Allocating guaranteed resources and optimizing system parameters for Cloud-native Network Function (CNF) deployments introduce significant complexity in management. The performance of CNFs deployed on shared resources always depends on their incoming traffic and their competition for the underlying resource, both of which are usually very dynamic. The complexity of managing their deployments across many physical resources can explode very quickly and become infeasible for taking runtime management decisions in closed-loops. In this work we experimentally analyze the impact of CNFs sharing Central Processing Unit (CPU) and memory resources and make a key observation that increasing the number of deployed CNFs results in an increase of packet service time variability which in turn leads to longer waiting times. We also propose a model of CNF performance when sharing these resources, and discuss its extension to a general compute-network resource model for CNF performance estimation in closed management loops.

In recent years, Radio Frequency Spatial Networks (RF-SNs) have struggled to meet the increasing demand for higher data rates due to their limited bandwidth. To address this challenge, Free Space Optical Spatial Networks (FSO-SNs) have been proposed as a promising solution that aims to fulfill the stringent requirements for high data rates, enhanced security, and global coverage. Despite their potential, FSO-SNs face significant challenges due to their dynamic nature caused by the mobility of spatial nodes and fluctuating weather conditions, leading to frequent topology changes and inefficient routing. However, in RF-SNs, the dynamicity is mainly attributed to node mobility, since Radio Frequency (RF) is more robust to weather impairments. Consequently, the topology changes in RF-SNs are less frequent and do not pose routing issues as in FSO-SNs. This paper presents a comparative analysis of the dynamicity in RF-SNs and FSO-SNs, providing valuable insight into the rate of topology change. The analysis reveals that, on average, topology changes occur every 43.6 seconds in FSO-SNs, with a total of 1981 topology changes during a period of 24 hours, 61.4% of which occur within 41 seconds after the previous change. In contrast, RF-SNs experience only 441 topology changes, with only 20.6% of these occurring before 41 seconds after the previous change. Furthermore, we analyze the impact of weather conditions on the maximum achievable data rates for both FSO-SN and RF-SN technologies, observing drastic fluctuations in Free Space Optical (FSO) systems. This study provides a solid foundation for developing tailored routing solutions for FSO-SNs.

Cellular networks are carefully planned to provide sufficient coverage and capacity under normal circumstances. However, in emergency scenarios like floodings, wildfires, earthquakes or even terrorist attacks, part of the network may no longer be operational while at the same time a traffic hotspot may occur as a consequence of such an event. In such scenarios it is of utmost importance to quickly restore wireless coverage with sufficient capacity. To achieve this goal, we consider the dynamic deployment of drone-mounted base stations and propose a data-driven algorithm which optimizes the positions of the drone base stations based on measurement values that are readily available. We demonstrate that the use of well-positioned drones yields significant performance improvements and that the proposed method outperforms relevant benchmarks and is able to achieve close to optimal performance. For example, in rural scenarios we may observe in certain cases a twofold reduction in the fraction of failed calls compared to a pre-planned deployment of drones, and an even greater improvement over a setting without drones.

Reliable mobile communications is of critical importance, and should be maintained even in case of extremely crowded events or emergency scenarios. In such scenarios the deployment of drone-mounted base stations offers an agile and cost-efficient way to sustain coverage and/or provide capacity relief. In this paper we develop an analytical method to estimate the blocking and coverage probabilities of drone-assisted cellular networks using information that is readily available from network planning tools. We demonstrate how this method can be used to determine the minimum required number of drones and their corresponding locations for a given target performance level.

Social VR aims at enabling people located at different places to communicate and interact with each other in a natural way. It poses extremely strong throughput and latency requirements on the underlying communication networks. This paper investigates the potential of using cross-layer design approaches for radio access scheduling in order to realize these challenging requirements in (beyond) 5G networks. In particular, we provide an in-depth simulation study of the performance/capacity gains that can be achieved by exploiting the end-to-end latency budget and/or video frame type as cross-layer information in the scheduling decisions, and show how the benefits depend on the actual social VR scenario. This study further reveals the importance of using application-level metrics such as PSNR or SSIM rather than traditional network-level metrics like the packet drop rate in the performance assessment.

In this demo, we show a system where real-time processing modules for immersive conferencing streams can be dynamically relocated between user device and edge computing nodes, with minimal visual impact on the resulting stream.

With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10thuser throughput percentile by up to 125% for eMBB use case and decreases the 90thresource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.

