STACK: Pile logicielle pour les infrastructures massivement géo distribuées

To support the large and various applications generated by the Internet of Things (IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution, and heterogeneity of edge computational nodes make service placement in such infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complicate the deployment problem. This article presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new classification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.

Security has become a critical issue for Industry4.0 due to different emerging cyber-security threats. Recently, many deep learning (DL) approaches have focused on intrusion detection. However, such approaches often require sending data to a central entity. This in turn raises concerns related to privacy, efficiency, and latency. Despite the huge amount of data generated by the Internet of Things (IoT) devices in Industry 4.0, it is difficult to get labeled data, because data labeling is costly and time-consuming. This poses many challenges for several DL approaches, which require labeled data. In order to deal with these issues, new approaches should be adopted. This article proposes a novel federated semisupervised learning scheme that takes advantage of both unlabeled and labeled data in a federated way. First, an autoencoder (AE) is trained on each device (using unlabeled local/private data) to learn the representative and low-dimensional features. Then, a cloud server aggregates these models into a global AE using federated learning (FL). Finally, the cloud server composes a supervised neural network, by adding fully connected layers (FCN) to the global encoder (the first part of the global AE) and trains the resulting model using publicly available labeled data. Extensive case studies on two real-world industrial datasets demonstrate that our model: (a) ensures that no local private data is exchanged; (b) detects attacks with high classification performance, (c) works even when only a few amounts of labeled data are available; and (d) haslow communication overhead.

From the perspective of telecommunications, next-generation networks, or beyond 5G, will inevitably face the challenge of a growing number of users and devices. Such growth results in high-traffic generation with limited network resources. Thus, the analysis of the traffic and the precise forecast of user demands is essential for developing an intelligent network. In this line, Machine Learning (ML) and especially Deep Learning (DL) models can further benefit from the huge amount of network data. They can act in the background to analyze and predict traffic conditions more accurately than ever and help to optimize the design and management of network services. Recently, a significant amount of research effort has been devoted to this area, greatly advancing network traffic prediction (NTP) abilities. In this article, we bring together NTP and DL-based models and present recent advances in DL for NTP. We provide a detailed explanation of popular approaches and categorize the literature based on these approaches. In addition, as a technical study, we conduct different data analyses and experiments with several DL-based models for traffic prediction. Finally, discussions regarding the challenges and future directions are provided.

Today's emerging needs (Internet of Things applications, Network Function Virtualization services, Mobile Edge computing, etc.) are challenging the classic approach of deploying a few large data centers to provide cloud services. A massively distributed Cloud-Edge architecture could better fit these new trends' requirements and constraints by deploying on-demand infrastructure services in Point-of-Presences within backbone networks. In this context, a key feature is establishing connectivity among several resource managers in charge of operating, each one a subset of the infrastructure. After explaining the networking management challenges related to distributed Cloud-Edge infrastructures, this article surveys and analyzes the characteristics and limitations of existing technologies in the Software Defined Network field that could be used to provide the inter-site connectivity feature. We also introduce Kubernetes, the new de facto container orchestrator platform, and analyze its use in the proposed context. This survey is concluded by providing a discussion about some research directions in the field of SDN applied to distributed Cloud-Edge infrastructures' management.

Research on cache-based side-channel attacks shows the security impact of these attacks on cloud computing. Therefore, the detection of cache-based side-channel attacks has received more attention in IaaS cloud infrastructures because of improvements in the attack techniques. However, such detection requires high resolution information, and it is also a challenging task because of the fine-granularity of the attacks. In this paper, we present an approach to detect cross-VM cache-based side-channel attacks through using hardware fine-grained information provided by Intel Cache Monitoring Technology (CMT) and Hardware Performance Counters (HPCs) following the Gaussian anomaly detection method. The approach shows a high detection rate with 2% performance overhead on the computing platform.

This paper investigates whether the use of Constraint Programming (CP) could enable the development of a generic and easy-to-upgrade placement service for Fog Computing. Our contribution is a new formulation of the placement problem, an implementation of this model leveraging Choco-solver and an evaluation of its scalability in comparison to recent placement algorithms. To the best of our knowledge, our study is the first one to evaluate the relevance of CP approaches in comparison to heuristic ones in this context. CP interleaves inference and systematic exploration to search for solutions, letting users on what matters: the problem description. Thus, our service placement model not only can be easily enhanced (deployment constraints/objectives) but also shows a competitive tradeoff between resolution times and solutions quality.

