose a novel metric to assess the efficiency of any recruitment policy in terms of the number of users contacted and the ones actually recruited. Performance evaluation, conducted in a real urban environment for a large number of participants, reveals the effectiveness of sociability-driven user recruitment as the average number of recruited users improves by at least a factor of two.

Incorporating energy efficiency into the design of modern communication systems has become an important area of research. However, while most of the proposed solutions are devoted to making network hardware energy efficient, very few works focus on energy efficiency as a fundamental design parameter of network protocols. This paper proposes an analytical model for energy consumption of TCP which relates energy consumption to protocol operation cycles. Based on this model a number of optimization techniques are proposed to reduce energy consumption of TCP. The experiments, performed using NS2 simulations, demonstrate that energy savings can be as high as 93% for multiple TCP flows.

Mobile cloud applications have become extremely popular in the last years. Location-based services, navigation, online gaming and social networking are a representative set of “always on” cloud applications in which the same or partially overlapping content is delivered to multiple users. Network coding is a well matching solution to improve content delivery. In this paper we propose the vNC-CELL technique, which uses network coding to combine information flows carrying the same or overlapping content that has to be delivered to co-located users. vNC-CELL executes coding functionalities in a mobile cloud through virtualization as these operations are computationally intensive if performed locally at the base station. Performance evaluation obtained from NS-3 simulations confirms vNC-CELL ability to improve network throughput and reduce download times for the users.

The prosperous growth of the Internet-of-Things industry attracts numerous interests in employing edge clouds (a.k.a. cloudlets) to enhance the performance of mobile services and applications. Most existing research has been focused on offloading computational tasks from mobile devices to a single cloudlet or a central location, yet overlooked the issue of jointly coordinating the offloaded tasks in a system of multiple cloudlets. In this paper, we fill this gap by investigating the assignment and the scheduling of mobile computational tasks over multiple cloudlets, while optimizing the overall cost efficiency by leveraging the heterogeneity of cloudlets. We model both data transfer and computation in terms of monetary and time costs, with task deadlines guaranteed. We formulate the problem as a mixed integer program and prove its NP-hardness. By introducing admission control for the cloudlet provider to shape the system workload, we transform our problem into maximizing the task admission rate over the two coupled phases: data transfer and computation. We propose an efficient two-phase scheduling algorithm, and demonstrate that, compared with the conventional approach of always selecting the closest cloudlet, our approach achieves significantly higher admission rate with up to 20% reduction in the average cost of all offloaded tasks.

This work presents an on-line, energy- and communication-aware scheduling strategy for SaaS applications in data centers. The applications are composed of various services and represented as workflows. Each workflow consists of tasks related to each other by precedence constraints and represented by Directed Acyclic Graphs (DAGs). The proposed scheduling strategy combines advantages of state-of-the-art workflow scheduling strategies with energy-aware independent task scheduling approaches. The process of scheduling consists of two phases. In the first phase, virtual deadlines of individual tasks are set in the central scheduler. These deadlines are determined using a novel strategy that favors tasks which are less dependent on other tasks. During the second phase, tasks are dynamically assigned to computing servers based on the current load of network links and servers in a data center. The proposed approach, called Minimum Dependencies Energy-efficient DAG (MinD+ED) scheduling, has been implemented in the GreenCloud simulator. It outperforms other approaches in terms of energy efficiency, while keeping a satisfiable level of tardiness.

Cloud computing has become a de facto approach for service provisioning over the Internet. It operates relying on a pool of shared computing resources available on demand and usually hosted in data centers. Assessing performance and energy efficiency of data centers becomes fundamental. Industries use a number of metrics to assess efficiency and energy consumption of cloud computing systems, focusing mainly on the efficiency of IT equipment, cooling and power distribution systems. However, none of the existing metrics is precise enough to distinguish and analyze the performance of data center communication systems from IT equipment. This paper proposes a framework of new metrics able to assess performance and energy efficiency of cloud computing communication systems, processes and protocols. The proposed metrics have been evaluated for the most common data center architectures including fat-tree three-tier, BCube and DCell.

Heterogeneous architectures have become more popular and widespread in the recent years with the growing popularity of general-purpose processing on graphics processing units, low-power systems on a chip, multi- and many-core architectures, asymmetric cores, coprocessors, and solid-state drives. The design and management of cloud computing data-centers must adapt to these changes while targeting objectives of improving system performance, energy efficiency and reliability. This paper presents HEROS, a novel load balancing algorithm for energy-efficient resource allocation in heterogeneous systems. HEROS takes into account the heterogeneity of a system during the decision-making process and uses a holistic representation of the system. As a result, servers that contain resources of multiple types (computing, memory, storage and networking) and have varying internal structures of their components can be utilized more efficiently.

Selecting the appropriate cloud services and cloudproviders according to the cloud users requirements is becoming a complex task, as the number of cloud providers increases. Cloud providers offer similar kinds of cloud services, but they are different in terms of price, quality of service, customer experience, and service delivery. The most challenging issue of the current cloud computing business is that cloud providers commit a certain Service Level Agreement (SLA), with cloud users, but there is little or no verification mechanisms which ensure that cloud providers are providing cloud services according to their commitment. In the current literature, there is a lack of an evaluation model which provides the real status of cloud providers for the cloud users. In this paper, an evaluation model is proposed, which verifies the quality of cloud services delivered for each service and provides the service status of the cloud providers. Finally, evaluation results obtained from cloud auditors are visualized in an ordered performance heat map, showing the cloud providers in a decreasing ordering of overall service quality. In this way, the proposed service quality evaluation model represents a visual recommender system for cloud service brokers and cloud users.