The use of drone base stations offers an agile mechanism to safeguard coverage and provide capacity relief when cellular networks are under stress. Such stress conditions can occur for example in case of special events with massive crowds or network outages. In this paper we focus on a disaster scenario with emergence of a hotspot, and analyze the impact of the drone position (altitude, horizontal position) and selection bias on the network performance. We determine the optimal settings of these control parameters as a function of the hotspot location, and demonstrate that the optimized values can drastically reduce the fraction of failed calls.

One of the most challenging applications targeted by evolving (beyond-)5G technology is virtual reality (VR). Particularly, 'Social VR' applications provide a fully immersive experience and sense of togetherness to users residing at different locations. To support such applications the network must deal with huge traffic demands, while keeping end-to-end latencies low. Moreover, the radio access network must deal with the volatility and vulnerability of mmWave radio channels, where even small movements of the users may have substantial effects on the Quality of Experience. We present an integral modelling framework for feasibility assessment and performance optimization of the radio access network for Social VR applications in indoor office scenarios. Using the presented modelling approach, we conduct an extensive simulation-based assessment to determine the performance impact of head motion, the frequency band (3.5 GHz, 26 GHz) and radio network configurations, and derive the required carrier bandwidth for a range of 'Social VR' scenarios. Insights into these issues are a prerequisite for setting up guidelines for network deployment and configuration as well as for the development of (AI/ML-based) methods for dynamic resource management to optimally support Social VR applications.

Drone base stations can help safeguard coverage and provide capacity relief when cellular networks are under stress. Examples of such stress scenarios are events with massive crowds or network outages. In this paper we focus on a disaster scenario with emergence of a traffic hotspot, where agile drone positioning and load management is a critical issue. In order to address this challenge, we propose and assess a data-driven algorithm which leverages real-time measurements to dynamically optimize the 3D position of the drone as well as a cell selection bias tuned for optimized load management. We compare the performance with three benchmark scenarios: i) no drone; ii) a drone positioned above the failing site; and iii) a drone with a statically optimized position and cell selection bias. The results demonstrate that the proposed algorithm significantly improves the call success rate and achieves close to optimal performance.

Abstract Due to the rising concerns of energy consumption in wireless networks, base station (BS) sleeping strategies were introduced to save energy in low traffic scenarios. In this paper we analyse a weighted trade-off between energy consumption and user-perceived performance in dense cellular networks. We present an optimization problem representing this trade-off and derive properties of its optimal solutions. Using these properties we design a self-organizing strategy that dynamically (online) makes load-aware user association and BS operation decisions. Our strategy is self-organizing in the sense that it does not need any information or optimization beforehand, it simply relies on real-time load measurements at the BSs and user-reported SINR values. We furthermore present extensive simulation results, demonstrating the effectiveness of our self-organizing strategy and the impact of increased energy consumption on the user-perceived performance.

Network densification has emerged as a powerful paradigm to boost spectral efficiency and accommodate the continual rise in demand for wireless capacity. In dense cell deployments however, overlapping coverage areas may cause highly varying interference conditions among different cells. Moreover, denser networks experience more temporal load fluctuations due to daily and hourly changing usage patterns. The currently applied universal reuse frequency allocation is not suitable to deal with these issues, and needs to be tailored to dense cell deployments to ensure adequate performance in such scenarios. In this paper we present a dynamic, load aware and self-adapting frequency allocation scheme designed for dense cellular networks: the DyCRA scheme (Dynamic Cost/Reward based Allocation). The scheme makes decisions based on cost-reward trade-offs: rewards arise in the form of capacity, and costs arise in the form of interference (under spatial reuse). We quantify these costs and rewards based on SINR, and use periodic load estimates to determine if access points are in need of extra frequencies, or can spare them, and the cost/reward structure is used to determine which frequencies are allocated or released. Extensive simulation results show that the DyCRA scheme provides efficient resource allocations that adapt to changing traffic conditions and yields significant performance gains in scenarios with nonstationary traffic demands.

Multi-access Edge Computing (MEC) is an emerging technology aimed to improve the communication latency and scalability e.g. of cloud-based connected vehicle applications and services. This is accomplished by bringing services closer to the end users, that is, to the network edge. In a high-mobility scenario, such as one involving vehicles, there will be a need for handovers between different MEC servers in order to maintain the required communication latency. Part of the application data may also be relevant to multiple MEC servers, covering overlapping geographical areas or being hosted by different network operators. This paper focuses on these situations and studies the problem of maintaining service continuity and synchronization of relevant data among multiple MEC servers to support vehicular applications. The analysis is conducted in the context of two example applications with different requirements, namely platoon management and shared world model, with promising results regarding their suitability for future implementation.

