Enabling multicast slices in edge networks

Telecommunication networks are undergoing a disruptive transition towards distributed mobile edge networks with virtualized network functions (VNFs) (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) within the proximity of users. This transition will enable network services, especially IoT applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this paper we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into Integer Linear Programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a non-trivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on Reinforcement Learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real test-bed. Experimental results demonstrate that the proposed algorithms is promising

Efficient Virtualized Network Service Provisioning in Mobile Edge Computing

There is a substantial growth in the usage of mobile devices. These devices, including smartphones, sensors, and wearables, are limited by their computational and energy capacities, due to their portable size. Mobile edge computing (MEC), which provides cloud resources at the edge of mobile network in close proximity to mobile users, is a promising technology to reduce response delays, ensure network operation efficiency, and improve user service satisfaction. Mobile edge computing is a promising technology to leverage the capability of mobile devices to offload tasks to nearby edge-clouds (cloudlets) for processing. Furthermore, Network Function Virtualization (NFV) is another promising technique that implements various network functions for many applications as pieces of software in servers or cloudlets in MEC networks. The provisioning of virtualized network services in MEC can improve user service experiences, simplify network service deployment, and ease network resource management. In this thesis, we will focus on the efficient virtualized network service provisioning in MEC networks by judicious resource allocations and request admissions to maximize network throughput and minimize request admission cost in different application scenarios. We firstly address dynamic request admissions with service function chain requirements in MEC with the objective to maximize the profit collected by the network service provider, assuming that the cloudlets are located at different geographical locations and electricity prices at different locations are different. We formulate an integer linear programming (ILP) solution to the offline problem, and devise an online algorithm with a provable competitive ratio for the online problem when requests arrive one by one without the knowledge of future request arrivals. We then study NFV-enabled multicasting that is a fundamental routing problem in an MEC network, subject to resource capacities on both its cloudlets and links. We devise an admission framework for single NFV-enabled multicast request admission with the aim to minimize request admission cost. We then develop an efficient algorithm for the throughput maximization problem for the admissions of a given set of NFV-enabled multicast requests. We also devise an online algorithm with a provable competitive ratio for the online NFV-enabled multicast request admissions. We thirdly investigate virtualized network function service provisioning for mobile users in MEC that takes into account user mobility and service delay requirements. We formulate two novel optimization problems of user service request admissions with the aims to maximize the accumulative network utility and accumulative network throughput for a given time horizon, respectively, where network utility is a submodular function that can be used to tradeoff between individual user service satisfaction and accumulative network throughput. We then devise a constant approximation algorithm for the utility maximization problem. We also develop an online algorithm for the accumulative throughput maximization problem. We fourthly explore a non-trivial tradeoff between different types of resources in NFV-enabled request scheduling in MEC with an objective to minimize request admission cost, through introducing a novel concept - load factor. We formulate the cost minimization problem that admits all requests by assuming that there is sufficient computing resource in MEC to accommodate the requested VNF instances of all requests, for which we formulate an ILP solution and two efficient heuristic algorithms. We also deal with the problem under the computing resource constraint, for which we formulate an ILP solution when the problem size is small; otherwise, we devise efficient algorithms for it. We finally summarize the thesis and explore several potential research topics that are based on the work in this thesis

COCAM: a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning in multi-clouds environment

The evolution of the Internet of Things technology (IoT) has boosted the drastic increase in network traffic demand. Caching and multicasting in the multi-clouds scenario are effective approaches to alleviate the backhaul burden of networks and reduce service latency. However, existing works do not jointly exploit the advantages of these two approaches. In this paper, we propose COCAM, a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning to minimize the transmission number in the multi-clouds scenario with limited storage capacity in each edge cloud. Specifically, by integrating a cooperative transmission model with the caching model, we provide a concrete formulation of the joint problem. Then, we cast this decision-making problem as a multi-agent extension of the Markov decision process and propose a multi-agent actor-critic algorithm in which each agent learns a local caching strategy and further encompasses the observations of neighboring agents as constituents of the overall state. Finally, to validate the COCAM algorithm, we conduct extensive experiments on a real-world dataset. The results show that our proposed algorithm outperforms other baseline algorithms in terms of the number of video transmissions

Efficient Resource Allocation for Throughput Maximization in Next-Generation Networks

