Hybrid Chaos Particle Swarm Optimization algorithm for smart Cloud Service System based on optimization resource scheduling and allocation

To enhance the smart Cloud Service System for diverse user requirements in 5G and other service networks, this study leverages resource utilization and multi-tenancy network slicing operation costs. Specifically, we propose a multi-tenancy network resource allocation strategy based on the Chaos Particle Swarm Optimization (CPSO) algorithm. In a multi-tenancy network (MTN), we lease the wireless spectrum resources of the infrastructure provider’s base station, construct access service slices as network slice services, and offer network access services to users. Introduce detailed formulation of the relationship between MTN and users, represented as a multi-master and multi-slave construct that defines the strategy space and profit function after MTN decision-making. Reverse induction is used to analyze the proposed model, and a distributed iterative algorithm is proposed to obtain the optimal throughput demand of users and the optimal slice cost of MTN. Simulation results demonstrate that the proposed strategy can effectively enhance resource utilization and user satisfaction while reducing energy consumption

Network Function Virtualization in Dynamic Networks: A Stochastic Perspective

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordAs a key enabling technology for 5G network softwarization, Network Function Virtualization (NFV) provides an efficient paradigm to optimize network resource utility for the benefits of both network providers and users. However, the inherent network dynamics and uncertainties from 5G infrastructure, resources and applications are slowing down the further adoption of NFV in many emerging networking applications. Motivated by this, in this paper, we investigate the issues of network utility degradation when implementing NFV in dynamic networks, and design a proactive NFV solution from a fully stochastic perspective. Unlike existing deterministic NFV solutions, which assume given network capacities and/or static service quality demands, this paper explicitly integrates the knowledge of influential network variations into a twostage stochastic resource utilization model. By exploiting the hierarchical decision structures in this problem, a distributed computing framework with two-level decomposition is designed to facilitate a distributed implementation of the proposed model in large-scale networks. The experimental results demonstrate that the proposed solution not only improves 3∼5 folds of network performance, but also effectively reduces the risk of service quality violation.The work of Xiangle Cheng is partially supported by the China Scholarship Council for the study at the University of Exeter. This work is also partially supported by the UK EPSRC project (Grant No.: EP/R030863/1)

VNF placement optimization at the edge and cloud

Network Function Virtualization (NFV) has revolutionized the way network services are offered to end users. Individual network functions are decoupled from expensive and dedicated middleboxes and are now provided as software-based virtualized entities called Virtualized Network Functions (VNFs). NFV is often complemented with the Cloud Computing paradigm to provide networking functions t

Optimal VNFs placement in CDN Slicing over Multi-Cloud Environment

| openaire: EC/H2020/723172/EU//5GPagodaThis paper introduces a Content Delivery Network as a Service (CDNaaS) platform that allows dynamic deployment and life-cycle management of virtual Content Delivery Network (CDN) slices running across multiple administrative cloud domains. The CDN slice consists of four Virtual Network Function (VNF) types, namely virtual transcoders, virtual streamers, virtual caches, and a CDN-slice-specific Coordinator for the management of the slice resources across the involved cloud domains. To create an efficient CDN slice, the optimal placement of its composing VNFs using adequate amount of virtual resources for each VNF is of vital importance. In this vein, this paper devises mechanisms for allocating an appropriate set of VNFs for each CDN slice to meet its performance requirements and minimize as much as possible the incurred cost in terms of allocated virtual resources. A mathematical model is developed to evaluate the performance of the proposed mechanisms. We first formulate the VNF placement problem as two Linear Integer problem models, aiming at minimizing the cost and maximizing the Quality of Experience (QoE) of the virtual streaming service. By applying the bargaining game theory, we ensure an optimal trade-off solution between the cost efficiency and QoE. Extensive simulations are conducted to evaluate the effectiveness of the proposed models in achieving their design objectives and encouraging results are obtained.Peer reviewe

Towards the Softwarization of Content Delivery Networks for Component and Service Provisioning

