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Network Slicing is becoming a mature technology in recent years due to the continuous efforts conducted by the standards organizations, academic units and operators. However, current Network Slice (NS) demonstrations are not fully aligned with the ongoing standardization activities, particularly with the Network Slice Subnet Management Function (NSSMF) defined by 3GPP. This paper provides a novel Framework for NS Subnet Management, called NESSMA, that satisfies a list of requirements derived from an exhaustive analysis of up-to-date NS standards. The Framework is designed to jointly manage NS Subnets and their supported services having in mind its integration with NFV-MANO.

Currently, the intrinsic nature of cloud computing offers new opportunities for scheduling unit of works as containers. In this paper we present a new Scheduling and Allocation Framework for Containers (SAFC) based on an economic model. The novelty of our framework is to gives the possibility to specify ranges on the resources demand for containers instead of specifying a fixed amount of resource. Thus, the goal and the new developments in this paper are to propose a general framework which dynamically schedules containers and decides, on the fly, the number of resources that must be allocated to optimize the use of cloud resources and to maximize the number of containers executed. The key idea of the paper is in relaxing strict constraints on the requested number of resources and on letting the system to adjust the amount of resources according to the execution context, such as the peaks of activities. The SAFC framework is evaluated using the Linux Containers (LXC) inside the Grid’5000 testbed.

The swarm robot network is the widely used wireless communication technology, and significantly, it has higher uplink and downlink throughput than the conventional communication networks. Apart from the appreciable advantages, there are various challenges like minimized data retention rate, low data transmission rate, countless handovers over a short time, and a short transmission range due to higher data transmission usage. To resolve these challenges, Scalable Network Architecture with Dual Connectivity Application (SN-DCA) in control and data plane is proposed to improve data retention rates and minimize network usage by controlling unwanted access in the distributive clouds during transmission. Here, Two-stage control data plane splitting (TSCDP) is introduced to enhance network usage by optimizing unwanted access. It further improves the data retention rate during transmission. The analytical performance of the proposed techniques has been validated computationally and examined through simulation studies. The Simulation results show that these techniques achieve a higher performance ratio in the network with a minimum transmission delay and a high data retention rate.

Recently, containers have been used extensively in the cloud computing field, and several frameworks have been proposed to schedule containers using a scheduling strategy. The main idea of the different scheduling strategies consist to select the most suitable node, from a set of nodes that forms the cloud platform, to execute each new submitted container. The Spread scheduling strategy, used as the default strategy in the Docker Swarmkit container scheduling framework, consists to select, for each new container, the node with the least number of running containers. In this paper, we propose to improve the Spread strategy by presenting a new container scheduling strategy based on the power consumption of heterogeneous cloud nodes. The novelty of our approach consists to make the best compromise that allows to reduce the global power consumption of an heterogeneous cloud infrastructure. The principle of our strategy is based on learning and scheduling steps which are applied each time a new container is submitted by a user. Our proposed strategy is implemented in Go language inside Docker Swarmkit. Experiments demonstrate the potential of our strategy under different scenarios.

In this paper we target the class of modal clustering methods where clusters are defined in terms of the local modes of the probability density function which generates the data. The most well-known modal clustering method is the k-means clustering. Mean Shift clustering is a generalization of the k-means clustering which computes arbitrarily shaped clusters as defined as the basins of attraction to the local modes created by the density gradient ascent paths. Despite its potential, the Mean Shift approach is a computationally expensive method for unsupervised learning. Thus, we introduce two contributions aiming to provide clustering algorithms with a linear time complexity, as opposed to the quadratic time complexity for the exact Mean Shift clustering. Firstly we propose a scalable procedure to approximate the density gradient ascent. Second, our proposed scalable cluster labeling technique is presented. Both propositions are based on Locality Sensitive Hashing (LSH) to approximate nearest neighbors. These two techniques may be used for moderate sized datasets. Furthermore, we show that using our proposed approximations of the density gradient ascent as a pre-processing step in other clustering methods can also improve dedicated classification metrics. For the latter, a distributed implementation, written for the Spark/Scala ecosystem is proposed. For all these considered clustering methods, we present experimental results illustrating their labeling accuracy and their potential to solve concrete problems., Comment: Algorithms are available at https://github.com/Clustering4Ever/Clustering4Ever

