Security is a major challenge in swarm of drones. Network intrusion detection systems (IDS) are deployed to analyze and detect suspicious packet flows. Traditionally, they are implemented independently on each drone. However, due to heterogeneity and resource limitations of drones, IDS algorithms can fall short in satisfying Quality of Service (Qo$S$) metrics, such as latency and accuracy. We argue that a drone can make profit from the swarm by delegating part of the analysis of their packet flows to neighbor drones that have more processing power to enforce security. In this paper, we propose two solving methods to distribute the packet flows to analyze among drones in a way to ensure that it is processed with a minimum communication overhead to limit the attack surface, while ensuring Qo$S$metrics imposed by the drone mission. First, we propose a formulation of the distribution problem using both an Integer Linear Programming (ILP) and a Maximum-Flow Minimum-Cost (MFMC). Furthermore, we propose two specific solving methods for the distribution problem: (1) a Greedy Heuristic (GH), a non-exact solving method, but with small time overhead, and (2) an Adapted Edmonds-Karp (AEK) algorithm, an exact method, but with a higher time overhead. GH proved to be a very fast solution (up to more than 2000x faster than ILP with Branch and Bound), while AEK solution proved to find the exact solution even when the problem is very difficult.

Drone swarms are increasingly being used to perform critical missions, such as inspection of ports and industrial installations. Each drone can embed heterogeneous execution platforms to successfully perform various computing tasks. As security threats may disrupt the progression of the drone mission, network intrusion detection systems (IDSs) are used. They analyze network traffic to detect malicious behaviors, but generally rely on resource-hungry machine learning models. To adapt to the dynamic nature of the mission, it is necessary to embed several IDS implementations leveraging heterogeneous computing resources of the drone and presenting a trade-off between security, throughput, and energy consumption. To address this issue, we propose, in this paper, an end-to-end flow composed of an offline phase to choose the IDS implementations to embed on the drone platform and an online phase to select the best implementation online considering the mission conditions at a given time. We devised a MILP formulation for the offline phase that proved to provide a 89.41% better Inverted Generational Distance (IGD) than a random choice. For the online phase, we investigated several solutions and designed a novel optimized strategy that proved to be around 16.76 times faster than TOPSIS while having comparable QoS metrics.

Swarms of drones are more and more used for critical missions and need to be protected against malicious users. Intrusion Detection Systems (IDS) are used to analyze network traffic in order to detect possible threats. Modern IDSs rely on machine learning models for such a sake. Because of the absence of central management in swarms of drones, IDSs constitute a good second-line protective measure. Investigating the execution of IDS (resource-hungry) algorithms on drone (resource-constrained) devices is crucial when it comes to optimizing energy, response time, memory footprint and algorithm precision. In addition, embedded platforms used in drones often incorporate heterogeneous computing platforms on which IDSs could be executed. In this paper, we present a methodology and results about characterizing the execution of different IDS models on various platform (CPUs, GPUs). In effect, as swarm of drones operate in different mission contexts (e.g. criticity level) and states (e.g. energy budget, memory footprint), it is important to explore which IDS model to run on which platforms for a given mission in a given context. For this sake, we evaluated several metrics on different platforms: energy and resource consumption, accuracy for malicious traffic detection and response time. The models tested (RF, CNN, DNN) have shown different performance according to the measured metrics and the chosen platform and proved to be relevant in different mission states.

Cloud federation enables service providers to collaborate to provide better services to customers. For cloud storage services, optimizing customer object placement for a member of a federation is a real challenge. Storage, migration, and latency costs need to be considered. These costs are contradictory in some cases. In this article, we modeled object placement as a multi-objective optimization problem. The proposed model takes into account parameters related to the local infrastructure, the federated environment, customer workloads, and their SLAs. For resolving this problem, we propose CDP-NSGAII IR , a Constraint Data Placement matheuristic based on NSGAII with Injection and Repair functions. The injection function aims to enhance the solutions’ quality. It consists to calculate some solutions using an exact method then inject them into the initial population of NSGAII. The repair function ensures that the solutions obey the problem constraints and so prevents from exploring large sets of unfeasible solutions. It reduces drastically the execution time of NSGAII. Experimental results show that the injection function improves the HV of NSGAII and the exact method by up to 94% and 60%, respectively, while the repair function reduces the execution time by an average of 68%.