Network Function Virtualization involves implementing network functions (e.g., virtualized LTE component) in software that can run on a range of industry standard server hardware, and can be migrated or instantiated on demand. A prediction service hosted on cloud infrastructures enables consumers to request the prediction information on-demand and respond accordingly. In this paper we introduce MOBaaS, which is a network function of Mobility and Bandwidth prediction cloudified over the cloud computing infrastructure. We implemented the service orchestration framework of MOBaaS, which can easily be setup and integrated with any other cloud-based LTE entities to provide prediction information about the future location of mobile user(s) as well as the network link(s) bandwidth availability. This information can be used to generate required triggers for on-demand deployment or scaling-up/down of virtualized network components as well as for the self-adaptation procedures and optimal network function configuration. We also describe the performance evaluation of the MOBaaS cloudification procedures and present an example of the benefit of such a prediction service.

In this paper we discuss the major modifications required in the current LTE network to realize a decentralized LTE architecture and develop a novel IP mobility management solution for it. The proposed solution can handle traffic redirecting and IP address continuity above the distributed anchor points in a scalable and resource efficient manner. Our approach is based on the NAT (Network Address Translation) mechanism, which is a well- known and widely used procedure in the current Internet. We extend the NS3-LENA to implement a decentralized LTE network as well as the proposed scheme. The evaluation results show that the proposed solution efficiently fulfills the functionality and performance requirements (e.g.,latency and signaling load) related to the mobility management.

The core architecture of current mobile networks does not scale well to cope with future traffic demands owing to its highly centralized composition. Typically, it is believed that decentralization of the network architecture would be a sustainable approach to deal with ever growing amount of mobile data traffic. Nevertheless, the decentralization strategy of network architecture has not been properly examined through quantitative performance studies. Given that LTE will be the leading mobile networking technology in the coming 5–10 years, we conduct a hybrid study model to compare performance of current and future (decentralized) LTE network architectures. Particularly, our analysis presents numerical results quantifying impact of the number of attached nodes on the load at network routers and links, on the latency, and on the processing cost of the user’s data and control planes. Analytical results demonstrate that decentralization of the LTE network architecture achieves higher performance compared to the current architecture and improves the latency and cost of data packet delivery more than 10 and 6 times, respectively. Furthermore, it is also observed that GTP outperforms PMIP for all studied performance metrics in the decentralized architecture and provides about twofold better latency and cost for data packet delivery and roughly 6 times lower data traffic load on the network routers.

The location of data centres is crucial when mobile network operators are moving towards cloudified mobile networks to optimize resource utilization and to improve performance of services. Quality of Experience (QoE) can be enhanced in terms of content access latency, by placing user content at locations where they will be present in the future. The Follow-Me Cloud (FMC) concept aims at optimising operations of moving Mobile Network Operators Services towards cloudified environments, where Information Centric Networking (ICN) and the appropriate content migration policies are of paramount importance. However, several factors need to be considered, including user movements and mobility prediction (MP), content popularity, and migration. This paper addresses all these aspects by implementing a fully integrated multi-criteria FMC and mobility prediction mechanisms (MP-FMC) on a cloud infrastructure. Experimental evaluation shows that MP-FMC can be orchestrated on-demand within a reasonable time frame, and it could deliver ≈ 33% improvement of content retrieval time.

The ever increasing traffic demands and the current trend of network and services virtualization calls for effective approaches for optimal use of network resources. In the future Internet multiple virtual networks will coexist on top of the same physical infrastructure, and these will compete for bandwidth resources. Link dimensioning can support fair share and allocation of bandwidth. Current approaches however, are ineffective at smaller timescales or require traffic measurements that are not easy to obtain. In this thesis we focused on easy to deploy and accurate link dimensioning approaches for the future Internet. The start point of our work is a dimensioning formula, proposed in 2006, built upon the assumption of Gaussian traffic. This formula is able to accurately estimate required capacity at very small timescales. To do so it requires traffic statistics that can be obtained from packet captures. The contribution of this thesis is threefold. First, we prove that the assumption of Gaussian traffic holds for current Internet traffic and, hence, the dimensioning formula can still be applied. Second, instead of relying on costly packet captures, we develop and validate link dimensioning approaches that estimate the needed traffic statistics from measurement data obtained via technologies that are largely found in today's networks (namely, sFlow and NetFlow/IPFIX). Our approaches are able to accurately estimate required capacity at timescales as low as 1ms. Last, we propose a link dimensioning approach that uses measured data from the recent and already widely available OpenFlow. We also investigate the quality of flow-level measurements in current implementations of OpenFlow, and demonstrate that these are not yet accurate enough for link dimensioning purposes.