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) have emerged as the foundation of the next-generation network architecture by introducing great flexibility and network automation capabilities, including automatic response to faults and load changes and programmatic provision of network resources and connections. It has been envisioned that the SDN- and NFV-based next-generation network architecture will play a critical role in providing network services to users, where the desired network services, including data transfer and policy enforcement, are fulfilled by allocating network resources using virtualization technologies. However, the disparity between ever-growing user demands and scarce network resources makes resource allocation exceptionally central to the performance of a network service, because only by effectively allocating these scarce resources can a network service provider satisfy users and maximize the gain from running the service. In this thesis, we study efficient resource allocation for network throughput maximization in next-generation networks, while meeting user resource demands and Quality of Service (QoS) requirements, subject to network resource capacities. This however poses great challenges, namely, (1) how to maximize network throughput, considering that both SDN-enabled switches and links are capacitated, (2) how to maximize the network throughput while taking into account network function and QoS requirements of users, (3) how to dynamically scale and readjust resource allocation for user requests, and (4) how to provision a network service that can satisfy user reliability requirements. To address these challenges, we provide a thorough study of network throughput maximization problems in the context of the next-generation network architecture, by formulating the problems as optimizations problems and developing novel optimization frameworks and algorithms for the problems. Specifically, this thesis makes the following contributions. Firstly, we consider dynamic user request admissions where user requests arrive one by one and the knowledge of future request arrivals is not given as a priori. We develop a novel cost model that accurately captures the usage costs of network resources and propose online algorithms with provable performance guarantees. Secondly, we study the problem of realizing user requests with network function requirements, with the objective of maximizing network throughput, while meeting user QoS requirements, subject to resource capacity constraints. For this problem, we develop two algorithms that strive for the trade-off between the accuracy/quality of a solution and the running time of obtaining the solution. Thirdly, we investigate maximization of network throughput by dynamically scaling network resources while minimizing the overall operational cost of a network. We propose a unified framework for two types of resource scaling {--} vertical scaling and horizontal scaling. Through non-trivial reductions of the problem of concern into several classic problems, we propose an algorithm that has been empirically demonstrated to deliver near-optimal solutions. Fourthly, we deal with the problem of reliability-aware provisioning of network resources for users, with the aim of maximizing network throughput. We devise an approximation algorithm with a logarithmic approximation ratio for the general case of this problem. We also develop constant-factor approximation and exact algorithm for two special cases of the problem, respectively. The formulated problem is a generalization of several classic optimization problems. Finally, in addition to extensive theoretical analyses, we also evaluate the performance of proposed algorithms empirically through experimental simulations based on real and synthetic datasets. Experimental results show that the proposed algorithms significantly outperform existing algorithms

Mobile Edge Cloud: Intelligent deployment and services for 5G Indoor Network

This thesis was submitted for the award of doctor of Philosophy and was awarded by Brunel University LondonFifth-Generation (5G) mobile networks are expected to perform according to the stringent performance targets assigned by standardization committees. Therefore, significant changes are proposed to the network infrastructure to achieve the expected performance levels. Network Function Virtualization, cloud computing and Software Defined Networks are some of the main technologies being utilised to ensure flexible network design, with optimum performance and efficient resource utilization. The aforementioned technologies are shifting the network architecture into service-based rather device-based architecture. In this regard, this thesis provides experimental investigation, design, implementation and evaluation of various multimedia services along with integration design and caching solution for 5G indoor network. The multimedia services are targeting the enhancement of UEs’ Quality of Experience, by exploiting the intelligence offered by the synergy between SDN and NFV technologies, to design and develop new multimedia solutions with improved QoE. The caching solution is designed to achieve a good trade-off between latency reduction and resource utilization that satisfies efficient network performance and resource utilization. The proposed network integration design targets deploying IoRL gNB with its innovative intelligent services. It have successfully achieved lower overhead signalling compared to the traditional network architectures. Whilst all of the proposed solutions have proven to provide enhancement to the system performance, the testing results for the multimedia services showed high QoS performance parameters in the form of zero packet loss due to route switching, very high throughput and 0.03 ms jitter. The caching solution test results provided up to 300% server utilization improvement (based on the deployed scenario) with negligible extra delay cost (0.5ms). As for the proposed integration design, the quantification of the performance enhancement is represented by the amount of the reduced overhead signalling. In the case of Intra-secondary gNB handover within the same Main eNB, the back-haul signalling for the AMF was reduced 100% while the overall overhead signalling is reduced by 50% compared to traditional deployment architecture.European Union’s Horizon 2020 research progra