Most cloud data centers are over-provisioned and underutilized, primarily to handle peak loads and sudden failures. This has motivated many researchers to reclaim the unused resources, which are by nature ephemeral, to run data-intensive applications at a lower cost. Hadoop MapReduce is one of those applications. However, it was designed on the assumption that resources are available as long as users pay for the service. In order to make it possible for Hadoop to run on unused (ephemeral) resources, we have designed a heterogeneity and volatility-aware holistic scheduler consisting of three different components: (1) A MapReduce task and job scheduler that relies on a global vision of resource utilization predictions, (2) a scheduler-based data placement strategy that improves the data locality, and (3) a reactive QoS controller that ensures customers' service-level agreement (SLA) and minimizes interference between co-located workloads. Our framework makes it possible to take advantage of ephemeral resources efficiently. Indeed, for a given set of jobs, it reduces the overall execution time by up to 47.6% and an average of 18.7% as compared to state-of-the-art strategies.

ARINC 653 introduces the concept of partition that allows time and space isolation in real-time avionic systems. Tasks are assigned to partitions according to various objective functions or constraints such as safety, performance, and security. Some of these objective functions may be conflicting as an improvement of one objective leads to a decrease of another. For example, improving safety by active redundancy may decrease performance. In this paper, we investigate the conflicting aspect between schedulability and security in Time and Space Partitioning (TSP) systems. Many researches have shown that enforcing the security of a system results in an overhead affecting its schedulability. We formulate a design space exploration (DSE) process with a meta-heuristic to explore solutions defined by the tasks to partitions assignment according to security requirements and timing constraints. Experiments are conducted with the Cheddar scheduling analyzer to characterize applications that are concerned by this conflicting issue and to evaluate the tradeoffs between schedulability and security.

Fog computing consists in extending Cloud services down to the network edge by using resources such as base stations, routers and switches. It presents a dense, heterogeneous and geo-distributed infrastructure which pushes to investigate how data are placed within this infrastructure in order to minimize service latency, network utilization and energy consumption. iFogSim is a Fog and IoT environments simulator dedicated to manage IoT services in a Fog infrastructure. In this paper, we present an extension to iFogSim to be able to model and simulate scenarios with strategies aiming to optimize data placement in Fog and IoT contexts. Data placement problem is NP-Hard due to the large number of Fog nodes and the high amount of data to be placed. Thus, we added a support to divide and conquer strategies to subdivide the issued infrastructure into several parts hence reducing the data placement computing time. Moreover, the extension involves a generic smart city scenario with different workloads making it possible for the users to investigate the behavior of their strategies using various workloads. In order to optimize the execution time of the simulations, we parallelized the Floyd-Warshall algorithm. This algorithm is used in iFogSim to compute all shortest paths between nodes in order to simulate data transmission. We have evaluated this extension using the proposed smart city scenario with various infrastructure configurations. The experiments show that our extension has a small overhead in terms of simulation time and memory utilization.

The endoplasmic reticulum (ER) is an intricate network that pervades the entire cortex of plant cells and its geometric shape undergoes drastic changes. This paper proposes a mathematical model to reconstruct geometric network dynamics by combining the node movements within the network and topological changes engendered by these nodes. The network topology in the model is determined by a modified optimization procedure from the work (Lemarchand, et al. 2014) which minimizes the total length taking into account both degree and angle constraints, beyond the conditions of connectedness and planarity. A novel feature for solving our optimization problem is the use of "lifted" angle constraints, which allows one to considerably reduce the solution runtimes. Using this optimization technique and a Langevin approach for the branching node movement, the simulated network dynamics represent the ER network dynamics observed under latrunculin B treated condition and recaptures features such as the appearance/disappearance of loops within the ER under the native condition. The proposed modeling approach allows quantitative comparison of networks between the model and experimental data based on topological changes induced by node dynamics. An increased temporal resolution of experimental data will allow a more detailed comparison of network dynamics using this modeling approach.

Solid State Drives (SSD) are integrated together with Hard Disk Drives (HDD) in Hybrid Storage Systems (HSS) for Cloud environment. When it comes to storing data, some placement strategies are used to find the best location (SSD or HDD). These strategies should minimize the cost of data placement while satisfying Service Level Objectives (SLO). This paper presents two Cost based Object Placement Strategies (COPS) for DBaaS objects in HSS: a Genetic based approach (G-COPS) and an ad-hoc Heuristic approach (H-COPS) based on incremental optimization. While G-COPS proved to be closer to the optimal solution in case of small instances, H-COPS showed a better scalability as it approached the exact solution even for large instances (by 10% in average). In addition, H-COPS showed small execution times (few seconds) even for large instances which makes it a good candidate to be used in runtime. Both H-COPS and G-COPS performed better than state-of-the-art solutions as they satisfied SLOs while reducing the overall cost by more than 40% for problems of small and large instances.