Link dimensioning is used by network operators to properly provision the capacity of their network links. Proposed methods for link dimensioning often require statistics, such as traffic variance, that need to be calculated from packet-level measurements. In practice, due to increasing traffic volume, operators deploy packet sampling techniques aiming to reduce the burden of traffic monitoring, but little is known about how link dimensioning is affected by such measurements. In this paper, we make use of a previously proposed and validated dimensioning formula that requires traffic variance to estimate required link capacity. We assess the impact of three packet sampling techniques on link dimensioning, namely, Bernoulli, n-in-N and sFlow sampling. To account for the additional variance introduced by the sampling algorithms, we propose approaches to better estimate traffic variance from sampled data according to the employed technique. Results show that, depending on sampling rate and link load, packet sampling does not negatively impact on link dimensioning accuracy even at very short timescales such as 10 ms. Moreover, we also show that the loss of inter-arrival time of sampled packets due to the exporting process in sFlow does not harm the estimations, given that an appropriate sampling rate is used. Our study is validated using a large dataset consisting of traffic packet traces captured at several locations around the globe.

For effective capacity management in access networks, it is essential to have a good insight in the service quality perceived by the users. As users share the service capacity available, one would want to know how the achieved per-user throughput fluctuates over time. In this paper we present a novel method that assesses the per-user throughput performance on the basis of throughput measurements at equidistant points in time. Our method relies on a queueing-theoretic framework, and allows us to explicitly quantify various statistics concerning the minimum per-user throughput obtained in a sample period, given the measured per-user throughput at the end points of that sample period, as well as the measured utilization during the interval. In an extensive numerical study we show the impact of important system parameters on these statistics. In addition, using illustrative examples, we demonstrate how the developed method can be used in practice for capacity management, with a specific focus on applications in cable access networks, an application for which our approach is particularly suitable.

It is a real challenge for cloud providers to offer cloud resources that are 'virtually unlimited and can be appropriated in any quantity at any time' in a cost-effective way. In this paper, we propose quantitative models that enable cloud providers to make an informed trade-off between cost and quality. Distinguishing between public and private cloud environments we consider infinite and finite source models respectively. In both cases either homogeneous or heterogeneous cloud resource requests are considered. These models can be applied to cloud dimensioning based on request blocking probability, an important SLA parameter. We derive a novel, insightful method that makes it possible to compute resource requirements in private clouds with heterogeneous resource requests. We show the importance of applying finite source models in the context of private clouds. We also use the proposed models to quantify the benefits of cloud federations.

Due to the inherent stochastic nature of services execution environment within service oriented systems, a runtime adaptation of the given composition may be required. We address a runtime service adaptation mechanism based on conditional retries for the orchestrated web services. The conditional retry may be issued while a concrete service within composition is executed. The retry could either invoke the same concrete service or a functionally equivalent web service that implements the same task. We use dynamic programming to determine the optimal time instances at which the current request should be terminated before request replication. The calculation takes into account different QoS parameters like services' response -- time distributions and cost -- relating parameters, and the solution optimizes the expected revenue of composite service provider. We illustrate the benefits of our approach by numerical calculations, and discuss the impact of considered QoS parameters to the solution at hand.

In this paper we consider the general problem of resource provisioning within cloud computing. We analyze the problem of how to allocate resources to different clients such that the service level agreements (SLAs) for all of these clients are met. A model with multiple service request classes generated by different clients is proposed to evaluate the performance of a cloud computing center when multiple SLAs are negotiated between the service provider and its customers. For each class, the SLA is specified by the request rejection probabilities of the clients in that class. The proposed solution supports cloud service providers in the decision making about 1) defining realistic SLAs, 2) the dimensioning of data centers, 3) whether to accept new clients, and 4) the amount of resources to be reserved for high priority clients. We illustrate the potential of the solution by a number of experiments conducted for a large and therefore realistic number of resources.

We investigate dynamic decision mechanisms for composite web services maximizing the expected revenue for the providers of composite services. A composite web service is represented by a (sequential) workflow, and for each task within this workflow, a number of service alternatives may be available. These alternatives offer the same functionality at different price-quality levels. After executing a sub-service, it is decided which alternative of the next sub-service in the workflow is invoked. The decisions optimizing expected revenue are based on observed response times, costs and response-time characteristics of the alternatives as well as end-to-end response-time objectives and corresponding rewards and penalties. We propose an approach, based on dynamic programming, to determine the optimal, dynamic selection policy. Extensive numerical examples show significant potential gain in expected revenues using the dynamic approach compared to other, non-dynamic approaches.