Wearable devices are becoming increasingly popular and are expected to become essential in our everyday life. Despite continuous improvement of hardware, the lifetime of mobile devices and their capabilities still remain a concern. Small size of batteries of smart watches, glasses, helmets and gloves limits the amount of computing, storage and communication resources. Mobile cloud computing can augment the capabilities of wearable devices by helping to execute some of the computing tasks in the cloud. Such computational offloading helps to preserve battery power at the cost of more intensive communications with the cloud. In this paper, we present a model and comprehensive analysis for computational offloading between wearable devices and clouds in realistic setups.

Cloud computing is a computing model where users access ICT services and resources without regard to where the services are hosted. Communication resources often become a bottleneck in service provisioning for many cloud applications. Therefore, data replication which brings data (e.g., databases) closer to data consumers (e.g., cloud applications) is seen as a promising solution. In this paper, we present models for energy consumption and bandwidth demand of database access in cloud computing datacenter. In addition, we propose an energy efficient replication strategy based on the proposed models, which results in improved Quality of Service (QoS) with reduced communication delays. The evaluation results obtained with extensive simulations help to unveil performance and energy efficiency tradeoffs and guide the design of future data replication solutions.

Data traffic from mobile devices experiences unprecedented growth, which current cellular network capacities cannot sustain. Traffic offloading to other type of networks, such as WiFi, can be used to reduce load in cellular networks. In this paper, we propose a novel solution, which unlike other existing methodologies, implements tight cooperation with the cellular network to optimize traffic offloading. The cellular network provides information about channel usage statistics, user mobility patterns, available resources and other parameters. The offloading decisions aim at optimizing the balance between user application requirements and availability of network resources. The validation results, obtained from NS-3 simulations, confirm effectiveness of the proposed solution in balancing cellular traffic load while ensuring QoS.

This poster shows the performance evaluation of UnaCloud Opportunistic Computing IaaS. We analyze from an HPC perspective, two virtualization frameworks Virtual Box and VMware ESXi and compare them over this particular opportunistic cloud environment. The benchmarks consist of two set of tests, High Performance Linpack and IOzone, that examine the performance and the Input/Output response. The purpose of the experiments is to evaluate the behavior of the different virtual environments over an opportunistic cloud environment and investigate how these are affected by different percentage of end-users. The results show a better performance for Virtual Box than VMware and the other way around for I/O response. Nevertheless, the experiments shows that VBox have more robustness than VMware.

Energy efficiency remains a prevalent concern in the development of future HPC systems. Thus the next generations of supercomputers are foreseen to be developed as hybrid systems featuring traditional processors, accelerators (such as GPGPUs) and/or low-power processor architectures (ARM, Intel Atom, etc.) primarily designed for the mobile and embedded devices market. Also, a confluence with the Cloud Computing (CC) paradigm is anticipated, driven by economic sustainability factors. However, the performance impact of running Cloud middleware on such crossbred platforms remains to be explored, especially on low power processors. In this context, this paper brings two main contributions: (1) the design and implementation of BACH, a framework able to execute automated performance evaluations of Cloud and HPC cluster environments, (2) the concrete validation of the framework for the evaluation of the modern Open Stack Infrastructure-as-a-Service (IaaS) middleware, deployed on a cutting-edge cluster based on ultra low power energy efficient ARM processors. The efficiency in itself is measured with synthetic HPC benchmarks: HPCC (incorporating the well known HPL), HPCG and real world applications from the bioinformatics domain - GROMACS and ABySS. The experimental evaluation revealed an average 24% performance drop in performance for compute-intensive tasks and 65.6% drop in communication capacity compared to the native environment without the IaaS solution, showing a non-negligible impact on the tested platform. To our knowledge, this is one of the first studies of this type, since deployment attempts of the Open Stack infrastructure on top of ARM platforms are in early stages, and are generally performed only for demonstration purposes.

The popularity of cloud applications surged in the last years. Billions of mobile devices remain always connected. Location services, online games, social networking and navigation are a just few examples of "always on" cloud applications in which the same or partially overlapping content is delivered to multiple users. In this paper, we propose a technique, called NC-CELL, which uses network coding to foster content distribution in mobile cellular networks. Specifically, NC-CELL implements a software module at mobile base stations (or eNodeBs) which scans in transit traffic and looks for opportunities to code packets destined to different mobile users together. The proposed approach can significantly improve cell throughput and is particularly relevant for delay tolerant content distribution.

SUMMARY Virtualization is emerging as the prominent approach to mutualise the energy consumed by a single server running multiple virtual machines instances. The efficient utilisation of virtualized servers and/or computing resources requires understanding of the overheads in energy consumption and the throughput especially on high‐demanding high‐performance computing (HPC) platforms. In this paper, a novel holistic model for the power of virtualized computing nodes is proposed. Moreover, we create and validate instances of the proposed model using concrete measures taken during a benchmarking process that reflects an HPC usage, that is, HPC challenge, IOZone and Bonnie++, conducted using two different hardware configurations on Grid'5000 platform, based on Intel and Advanced Micro Devices (AMD) processors and three widespread virtualization frameworks, namely, Xen, Kernel‐based virtual machine and VMware ESXi. The proposed holistic model of machine power takes into account the impact of utilisation metrics of the machine's components, as well as the employed application, virtualization and hardware. The model is further derived using tools such as multiple linear regressions or neural networks that prove its elasticity, applicability and accuracy. The purpose of the model is to enable the estimation of energy consumption of virtualized platforms, aiming to make possible the optimization, scheduling or accounting in such systems or their simulation. Copyright © 2014 John Wiley & Sons, Ltd.

The review of the requirements of different cloud applications identified the need to consider communication processes explicitly and equally to the computing tasks. Following this observation, we propose a new communication-aware model for cloud computing applications, called CA-DAG. This model is based on Directed Acyclic Graphs (DAGs) that in addition to computing vertices include separate vertices to represent communications. Such a representation allows making separate resource allocation decisions, assigning processors to handle computing jobs and network resources for information transmissions, such as application database requests.

