Dynamic Auto Scaling Algorithm (DASA) for 5G Mobile Networks

Network Function Virtualization (NFV) enables mobile operators to virtualize their network entities as Virtualized Network Functions (VNFs), offering fine-grained on-demand network capabilities. VNFs can be dynamically scale-in/out to meet the performance desire and other dynamic behaviors. However, designing the auto-scaling algorithm for desired characteristics with low operation cost and low latency, while considering the existing capacity of legacy network equipment, is not a trivial task. In this paper, we propose a VNF Dynamic Auto Scaling Algorithm (DASA) considering the tradeoff between performance and operation cost. We develop an analytical model to quantify the tradeoff and validate the analysis through extensive simulations. The results show that the DASA can significantly reduce operation cost given the latency upper-bound. Moreover, the models provide a quick way to evaluate the cost- performance tradeoff and system design without wide deployment, which can save cost and time

Dynamic Resource Provisioning of a Scalable E2E Network Slicing Orchestration System

Network slicing allows different applications and network services to be deployed on virtualized resources running on a common underlying physical infrastructure. Developing a scalable system for the orchestration of end-to-end (E2E) mobile network slices requires careful planning and very reliable algorithms. In this paper, we propose a novel E2E Network Slicing Orchestration System (NSOS) and a Dynamic Auto- Scaling Algorithm (DASA) for it. Our NSOS relies strongly on the foundation of a hierarchical architecture that incorporates dedicated entities per domain to manage every segment of the mobile network from the access, to the transport and core network part for a scalable orchestration of federated network slices. The DASA enables the NSOS to autonomously adapt its resources to changes in the demand for slice orchestration requests (SORs) while enforcing a given mean overall time taken by the NSOS to process any SOR. The proposed DASA includes both proactive and reactive resource provisioning techniques). The proposed resource dimensioning heuristic algorithm of the DASA is based on a queuing model for the NSOS, which consists of an open network of G/G/m queues. Finally, we validate the proper operation and evaluate the performance of our DASA solution for the NSOS by means of system-level simulations.This research work is partially supported by the European Union’s Horizon 2020 research and innovation program under the 5G!Pagoda project, the MATILDA project and the Academy of Finland 6Genesis project with grant agreement No. 723172, No. 761898 and No. 318927, respectively. It was also partially funded by the Academy of Finland Project CSN - under Grant Agreement 311654 and the Spanish Ministry of Education, Culture and Sport (FPU Grant 13/04833), and the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (TEC2016-76795-C6- 4-R)

ASA: Adaptive VNF Scaling Algorithm for 5G Mobile Networks

5G mobile networks introduce Virtualized Network Functions (VNFs) to provide flexible services for incoming huge mobile data traffic. Compared with fixed capacity legacy network equipment, VNFs can be scaled in/out to adjust system capacity. However, hardware-based legacy network equipment is designed dedicatedly for its purpose so that it is more efficient in terms of unit cost. One challenge is to best use VNF resources and to balance the traffic between legacy network equipment and VNFs. To address this challenge, we first formulate the problem as a cost-performance tradeoff, where both VNF resource cost and system performance are quantified. Then, we propose an adaptive VNF scaling algorithm to balance the tradeoff. We derive the suitable VNF instances to handle data traffic with minimizing cost. Through extensive simulations, the adaptive algorithm is proven to provide good performance

Design and Analysis of Deadline and Budget Constrained Autoscaling (DBCA) Algorithm for 5G Mobile Networks

In cloud computing paradigm, virtual resource autoscaling approaches have been intensively studied recent years. Those approaches dynamically scale in/out virtual resources to adjust system performance for saving operation cost. However, designing the autoscaling algorithm for desired performance with limited budget, while considering the existing capacity of legacy network equipment, is not a trivial task. In this paper, we propose a Deadline and Budget Constrained Autoscaling (DBCA) algorithm for addressing the budget-performance tradeoff. We develop an analytical model to quantify the tradeoff and cross-validate the model by extensive simulations. The results show that the DBCA can significantly improve system performance given the budget upper-bound. In addition, the model provides a quick way to evaluate the budget-performance tradeoff and system design without wide deployment, saving on cost and time