Long Term Evolution (LTE) is the latest cellular system that is being standardized by the 3rdGeneration Partnership Project (3GPP) and is expected to substantially improve end-user throughput and reduce user plane latency, while at the same time significantly improve user experience with full mobility. To realize these expectations, 3GPP is investigating the relay station (RS) usage in LTE networks, which is an intermediate station placed between a Mobile Station (MS) and evolved Node-Bs (eNode-Bs). The RS positioning is an important factor that severely influences the LTE uplink performance. This paper analyses the impact of RS positioning on the LTE uplink performance at flow level by using numerical and simulation experiments.

In this paper we investigate sequential decision mechanisms for composite web services. After executing each sub-service within a sequential workflow, decisions are made whether to terminate or continue the execution of the workflow. These decisions are based on observed response times, expected rewards, and typical Service Level Agreement parameters such as costs, penalties, and agreed response-time objectives. We propose a model for the sequential decision-making process within which we explore a couple of decision algorithms. We benchmarked these algorithms against the profit made when executing the workflow without decision-making. We show that algorithm based on backward recursion principle of dynamic programming is optimal with respect to profit. Next, we analyse the structure of erroneous decisions for both algorithms and show that significant profit gains can be obtained by sequential decision making.

Advanced packet scheduling schemes in 3G/3G+ mobile networks provide one or more parameters to optimise the trade-off between QoS and resource efficiency. In this paper we study the sensitivity of the optimal parameter setting for packet scheduling in LTE radio networks with respect to various traffic and environment aspects. For our investigations we consider a reference packet scheduling algorithm containing elements of proportional fairness and packet urgency to support mixes of real-time and non-real-time traffic. We present extensive simulation results showing the impact of traffic characteristics (like file size distribution, traffic mix) and environment conditions (regarding e.g. multipath fading and shadowing) on the optimal parameter setting. Although, in some cases, efficiency gains of about twenty percent can be achieved by proper tuning of the scheduling parameters, the overall view from our investigations is that a single, robust setting of the parameters can be determined which provides near optimal trade-offs under almost all practically relevant conditions.

The UMTS enhanced uplink (EUL) is expected to provide higher capacity, increased data rates, and smaller latency on the communication link from users towards the network. A key mechanism in EUL traffic handling is the packet scheduler, for which a number of basic schemes can be identified (one-by-one, partial parallel, and full parallel). In this paper we analyze the interaction between the EUL scheduling scheme deployed in the network and the inter-cell interference. On the one hand, different scheduling schemes cause different inter-cell interference patterns on neighbouring cells. On the other hand, the different schemes are affected by inter-cell interference in different ways. The scheduling schemes are evaluated and compared under different approaches for reserving part of the allowed noise rise at the base station for inter-cell interference. For our analysis, we have developed a hybrid analytical/simulation approach allowing for fast evaluation of performance measures such as the mean flow transfer time and fairness expressing how the performance depends on the user's location. This approach takes into account both the packet-level characteristics and the flow-level dynamics due to the random user behaviour.

Personal networks (PNs) are future communication systems that combine wireless and infrastructure based networks to provide users a variety of services anywhere and anytime. PNs introduce new design challenges due to the heterogeneity of the involved technologies, the need for self-organization, the dynamics of the PN composition, the application-driven nature, the co-operation with infrastructure-based networks, and the security hazards. This paper discusses the challenges of security, service discovery and QoS provisioning in designing self-organized PNs and combines them all into an integrated architectural framework

We present a flow-level performance evaluation for a umtshsdpa network. The results provide thorough insights into the performance impact of a number of key system, environment and traffic aspects, e.g. terminal location, the presence of intercellular interference, the intrinsic feedback delay in the channel quality indications, soft combining of retransmissions and the applied packet scheduling scheme. The contribution of the identified key aspects in the experienced service quality and spatial fairness is assessed by evaluating gradually more 'complete' scenarios.

The authors present a performance study of the Gauss asynchronous transfer mode (ATM) switch with respect to cell loss. The Gauss switch is a recently proposed, high-performance, output buffered ATM switch that can easily be implemented. In general, if an ATM switch suffers from cell loss, the exact behavior of this cell loss as a function of parameters such as load, traffic business, internal speed, etc., has to be known. The study focuses on cell loss in the Gauss switch due to its reduced internal speed. Analytical results for the cell loss probability are provided and compared to corresponding quantities for the well-known knockout switch. The comparison shows that the Gauss switch yields significantly smaller cell loss probabilities than the knockout switch.>