UnaCloud is an opportunistic based cloud infrastructure (IaaS) that allows to access on-demand computing capabilities using commodity desktops. Although UnaCloud maximizes the use of idle resources to deploy virtual machines, it does not use energy-efficient resource allocation algorithms. In this paper, we design and develop different energy-aware algorithms to operate in an energy-efficient way and at the same time to guarantee the performance of the UnaCloud users. Performance tests with different algorithms and scenarios using real trace workloads from UnaCloud, show how different policies can change the energy consumption patterns and reduce the energy consumption in the opportunistic cloud infrastructure. The results show that some algorithms can reduce the energy-consumption power up to 30% over the percentage earned by the opportunistic environment.

This paper proposes a theoretical and experimental analysis of the expected running time for an elitist parallel Evolutionary Algorithm (pEA) based on an island model executed over small-world networks. Our study assumes the resolution of optimization problems based on unimodal pseudo-boolean funtions. In particular, for such function with d values, we improve the previous asymptotic upper bound for the expected parallel running time from O(d√n) to O(d log n). This study is a first step towards the analysis of influence of more complex network topologies (like random graphs created by P2P networks) on the runtime of pEAs. A concrete implementation of the analysed algorithm have been performed on top of the ParadisEO framework and run on the HPC platform of the University of Luxembourg (UL). Our experiments confirm the expected speedup demonstrated in this article and prove the benefit that pEA can gain from a small-world network topology.

The development of large scale data center and cloud computing optimization models led to a wide range of complex issues like scaling, operation cost and energy efficiency. Different approaches were proposed to this end, including classical resource allocation heuristics, machine learning or stochastic optimization. No consensus exists but a trend towards using many-objective stochastic models became apparent over the past years. This work reviews in brief some of the more recent studies on cloud computing modeling and optimization, and points at notions on stability, convergence, definitions or results that could serve to analyze, respectively build accurate cloud computing models. A very brief discussion of simulation frameworks that include support for energy-aware components is also given.

Management and optimization of cloud infrastructures combine multiple challenges. The optimization of data centers targets such objectives as performance, reliability, energy consumption, and security. To achieve these goals, multiple actions can be taken, for example, task and virtual machine allocation or infrastructure management. In this work we propose a model for representation of computing, memory, storage, and communication resources in cloud computing data centers. This model is relevant for the characterization of cloud applications, virtual machines, as well as physical servers. The performance evaluation and validation of the proposed model is carried out using the Green Cloud simulator. The obtained results show good agreement with the design objectives and confirm validity of the assumptions.

A key factor for accurate vehicular ad hoc networks (VANET) simulation is the quality of its underlying mobility model. VehILux is a recent vehicular mobility model that generates traces using traffic volume counts and real-world map data. This model uses probabilistic attraction points which values require optimization to provide realistic traces. Previous sensitivity analysis and application of genetic algorithms (GAs) on the Luxembourg problem instance have outlined this model's limitations. In this article, we first propose an extension of the model using a higher number of auto-generated attraction points. Then its decomposition on the Luxembourg instance using geographical information is proposed as a way to break epistatic links and hence make its optimization using cooperative coevolutionary genetic algorithms (CCGAs) more efficient. Experimental results demonstrate the significant realism increase brought by both the VehILux model enhancements and the CCGA compared to the generational and cellular GAs.

The size and complexity of communication satellite payloads have been increasing very quickly over the last years and their configuration / reconfiguration have become very difficult problems. In this work, we propose to compare the efficiency of three well-known metaheuristic methods to solve an initial configuration problem, which objective is to minimise the length of the longest channel path. Experiments are conducted on real-world problem instances with realistic operational constraints (e.g., a maximum computation time of 10 minutes) and Wilcoxon test is used to determine with statistical confidence what technique is more suitable and what are its limitations. The results of this work will serve as an initial step in our research to design hybrid approaches to push even further the solving capabilities, i.e., tackling bigger payloads and more channels to activate.

The energy consumption in data centers is drastically increasing and becoming a significant portion in the data center operating expenses. Enabling a sleep mode in the idle computing servers and network hardware is the most efficient method to avoid unnecessary power consumption. However, changes in the power modes introduce considerable delays. Moreover, inability to wake up a sleeping server immediately requires an availability of a pool of idle servers able to accommodate incoming load in the short term to prevent QoS degradation. In this paper we investigate the amount of computing servers and network hardware needed to accommodate different incoming load patters in the data centers. Furthermore, we propose to build these servers on energy efficient hardware, which is costly but can scale its power consumption with the offered load levels. The evaluation results show that the proposed methodology can save up to $750 per server per year on average.

With a growing concern on the considerable energy consumed by HPC platforms and data centers, research efforts are targeting green approaches with higher energy efficiency. In particular, virtualization is emerging as the prominent approach to mutualize the energy consumed by a single server running multiple VMs instances. Even today, it remains unclear whether the overhead induced by virtualization and the corresponding hypervisor middleware suits an environment as high-demanding as an HPC platform. In this paper, we analyze from an HPC perspective the three most widespread virtualization frameworks, namely Xen, KVM, and VMware ESXi and compare them with a baseline environment running in native mode. We performed our experiments on the Grid'5000 platform by measuring the results of the reference HPL benchmark. Power measures were also performed in parallel to quantify the potential energy efficiency of the virtualized environments. In general, our study offers novel incentives toward in-house HPC platforms running without any virtualized frameworks.

