For many years, the continuous proliferation of mobile devices and their applications generate a surge of signaling traffic in the control plane of the mobile packet core network. As a consequence, the control plane will potentially become a bottleneck if not properly managed. We focus on the load balancing of a virtualized and distributed Mobility Management Entity (vMME), which is the key control plane element in the 4G and 5G non-standalone cores. Most of existing works use the simple and static load balancing approaches such as roundrobin and consistent hashing, which do not work well in a heterogeneous environment. In this paper, we developed three adaptive algorithms in which the balancing decision takes into account the dynamics of the system such as the vMME load, the completion time of a request served by a vMME, and the number of pending requests queued at a vMME. The evaluation of our three proposed load-balancing algorithms in an Open5GCore testbed suggests that the latency-aware scheme helps shorten the completion time of the signaling requests by up to five times the static and dynamic schemes in those cases the link delay between the load balancer and the vMMEs differ significantly.

In the fifth generation (5G) mobile networks, the number of user plane functions has increased, and, in contrast to previous generations. They can be deployed in a decentralized way and auto-scaled independently from their control plane functions. Moreover, the performance of the user plane functions can be boosted with the adoption of advanced acceleration techniques such as Vector Packet Processing (VPP). However, the increased number of user plane functions has also made load balancing a necessity, something we find has so far received little attention. Moreover, the introduction of VPP poses a challenge to the design of the auto-scaling of user-plane functions. In this paper, we address these two challenges by proposing a novel performance indicator for making better auto-scaling decisions, and by proposing three new dynamic load-balancing algorithms for the user plane of a VPP-based, softwarized 5G network. The novel performance indicator is estimated based on the VPP vector rate, and is used as a threshold for the auto-scaling process. The dynamic load-balancing algorithms take into account the number of bearers allocated for each user plane function and their VPP vector rate. We validated and evaluated our proposed solution in a 5G testbed. Our experiment results show that the scaling helps to reduce the packet latency for the user plane traffic, and our proposed load-balancing algorithms seem to give a better distribution of traffic load as compared to traditional static algorithms.

In the fifth generation (SG) mobile networks, the number of user-plane gateways has increased, and, in contrast to previous generations they can be deployed in a decentralized way and auto-scaled independently from their control-plane functions. Moreover, the performance of the user-plane gateways can be boosted with the adoption of advanced acceleration techniques such as Vector Packet Processing (VPP). However, the increased number of user-plane gateways has also made load balancing a necessity, something we find has so far received little attention. Moreover, the introduction of VPP poses a challenge to the design of the auto-scaling of user- plane gateways. In this paper, we address these two challenges by proposing a novel performance indicator for making better auto-scaling decisions, and by proposing three new dynamic load- balancing algorithms for the user plane of a VPP-based, softwarized SG network. The novel performance indicator is estimated based on the VPP vector rate and is used as a threshold for the auto-scaling process. The dynamic load-balancing algorithms take into account the number of bearers allocated for each user-plane gateway and their VPP vector rate. We validate and evaluate our proposed solution in a SG testbed. Our experiment results show that the scaling helps to reduce the packet latency for the user-plane traffic, and that our proposed load-balancing algorithms can give a better distribution of traffic load as compared to traditional static algorithms.

The mobile packet core – a central part of the overall mobile cellular network – has a long history of evolution. Through the years, its architecture has been drastically changed to meet the demand coming from the fast growth in the number of devices as well as the introduction of new types of applications and services. On one hand, the number of new devices and subscribers keeps increasing at an unprecedented rate, which can still give rise to scalability issues in the mobile packet core if it is not properly managed. On the other hand, the introduction of new types of services brings with it a new set of requirements, such as low-latency and high reliability. The network softwarization is widely considered as a promising approach to address these two challenges.

This thesis focuses on enhancing the scalability of a softwarized mobile packet core network for 5G and beyond, and the communication latency it provides. Moreover, the thesis provides an extensive survey of existing softwarized mobile packet core network solutions, identifying important questions and gaps for future research. This thesis also explores the possibility of leveraging the network softwarization concept to design a softwarized mobile packet core network to support multicast and broadcast services. In order to tackle the scalability issue in a softwarized mobile packet core, the thesis proposes several dynamic and adaptive load-balancing algorithms to efficiently manage the traffic load in both the control and the user plane. These load-balancing algorithms take into account different factors such as the current load of virtualized network functions and the estimation of the communication latency. On the latency aspect, the thesis studies the feasibility of using a softwarized mobile packet core for delivering time-critical messages in a smart-grid environment, and proposes several deployable communication solutions to support the study.