The recent development of smart devices has lead to an explosion in data generation and heterogeneity. Hence, current networks should evolve to become more intelligent, efficient, and most importantly, scalable in order to deal with the evolution of network traffic. In recent years, network softwarization has drawn significant attention from both industry and academia, as it is essential for the flexible control of networks. At the same time, machine learning (ML) and especially deep learning (DL) methods have also been deployed to solve complex problems without explicit programming. These methods can model and learn network traffic behavior using training data/environments. The research community has advocated the application of ML/DL in softwarized environments for network traffic management, including traffic classification, prediction, and anomaly detection. In this paper, we survey the state of the art on these topics. We start by presenting a comprehensive background beginning from conventional ML algorithms and DL and follow this with a focus on different dimensionality reduction techniques. Afterward, we present the study of ML/DL applications in sofwarized environments. Finally, we highlight the issues and challenges that should be considered.

Over the last years, research activities on blockchain technologies have fairly increased. Firstly introduced with Bitcoin, some projects have since emerged to create or improve blockchain features like privacy while others propose to overcome technical limitations such as scalability and energy consumption. New proposals are often evaluated with ad hoc tools and experimental environments. Reproducibility and comparison of these new contributions with the state of the art of the blockchain technologies are therefore complicated. To the best of our knowledge, only a few tools partially address the design of a generic benchmarking of blockchain technologies (e.g., load generation). In this paper, we introduce BCTMark, a generic framework for benchmarking blockchain technologies on an emulated network in a reproducible way. To illustrate the portability of experiments using BCTMark, we have conducted some experiments on two different testbeds: a cluster of Dell PowerEdge R630 servers (Grid'5000) and one of Raspberry Pi 3+. Experiments have been conducted on three different blockchain systems (Ethereum Clique/Ethash and Hyperledger Fabric) to measure their CPU consumption and energy footprint for different numbers of clients.

Network Traffic Classification enables a number of practical applications ranging from network monitoring to resource management, with security implications as well. Nowadays, traffic classification has become a challenging task in order to distinguish among a variety of applications due to the huge amount of generated traffic. Therefore, developing Machine Learning (ML) models, which can successfully identify network applications, is one of the most important tasks. However, among the ML models applied to network traffic classification so far, no model outperforms all the others. To solve these issues, this paper proposes a novel Deep Learning (DL)-based approach that incorporates multiple Decision Tree based models. This approach employs a non-linear blending ensemble method by combining tree-based classifiers through DL in order to maximize generalization accuracy. This ensemble consists of two levels called base classifiers and meta-classifiers. In the first level, Decision Tree-based models are used as the base classifiers while in the second level, DL is used as a meta-model to combine the outputs of the base classifiers. Using two publicly available datasets, we show that our proposed ensemble is suitable for network traffic classification and outperforms the linear blending (using logistic regression as meta-model) as well as several well-known ML models, which are Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), AdaBoost, K-Nearest Neighbors (KNN), LightGBM, Catboost, and XGBoost.

Edge computing promises to extend Clouds by moving computation close to data sources to facilitate short-running and low-latency applications and services. Providing fast and predictable service provisioning time presets a new and mounting challenge, as the scale of Edge-servers grows and the heterogeneity of networks between them increases. This paper is driven by a simple question: can we place container images across Edge-servers in such a way that an image can be retrieved to any Edge-server fast and in a predictable time. To this end, we present KCBP and KCBP-WC, two container image placement algorithms which aim to reduce the maximum retrieval time of container images. KCBP and KCBP-WC are based on k-Center optimization. However, KCBP-WC tries to avoid placing large layers of a container image on the same Edge-server. Evaluations using trace-driven simulations show that KCBP and KCBP-WC can be applied to various network configurations and reduce the maximum retrieval time of container images by 1.1x to 4x compared to state-of-the-art placements (i.e., Best-Fit and Random).