This paper presents a new resource allocation framework based on SLA (Service Level Agreements) classes for cloud computing environments. Our framework is proposed in the context of containers with two qualitative and two quantitative SLAs classes to meet the needs of users. The two qualitative classes represent the satisfaction time criterion, and the reputation criterion. Moreover, the two quantitative classes represent the criterion over the number of resources that must be allocated to execute a container and the redundancy (number of replicas) criterion. The novelty of our work is based on the possibility to adapt, dynamically, the scheduling and the resources allocation of containers according to the different qualitative and quantitative SLA classes and the activities peaks of the nodes in the cloud. This dynamic adaptation allows our framework a flexibility for efficient global scheduling of all submitted containers and for efficient management, on the fly, of the resources allocation. The key idea is to make the specification on resources demand less rigid and to ask the system to decide on the precise number of resources to allocate to a container. Our framework is implemented in C++ and it is evaluated using Docker containers inside the Grid’5000 testbed. Experimental results show that our framework gives expected results for our scenario and provides with good performance regarding the balance between objectives.

Cloud computing is regarded as the best generation of computation that relies on shared computing resources rather than having local servers or personal devices to handle different applications. Knowing that, from one side, computing resources can be dynamically allocated according to consumer requirements and from the other side, virtual machine deployment preferences has an important role in Cloud computing for improving resource use. In this paper, we propose an online placement strategy of services on dynamic availability resources in order to reduce energy consumption. Dynamic deployment of services is a key issue of Cloud computing; there are still many areas to be studied, especially for dynamic environments. Unlike the infrastructure topology of the traditional Clouds, i.e. the data centers to which users are connected, in the volunteer Cloud, users and data centers can process multiple requests. Indeed, the volunteer nodes are useful for the Cloud elasticity. However, they are not usually available. The dynamic availability is one of the biggest concerns for the consumers. In addition, live migration of services is a widely used technique for dynamically allocating resources in a volunteer Cloud to overcome such issue. In this work, we investigate the problem of energy efficient online task allocation in volunteer Cloud environment, where the dynamic services can be allocated to a set of dynamic availability resources. We propose through this paper an online heuristic called Dynamic Shortest Path Strategy (DSPS) in order to generate a good planning that minimizes the energy consumption of applications. Our contribution respects various constraints such as availability, capacity of machines and duplications factor of applications. A series of experiments are illustrated to validate the potential of our approach.

This paper presents two practical Large Scale Multi-Objectives Scheduling (LSMOS) strategies, proposed for Cloud Computing environments. The goal is to address the problems of companies that manage a large cloud infrastructure with thousands of nodes, and would like to optimize the scheduling of several requests submitted online by users. In our context, requests submitted by users are configured according to multi-objectives criteria, as the number of used CPUs and the used memory size, to take an example. The novelty of our strategies is to select effectively, from a large set of nodes forming the Cloud Computing platform, a node that execute the user request such that this node has a good compromise among a large set of multi-objectives criteria. In this paper, first we show the limit, in terms of performance, of exact solutions. Second, we introduce approximate algorithms in order to deal with high dimensional problems in terms of nodes number and criteria number. The proposed two scheduling strategies are based on exact Kung multi-objectives decision algorithm and k-means clustering algorithm or LSH hashing (random projection based) algorithm. The experiments of our new strategies demonstrate the potential of our approach under different scenarios.