The use of small-world graphs as a topology structure for the population of Evolutionary Algorithms (EAs) has been recently proposed in the literature. The motivation is clear: the high clustering coefficient and low characteristic path length of such networks makes them suitable for fast local information dissemination, while at the same time preventing it from quickly spreading on the whole population, as it happens in panmictic populations. However, even though several papers addressed this issue so far, only a few of them are able to provide competitive results with other panmictic and/or decentralized population EAs with similar configurations. Therefore, we perform ax study in this work, both theoretically and empirically, on the most appropriate mechanisms to generate SW topologies for Genetic Algorithms (a family of EA). The algorithms are analyzed in terms of efficiency and efficacy, and the best studied variant is validated versus other GAs using well known centralized and decentralized population structures, outperforming them.

This article introduces asynchronous implementations of selected synchronous cooperative co-evolutionary multi-objective evolutionary algorithms (CCMOEAs). The CCMOEAs chosen are based on the following state-of-the-art multi-objective evolutionary algorithms (MOEAs): Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-objective Cellular Genetic Algorithm (MOCell). The cooperative co-evolutionary variants presented in this article differ from the standard MOEAs architecture in that the population is split into islands, each of them optimizing only a sub-vector of the global solution vector, using the original multi-objective algorithm. Each island evaluates complete solutions through cooperation, i.e., using a subset of the other islands current partial solutions. We propose to study the performance of the asynchronous CCMOEAs with respect to their synchronous versions and base MOEAs on well kown test problems, i.e. ZDT and DTLZ. The obtained results are analyzed in terms of both the quality of the Pareto front approximations and computational speedups achieved on a multicore machine.

The first step in the evaluation of vehicular ad hoc networks (VANETs) applications is based on simulations. The quality of those simulations not only depends on the accuracy of the network model but also on the degree of reality of the underlying mobility model. VehILux-a recently proposed vehicular mobility model, allows generating realistic mobility traces using traffic volume count data. It is based on the concept of probabilistic attraction points. However, this model does not address the question of how to select the best values of the probabilities associated with the points. Moreover, these values depend on the problem instance (i.e. geographical region). In this article we demonstrate how genetic algorithms (GAs) can be used to discover these probabilities. Our approach combined together with VehILux and a traffic simulator allows to generate realistic vehicular mobility traces for any region, for which traffic volume counts are available. The process of the discovery of the probabilities is represented as an optimisation problem. Three GAs-generational GA, steady-state GA, and cellular GA-are compared. Computational experiments demonstrate that using basic evolutionary heuristics for optimising VehILux parameters on a given problem instance permits to improve the model realism. However, in some cases, the results significantly deviate from real traffic count data. This is due to the route generation method of the VehILux model, which does not take into account specific behaviour of drivers in rush hours.

Realistic simulations of Vehicular Ad hoc Networks (VANETs) are necessary to evaluate novel technologies based on such networks and to prove benefits obtained from their implementation. This survey gathers from several research domains aspects that increase the quality of VANET simulations. It explains a multi-fold nature of VANETs and presents main building blocks of their simulation-traffic and network simulators. The paper proposes a comprehensive architecture for VANET simulation platform that focuses on producing reliable results. The architecture contains traffic and network simulators that communicate with each other in a dynamic and bi-directional way. The concept of a realistic traffic generator is introduced. It uses real-world data (e.g. maps, traffic volume counts) to model an activity-based traffic varying in time. The traffic generator aims at reproducing accurate vehicular traces for urban scenario. A higher level of realism can be obtained by modelling of human behaviour with intelligent agents and by the implementation of related subsystems, like traffic management and control or weather factors.

Welcome to the first workshop on Vehicular Networks from Theory to Practice, VTP 2012. Vehicular networks have emerged as a very active area of research with large scale efforts and investments on both sides of the Atlantic. The services these networks promise to enable will lead to leap changes in road traffic safety and management and potentially revolutionize modern traffic systems. However, these network constructs are also largely untested and many unknowns exist before they can be realized in live systems. This workshop provides a forum for researchers and practitioners to meet and discuss all aspects of bringing theoretical results to practical implementations.

The security issues raised by the Cloud paradigm are not always tackled from the user point of view. For instance, considering an Infrastructure-as-a-Service (IaaS) Cloud, it is currently impossible for a user to certify in a reliable and secure way that the environment he deployed (typically a Virtual Machine(VM)) has not been corrupted, whether by malicious acts or not. Yet having this functionality would enhance the confidence on the IaaS provider and therefore attract new customers. This paper fills this need by proposing CERTICLOUD, a novel approach for the protection of IaaS platforms that relies on the concepts developed in the Trusted Computing Group (TCG) together with hardware elements, i.e., Trusted Platform Module (TPM) to offer a secured and reassuring environment. Those aspects are guaranteed by two protocols: TCRR and Verify MyVM. When the first one asserts the integrity of a remote resource and permits to exchange a private symmetric key, the second authorizes the user to detect trustfully and on demand any tampering attempt on its running VM. These protocols being key components in the proposed framework, we take very seriously their analysis against known cryptanalytic attacks. This is testified by their successful validation by AVISPA and Scyther, two reference tools for the automatic verification of security protocols. The CERTICLOUD proposal is then detailed: relying on the above protocols, this platform provides the secure storage of users environments and their safe deployment onto a virtualization framework. While the physical resources are checked by TCRR, the user can execute on demand the Verify MyVM protocol to certify the integrity of its deployed environment. Experimental results operated on a first prototype of CERTICLOUD demonstrate the feasibility and the low overhead of the approach, together with its easy implementation on recent commodity machines.