Content Delivery Networks (CDNs) are common systems nowadays to deliver content (e.g. Web pages, videos) to geographically distributed end-users over the Internet. Leveraging geographically distributed replica servers, CDNs can easily help to meet the required Quality of Service (QoS) in terms of content quality and delivery time. Recently, the dominating surge in demand for rich and premium content has encouraged CDN providers to provision value-added services (VAS) in addition to the basic services. While video streaming is an example of basic CDN services, VASs cover more advanced services such as media management. Network softwarization relies on programmability properties to facilitate the deployment and management of network functionalities. It brings about several benefits such as scalability, adaptability, and flexibility in the provisioning of network components and services. Technologies, such as Network Functions Virtualization (NFV) and Software Defined Networking (SDN) are its key enablers. There are several challenges related to the component and service provisioning in CDNs. On the architectural front, a first challenge is the extension of the CDN coverage by on-the-fly deployment of components in new locations and another challenge is the upgrade of CDN components in a timely manner, because traditionally, they are deployed statically as physical building blocks. Yet, another architectural challenge is the dynamic composition of required middle-boxes for CDN VAS provisioning, because existing SDN frameworks lack features to support the dynamic chaining of the application-level middle-boxes that are essential building blocks of CDN VASs. On the algorithmic front, a challenge is the optimal placement of CDN VAS middle-boxes in a dynamic manner as CDN VASs have an unknown end-point prior to placement. This thesis relies on network softwarization to address key architectural and algorithmic challenges related to component and service provisioning in CDNs. To tackle the first challenge, we propose an architecture based on NFV and microservices for an on-the-fly CDN component provisioning including deployment and upgrading. In order to address the second challenge, we propose an architecture for on-the-fly provisioning of VASs in CDNs using NFV and SDN technologies. The proposed architecture reduces the content delivery time by introducing features for in-network caching. For the algorithmic challenge, we study and model the problem of dynamic placement and chaining of middle-boxes (implemented as Virtual Network Function (VNF)) for CDN VASs as an Integer Linear Programming (ILP) problem with the objective of minimizing the cost while respecting the QoS. To increase the problem tractability, we propose and validate some heuristics

Machine Learning for Next-generation Content Delivery Networks: Deployment, Content Placement, and Performance Management

With the explosive demands for data and the growth in mobile users, content delivery networks (CDNs) are facing ever-increasing challenges to meet end-users quality-of-experience requirements, ensure scalability and remain cost-effective. These challenges encourage CDN providers to seek a solution by considering the new technologies available in today’s computer network domain. Network Function Virtualization (NFV) is a relatively new network service deployment technology used in computer networks. It can reduce capital and operational costs while yielding flexibility and scalability for network operators. Thanks to the NFV, the network functions that previously could be offered only by specific hardware appliances can now run as Virtualized Network Functions (VNF) on commodity servers or switches. Moreover, a network service can be flexibly deployed by a chain of VNFs, a structure known as the VNF Forwarding Graph or VNF-FG. Considering these advantages, the next-generation CDN will be deployed using NFV infrastructure. However, using NFV for service deployment is challenging as resource allocation in a shared infrastructure is not easy. Moreover, the integration of other paradigms (e.g., edge computing and vehicular network) into CDN will compound the complexity of content placement and performance management for the next-generation CDNs. In this regard, due to their impacts on final service and end-user perceived quality, the challenges in service deployment, content placement, and performance management should be addressed carefully. In this thesis, advanced machine learning methods are utilized to provide algorithmic solutions for the abovementioned challenges of the next generation CDNs. Regarding the challenges in the deployment of the next-generation CDNs, we propose two deep reinforcement learning-based methods addressing the joint problems of VNF-FG’s composition and embedding, as well as function scaling and topology adaptation. As for content placement challenges, a deep reinforcement learning-based approach for content migration in an edge-based CDN with vehicular nodes is proposed. The proposed approach takes advantage of the available caching resources in the proximity of the full local caches and efficiently migrates contents at the edge of the network. Moreover, for managing the performance quality of an operating CDN, an unsupervised machine learning anomaly detection method is provided. The proposed method uses clustering to enable easier performance analysis for next-generation CDNs. Each proposed method in this thesis is evaluated by comparison to the state-of-the-art approaches. Moreover, when applicable, the optimality gaps of the proposed methods are investigated as well

Leveraging Cloud-based NFV and SDN Platform Towards Quality-Driven Next-Generation Mobile Networks

Network virtualization has become a key approach for Network Service Providers (NSPs) to mitigate the challenge of the continually increasing demands for network services. Tightly coupled with their software components, legacy network devices are difficult to upgrade or modify to meet the dynamically changing end-user needs. To virtualize their infrastructure and mitigate those challenges, NSPs have started to adopt Software Defined Networking (SDN) and Network Function Virtualization (NFV). To this end, this thesis addresses the challenges faced on the road of transforming the legacy networking infrastructure to a more dynamic and agile virtualized environment to meet the rapidly increasing demand for network services and serve as an enabler for key emerging technologies such as the Internet of Things (IoT) and 5G networking. The thesis considers different approaches and platforms to serve as an NFV/SDN based cloud applications while closely considering how such an environment deploys its virtualized services to optimize the network and reducing their costs. The thesis starts first by defining the standards of adopting microservices as architecture for NFV. Then, it focuses on the latency-aware deployment approach of virtual network functions (VNFs) forming service function chains (SFC) in a cloud environment. This approach ensures that NSPs still meet their strict quality of service and service level agreements while considering both functional and non-functional constraints of the NFV-based applications such as, delay, resource allocation, and intercorrelation between VNF instances. In addition, the thesis proposes a detailed approach on recovering and handling of those instances by optimizing the decision of migrating or re-instantiating the virtualized services upon a sudden event (failure/overload…). All the proposed approaches contribute to the orchestration of NFV applications to meet the requirements of the IoT and NGNs era