Smart buildings present challenging opportunities and issues regarding the intelligence we push in the framework that manage the building. Among them the scheduling and allocation of tasks/jobs. Based on the expertises of Qarnot Computing and LIPN Laboratory, we address three relevant matters of concern for smart buildings. First of all, we explain the economic model that the Qarnot middleware implements for managing, in a distributed way, smart buildings. The key idea is to consider the building as a data-center, managed by a cloud middleware. Then we introduce the key properties we consider as important in the context of the construction of a middleware for buildings. At last, this paper presents an efficient scheduling and allocation system according to an innovative economic model. The idea is to presents a new container scheduling system based on SLA (Service Level Agreements) classes and which is used in smart building with cloud computing environment. The novelty of our system is based on the possibility to adapt, dynamically, the scheduling and the resources allocation of containers according to the different SLA classes and the activities peaks of the nodes in the cloud i.e. the building. Experimental results show that our system gives expected results for our scenario and provides with good performance regarding the balance between objectives.

Servers have different energy efficiency and energy proportionality (EP) due to their hardware configuration (i.e., CPU generation and memory installation) and workload. However, current virtual machine (VM) scheduling in virtualized environments will saturate servers without considering their energy efficiency and EP differences. This article will discuss EASE, the energy efficiency and proportionality aware VM scheduling approach. EASE first executes customized computing intensive, memory intensive, and hybrid benchmarks to calculate a server's energy efficiency and EP. Then it schedules VMs to servers to keep them working at their peak energy efficiency point (or optimal working range). This step improves the overall energy efficiency of the cluster and the data center. For performance guarantee, EASE migrates VMs from servers under highly contending conditions. The experimental results on real clusters show that power consumption can be saved 37.07% ~ 49.98% in the homogeneous cluster. The average completion time of the computing intensive VMs increases only 0.31 % ~ 8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22 %. The job completion time can be saved by 53.80%.

The paper discusses various aspects of interest regarding the data management in the context of executing scientific workflows in the cloud. We offer a proper service-based solution e.g. Workflow as a Service, considering additional factors such as the control of data propagation and service level agreements. One important aspect is the optimization of resource allocation, and to accomplish this a dynamic allocation approach, based on certain heuristics, is adopted and tested. The aim of this paper is to introduce the basic concepts and some implementation details for a two layers based scheduling system that globally takes into account data access and space requirements. At the cloud level, we propose a scheduling strategy based on different Service Level Agreement (SLA) classes. The novelty of our strategy consists in using the SLA class of the user to provision a container that will execute the service, based on a dynamic computation of the number of resources. The user do not specify the exact amount of resources but a range. The key idea of the paper is in relaxing strict constraints on the number of resources for data management and on letting the "System" to adjust the resources number according to the execution context. The first and second layers are validated through simulation and emulation conducted on the Grid'5000 testbed and for an heterogeneous context. The results of our experiments demonstrate the potential of our approaches, as general approaches for a better control of data movement and placement in cloud to execute scientific workflows. We also provide with insights to coupling the two proposed layers in a coherent way.

International audience; One of the most important challenges for the Internet of Things (IoT) is the implementation of edge computing platforms. In order to improve the response time, the confidentiality, or the energy consumption of IoT applications, part of IoT services must be operated on servers, deployed close to connected devices. This said, much remains to be done for the practical realization of this vision. What are edge servers? Where will they be deployed? These are some questions that are poorly addressed in the literature. In this paper, we propose to build an edge computing framework around new datacenter models in which computing servers are seamlessly integrated in buildings. The datacenter models we are considering have been developed around the concept of data furnace. This concept has been implemented in European cities for district heating. Our paper introduces a new processing model in which edge and distributed cloud computing are operated with the same data furnace servers. We then discuss the challenges in the utilization of this model for edge computing. Our discussion deals with urban integration, service computing frameworks and performance issues. Finally, we end with an impact analysis of our proposition on the future of cloud computing.