Communication within substatation automation systems in a smart grid environment is often time-critical. The time required for exchanging information between intelligent devices should be within a few milliseconds. The International Electrotechnical Commission (IEC) 61850 standard has proposed the Generic Object Oriented Substation Event (GOOSE) protocol to achieve this goal. However, the transmission of GOOSE messages is often limited to a small Local Area Network (LAN). In this demo, we demonstrate the feasibility of using 5G for GOOSEbased time critical communication in a large-scale smart-grid environment, and present a deployable 5G core solution using container-based virtualization technology. The radio part of the demo is emulated. The demo also shows that the delay introduced by the core network is in the order of sub milliseconds, while the one-way delay without a real radio access network is less than 1 ms, which is well below the total delay budget for GOOSE.

5G with its great capabilities is believed to play a crucial role in different vertical sectors such as automotive, healthcare, and energy, etc. In this paper, we present a study on the adoption of 5G into a smart grid environment, in particular, a power grid substation automation system. In such a system, the communication between electrical devices is often within a few milliseconds. To verify the proposed solution, we conducted a set of experiments using the Generic Object Oriented Substation Event (GOOSE) protocol, a standard protocol used in power grid substation automation systems, over a virtualized 5G network. Our experimental results show that the delay introduced by the core network is in the order of sub milliseconds, while the one-way delay without a real radio access network, and without background traffic, is less than 1 ms. Moreover, these delays can be significantly reduced with a container-based deployment rather than a virtual machine-based one.

Protection and automation in a smart grid environment often have stringent real-time communication requirements between devices within a substation, as well as between distantly located substations. The Generic Object Oriented Substation Event (GOOSE) protocol has been proposed to achieve this goal as it allows to transfer time-critical information within a few milliseconds. However, the transmission of GOOSE messages is often limited to a small Local Area Network (LAN). An earlier work has proposed to use the fifth generation of mobile networks (5G) as a means to transport IP-based GOOSE messages in a large-scale smart grid environment. On the basis of this work, this paper designs and implements an alternative solution for Ethernet-based GOOSE communication over a virtualized 5G core network that does not require any modification of the existing network protocol stack and thus is much easier to deploy. Our experimental results show that the delay introduced by the core network is in the order of sub milliseconds, while the oneway delay without a real radio access network, and without background traffic, is less than 1 ms. Moreover, these delays can be significantly reduced with a container-based deployment rather than a virtual machine-based one. Assuming a 1-ms delay budget for a 5G radio access network, our evaluation confirms that it is indeed feasible to use 5G for GOOSE transmission in IEC 61850 substation automation systems.

Protection and automation in a smart grid environmentoften have stringent real-time communication requirementsbetween devices within a substation as well as between distantlylocated substations. The Generic Object Oriented SubstationEvent (GOOSE) messaging service has been proposed to achievethis goal as it allows to transfer time-critical information within afew milliseconds. However, the transmission of GOOSE messagesare often limited to a small Local Area Network (LAN).In this paper, we propose the use of the fifth generation ofmobile networks (5G) as a means to transport GOOSE messagesin a large scale smart grid environment. The end-to-end delay ismeasured between GOOSE devices over an 5G network with thefocus on the core network using the Open5GCore platform in alab environment. Although there is a lack of a real radio accessnetwork, the experimental results confirm that the delay withinthe rest of the 5G network is small enough for it to be feasiblefor inter-substation GOOSE transmissions.

In this paper, we aim at tackling the scalability problem of the Mobility Management Entity (MME), which plays a crucial role of handling control plane traffic in the current 4G Evolved Packet Core as well as the next generation mobile core, 5G. One of the solutions to this problem is to virtualize the MME by applying Network Function Virtualization principles and then deploy it as a cluster of multiple virtual MME instances (vMMEs) with a front-end load balancer. Although several designs have been proposed, most of them assume the use of simple algorithms such as random and round-robin to balance the incoming traffic without any performance assessment. To this end, we implemented a weighted round robin algorithm which takes into account the heterogeneity of resources such as the capacity of vMMEs. We compare this algorithm with a random and a round-robin algorithm under two different system settings. Experimental results suggest that carefully selected load balancing algorithms can significantly reduce the control plane latency as compared to simple random or round-robin schemes.

Currently, improving the performance of Big Data in general and velocity in particular is challenging due to the inefficiency of current network management, and the lack of coordination between the application layer and the network layer to achieve better scheduling decisions, which can improve the Big Data velocity performance. In this chapter, we discuss the role of recently emerged software defined networking (SDN) technology in helping the velocity dimension of Big Data. We start the chapter by providing a brief introduction of Big Data velocity and its characteristics and different modes of Big Data processing, followed by a brief explanation of how SDN can overcome the challenges of Big Data velocity. In the second part of the chapter, we describe in detail some proposed solutions which have applied SDN to improve Big Data performance in term of shortened processing time in different Big Data processing frameworks ranging from batch-oriented, MapReduce-based frameworks to real-time and stream-processing frameworks such as Spark and Storm. Finally, we conclude the chapter with a discussion of some open issues.