Audio streaming over wireless local area network (WLAN) has an ability to transmit audio data ranging from short messages to music station. A popular application of this is in setting up a community radio, which is in demand, especially in developing countries where frequency spectrum management is still problematic. Audio streaming has to assure high quality of content reception without any packet loss, if possible. One such suitable coder-decoder (CODEC) is the Advanced Audio Coding (AAC). Packetization interval for audio streaming is usually set at a default value of 20 ms. This work develops an AAC Audio Streaming over WLAN 802.11g. It studies the effect of different packetization intervals has on the maximum number of possible connections (i.e. receivers) without any packet loss. The simulated environment comprises of 1 access point with 300 m2. Connecting media server to access point via wireless link is adopted for its mobility and ease of deployment in rural area. The range of packetization intervals under this study is from 20 ms. to 200 ms. The study reveals that the commonly use default value of 20 ms. can manage up to 13 connections. Under the scenario of this study, it was found that the best packetization interval is 70 ms. with the maximum of 33 connections. The study also reveals that packet delay is not an issue in this approach as all are under the acceptable standard of 400 ms.

Full audio bandwidth with very low algorithmic delay CODEC is state-of-the-art in CODEC technology. This type of CODEC is expected to support full frequency range for human hearing. It will enable future multipurpose audio applications, especially those which require high quality of audio signal with very low delay. A popular choice for this type of CODEC is the Constrained Energy Lapped Transform (CELT) CODEC since it is an open source and royalty free. This study is concerned with the effect of packetization interval for using CELT CODEC in bidirectional communication over WLAN 802.11g with respect to packet loss and end-to-end delay. Possible bitrates used in CELT were also investigated. The result reveals that the commonly default 20 ms. packetization interval is not always suitable for all network conditions. High packetization interval (more than 60 ms) interval are not recommended as packet loss can be quite high and end-to-end delay can exceed the 150 ms. ITU recommended value. Using packetization interval at 30 ms. is recommended, apart from achieving better performance, it allows CODEC bitrate to be varied while packet loss is still acceptable and end-to-end delay is still within the recommended value. A practical application for this study is a synchronous remote music session practice between two persons.

In this work we focus on defining how dynamism can be modeled in the context of multi-objective optimization. Based on this, we construct a component oriented classification for dynamic multi-objective optimization problems. For each category we provide synthetic examples that depict in a more explicit way the defined model. We do this either by positioning existing synthetic benchmarks with respect to the proposed classification or through new problem formulations. In addition, an online dynamic MNK-landscape formulation is introduced together with a new comparative metric for the online dynamic multi-objective context.

We address the problem of scheduling precedence-constrained scientific applications on a heterogeneous distributed processor system with the twin objectives of minimizing simultaneously energy consumption and schedule length. Previous research efforts on scheduling have focused on the minimization of a quality of service metric based on the completion time of applications (e.g., the schedule length). Recently, many researchers are working on the design of new scheduling algorithms that consider the minimization of energy consumption. We report a new scheduling algorithm accounting for both objectives. The new scheduling algorithm is based on a multi-start randomized adaptive search technique (GRASP framework) that adopts Dynamic Voltage Scaling technique to minimize energy consumption. This technique enables processors to operate in different voltage supply levels at the cost of sacrificing clock frequencies. This multiple voltage implies a trade-off between the quality of the schedules and energy consumption. Therefore, the new proposed approach is designed as a multi-objective algorithm that simultaneously optimize both objectives. Simulation results on a set of real-world applications emphasize the robust performance of the proposed approach.

In mobile ad hoc networks (or MANETs) devices usually rely on batteries, so the lifetime of these networks highly depends on the energy consumption of the devices composing them. In this paper, we improve the performance of the adaptive enhanced distance based broadcasting algorithm, AEDB, as well as we optimize it using a hybrid multi-objective algorithm, CellDE. The optimization is done by maximizing the coverage achieved by the broadcast process while minimizing at the same time both, the energy consumption and the broadcast time. CellDE provides a wide range of different solutions for the configuration of AEDB, outperforming all of them the performance of the protocol using the original configuration.

Differential evolution (DE) algorithms compose an efficient type of evolutionary algorithm (EA) for the global optimization domain. Although it is well known that the population structure has a major influence on the behavior of EAs, there are few works studying its effect in DE algorithms. In this paper, we propose and analyze several DE variants using different panmictic and decentralized population schemes. As it happens for other EAs, we demonstrate that the population scheme has a marked influence on the behavior of DE algorithms too. Additionally, a new operator for generating the mutant vector is proposed and compared versus a classical one on all the proposed population models. After that, a new heterogeneous decentralized DE algorithm combining the two studied operators in the best performing studied population structure has been designed and evaluated. In total, 13 new DE algorithms are presented and evaluated in this paper. Summarizing our results, all the studied algorithms are highly competitive compared to the state-of-the-art DE algorithms taken from the literature for most considered problems, and the best ones implement a decentralized population. With respect to the population structure, the proposed decentralized versions clearly provide a better performance compared to the panmictic ones. The new mutation operator demonstrates a faster convergence on most of the studied problems versus a classical operator taken from the DE literature. Finally, the new heterogeneous decentralized DE is shown to improve the previously obtained results, and outperform the compared state-of-the-art DEs.

A key issue related to the distributed memory multiprocessors architecture for achieving high performance computing is the efficient scheduling of heavily communicated parallel applications such that the total execution time is minimized. Therefore, this paper provides a genetic algorithm based on task clustering techniques for scheduling parallel applications with large communication delays on distributed memory parallel systems. The genetic algorithm is improved with the introduction of some extra knowledge about the scheduling problem. This knowledge is represented by a class of clustering heuristic which is based on structural properties of the parallel application. The major feature of the proposed algorithm is that it takes advantage of the effectiveness of task clustering for reducing communication delays combined with the ability of the genetic algorithms for exploring and exploiting information of the search space of the scheduling problem. The algorithm is assessed by simulation run on some families of traced graphs which represents some of the numerical parallel application programs, and a set of randomly generated applications. Simulation results showed that this algorithm significantly improves the performance of related approaches.