The field of life imaging spans a large spectrum of scientific study from mathematics and computer science to medical, passing by physics, biology, etc. The challenge of IDV project is to enrich a multi-parametrized, quantitative, qualitative, integrative, and correlative life imaging in health. It deals with linking the current research developments and applications of life imaging in medicine and biology to develop computational models and methods for imaging and quantitative image analysis and validate the added diagnostic and therapeutic value of new imaging methods and biomarkers.

Traditional Cloud computing has emerged as a new paradigm for providing computing resources on demand and outsourcing software and hardware infrastructures. Cloud computing is rapidly changing the way IT services are made available and managed. These services can be requested by several Cloud providers, hence the need for networking between IT service components distributed in geographically diverse locations. Like the traditional Cloud computing, the volunteer computing paradigm has become increasingly important. For this paradigm, the resources on each personal machine are shared, thanks to the will of their owners. Cloud and volunteer paradigms have been recently seen as complementary technologies to better exploit the use of local resources. Besides execution time and cost, energy consumption is also becoming more important in the Cloud computing environments. Thus, it has become a major concern for the widespread deployment of Cloud data centers. Among methods that can overcome this problem, we are interested in planning services that improve the use of data center resources in a dynamic environment. In this context, we propose throughout this paper a heuristic that predicts the allocation of dynamic and independent services to reduce the total energy consumption. Our proposal respects various constraints: availability, capacity of machines and the number of applications duplications. A series of experiments illustrates and validates the potential of our approach.

The exponential increment in data size poses new challenges for computer scientists, giving rise to a new set of methodologies under the term Big Data. Many efficient algorithms for machine learning have been proposed, facing up time and memory requirements. Nevertheless, with hardware acceleration, multiple software instructions can be integrated and executed into a single hardware die. Current researches aim at eliminating the burden for the user in using multiple processor types. In this paper we propose our return of experience on a new way of implementing machine learning algorithms on heterogeneous hardware. To explore our vision, we use a parallel Mean-shift algorithm, developed at LIPN as our case study to investigate issues in building efficient Machine Learning libraries for heterogeneous systems. The ultimate goal is to provide a core set of building blocks for Machine Learning programming that could serve either to build new applications on heterogeneous architectures or to control the evolution of the underlying platform. We thus examine the difficulties encountered during the implementation of the algorithm with the aim to discover methodologies for building systems based on heterogeneous hardware. We also discover issues and building blocks for solving concrete machine learning (ML) problems on the Chisel software stack we use for this purpose.

Cloud computing is one of the most popular technologies nowadays because of its wide utilities and various benefits in several IT companies all over the world. However, in front of the increasing users' requests for computing services, cloud providers are encouraged to deploy large data centers, which consumes very large amount of energy and contribute to,,,, high operational costs. Among the effects, carbon dioxide emission rate is growing each day due to the huge amount of power consumption. This energy efficiency is an important issue in cloud computing, mainly due to the required electrical power to run these systems and to cool them. Therefore, energy consumption has become a major concern for the widespread deployment of Cloud data centers. The growing importance for parallel applications in the Cloud introduces significant challenges in,,,, reducing energy consumption from hosted servers. This paper addresses the problem of placing independent applications on the physical servers (hosts) of a Cloud infrastructure. We proposed a novel heuristic to allocate applications so that total energy consumption is reduced. Our proposal respects various constraints e.g. the machines availability, capability and the duplication of applications. Experiments are illustrated to validate the potential of our approach.

This paper presents a new scheduling and resource management allocation system based on an economic model related to different classes for SLAs (Service Level Agreements). The goal is to address the problems of companies that manage a private infrastructure of machines, and would like to optimize the scheduling of several requests submitted online by users. Each request is an application which is executed using a set of computing resources. Our economic model has two SLAs classes (a qualitative one and a quantitative one). The qualitative class represents the satisfaction time criteria, i.e. the user waiting time before the execution of its requests. Moreover, the quantitative class represents the number of resources that must be allocated to execute the user request. As a first contribution, our system allocates dynamically, for each selected request, a set of computing cores according to the quantitative SLA class and the load of the parallel machines across the infrastructure. To choose the machine that will execute a selected request, we propose to use a Bin Packing heuristic to minimize the number of used machines and reduce the cost of the infrastructure. As a second contribution, simulations of our system are conducted on Prezi and Google Cloud Data traces and they demonstrate the potential of our approach under different scenario.

