Measuring Interaction QoE in Internet Videoconferencing

The energy demands of modern mobile devices are outstripping their battery lives. As a result, energy efficiency—fitting an energy budget and maximizing the utility of applications under given battery constraints—has become an important system design consideration. Because network communication incurs high energy costs in mobile applications, middleware presents a promising target for energy optimizations. Unfortunately, mainstream middleware mechanisms are oblivious to the highly volatile nature of mobile networks, operating over which energy efficiently requires aligning the middleware communication patterns with the network conditions in place. In this paper, we present a novel middleware architecture that optimizes energy consumption by adapting various facets of middleware functionality (e.g., data communication, encoding, and compression) dynamically in response to fluctuations in network conditions. By means of a simple configuration file, the programmer can specify the policies to follow for various parts of the communication functionality and how policies should be triggered by changes in network conditions. As compared to mainstream middleware mechanisms, our reference implementation improves the energy efficiency of mobile applications. Specifically, our benchmarks and case studies demonstrate that the new middleware architecture can reduce the energy budget of a typical third-party mobile application by as much as 30%.

This article proposes an access-point and channel selection method for Internet of Things environments. Recently, the number of wireless nodes has increased with the growth of Internet of Things technologies. In order to accommodate traffic generated by the wireless nodes, we need to utilize densely placed wireless access-points. This article introduces a joint optimization problem of access-point and channel selection for such an environment. The proposed method deals with the optimization problem, using Markov approximation which adapts to dynamic changes in network conditions. Markov approximation is a distributed optimization framework, where a network is optimized by individual behavior of users forming a time-reversible continuous-time Markov chain. The proposed method searches optimal solution for the access-point and channel selection problem on the time-reversible continuous-time Markov chain. Simulation experiments demonstrate the effectiveness of the proposed method.

In live peer-to-peer (P2P) streaming, each peer (child) has a number of supplying parents whose packets have to be scheduled and delivered in time for continuous playback at the child. It is challenging to develop a scheduling algorithm that achieves low delay given heterogeneous bandwidth, propagation delays and available content in all the parents. This paper proposes a novel, simple and effective scheduling scheme called Pattern-Push. As compared to the traditional mesh-pull, pattern-push does not require continuous buffermap advertisements from the parents, and operates on the packet level instead of the larger segment level. In pattern-push, each parent pushes its packets according to a pattern as indicated by a starting packet ID and a cycle bitmap. Pattern-push requires only minimal feedback from the child, as the pattern only needs to be changed when the child detects a marked change in network conditions or its parents. Simulation results show that pattern-push achieves a significantly lower delay and overhead as compared with both traditional and recent scheduling algorithms proposed in the literature.

지터라고 불리는 패킷 도착 지연의 편차는 모바일 단말기에서 Voice over Internet Protocol (VoIP)음성의 품질을 저하시키는 주요한 원인 중 하나이다. 이 문제를 해결하기 위해서 본 논문에서는 VoIP 음질향상을 위한 적응적 지터추정 기반의 플레이아웃 스케줄링 방법을 제안한다. 제안된 알고리즘은 예측된 네트워크 지연의 변화에 따라 각각의 패킷을 신장 혹은 압축하여 전송 지터의 영향에 대응한다. 이와 함께, 네트워크 지터 추정은 신속한 지연 스파이크 검출과 네트워크 상태 변화에 대한 반응을 포함한다. 실험결과는 제안된 알고리즘이 불안정한 네트워크 환경에서 고음질을 제공하는 것을 보여 주었다. Packet arrival-delay variation, so-called 'jitter' is one of the main factors that degrade the quality of voice in mobile devices at the Voice over Internet Protocol (VoIP). To resolve this issue, a playout scheduling based on adaptive jitter estimation for enhancing VoIP speech quality is proposed. The proposed algorithm copes with the effect of transmission jitter by expanding or compressing each packet according to the predicted network delay and variations. Additionally, the active network jitter estimation incorporates rapid detection of delay spikes and reacts to changes in network conditions. The experimental results have shown that the proposed algorithm delivers high voice quality in unstable network environment.