We study the problem of efficiently allocating incoming independent tasks onto the resources of a Grid system. Typically, it is assumed that the estimated time to compute each task on every machine is known. We are making the same assumption in this work, but we allow the existence of inaccuracies in these values. Our schedule will be robust versus such inaccuracies, ensuring that even when the estimated time to compute all the tasks is increased by a given percentage, the makespan of the schedule (i.e., the time when the last machine finishes its tasks) will not grow behind that percentage. We propose a new multi-objective definition of the problem, optimizing at the same time the makespan of the schedule and its robustness. Four well-known multi-objective evolutionary algorithms are used to find competitive results to the new problem. Finally, a new population initialization method for scheduling problems is proposed, leading to more efficient and accurate algorithms.

When dealing with exponential search spaces and when no special knowledge is available on global optima, initial populations for population-based meta-heuristics should be uniformly distributed on the search space in order to sample basins of attraction of all local optima. In this paper, we propose a new initialization strategy for permutation problems. The new method is based on an original tree representation of the search space. Such representation was previously used for exact methods but never for meta-heuristics. The proposed method has been tested using a parallel Genetic Algorithm implemented in the ParadisEO framework and experimented on the Nationwide Grid5000 experimental grid using the Q3AP (3D QAP) permutation problem. The preliminary results are promising.

Reciprocity-based cooperation on packet forwarding in mobile ad hoc networks means that before passing on a packet to the next hop intermediate nodes verify whether the sender of the packet is trustworthy (i.e., cooperative in the past) or not. One of the key questions is what data should be used to evaluate the trustworthiness. This paper demonstrates that if cooperation is based on indirect reciprocity and a classic watchdog-based mechanism for data collection is used, discarding packets can be seen as an act of altruistic punishment. An intermediate node that decides to discard packets from a selfish sender pays the cost (expressed in decrease of trustworthiness among other nodes). However, if the cost of punishing free-riders is too high then nobody has the incentive to be the punisher. This paper demonstrates that the cost is significant and reduces an overall performance of the network. Using computational experiments it is shown that a simple modification of the classic watchdog-based trust data collection mechanism can result in minimisation of the cost and improvement of the throughput of the network.

Cloud computing data centers are becoming increasingly popular for the provisioning of computing resources. The cost and operating expenses of data centers have skyrocketed with the increase in computing capacity. Several governmental, industrial, and academic surveys indicate that the energy utilized by computing and communication units within a data center contributes to a considerable slice of the data center operational costs. In this paper, we present a simulation environment for energy-aware cloud computing data centers. Along with the workload distribution, the simulator is designed to capture details of the energy consumed by data center components (servers, switches, and links) as well as packet-level communication patterns in realistic setups. The simulation results obtained for two-tier, three- tier, and three-tier high-speed data center architectures demonstrate the effectiveness of the simulator in utilizing power management schema, such as voltage scaling, frequency scaling, and dynamic shutdown that are applied to the computing and networking components.

In this contribution, we present SHARC, a Sharper Heuristic for Assignment of Robust Communities. This algorithm performs distributed network partitioning into communities using epidemic propagation of community labels and the computation of a neighborhood similarity metric. Due to its decentralized nature, SHARC is scalable and well suited for networks where no global knowledge nor node coordination exist, like ad hoc networks. Besides, SHARC is computationally efficient and does not depend on configuration parameters. We validated our approach and compared it to alternative solutions using static and dynamic networks. Results show that SHARC provides a sharper and more robust community assignment and prevents the domination of a single community in both static and dynamic networks.

VANETs are ad hoc networks in which devices are vehicles moving at high speeds. This kind of network is getting more and more importance since it has many practical and important applications, like multimedia file sharing (e.g., maps, music, news, weather), or dissemination of alarm messages (e.g., accidents, traffic jams, bad road conditions). One important problem faced in ad hoc networks is network partitioning, causing the formation of isolated clusters, and preventing devices in different clusters from communicating. Usually, devices composing the ad hoc network are provided with other communication interfaces rather than Wi-Fi and/or Bluetooth that allow them to connect to remote devices, such as GPRS/HSDPA. Additionally, there exists some network infrastructure in cities or roads that could be used by VANETs (e.g. hotspots). By taking advantage of these technologies and infrastructures, devices could be able to form a hybrid network, establishing remote links between them (called bypass links) in order to improve the network connectivity by joining, for example, separate clusters. In this work, we face the problem of optimizing the number and location of these remote connections for maximizing the QoS of the network. We use an efficient genetic algorithm with structured population, called cellular genetic algorithm (cGA), to optimize this hard problem. The evaluation of the quality of the network connectivity is made using small world properties. Our goal is to find highly accurate solutions (that could be used as reference values for future works) and then analyze the influence of the quality of the solutions in the real behavior of the network. This is achieved by using the JANE simulator to disseminate a message in the network using two broadcasting protocols having different features.

We deal in this paper with the important problem of partitioning in ad hoc networks. In our approach, we assume that some devices might have other communication interfaces rather than Wi-Fi and/or Bluetooth allowing to connect remote devices (e.g., technologies such as GPRS or HSDPA). This would allow us to build hybrid networks for overcoming the network partitioning. Hence, the problem considered in this work is to establish remote links between devices (called bypass links) in order to maximize the QoS of the network by optimizing its properties to make it small world. Additionally, the number of this kind of links in the network should be minimized as well, since we consider that not all the devices have these communication capabilities, or it could be a requirement to minimize the use of the long range network (for example, in the case its use supposes some cost). We face the problem with four different GAs (both parallel and sequential) and compare their behaviors on six different network instances. All the algorithms were tested with a new encoding of the problem, which is demonstrated to provide more accurate results than the previously existing one.

