The demand for mobile communication is continuously increasing, and mobile devices are now the communication device of choice for many people. To guarantee connectivity and performance, mobile devices are typically equipped with multiple interfaces. To this end, exploiting multiple available interfaces is also a crucial aspect of the upcoming 5G standard for reducing costs, easing network management, and providing a good user experience. Multi-path protocols, such as multi-path TCP (MPTCP), can be used to provide performance optimization through load-balancing and resilience to coverage drops and link failures, however, they do not automatically guarantee better performance. For instance, low-latency communication has been proven hard to achieve when a device has network interfaces with asymmetric capacity and delay (e.g., LTE and WLAN). For multi-path communication, the data scheduler is vital to provide low latency, since it decides over which network interface to send individual data segments. In this paper, we focus on the MPTCP scheduler with the goal of providing a good user experience for latency-sensitive applications when interface quality is asymmetric. After an initial assessment of existing scheduling algorithms, we present two novel scheduling techniques: the block estimation (BLEST) scheduler and the shortest transmission time first (STTF) scheduler. BLEST and STTF are compared with existing schedulers in both emulated and real-world environments and are shown to reduce web object transmission times with up to 51% and provide 45% faster communication for interactive applications, compared with MPTCP's default scheduler.

Information-centric networking (ICN) with its design around named-based forwarding and in-network caching holds great promises to become a key architecture for the future Internet. Still, despite its attractiveness, there are many open questions that need to be answered before wireless ICN becomes a reality, not least about its congestion control: Many of the proposed hop-by-hop congestion control schemes assume a fixed and known link capacity, something that rarely – if ever – holds true for wireless links. As a first step, this paper demonstrates that although these congestion control schemes are able to fairly well utilise the available wireless link capacity, they greatly fail to keep the link delay down. In fact, they essentially offer the same link delay as in the case with no hop-by-hop, only end- to-end, congestion control. Secondly, the paper shows that by complementing these congestion control schemes with an easy- to-implement, packet-train link estimator, we reduce the link delay to a level significantly lower than what is obtained with only end-to-end congestion control, while still being able to keep the link utilisation at a high level. 

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.

TCP BBR (Bottleneck Bandwidth and Round-trip propagation time) is a new TCP variant developed at Google, and which, as of this year, is fully deployed in Googles internal WANs and used by services such as Google.com and YouTube. In contrast to other commonly used TCP variants, TCP BBR is not loss-based but model-based: It builds a model of the network path between communicating nodes in terms of bottleneck bandwidth and minimum round-trip delay and tries to operate at the point where all available bandwidth is used and the round-trip delay is at minimum. Although, TCP BBR has indeed resulted in lower latency and a more efficient usage of bandwidth in fixed networks, its performance over cellular networks is less clear. This paper studies TCP BBR in live mobile networks and through emulations, and compares its performance with TCP NewReno and TCP CUBIC, two of the most commonly used TCP variants. The results from these studies suggest that in most cases TCP BBR outperforms both TCP NewReno and TCP CUBIC, however, not so when the available bandwidth is scarce. In these cases, TCP BBR provides longer file completion times than any of the other two studied TCP variants. Moreover, competing TCP BBR flows do not share the available bandwidth in a fair way, something which, for example, shows up when shorter TCP BBR flows struggle to get its fair share from longer ones. 

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.

Mobile internet usage has significantly raised over the last decade and it is expected to grow to almost 4 billion users by 2020. Even after the great effort dedicated to the improvement of the performance, there still exist unresolved questions and problems regarding the interaction between TCP and mobile broadband technologies such as LTE. This chapter collects the behavior of distinct TCP implementation under various network conditions in different LTE deployments including to which extent the performance of TCP is capable of adapting to the rapid variability of mobile networks under different network loads, with distinct flow types, during start-up phase and in mobile scenarios at different speeds. Loss-based algorithms tend to completely fill the queue, creating huge standing queues and inducing packet losses both under stillness and mobility circumstances. On the other side delay-based variants are capable of limiting the standing queue size and decreasing the amount of packets that are dropped in the eNodeB, but they are not able under some circumstances to reach the maximum capacity. Similarly, under mobility in which the radio conditions are more challenging for TCP, the loss-based TCP implementations offer better throughput and are able to better utilize available resources than the delay-based variants do. Finally, CUBIC under highly variable circumstances usually enters congestion avoidance phase prematurely, provoking a slower and longer start-up phase due to the use of Hybrid Slow-Start mechanism. Therefore, CUBIC is unable to efficiently utilize radio resources during shorter transmission sessions.

The increase in traffic volumes as well as the heterogeneity in network infrastructure in the upcoming 5G cellular networks will lead to a dramatic increase in volumes of control traffic, i.e., signaling traffic, in the networks. Moreover, the increasing number of low-power devices with an on-off behavior to save energy will generate extra control traffic. These increased traffic volumes for signaling traffic, often generated as bursts of messages, will challenge the signaling application timing requirements on transmission. One of the major transport protocols deployed for signaling traffic in cellular networks is the Stream Control Transmission Protocol (SCTP), with support for multiple paths as well as for independent data flows. This paper evaluates transmission over several paths in SCTP to keep the latency low despite increasing traffic volumes. We explore different transmission strategies and find that concurrent multipath transfer over several paths will significantly reduce latency for transmission over network paths with the same or similar delay. Still, over heterogeneous paths, careful, continuous sender scheduling is crucial to keep latency low. To this end, we design and evaluate a sender scheduler that considers path characteristics as well as queuing status and data flows of different priority to make scheduling decisions. Our results indicate that by careful dynamic sender scheduling, concurrent multipath transfer could lead to reduced latency for signaling traffic irrespective of path or traffic characteristics.

Network Function Virtualization (NFV) is a promising solution for telecom operators and service providers to improve business agility, by enabling a fast deployment of new services, and by making it possible for them to cope with the increasing traffic volume and service demand. NFV enables virtualization of network functions that can be deployed as virtual machines on general purpose server hardware in cloud environments, effectively reducing deployment and operational costs. To benefit from the advantages of NFV, virtual network functions (VNFs) need to be provisioned with sufficient resources and perform without impacting network quality of service (QoS). To this end, this paper proposes a model for VNFs placement and provisioning optimization while guaranteeing the latency requirements of the service chains. Our goal is to optimize resource utilization in order to reduce cost satisfying the QoS such as end- to-end latency. We extend a related VNFs placement optimization with a fine-grained latency model including virtualization overhead. The model is evaluated with a simulated network and it provides placement solutions ensuring the required QoS guarantees. 

Network Function Virtualization (NFV) is a promising solution for telecom operators and service providers to improve business agility, by enabling a fast deployment of new services, and by making it possible for them to cope with the increasing traffic volume and service demand. NFV enables virtualization of network functions that can be deployed as virtual machines on general purpose server hardware in cloud environments, effectively reducing deployment and operational costs. To benefit from the advantages of NFV, virtual network functions (VNFs) need to be provisioned with sufficient resources and perform without impacting network quality of service (QoS). To this end, this paper proposes a model for VNFs placement and provisioning optimization while guaranteeing the latency requirements of the service chains. Our goal is to optimize resource utilization in order to reduce cost satisfying the QoS such as end-to-end latency. We extend a related VNFs placement optimization with a fine-grained latency model including virtualization overhead. The model is evaluated with a simulated network and it provides placement solutions ensuring the required QoS guarantees.