Analysis of scaling policies for NFV providing 5G/6G reliability levels with fallible servers

The softwarization of mobile networks enables an efficient use of resources, by dynamically scaling and re-assigning them following variations in demand. Given that the activation of additional servers is not immediate, scaling up resources should anticipate traffic demands to prevent service disruption. At the same time, the activation of more servers than strictly necessary results in a waste of resources, and thus should be avoided. Given the stringent reliability requirements of 5G applications (up to 6 nines) and the fallible nature of servers, finding the right trade-off between efficiency and service disruption is particularly critical. In this paper, we analyze a generic auto-scaling mechanism for communication services, used to de(activate) servers in a cluster, based on occupation thresholds. We model the impact of the activation delay and the finite lifetime of the servers on performance, in terms of power consumption and failure probability. Based on this model, we derive an algorithm to optimally configure the thresholds. Simulation results confirm the accuracy of the model both under synthetic and realistic traffic patterns as well as the effectiveness of the configuration algorithm. We also provide some insights on the best strategy to support an energy-efficient highly-reliable service: deploying a few powerful and reliable machines versus deploying many machines, but less powerful and reliable.The work of Jorge Ortin was funded in part by the Spanish Ministry of Science under Grant RTI2018-099063-B-I00, in part by the Gobierno de Aragon through Research Group under Grant T31_20R, in part by the European Social Fund (ESF), and in part by Centro Universitario de la Defensa under Grant CUD-2021_11. The work of Pablo Serrano was partly funded by the European Commission (EC) through the H2020 project Hexa-X (Grant Agreement no. 101015956), and in part by Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52PID2019-108713RB-C52/AEI/ 10.13039/501100011033). The work of Jaime Garcia-Reinoso was partially supported by the EC in the framework of H2020-EU.2.1.1. 5G EVE project (Grant agreement no. 815074). The work of Albert Banchs was partially supported by the EC in the framework of H2020-EU.2.1.1. 5G-TOURS project (Grant agreement no. 856950) also partially supported by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52PID2019- 108713RB-C52/AEI/10.13039/501100011033)

METODY ZAPEWNIENIA BEZPIECZEŃSTWA DANYCH W STANDARDACH MOBILNYCH

The analysis of mobile communication standards is carried out, the functional structure and interfaces of interaction between the structural elements of the cellular network are considered. To understand the principle of communication according to the GSM standard, a block diagram of a mobile switching center (MSC), base station equipment (BSS), control and service center (MCC), mobile stations (MS) is presented. The main algorithms for ensuring the confidentiality and security of mobile subscribers' data, in different types of standards, as well as the vulnerabilities of information flows are considered. In particular, the following dangerous types of attacks have been identified, to which mobile network subscribers are sensitive: sniffing; leakage of personal data; leakage of geolocation data; spoofing; remote capture of SIM-card, execution of arbitrary code (RCE); denial of service (DoS). It is established that the necessary function of the mobile network is the identification of subscribers, which is performed by IMSI, which is recorded in the SIM card of the subscriber and the HLR of the operator. To protect against spoofing, the network authenticates the subscriber before starting its service. In the case of subscriber identification, the subscriber and the network operator are protected from the effects of fraudulent access. In addition, the user must be protected from eavesdropping. This is achieved by encrypting the data transmitted over the radio interface. Thus, user authentication in UMTS, as well as in the GSM network, is carried out using encryption with a common key using the "hack-response" protocol (the authenticating party sends a random number to the authenticated party, which encrypts it according to a certain algorithm using a common key and returns the result back).Przeprowadzana jest analiza standardów komunikacji mobilnej, rozważana jest struktura funkcjonalna i interfejsy interakcji między elementami strukturalnymi sieci komórkowej. Aby zrozumieć zasadę komunikacji w standardzie GSM, przedstawiono schemat blokowy centrali ruchomej (MSC), wyposażenia stacji bazowej (BSS), centrum sterowania i obsługi (MCC), stacji ruchomych (MS). Rozważane są główne algorytmy zapewniające poufność i bezpieczeństwo danych abonentów telefonii komórkowej, w różnych typach standardów, a także podatności na przepływ informacji. W szczególności zidentyfikowano następujące niebezpieczne rodzaje ataków, na które podatni są abonenci sieci komórkowych: sniffing; wyciek danych osobowych; wyciek danych geolokalizacyjnych; podszywanie się; zdalne przechwytywanie karty SIM, wykonanie dowolnego kodu (RCE); odmowa usługi (DoS). Ustalono, że niezbędną funkcją sieci komórkowej jest identyfikacja abonentów, która jest realizowana przez IMSI, która jest zapisywana na karcie SIM abonenta i HLR operatora. Aby zabezpieczyć się przed podszywaniem się, sieć uwierzytelnia subskrybenta przed uruchomieniem usługi. W przypadku identyfikacji abonenta, abonent i operator sieci są chronieni przed skutkami nieuprawnionego dostępu. Ponadto użytkownik musi być chroniony przed podsłuchem. Osiąga się to poprzez szyfrowanie danych przesyłanych przez interfejs radiowy. Tak więc uwierzytelnianie użytkownika w UMTS, jak również w sieci GSM, odbywa się z wykorzystaniem szyfrowania wspólnym kluczem z wykorzystaniem protokołu „hack-response” (strona uwierzytelniająca wysyła do strony uwierzytelnianej losową liczbę, która ją szyfruje zgodnie z pewien algorytm używający wspólnego klucza i zwraca wynik z powrotem)