In the absence of a priori knowledge about global optima, initial populations in genetic algorithms (GAs) should at least be diversified, especially while dealing with large spaces. On the other hand, the use of parallel models for GAs helps to solve large instances. We will focus on the island model. In this paper we propose an island initialization technique for permutation-based problems. We exploit a virtual tree organisation commonly used in exact methods (Branch and Bound) to generate a fully disjoint and well distributed (over the search space) initial population in each island. This method can be used for all permutation-based problems (QAP, Flow-shop, Q3AP..). regardless of the number of permutations. Experiments are performed over Q3AP benchmarks using a $10$ island model. The results shows the efficiency of the proposed method especially for large instances.

The paper proposes a multiobjective approach to the problem of malicious network traffic classification, with specificity and sensitivity criteria as objective functions for the problem. The multiobjective version of Gene Expression Programming (GEP) called moGEP is proposed and applied to find proper classifiers in the multiobjective search space. The purpose of the classifiers is to discriminate information about the network traffic obtained from Idiotypic Network-based Intrusion Detection System (INIDS), transformed into time series. The proposed approach is validated using the network traffic simulator ns2. Classifiers of high accuracy are obtained and their diversity offers interesting possibilities to the domain of network security.

The perceptron problem (PP) appeared for the first time in the learning machines and is very useful for zero-knowledge identification schemes in cryptology. The problem is NP-complete and no deterministic algorithm is known to date. In this paper we develop a deterministic method based on DC (Difference of Convex functions) programming and DCA (DC optimization Algorithms), an innovative approach in nonconvex programming framework. We first formulate the PP as a concave minimization programming problem. Then, we show how to apply DC programming and DCA for the resulting problem. Numerical results demonstrate that the proposed algorithm is promising: its is very fast and can efficiently solve the Perceptron Problem with large sizes.

In this paper we propose a parallel hybrid genetic method for solving Quadratic 3-dimensional Assignment Problem (Q3AP). This problem is proved to be computationally NP-hard. The parallelism in our algorithm is of two hierarchical levels. The first level is an insular model where a number of GAs (genetic algorithms) evolve in parallel. The second level is a parallel transformation of individuals in each GA. Implementation has been done using ParadisEO1 framework, and the experiments have been performed on GRID5000, the French nation-wide computational grid. To evaluate our method, we used three benchmarks derived from QAP instances of QAPLIB and the results are compared with those reported in the literature. The preliminary results show that the method is promising. The obtained solutions are close to the optimal values and the execution is efficient.

A mobile wireless ad hoc network (MANET) consists of a number of devices that form a temporary network operating without support of a fixed infrastructure. The correct operation of such a network requires its users to cooperate on the level of packet forwarding. However, a distributed nature of MANET, lack of a single authority, and limited battery resources of participating devices may lead to a noncooperative behavior of network users, resulting in a degradation of the network throughput. Thus, a cooperation enforcement system specifying certain packet forwarding strategies is a necessity is such networks. In this work we investigate general properties of such a system. We introduce a Prisoner's Dilemma-based model of packet forwarding and next using an evolutionary game-theoretical approach we demonstrate that cooperation very likely to be developed on the basis of conditionally cooperative strategies similar to the TIT-FOR-TAT strategy.

A reliable wireless ad hoc network has to be secured against a selfish behavior. In such a network environment nodes have no incentives to participate actively in packet forwarding. Thus, selfishness is a rational choice for network participants. In this paper we demonstrate how using a strategy based packet forwarding approach can increase throughput in such networks and at the same time can minimize the usage of resources of participating nodes. The strategy defines conditions under which packets are being forwarded. It is based on the notions of trust and activity of the node originating the packet. We demonstrate that selfish behavior becomes unattractive when a certain number of nodes is using this approach. A genetic algorithm (GA) is applied to evolve good strategies, while for evaluation purposes of the strategies a game theoretical model of the ad hoc network is used.

The DNA fragment assembly problem is to re construct a DNA chain from multiple fragments that have previously been sequenced in a laboratory. This is a critical step in any genomic project, since the resulting chains are the basis of all the work. Therefore, the quality of these chains is a prime importance to the correct development of the project. The methods typically applied to this problem usually encounter difficulties on large instances, so more efficient techniques are necessary. In this context, this work proposes a new method combining a general purpose metaheuristic (an advanced cellular genetic algorithm which automatically regulates the intensity of the search) with a local search method specifically designed for this problem (PALS). This local search method (recently published) finds very accurate solutions in very short times. As a result, our proposal is a very accurate and efficient hybrid technique clearly outperforming the other existing ones.

In this paper we present a novel intrusion detection architecture based on Idiotypic Network Theory (INIDS), that aims at dealing with large scale network attacks featuring variable properties, like Denial of Service (DoS). The proposed architecture performs dynamic and adaptive clustering of the network traffic for taking fast and effective countermeasures against such high-volume attacks. INIDS is evaluated on the MITpsila99 dataset and outperforms previous approaches for DoS detection applied to this set.

The branch and bound (B&B) algorithm is one of the most used methods to solve in an exact way combinatorial optimization problems. In a previous article, we proposed a new approach of the parallel B&B algorithm for distributed systems using the farmer-worker paradigm. However, the new farmer-worker approach has a disadvantage: some nodes of the B&B tree can be explored by several B&B processes. To prevent this redundant work and speed up, we propose a new P2P approach inspired from the strategies of existing P2P systems like Napster and JXTA. Validation is performed by experimenting the two approaches on mono-objective flow-shop problem instances using 500 processors belonging to the French national grid, Grid'5000. The obtained results prove the efficiency of the proposed P2P approach. Indeed, the execution time obtained with the P2P version, even if more communicative, is better than the farmer-worker's one.