With the increasing development of 5G/Beyond 5G and network softwarization techniques, we have more flexibility and agility in the network. This can be exploited by Machine Learning (ML) to integrate intelligence in the network and improve network as well as service management in edge-cloud environment. Intrusion detection systems (IDS) is one of the challenging issues for managing network. However, traditional approaches in this domain require all data (and their associated labels) to be centralized at the same location. In this context, such approaches lead to: (i) a large bandwidth overhead, as raw data needs to be transmitted to the server, (ii) low incentives for devices to send their private data, and (iii) large computing and storage resources needed on the server side to label and treat all this data. In this paper, to cope with the above limitations, we propose a semi-supervised federated learning model for IDS. Moreover, we use network softwarisation for automation and deployment. Our model combines Federated Learning and Semi-Supervised Learning where the clients train unsupervised models (using unlabeled data) to learn the representative and low-dimensional features and the server conducts a supervised model (using labeled data). We evaluate this approach on the well-known UNSW-NB15 dataset and the experimental results demonstrate that our approach can achieve accuracy and detection rates up to 84.32% and 83.10%, respectively while keeping the data private with limited overhead.

Most current infrastructures for cloud computing leverage static and greedy policies for the placement of virtual machines. Such policies impede the optimal allocation of resources from the infrastructure provider viewpoint. Over the last decade, more dynamic and often more efficient policies based, e.g., on consolidation and load balancing techniques, have been developed. Due to the underlying complexity of cloud infrastructures, these policies are evaluated either using limited scale testbeds/in-vivo experiments or ad-hoc simulators. These validation methodologies are unsatisfactory for two important reasons: they (i) do not model precisely enough real production platforms (size, workload variations, failure, etc.) and (ii) do not enable the fair comparison of different approaches. More generally, new placement algorithms are thus continuously being proposed without actually identifying their benefits with respect to the state of the art. In this article, we show how VMPlaceS, a dedicated simulation framework enables researchers (i) to study and compare VM placement algorithms from the infrastructure perspective, (ii) to detect possible limitations at large scale and (iii) to easily investigate different design choices. Built on top of the SimGrid simulation platform, VMPlaceS provides programming support to ease the implementation of placement algorithms and runtime support dedicated to load injection and execution trace analysis. To illustrate the relevance of VMPlaceS, we first discuss a few experiments that enabled us to study in details three well known VM placement strategies. Diving into details, we also identify several modifications that can significantly increase their performance in terms of reactivity. Second, we complete this overall presentation of VMPlaceS by focusing on the energy efficiency of the well-know FFD strategy. We believe that VMPlaceS will allow researchers to validate the benefits of new placement algorithms, thus accelerating placement research and favouring the transfer of results to IaaS production platforms.

Graph processing is an emerging computation model for a wide range of applications and graph partitioning is important for optimizing the cost and performance of graph processing jobs. Recently, many graph applications store their data on geo-distributed datacenters (DCs) to provide services worldwide with low latency. This raises new challenges to existing graph partitioning methods, due to the multi-level heterogeneities in network bandwidth and communication prices in geo-distributed DCs. In this article, we propose an efficient graph partitioning method named Geo-Cut, which takes both the cost and performance objectives into consideration for large graph processing in geo-distributed DCs. Geo-Cut adopts two optimization stages. First, we propose a cost-aware streaming heuristic and utilize the one-pass streaming graph partitioning method to quickly assign edges to different DCs while minimizing inter-DC data communication cost. Second, we propose two partition refinement heuristics which identify the performance bottlenecks of geo-distributed graph processing and refine the partitioning result obtained in the first stage to reduce the inter-DC data transfer time while satisfying the budget constraint. Geo-Cut can be also applied to partition dynamic graphs thanks to its lightweight runtime overhead. We evaluate the effectiveness and efficiency of Geo-Cut using real-world graphs with both real geo-distributed DCs and simulations. Evaluation results show that Geo-Cut can reduce the inter-DC data transfer time by up to 79 percent (42 percent as the median) and reduce the monetary cost by up to 75 percent (26 percent as the median) compared to state-of-the-art graph partitioning methods with a low overhead.

Cloud computing by far brings a lot of undeniable advantages. Accordingly, many research works aim to evaluate the characteristics of cloud systems on many aspects such as performance, workload, cost, provisioning policies, and resources management. In order to setup a cloud system for running rigorous experiments, ones have to overcome a huge amount of challenges and obstacles to build, deploy, and manage systems and applications. Cloud simulation tools help researchers to focus only on the parts they are interesting about without facing the aforementioned challenges. However cloud simulators still do not provide accurate models for Virtual Machine (VM) operations. This leads to incorrect results in evaluating real cloud systems. Following previous works on live-migration, we present in this paper an experiment study we conducted in order to propose a first-class VM boot time model. Most cloud simulators often ignore the VM boot time or give a naive model to represent it. After studying the relationship between the VM boot time and different system parameters such as CPU utilization, memory usage, I/O and network bandwidth, we introduce a first boot time model that could be integrated in current cloud simulators. Through experiments, we also confirmed that our model correctly reproduced the boot time of a VM under different resources contention.