Performance Modeling of Softwarized Network Services Based on Queuing Theory with Experimental Validation

Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs). However, the realization of such a scenario requires a way to determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is known as resource dimensioning, and it can be efficiently tackled by performance modeling. In this vein, this paper describes an analytical model based on an open queuing network of G/G/m queues to evaluate the response time of SNSs. We validate our model experimentally for a virtualized Mobility Management Entity (vMME) with a three-tiered architecture running on a testbed that resembles a typical data center virtualization environment. We detail the description of our experimental setup and procedures. We solve our resulting queueing network by using the Queueing Networks Analyzer (QNA), Jackson’s networks, and Mean Value Analysis methodologies, and compare them in terms of estimation error. Results show that, for medium and high workloads, the QNA method achieves less than half of error compared to the standard techniques. For low workloads, the three methods produce an error lower than 10%. Finally, we show the usefulness of the model for performing the dynamic provisioning of the vMME experimentally.This work has been partially funded by the H2020 research and innovation project 5G-CLARITY (Grant No. 871428)National research project 5G-City: TEC2016-76795-C6-4-RSpanish Ministry of Education, Culture and Sport (FPU Grant 13/04833). We would also like to thank the reviewers for their valuable feedback to enhance the quality and contribution of this wor

Design and analysis of dynamic block-setup reservation algorithm for 5G network slicing

In 5G, network functions can be scaled out/in dynamically to adjust the capacity for network slices. The scale-out/-in procedure, namely autoscaling, enhances performance by scaling out instances and reduces operational costs by scaling in instances. However, the autoscaling problems in 5G networks are different from those in traditional cloud computing. The 5G network functions must be considered the simultaneous deployment of multiple instances; moreover, the deployment of 5G network functions is more frequent than that of traditional cloud computing. Both the number and timing of deployment will substantially affect the cost-effectiveness of the system. In this paper, we first identify the autoscaling issues specifically based on the 3GPP standards. We develop a low-complexity analytical queuing model to formulate the problem and quantify a set of performance metrics with closed-form solutions. The proposed analytical model and closed-form solutions are cross-validated by extensive simulations. The analytical model offers design insights and theoretical guidelines, helping us study the effectiveness of reservations. We proposed a dynamic block-setup reservation algorithm (DBRA) to find the optimal reserved number and threshold value of network slices. Therefore, mobile operators can balance the system's cost-effectiveness without large-scaled testing and real deployment, saving cost on time and money