Internet of Things (IoT) will be one of the driving application for digital data generation in the next years as more than 50 billions of objects will be connected by 2020. IoT data can be processed and used by different devices spread all over the network. The traditional way of centralizing data processing in the Cloud can hardly scale because it cannot satisfy many of the latency critical IoT applications. In addition, it generates a too high network traffic when the number of objects and services increase. Fog infrastructure provides a beginning of an answer to such an issue. In this paper, we present a data placement strategy for Fog infrastructures called iFogStor. The objective of iFogStor is to take profit of the heterogeneity and location of Fog nodes to reduce the overall latency of storing and retrieving data in a Fog. We formulated the data placement problem as a Generalized Assignment Problem (GAP) and proposed two ways to solve it: 1) an exact solution using integer programming and 2) a heuristic one based on geographical zoning to reduce the solving time. Both solutions proved very good performance as they reduced the latency by more than 86% as compared to a Cloud based solution and by 60% as compared to a naive Fog solution. Using geographical zoning heuristic can allow solving problems with large number of Fog nodes efficiently and in a couple of seconds making iFogStor feasible in runtime and scalable.

Real-time embedded systems may be composed of a large number of time constrained functions. During software architecture design, these functions must be assigned to tasks that will run the functions on the top of a real-time operating systems (RTOS). This is a challenging work due to the large number of valid candidate functions to tasks assignment solutions. Moreover, the impact of the assignment on the system performance criteria (often conflicting) should be taken into account in the architecture exploration. The automation of the design exploration by the use of metaheuristics such as multi-objective evolutionary algorithm (MOEA) is a suitable way to help the designers. MOEAs approximate near-optimal alternatives at a reasonable time when compared to an exact search method. However, for large-scale systems even a MOEA method is impractical due to the increased time required to solve a problem instance. To tackle this problem, we present in this article a parallel implementation of the Pareto Archived Evolution Strategy (PAES) algorithm used as a MOEA for the design exploration. The proposed parallelization method is based on the well-known Master-Slave paradigm. Additionally, it involves a new selection scheme in the PAES algorithm. Results of experimentations provide evidence that, on one hand, the parallel approach can considerably speed up the design exploration and the optimization processes. On the other hand, the proposed selection strategy improves the quality of obtained solutions as compared to the original PAES selection schema.

This article deals with real-time embedded system design and verification. Real-time embedded systems are frequently designed according to multi-tasking architectures that have timing constraints to meet. The design of real-time embedded systems expressed as a set of tasks raises a major challenge since designers have to decide how functions of the system must be assigned to tasks. Assigning each function to a different task will result in a high number of tasks, and then in higher preemption overhead. In contrast, mapping many functions on a limited number of tasks leads to a less flexible design which is more expensive to change when the functions of the system evolve. This article presents a method based on an optimization technique to investigate the assignment of functions to tasks. We propose a multi-objective evolution strategy formulation which both minimizes the number of preemptions and maximizes task laxities. Our method allows designers to explore the search space of all possible function to task assignments and to find good tradeoffs between the two optimization objectives among schedulable solutions. After explaining our mapping approach, we present a set of experiments which demonstrates its effectiveness for different system sizes.

In home network, to manage network bandwidth usage, one solution is to control the server outgoing traffic with a token bucket policy. Hull-based token bucket parameter setting allows the guarantee of Quality of Service for variable bitrate video streaming. Hull is an abstraction of the bitrate, following the bandwidth requirement evolution dynamically. In the case of multi-streaming, we investigate a shifting technique to reduce the peaks impact. Postponing the streaming starting time of a video helps to decrease the maximum required bandwidth. The technique is then mixed with the hull-based reservation. Simulations show the effectiveness of the combined approaches to optimize bandwidth usage, guaranteeing the best QoS for streaming. Online utilization is also discussed.

Modern development methodologies from the industry and the academia for complex real-time systems define a stage in which application functions are deployed onto an execution platform. The deployment consists of the placement of functions on a distributed network of nodes, the partitioning of functions in tasks and the scheduling of tasks and messages. None of the existing optimization techniques deal with the three stages of the deployment problem at the same time. In this paper, we present a staged approach towards the efficient deployment of real-time functions based on genetic algorithms and mixed integer linear programming techniques. Application to case studies shows the applicability of the method to industry-size systems and the quality of the obtained solutions when compared to the true optimum for small size examples.

The synthesis of a valid and optimized deployment model from functional and platform models is a crucial issue in the development of distributed real-time systems. The synthesis consists in the allocation of functions/signals to execution nodes/communication buses, the mapping of functions/signals into tasks/messages and the priority assignment to tasks/messages. Current approaches provide partial solutions for the synthesis of a deployment model on distributed platforms, as either the allocation or the mapping is fixed a-priori. In order to tackle this problem we propose an optimization technique, based on two different mathematical programming formulations, to handle optimization of both allocation and mapping. The optimization is multi-objective and considers extensibility maximization, latency minimization and minimization of the number of tasks. The obtained solutions satisfy timing and platform resources requirements. An automotive case study shows the effectiveness of our approach.