Recent trends have caused a shift from services deployed solely in monolithic data centers in the cloud to services deployed in the fog (e.g. roadside units for smart highways, support services for IoT devices). Simultaneously, the variety and number of IoT devices has grown rapidly, along with their reliance on cloud services. Additionally, many of these devices are now themselves capable of running containers, allowing them to execute some services previously deployed in the fog. The combination of IoT devices and fog computing has many advantages in terms of efficiency and user experience, but the scale, volatile topology and heterogeneous network conditions of the fog and the edge also present problems for service deployment scheduling. Cloud service scheduling often takes a wide array of parameters into account to calculate optimal solutions. However, the algorithms used are not generally capable of handling the scale and volatility of the fog. This paper presents a scheduling algorithm, named Swirly, for large scale fog and edge networks, which is capable of adapting to changes in network conditions and connected devices. The algorithm details are presented and implemented as a service using the Kubernetes API. This implementation is validated and benchmarked, showing that a single threaded Swirly service is easily capable of managing service meshes for at least 300.000 devices in soft real-time.

The development of collaborative multimedia applications today follows a vertical development approach, which is a major inhibitor that drives up the cost of development and slows down the pace of innovation of new generations of collaborative applications. In this paper, we propose a network communication broker (NCB) that provides a unified higher-level abstraction that encapsulates the complexity of network-level communication control and media delivery for the class of multimedia collaborative applications. NCB expedites the development of next-generation applications with diverse communication logics. Furthermore, NCB-based applications can be easily ported to new network environments. In addition, the self-managing design of NCB supports dynamic adaptation in response to changes in network conditions and user requirements.

This paper proposes a new adaptive jitter estimation algorithm for voice-over IP networks. The adaptive network jitter estimation incorporates rapid detection of delay spikes and reacts to changes in network conditions. The proposed algorithm can more correctly estimate the network jitter than the conventional algorithm during spike intervals, thereby effectively reducing the jitter estimation error.

Present distributed applications are not typically designed to adapt to changes in network conditions. In addition, network-based adaptation does not typically consider the requirements of specific...

Web services provide interoperability to network hosts with different capabilities. Complex tasks can be performed by composing services, assuming sufficient service descriptions are provided. Researchers are just beginning to realize the importance of accounting for network properties during automated service composition. The work presented in this thesis considers dynamic, heterogeneous networks-one type of network-centric environment. The purpose of this research is to improve network-centric service composition. This is accomplished by converting the service composition problem to an automated planning under uncertainty problem and by reasoning about network properties at various stages of the planning process. This thesis presents a method of improving the agents' ability to construct, execute, and monitor plans in network-centric environments. There are two main contributions of this thesis: 1) generating qualitatively-different plans and 2) creating network-aware agents. As part of the former contribution, this thesis presents a comparison of methods used to create classical planning domains for distributed service composition problems. The other part of this contribution is an algorithm for guiding a plan-space planner to create qualitatively-different plans based on domain-dependent and network-centric plan evaluations. The second contribution pertains to network-awareness, which agents exhibit by reacting to changes in network conditions. This thesis describes methods of incorporating network-awareness into agents that 1) create plans, 2) execute plans, and 3) monitor plan execution. Experiments to validate the aforementioned contributions are presented in the context of an Improvised Explosive Device (IED) detection scenario. Several locations are monitored for IEDs using a variety of techniques including manual searching and visual change detection, as well as a variety of resources including humans, robots, and unmanned aerial vehicles (UAVs). Empirical results indicate that incorporating network-awareness into agents in dynamic, heterogeneous networks improves the overall service composition performance and effectiveness.

The availability of variety of communication devices offers a choice among networks with vastly different characteristics. No single protocol or application can be expected to perform well over all these networks. A mobile host is likely to encounter these different networks and needs to adapt accordingly. The problem of adapting to a changing network environment is further complicated, because changes in network conditions are usually transparent to higher layers of the protocol stack. In order to allow automatic adaptation of applications and protocols, awareness of link conditions and network environment is necessary. In this paper, we present a uniform mechanism based on ICMP messages for providing information about the environment to the protocol stack. We also show how protocols can adapt to changes in the environment, and in particular, demonstrate dynamic fine tuning of some of the well known protocols such as UDP and TCP. Performance measurements demonstrate that our framework imposes little overheads and improves protocol performance under changing network conditions.

An intersystem or vertical handover is a key enabling mechanism for next generations of mobile communication systems. A vertical handover can cause an abrupt change of up to two orders of magnitude in link bandwidth and latency. It is hard for end-to-end congestion control to adapt promptly to such changes. This is especially a concern for slowly responsive congestion control algorithms, such as TCP-Friendly Rate Control (TFRC). TFRC is designed to provide a smooth transmission rate for real-time applications and, therefore, is less responsive to changes in network conditions than TCP. Using measurements and simulation, we show that TFRC has significant difficulties adapting after a vertical handover. TFRC receives only a fraction of TCP throughput over a fast link, but can be grossly unfair to concurrent TCP flows after handover to a slow link. We show that two proposals based on overbuffering and an explicit handover notification are effective solutions to these problems. Using them, TFRC can quickly adapt to new link characteristics after a handover, while otherwise maintaining a smooth transmission rate.

