153 research outputs found

    A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

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    Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay

    A Review on Computational Intelligence Techniques in Cloud and Edge Computing

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    Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users’ requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This article provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions

    Emerging Edge Computing Technologies for Distributed Internet of Things (IoT) Systems

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    The ever-increasing growth in the number of connected smart devices and various Internet of Things (IoT) verticals is leading to a crucial challenge of handling massive amount of raw data generated from distributed IoT systems and providing real-time feedback to the end-users. Although existing cloud-computing paradigm has an enormous amount of virtual computing power and storage capacity, it is not suitable for latency-sensitive applications and distributed systems due to the involved latency and its centralized mode of operation. To this end, edge/fog computing has recently emerged as the next generation of computing systems for extending cloud-computing functions to the edges of the network. Despite several benefits of edge computing such as geo-distribution, mobility support and location awareness, various communication and computing related challenges need to be addressed in realizing edge computing technologies for future IoT systems. In this regard, this paper provides a holistic view on the current issues and effective solutions by classifying the emerging technologies in regard to the joint coordination of radio and computing resources, system optimization and intelligent resource management. Furthermore, an optimization framework for edge-IoT systems is proposed to enhance various performance metrics such as throughput, delay, resource utilization and energy consumption. Finally, a Machine Learning (ML) based case study is presented along with some numerical results to illustrate the significance of edge computing.Comment: 16 pages, 4 figures, 2 tables, submitted to IEEE Wireless Communications Magazin

    Allocation des ressources dans les environnements informatiques en périphérie des réseaux mobiles

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    Abstract: The evolution of information technology is increasing the diversity of connected devices and leading to the expansion of new application areas. These applications require ultra-low latency, which cannot be achieved by legacy cloud infrastructures given their distance from users. By placing resources closer to users, the recently developed edge computing paradigm aims to meet the needs of these applications. Edge computing is inspired by cloud computing and extends it to the edge of the network, in proximity to where the data is generated. This paradigm leverages the proximity between the processing infrastructure and the users to ensure ultra-low latency and high data throughput. The aim of this thesis is to improve resource allocation at the network edge to provide an improved quality of service and experience for low-latency applications. For better resource allocation, it is necessary to have reliable knowledge about the resources available at any moment. The first contribution of this thesis is to propose a resource representation to allow the supervisory xentity to acquire information about the resources available to each device. This information is then used by the resource allocation scheme to allocate resources appropriately for the different services. The resource allocation scheme is based on Lyapunov optimization, and it is executed only when resource allocation is required, which reduces the latency and resource consumption on each edge device. The second contribution of this thesis focuses on resource allocation for edge services. The services are created by chaining a set of virtual network functions. Resource allocation for services consists of finding an adequate placement for, routing, and scheduling these virtual network functions. We propose a solution based on game theory and machine learning to find a suitable location and routing for as well as an appropriate scheduling of these functions at the network edge. Finding the location and routing of network functions is formulated as a mean field game solved by iterative Ishikawa-Mann learning. In addition, the scheduling of the network functions on the different edge nodes is formulated as a matching set, which is solved using an improved version of the deferred acceleration algorithm we propose. The third contribution of this thesis is the resource allocation for vehicular services at the edge of the network. In this contribution, the services are migrated and moved to the different infrastructures at the edge to ensure service continuity. Vehicular services are particularly delay sensitive and related mainly to road safety and security. Therefore, the migration of vehicular services is a complex operation. We propose an approach based on deep reinforcement learning to proactively migrate the different services while ensuring their continuity under high mobility constraints.L'évolution des technologies de l'information entraîne la prolifération des dispositifs connectés qui mène à l'exploration de nouveaux champs d'application. Ces applications demandent une latence ultra-faible, qui ne peut être atteinte par les infrastructures en nuage traditionnelles étant donné la distance qui les sépare des utilisateurs. En rapprochant les ressources aux utilisateurs, le paradigme de l'informatique en périphérie, récemment apparu, vise à répondre aux besoins de ces applications. L’informatique en périphérie s'inspire de l’informatique en nuage, en l'étendant à la périphérie du réseau, à proximité de l'endroit où les données sont générées. Ce paradigme tire parti de la proximité entre l'infrastructure de traitement et les utilisateurs pour garantir une latence ultra-faible et un débit élevé des données. L'objectif de cette thèse est l'amélioration de l'allocation des ressources à la périphérie du réseau pour offrir une meilleure qualité de service et expérience pour les applications à faible latence. Pour une meilleure allocation des ressources, il est nécessaire d'avoir une bonne connaissance sur les ressources disponibles à tout moment. La première contribution de cette thèse consiste en la proposition d'une représentation des ressources pour permettre à l'entité de supervision d'acquérir des informations sur les ressources disponibles à chaque dispositif. Ces informations sont ensuite exploitées par le schéma d'allocation des ressources afin d'allouer les ressources de manière appropriée pour les différents services. Le schéma d'allocation des ressources est basé sur l'optimisation de Lyapunov, et il n'est exécuté que lorsque l'allocation des ressources est requise, ce qui réduit la latence et la consommation en ressources sur chaque équipement de périphérie. La deuxième contribution de cette thèse porte sur l'allocation des ressources pour les services en périphérie. Les services sont composés par le chaînage d'un ensemble de fonctions réseau virtuelles. L'allocation des ressources pour les services consiste en la recherche d'un placement, d'un routage et d'un ordonnancement adéquat de ces fonctions réseau virtuelles. Nous proposons une solution basée sur la théorie des jeux et sur l'apprentissage automatique pour trouver un emplacement et routage convenable ainsi qu'un ordonnancement approprié de ces fonctions en périphérie du réseau. La troisième contribution de cette thèse consiste en l'allocation des ressources pour les services véhiculaires en périphérie du réseau. Dans cette contribution, les services sont migrés et déplacés sur les différentes infrastructures en périphérie pour assurer la continuité des services. Les services véhiculaires sont en particulier sensibles à la latence et liés principalement à la sûreté et à la sécurité routière. En conséquence, la migration des services véhiculaires constitue une opération complexe. Nous proposons une approche basée sur l'apprentissage par renforcement profond pour migrer de manière proactive les différents services tout en assurant leur continuité sous les contraintes de mobilité élevée

    Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

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    In recent years, the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks. Such challenges can be potentially overcome by integrating communication, computing, caching, and control (i4C) technologies. In this survey, we first give a snapshot of different aspects of the i4C, comprising background, motivation, leading technological enablers, potential applications, and use cases. Next, we describe different models of communication, computing, caching, and control (4C) to lay the foundation of the integration approach. We review current state-of-the-art research efforts related to the i4C, focusing on recent trends of both conventional and artificial intelligence (AI)-based integration approaches. We also highlight the need for intelligence in resources integration. Then, we discuss integration of sensing and communication (ISAC) and classify the integration approaches into various classes. Finally, we propose open challenges and present future research directions for beyond 5G networks, such as 6G.Comment: This article has been accepted for inclusion in a future issue of China Communications Journal in IEEE Xplor
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