In this paper, we introduce a new parallel and distributed algorithm for the resolution of the satisfiability problem. The proposed algorithm is based on algorithm portfolio and is intended to be used for servicing requests in a distributed cloud. The core of our contribution is the modeling of the optimal resource sharing schedule in parallel executions and the proposition of heuristics for its approximation. For this purpose, we reformulate a computational problem introduced in prior work. The main assumption is that it is possible to learn the optimal resource sharing from traces collected on past executions on a representative set of instances. We show that the learning can be formalized as a set coverage problem. Then, we propose to solve it by approximation and dynamic programming algorithms. These algorithms are based on classical greedy algorithms for the maximum coverage problem. Finally, we conduct an experimental evaluation for comparing the performance of the various proposed algorithms. The results show that some algorithms become more competitive if we intend to determine the trade-off between their quality and the runtime required for their computation.

In this paper, we consider the energy-efficient coordination of a set of appliances in a smart-building. We introduce a theoretical formulation of the coordination problem and an Integer Linear Programming model for its resolution. Our formalization is complemented by an analysis of the properties and limits of the model. We also define a practical smart-building setting in which our formalization holds. Finally, we conduct several experiments with realistic data and we make a scalability and sensitivity analysis. The experimental results correspond to emulation on an industrial infrastructure for smart-buildings, the one of Qarnot computing. They are obtained in using the API of the company. They show that we can quickly solve the problem for small and medium-sized buildings and for realistic settings. They also open interesting questions regarding the optimal model of control in future intelligent buildings. Should residents let a collective intelligence to decide on the optimal control of its appliance or is it more appropriate that the user decides by itself?

This paper presents our initial ideas for a new scheduling strategy integrated in the Docker Swarm scheduler. The aim of this paper is to introduce the basic concepts and the implementation details of a new scheduling strategy based on different Service Level Agreement (SLA) classes. This strategy is proposed to answer to the problems of companies that manage a private infrastructure of machines, and would like to optimize the scheduling of several requests submitted online by users. Each request is a demand of creating a container. Currently, Docker Swarm has three basic scheduling strategies (spread, binpack and random), each one executes a container with a fixed number of resources. However, the novelty of our new strategy consists in using the SLA class of the user to provision a container that must execute the service, based on a dynamic computation of the number of CPU cores that must be allocated to the container according to the user SLA class and the load of the parallel machines in the infrastructure. Testing of our new strategy is conducted, by emulation, on different part of our general framework and it demonstrates the potential of our approach for further development.

Infrastructure-as-a-Service container-based virtualization technology is gaining significant interest in industry as an alternative platform for running distributed applications. With increasing scale of Cloud Computing architectures, faults are becoming a frequent occurrence. Checkpoint-Restart is a key method to survive to failures in this context. However, there is a need to reduce the amount of checkpointing data as the Cloud is based on the pay-as-you-go model. This paper addresses the issue of garbage collection in LXCloud-CR and contributes with a novel decentralized garbage collection component GC-CR. LXCloud-CR, a decentralized Checkpoint-Restart model, is able to take snapshots of Linux Container instances and it uses replication to increase snapshots availability. LXCloud-CR contains a versioning scheme for each replica. The disadvantage refers to snapshots availability issues with versioning as the number of useless files grows. GC-CR is a decentralized garbage collector (checkpoint deletion) component that attempts to identify and eliminate old snapshots versions from the system in order to free storage space. Large scale experiments on the Grid5000 testbed demonstrate the benefits of our proposal. Obtained results validate our model and show significant reduction of storage space consumption

