Hybrid Cloud Integration: Best Practices and Use Cases

Convention will not cause the amount of difficulty that may be imagined in a cloud data centre to increase. Businesses may embrace IT without having to pay a large initial cost by using computing in the cloud. Although there are many advantages to the Internet, model security is still a problem, which has a negative impact on cloud adoption. Underneath the data centre, the security issue becomes unmanageable, and the problem scope has expanded to include model design, multiple tenancies, elasticity, and multiple levels of dependency stack. To benefit from the new processing viewpoint that provides an innovative arrangement of activities for interaction to IT, the increased security risks in cloud-based computing must be overcome. The study's goal was to lessen security's barriers and hazards by using approaches and protection techniques to guarantee optimal data protection while preserving the option for the customer to choose the initial security degree. The globe has become a global village where individuals can cooperate, communicate, and exchange information quickly and securely thanks to the prevalence of smart devices that can access the internet. Clients may share possessions and utilize a variety of on-demand services using cloud computing environments. Workflow technology in the online environment is used to manage business processes, and because activities rely on one another, this poses a difficulty to efficiently utilize resources. This study proposes a Hybrid GA-PSO method to effectively distribute workloads among the available resources. In cloud computing settings, the Hybrid GA-PSO method seeks to balance the load of dependent activities among heterogeneous resources while reducing make span and cost. The experiment's findings demonstrate that, when compared to the GA, the PSO method, HSGA, WSGA, and MTCT computer programs, the GA-PSO approach reduces the workflow tasks' overall execution time. Moreover, it lowers the cost of performance

An adaptive scaling mechanism for managing performance variations in network functions virtualization: A case study in an NFV-based EPC

The scaling is a fundamental task that allows addressing performance variations in Network Functions Virtualization (NFV). In the literature, several approaches propose scaling mechanisms that differ in the utilized technique (e.g., reactive, predictive and machine learning-based). The scaling in NFV must be accurate both at the time and the number of instances to be scaled, aiming at avoiding unnecessary procedures of provisioning and releasing of resources; however, achieving a high accuracy is a non-trivial task. In this paper, we propose for NFV an adaptive scaling mechanism based on Q-Learning and Gaussian Processes that are utilized by an agent to carry out an improvement strategy of a scaling policy, and therefore, to make better decisions for managing performance variations. We evaluate our mechanism by simulations, in a case study in a virtualized Evolved Packet Core, corroborating that it is more accurate than approaches based on static threshold rules and Q-Learning without a policy improvement strategy

Mobile Edge Computing Potential in Making Cities Smarter

This paper proposes an approach to enhance users’ expe-rience of video streaming in the context of smart cities. The proposed approach relies on the concept of mobile edge computing (MEC) as a key factor in enhancing the Quality of Service (QoS). It sustains QoS by ensuring that applications/services follow the mobility of users, realizing the “Follow-me-Edge” concept. The proposed scheme en-forces an autonomic creation of MEC services to allow any-where-anytime data access with optimum Quality of Experience (QoE) and reduced latency. Considering its application in smart city scenar-ios, the proposed scheme represents an important solution for reduc-ing core network traffic and ensuring ultra-short latency, and that is through a smart MEC architecture capable of achieving 1 ms latency dream for the upcoming 5G mobile system

Improving traffic forecasting for 5G core network scalability: A Machine Learning approach

International audience5G is expected to provide network connectivity to not only classical devices (i.e. tablets, smartphones, etc) but also to the Internet Of Things (IOT), which will drastically increase the traffic load carried over the network. 5G will mainly rely on Network Function Virtualization (NFV) and Software Defined Network (SDN) to build flexible and on-demand instances of functional networking entities, via Virtual Network Functions (VNF). Indeed, 3GPP is devising a new architecture for the core network, which replaces point to point interfaces used in 3G and 4G, by a producer/consumer-based communication among 5G core network functions, facilitating deployment over a virtual infrastructure. One big advantage of using VNF, is the possibility of dynamically scaling, depending on traffic load (i.e. instantiate new resources to VNF when the traffic load increases, and reduce the number of resources when the traffic load decreases). In this paper, we propose a novel mechanism to scale 5G core network resources by anticipating traffic load changes through forecasting via Machine Learning (ML) techniques. The traffic load forecast is achieved by using and training a Neural Network on a real dataset of traffic arrival in a mobile network. Two techniques were used and compared: (i) Recurrent Neural Network (RNN), more specifically Long Short Term Memory Cell (LSTM); and (ii) Deep Neural Network (DNN). Simulation results showed that the forecast-based scalability mechanism outperforms the threshold-based solutions, in terms of latency to react to traffic change, and delay to have new resources ready to be used by the VNF to react to traffic increase

An SDN-based solution for horizontal auto-scaling and load balancing of transparent VNF clusters

© 2021 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/)This paper studies the problem of the dynamic scaling and load balancing of transparent virtualized network functions (VNFs). It analyzes different particularities of this problem, such as loop avoidance when performing scaling-out actions, and bidirectional flow affinity. To address this problem, a software-defined networking (SDN)-based solution is implemented consisting of two SDN controllers and two OpenFlow switches (OFSs). In this approach, the SDN controllers run the solution logic (i.e., monitoring, scaling, and load-balancing modules). According to the SDN controllers instructions, the OFSs are responsible for redirecting traffic to and from the VNF clusters (i.e., load-balancing strategy). Several experiments were conducted to validate the feasibility of this proposed solution on a real testbed. Through connectivity tests, not only could end-to-end (E2E) traffic be successfully achieved through the VNF cluster, but the bidirectional flow affinity strategy was also found to perform well because it could simultaneously create flow rules in both switches. Moreover, the selected CPU-based load-balancing method guaranteed an average imbalance below 10% while ensuring that new incoming traffic was redirected to the least loaded instance without requiring packet modification. Additionally, the designed monitoring function was able to detect failures in the set of active members in near real-time and active new instances in less than a minute. Likewise, the proposed auto-scaling module had a quick response to traffic changes. Our solution showed that the use of SDN controllers along with OFS provides great flexibility to implement different load-balancing, scaling, and monitoring strategies.Postprint (published version

Service based virtual RAN architecture for next generation cellular systems

Service based architecture (SBA) is a paradigm shift from Service-Oriented Architecture (SOA) to microservices, combining their principles. Network virtualization enables the application of SBA in cellular systems. To better guide the software design of this virtualized cellular system with SBA, this paper presents a software perspective and a positional approach to using fundamental development principles for adapting SBA in virtualized Radio Access Networks (vRANs). First, we present the motivation for using an SBA in cellular radio systems. Then, we explore the critical requirements, key principles, and components for the software to provide radio services in SBA. We also explore the potential of applying SBA-based Radio Access Network (RAN) by comparing the functional split requirements of 5G RAN with existing open-source software and accelerated hardware implementations of service bus, and discuss the limitations of SBA. Finally, we present some discussions, future directions, and a roadmap of applying such a high-level design perspective of SBA to next-generation RAN infrastructure.This work was supported in part by the European Union (EU) H2020 5GROWTH Project under Grant 856709, in part by the Generalitat de Catalunya under Grant 2017 SGR 1195, and in part by the National Program on Equipment and Scientific and Technical Infrastructure under the European Regional Development Fund (FEDER) under Grant EQC2018-005257-P

QoE-aware elasticity support in cloud-native 5G systems

International audienc

QoE-aware elasticity support in cloud-native 5G systems

International audienc