A review on green caching strategies for next generation communication networks

© 2020 IEEE. In recent years, the ever-increasing demand for networking resources and energy, fueled by the unprecedented upsurge in Internet traffic, has been a cause for concern for many service providers. Content caching, which serves user requests locally, is deemed to be an enabling technology in addressing the challenges offered by the phenomenal growth in Internet traffic. Conventionally, content caching is considered as a viable solution to alleviate the backhaul pressure. However, recently, many studies have reported energy cost reductions contributed by content caching in cache-equipped networks. The hypothesis is that caching shortens content delivery distance and eventually achieves significant reduction in transmission energy consumption. This has motivated us to conduct this study and in this article, a comprehensive survey of the state-of-the-art green caching techniques is provided. This review paper extensively discusses contributions of the existing studies on green caching. In addition, the study explores different cache-equipped network types, solution methods, and application scenarios. We categorically present that the optimal selection of the caching nodes, smart resource management, popular content selection, and renewable energy integration can substantially improve energy efficiency of the cache-equipped systems. In addition, based on the comprehensive analysis, we also highlight some potential research ideas relevant to green content caching

Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by both simulations with real datasets and system implementation in a real testbed. Evaluation results show that the performance of the proposed algorithm with distributed inference framework is promising, by reaching the accuracies of attribute recognition and person identification up to 92.9% and 96.6% respectively, and significantly reducing the inference delay by at least 40.6% compared with existing methods

Virtual Service Provisioning for Internet of Things Applications in Mobile Edge Computing

The Internet of Things (IoT) paradigm is paving the way for many new emerging technologies, such as smart grid, industry 4.0, connected cars, smart cities, etc. Mobile Edge Computing (MEC) provides promising solutions to reduce service delays for delay-sensitive IoT applications, where cloudlets are co-located with wireless access points in the proximity of IoT devices. Most mobile users have specified Service Function Chain (SFC) requirements, where an SFC is a sequence of Virtual Network Functions (VNFs). Meanwhile, edge intelligence arises to provision real-time deep neural network (DNN) inference services for users. To accelerate the processing of the DNN inference of a request in an MEC network, the DNN inference model usually can be partitioned into two connected parts: one part is processed on the local IoT device of the request; and the other part is processed on a cloudlet (server) in the MEC network. Also, the DNN inference can be further accelerated by allocating multiple threads of the cloudlet in which the request is assigned. In this thesis, we will focus on virtual service provisioning for IoT applications in MEC Environments. Firstly, we consider the user satisfaction problem of using services jointly provided by an MEC network and a remote cloud for delay-sensitive IoT applications, through maximizing the accumulative user satisfaction when different user services have different service delay requirements. A novel metric to measure user satisfaction of using a service is proposed, and efficient approximation and online algorithms for the defined problems under both static and dynamic user service demands are then devised and analyzed. Secondly, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements with the aim of minimizing service provisioning cost, where each IoT application has multiple data streams from different sources to be uploaded to the MEC network for processing and storage, while each data stream must pass through the network functions of the SFC of the IoT application, prior to reaching its destination. A service provisioning framework for such multi-source IoT applications is proposed, through uploading stream data from multiple IoT sources, VNF instance placement and sharing, in-network aggregation of data streams, and workload balancing among cloudlets. Efficient algorithms for service provisioning of multi-source IoT applications in MEC networks, built upon the proposed framework, are also proposed. Thirdly, we investigate a novel DNN inference throughput maximization problem in an MEC network with the aim to maximize the number of delay-aware DNN service requests admitted, by accelerating each DNN inference through jointly exploring DNN partitioning and inference parallelism. We devise a constant approximation algorithm for the problem under the offline setting, and an online algorithm with a provable competitive ratio for the problem under the online setting, respectively. Fourthly, we address a robust SFC placement problem with the aim to maximize the expected profit collected by the service provider of an MEC network, under the assumption of both computing resource and data rate demand uncertainties. We start with a special case of the problem where the measurement of the expected demanded resources for each request admission is accurate, under which we propose a near-optimal approximation algorithm for the problem by adopting the Markov approximation technique, which can achieve a provable optimality gap. Then, we extend the proposed approach to the problem of concern, for which we show that the proposed algorithm still is applicable, and the solution delivered has a moderate optimality gap with bounded perturbation errors on the profit measurement. Finally, we summarize the thesis work and explore several potential research topics that are based on the studies in this thesis