In this paper we present a novel intrusion detection architecture based on Idiotypic Network Theory (INIDS), that aims at dealing with large scale network attacks featuring variable properties, like Denial of Service (DoS). The proposed architecture performs dynamic and adaptive clustering of the network traffic for taking fast and effective countermeasures against such high-volume attacks. INIDS is evaluated on the MIT'99 dataset and outperforms previous approaches for DoS detection applied to this set.

In this paper we present a novel intrusion detection architecture based on idiotypic network theory (INIDS), that aims at dealing with large scale network attacks featuring variable properties, like denial of service (DoS). The proposed architecture performs dynamic and adaptive clustering of the network traffic for taking fast and effective counter-measures against such high-volume attacks. INIDS is evaluated on the MIT'99 dataset and outperforms previous approaches for DoS detection applied to this set.

When considering realistic mobility patterns, nodes in mobile ad hoc networks move in such a way that the networks most often get divided in a set of disjoint partitions. This presence of partitions is an obstacle to communication within these networks. Ad hoc networks are generally based on technologies allowing nodes in a geographical neighborhood to communicate for free, in a P2P manner. These technologies include IEEE802.11 (Wi-Fi), Bluetooth, etc. In most cases a communication infrastructure is available. It can be a set of access point as well as GMS/UMTS network. The use of such an infrastructure is billed, but it permits distant nodes to get in communication, through what we call "bypass links". The objective of our work is to improve the network connectivity by defining a set of long distance connections. To do this we consider the number of bypass links, as well as the two properties that build on the "small-world" graph theory: the clustering coefficient, and the characteristic path length. A fitness function, used for genetic optimization, is processed out of these three metrics. In this paper we investigate the use of two coevolutionary genetic algorithms (LCGA and CCGA) and compare their performance to a generational and a steady- state genetic algorithm (genGA and ssGA) for optimizing one instance of this topology control problem and present evidence of their capacity to solve it.

Cooperation enforcement is one of the key issues in ad hoc networks. In this paper we proposes a new strategy driven approach that aims at discouraging selfish behavior among network participants. Each node is using a strategy that defines conditions under which packets are being forwarded. Such strategy is based on the notion of trust and activity of the source node of the packet. This way network participants are forced to forward packets and to reduce the amount of time spent in a sleep mode. To evaluate strategies we use a new game theory based model of an ad hoc network. A genetic algorithm (GA) is applied to find good strategies. Experimental results show that our approach makes selfish behavior unattractive.

This work aims at optimizing injection networks, which consist in adding a set of long-range links (called bypass links) in mobile multi-hop ad hoc networks so as to improve connectivity and overcome network partitioning. To this end, we rely on small-world network properties, that comprise a high clustering coefficient and a low characteristic path length. We investigate the use of two genetic algorithms (generational and steady-state) to optimize three instances of this topology control problem and present results that show initial evidence of their capacity to solve it.

In this paper we address the problem of selfish behavior in ad hoc networks. We propose a strategy driven approach which aims at enforcing cooperation between network participants. Each node (player) is using a strategy that defines conditions under which packets are being forwarded. Such strategy is based on the notion of trust and activity of the source node of the packet. This way network participants are enforced to forward packets and to reduce the amount of time of being in a sleep mode. To evaluate strategies we use a new game theory based model of an ad hoc network. This model has some similarities with the iterated prisoner's dilemma under the random pairing game where randomly chosen players receive payoffs that depend on the way they behave. Our model of the network also includes a simple reputation collection and trust evaluation mechanisms. A genetic algorithm (GA) is applied to find good strategies. Experimental results show that approach can successfully enforce cooperation among ad hoc networks participants.

The problem of generation by cellular automata of high quality pseudorandom sequences useful in cryptography is considered in the paper. For this purpose one dimensional nonuniform cellular automata is considered. The quality of pseudorandom sequences generated by cellular automata depends on collective behavior of rules assigned to cellular automata cells. Genetic algorithm is used to find suitable rules from predefined earlier set of rules. It has been shown that genetic algorithm eliminates bad subsets of rules and founds subsets of rules, which provide high quality pseudorandom sequences. These sequences are suitable for symmetric key cryptography and can be used in different cryptographic modules.

The paper presents an approach based on principles of immune systems to the anomaly detection problem. Flexibility and efficiency of the anomaly detection system are achieved by building a model of network behavior based on the self-nonself space paradigm. Covering both self and nonself spaces by hyperrectangular structures is proposed. Structures corresponding to self-space are built using a training set from this space. Hyperrectangular detectors covering nonself space are created using niching genetic algorithm. A coevolutionary algorithm is proposed to enhance this process. Results of experiments show a high quality of intrusion detection, which outperform the quality of recently proposed approach based on hypersphere representation of self-space.

The paper presents an approach for the anomaly detection problem based on principles of immune systems. Flexibility and efficiency of the anomaly detection system are achieved by building a model of network behavior based on self-nonself space paradigm. Covering both self and nonself spaces by hyperrectangular structures is proposed. Structures corresponding to self-space are built using a training set from this space. Hyperrectangular detectors covering nonself space are created using niching genetic algorithm. Coevolutionary algorithm is proposed to enhance this process. Results of conducted experiments show a high quality of intrusion detection which outperforms the quality of recently proposed approach based on hypersphere representation of self-space

The work presents a new encryption algorithm based on one dimensional, uniform and reversible cellular automata (CA). A class of CA with rules specifically constructed to be reversible is used. The quality of encryption depends on the type of rules used, and randomness of the numbers used in the process of encryption.

Summary form only given. A new genetic algorithm for conformational analysis, field of pharmacy related to discovery and design of new drugs, is proposed. The objective is to find the optimal spatial configuration of a molecule, which corresponds to finding its energy minimum by rotation of torsion angles. Different evolutionary mechanisms have been studied and experimentally evaluated in order to fine-tune our algorithm. Finally the proposed solution is successfully applied to vitamin E.