In this paper we would like to share our experience for transforming a parallel code for a Computational Fluid Dynamics (CFD) problem into a parallel version for the RedisDG workflow engine. This system is able to capture heterogeneous and highly dynamic environments, thanks to opportunistic scheduling strategies. We show how to move to the field of "HPC as a Service" in order to use heterogeneous platforms. We mainly explain, through the CFD use case, how to transform the parallel code and we exhibit challenges to 'unfold' the task graph dynamically in order to improve the overall performance (in a broad sense) of the workflow engine. We discuss in particular of the impact on the workflow engine of such dynamic feature. This paper states that new models for High Performance Computing are possible, under the condition we revisit our mind in the direction of the potential of new paradigms such as cloud, edge computing.

We introduce the Qarnot platform, a new edge computing platform for the design of smart-buildings. Our proposition is based on a new model of servers that also serve as heaters. As a use case, we consider the recognition of acoustic events. We describe a reference architecture for the processing of acoustic flows in the Qarnot platform. We also present some experimental results on the recognition of alarm sounds.

This paper deals with the reduction of the number of comatose servers. The characteristic of such a server is to consume electricity while not delivering useful information services. According to recent studies, up to 30% of the servers (including those in datacenters) are comatose. The existence of these servers lowers the interest in clouds for green computing. Our paper assumes a cloud provider whose services are operated on a minimal number of dedicated servers in a datacenter, we also assume that this provider delivers software as a service (SaaS). In order to reduce the number of dedicated servers that could be comatose, we propose to automatically generate and run auto-tuning tasks that, in servers idle time, will learn to calibrate the execution of the software delivered by the provider. Our proposition follows the goal of delivering auto-tuning as an opportunistic cloud-service. For this purpose we introduce an opportunistic auto-tuning service (QTuning) that exploits servers idle time. The service is based on a theoretical model that includes two computational problems: an exploration and exploitation problem. In the former problem, the goal is to evaluate the performance of a software in different configurations. In the latter, assuming a set of performance results for a given software, the goal is to compute the best configurations to run the software with. Our second contribution is to propose and evaluate solutions for the exploitation problem.

We consider the implementation of an in-situ machine learning system with the computing model promoted by Qarnot computing. Qarnot introduced an utility computing model in which servers are distributed in homes and offices where they serve as heaters. The Qarnot servers also embed several sensors for temperature, humidity, CO 2 etc. Qarnot offers an adequate platform to develop in-situ workflows for smart-homes problems. To demonstrate this point, we consider a typical problem: the detection of alarm sounds. Our paper introduces a new orchestration system for in-situ workflows, in the Qarnot platform. We also consider a general parallel framework for training alarm sound classifiers and decline an implementation that makes use of our orchestrator. Finally, we evaluate the implemented framework on different aspects including: the accuracy (of the resulting classifiers) and the runtime gain of the parallelization.

The rapid growth in demand for Information Technologies (IT) resources has led to the creation of large-scale data centers. These latter consume an enormous amount of electrical energy, resulting in high operating costs and carbon dioxide emissions. Moreover, modern Cloud computing environments should provide a high quality of service (QoS) for their customers. Thus, energy consumption has become a major concern for the widespread deployment of Cloud data centers. The growing importance for parallel applications in the Cloud introduces significant challenges in reducing energy consumption from hosted servers. With Cloud, customers can transparently access to virtually unlimited resources. The volunteer computing paradigm has become increasingly important, where the saved resources on each personal machine are shared thanks to the will of their customers. Cloud and volunteer paradigms have recently been seen as complementary technologies to better exploit the use of local resources. This paper presents the most well-known research in the literature on the minimization of energy consumption in traditional Cloud computing as well as in the volunteer one. A novel approach, based on searching shortest path in a graph, is also introduced.