As fog computing brings processing and storage resources to the edge of the network, there is an increasing need of automated placement (i.e., host selection) to deploy distributed applications. Such a placement must conform to applications' resource requirements in a heterogeneous fog infrastructure, and deal with the complexity brought by Internet of Things (IoT) applications tied to sensors and actuators. This paper presents four heuristics to address the problem of placing distributed IoT applications in the fog. By combining proposed heuristics, our approach is able to deal with large scale problems, and to efficiently make placement decisions fitting the objective: minimizing placed applications' average response time. The proposed approach is validated through comparative simulation of different heuristic combinations with varying sizes of infrastructures and applications.

International audience

Despite the importance of experiment-driven research in the distributed computing community, there has been little progress in helping researchers conduct their experiments. In most cases, they have to achieve tedious and time-consuming development and instrumentation activities to deal with the specifics of testbeds and the system under study. In order to relieve researchers of the burden of those efforts, we have developedEnosLib: a Python library that takes into account best experimentation practices and leverages modern toolkits on automatic deployment and configuration systems.EnosLibhelps researchers not only in the process of developing their experimental artifacts, but also in running them over different infrastructures. To demonstrate the relevance of our library, we discuss three experimental engines built on top ofEnosLib, and used to conduct empirical studies on complex software stacks between 2016 and 2019 (database systems, communication buses and OpenStack). By introducingEnosLib, our goal is to gather academic and industrial actors of our community around a library that aggregates everyday experiment-driven research operations. A library that has been already adopted by open-source projects and members of the scientific community thanks to its ease of use and extension.

Recently, most large cloud providers, like Amazon and Microsoft, replicate their Virtual Machine Images (VMIs) on multiple geographically distributed data centers to offer fast service provisioning. Provisioning a service may require to transfer a VMI over the wide-area network (WAN) and therefore is dictated by the distribution of VMIs and the network bandwidth in-between sites. Nevertheless, existing methods to facilitate VMI management (i.e., retrieving VMIs) overlook network heterogeneity in geo-distributed clouds. In this paper, we design, implement and evaluate Nitro, a novel VMI management system that helps to minimize the transfer time of VMIs over a heterogeneous WAN. To achieve this goal, Nitro incorporates two complementary features. First, it makes use of deduplication to reduce the amount of data which will be transferred due to the high similarities within an image and in-between images. Second, Nitro is equipped with a network-aware data transfer strategy to effectively exploit links with high bandwidth when acquiring data and thus expedites the provisioning time. Experimental results show that our network-aware data transfer strategy offers the optimal solution when acquiring VMIs while introducing minimal overhead. Moreover, Nitro outperforms state-of-the-art VMI storage systems (e.g., OpenStack Swift) by up to 77%.

Today, stream data applications represent the killer applications for Edge computing: placing computation close to the data source facilitates real-time analysis. Previous efforts have focused on introducing light-weight distributed stream processing (DSP) systems and dividing the computation between Edge servers and the clouds. Unfortunately, given the limited computation power of Edge servers, current efforts may fail in practice to achieve the desired latency of stream data applications. In this vision paper, we argue that by introducing FPGAs in Edge servers and integrating them into DSP systems, we might be able to realize stream data processing in Edge infrastructures. We demonstrate that through the design, implementation, and evaluation of F-Storm, an FPGA-accelerated and general-purpose distributed stream processing system on Edge servers. F-Storm integrates PCIe-based FPGAs into Edge-based stream processing systems and provides accelerators as a service for stream data applications. We evaluate F-Storm using different representative stream data applications. Our experiments show that compared to Storm, F-Storm reduces the latency by 36% and 75% for matrix multiplication and grep application. It also obtains 1.4x and 2.1x improvement for these two applications, respectively. We expect this work to accelerate progress in this domain.

Distributed software built from components has become a mainstay of service-oriented applications, which frequently undergo reconfigurations to adapt to changes in their operating environment or their functional requirements. Given the complexity of distributed software and the adverse effects of incorrect reconfigurations, a suitable methodology is needed to ensure the correctness of reconfigurations in component-based systems. This survey gives the reader a global perspective over existing formal techniques that pursue this goal. It distinguishes different ways in which formal methods can improve the reliability of reconfigurations, and lists techniques that contribute to solving each of these particular scientific challenges.