Network monitoring is necessary so as to ensure high reliability and availability in telecom networks. One of the main challenges posed by state-of-the-art monitoring tools is the creation of network baselines. Such baselines include thresholds that can be used to determine whether monitored values (with a given context, e.g. time) represent normal network operation or not. The size and complexity of current (and future) networks makes it infeasible to manually determine and set baselines for each network operator and metric, let alone adapting the thresholds to changes in network conditions. This leads to the use of default baselines and/or setting baselines only once and never changing them throughout the lifetime of network elements. This does not only cause inefficient operation, but could have implications for network reliability and availability. In this paper, we present the design, implementation, and evaluation of DARN: a collection of analytics and machine learning-based algorithms aimed at ensuring that network baselines are automatically adapted to different metric evolution. DARN has been comprehensively evaluated on a deployment with real traffic to confirm accuracy of generated baselines, a 22% improvement in accuracy due to baseline adaptation, and a 72% reduction in false alarms.

Software-defined networks (SDNs) are expanding their presence beyond laboratories, campus networks, ISPs, and data centre networks, moving into various domains. Although originally designed for campus networks, SDNs face scalability challenges, especially with the use of OpenFlow. Addressing these challenges requires innovative traffic management mechanisms to efficiently handle the growing number of connected devices and the increasing volume of traffic from various types of applications. This article proposes an innovative method for link weight selection that incorporates multipath transmission and flow aggregation in the SDNs. This novel approach improves resource utilization in two key ways. First, it involves the preservation of bandwidth during congestion. Second, it minimizes internal resource usage, as illustrated by a reduction in the number of table entries in switches. Resources undergo optimization through the introduction of a novel mechanism for flow aggregation. This novel mechanism, coupled with multipath transmission, enables adaptive responses to dynamic changes in network conditions. The aggregation process leads to a reduced number of flow entries in the core switches compared to the conventional operation of OpenFlow. The proposed scenarios for link weight allocation allow for a reduction in the number of entries in the core switches by up to 99%. The application of the proposed method also results in an increase of 58% in traffic transmission.

The rapid growth of Internet of Things (IoT) devices, 5G connectivity, and data-driven services has increased the demand for intelligent, secure, and energy-efficient network infrastructures. Static routing choices, inflexible behavior, and high processing requirements of security schemes pose challenges in traditional Software-Defined Networking (SDN) models. In this paper, we present a new SDN framework in which Meta-Reinforcement Learning (Meta-RL) and Graph Attention Networks (GATs) are combined to support dynamic, topology-aware, and energy-efficient routing. Meta-RL enables the SDN controller to quickly adapt to changes in network conditions through prior knowledge, and GAT improves the learning process by focusing on the most relevant topological characteristics. The framework employs a hybrid cryptographic model based on Genus Weierstrass Curve Cryptography (GWCC), combined with chaotic map encryption to provide secure data transmission without adversely affecting real-time performance. The combined architecture enhances entropy and reduces processing latency. The proposed system is implemented and tested on a Mininet-based platform with a multi-hop structure using an OpenFlow topology. The results show significant improvements in throughput, latency, packet delivery ratio, energy efficiency, and Area Under the Curve (AUC) compared with current models, including Deep Q-Network (DQN), Q-learning-based Routing (QLR), and Elliptic Curve Cryptography (ECC)-based routing.

Along with recent advances in mobile networking and portable computing technologies, there is a trend in the telecommunications industry toward the development of efficient ubiquitous systems that can provide a set of bandwidth-intensive and real-time services to multiple users while supporting their full mobility. Large-scale deployment of 802.1 1-based technologies will play an integral part in the construction of such ubiquitous wireless mobile systems. A challenging task in the development of such networks is efficient provisioning of QoS-enabled services for mobile users. In this context, we propose a scheme that constantly monitors the overall network performance to perform admission control and traffic conditioning at the 802.11-based access points and mobile terminals. The focus is on service-level fairness, where different flows from the same traffic class can still receive the same QoS level even if they have different bit rates. Furthermore, given the mobility of users, the success of any resource allocation and admission control model depends on the continuity of QoS guarantees across different WLANs. This article proposes a dynamic service level agreement negotiation protocol that allows mobile terminals to perform handoffs between different WLANs while maintaining the agreed level or service. End users also can change their service levels in response to changes in network conditions.