Live migration is a technology that seamlessly relocates a virtualized service between physical hosts, which allows services to rapidly adapt to environmental changes. Despite the large amount of research, there is still a lack of understanding of its performance. Towards a better understanding of live migration, we build a testbed and use it to migrate a computation-intensive application with docker and KVM. We evaluate the service downtime, migration time, and network usage under different conditions. The results show that KVM outperforms docker in most of the scenarios, with some critical exceptions where only docker manages to perform miaration.

In wireless multi-hop and multi-path computer networks, such as the ones that potentially emerge with Internet of Things (IoT) deployments, channel coding has become a tried and tested solution to handle packet losses. Random Linear Network Coding (RLNC) is one of such block coding techniques, which also provides the unique feature of recoding already coded symbols on intermediate nodes, thereby increasing the general throughput of the network. However, the computation overhead of decoding on the receiver device counteracts the much desired low-latency requirements of various fifth generation (5G) use cases. Sliding-window RLNC is a technique, which can minimize such overheads. Nonetheless, recoding over a sliding window destroys the low-latency decodable properties of the scheme. In this paper we present a low-latency recoding technique that balances packet forwarding and recoding on the intermediate nodes in way that potentially preserves the sliding window characteristics. Our simulation results show that the performance of the proposed recoding algorithm achieves a two factor improvement in latency and losses, as well as a more than three times gain in throughput.

Live service migration with low service downtime is one of the main challenges in mobile edge cloud (MEC). In other words, it is important to guarantee that users continue to receive good service even if they cross through multiple MEC servers. In this paper, we propose a seamless service migration framework and apply it to an autonomous driving demonstration. We expect this paper to provide a perspective on how MEC can be used for practical autonomous driving in the near future.

Time-sensitive applications on wireless networks often demand high reliability and high throughput. Random Linear Network Coding (RLNC) is a forward error correction (FEC) method for wireless lossy channels that organizes the information in blocks, commonly called generations. Hybrid Automatic Repeat reQuest (HARQ) is a combination of a FEC method, such as RLNC, and an ARQ often used to ensure reliable data transmission. Many RLNC-based protocols provide in-order delivery, which helps in general cases but backfires in high-jitter channels. Multigeneration RLNC protocols are more resilient in high-jitter channels than conventional RLNC protocols because they are capable of handling multiple generations at the same time. However, they still struggle against losses. Despite the considerable amount of research done in this field, there is still no solution that combines HARQ with multigeneration RLNC protocols. Our contribution in this paper is two-fold: (i) we propose a new ARQ mechanism adapted for multigeneration RLNC protocols, and (ii) we compare its performance in terms of per-packet delay and throughput to conventional RLNC, HARQ RLNC, and multigeneration RLNC. The results show an increase in the decoding probability from 25% without ARQ to 80% with ARQ. Moreover, we observe a decrease of 35% in the average packet delay.

Blind Source Separation (BSS) for time-sensitive applications in the Internet of Things (IoT) results in a tradeoff between separation speed and accuracy. Data extraction has been widely employed recently to solve this problem. Although the introduction of current data extraction methods reduces the required time for separation, it is at the expense of separation quality. In this paper, we propose Adaptive extraction-based Independent Component Analysis (AeICA) to address these limitations. Specifically, the speed of separation is improved by using the extracted subset of the available data without affecting the overall separation accuracy, which we demonstrate through extensive numerical evaluations. In particular, AeICA reduces the total separation time by 50% to 75%, compared to the most remarkable related work.

Wireless Sensor Networks (WSNs) applications have attracted attention in Internet of Things (IoT) as a novel networking paradigm consisting of billions of small sensor nodes. These sensors collect environmental information and communicate with each other to provide solutions for real time IoT applications' requirements. Since the majority of applications require location-based services, it is necessary to improve the accuracy of localization algorithms. DV-Hop is one of the most attractive range-free localization algorithms in wireless sensor networks and several works have been undertaken to improve its accuracy, however, since sensor nodes have limited power resources, the energy consumption of nodes should be also considered. In this paper, we propose a method based on DV-Hop to improve both accuracy and power consumption. Each unknown node calculates the Hopsize of each anchor node according to the limited information it has from the network topology; therefore there is no need to broadcast the Hopsize from anchor nodes, and in this way energy can be saved. In the next step, we use Shuffled Frog Leaping Algorithm (SFLA) as an evolutionary algorithm to improve the accuracy of estimated Hopsizes and a hybrid Genetic-PSO algorithm is applied to the third step of DV-Hop to achieve more accurate values for unknown nodes' positions. Simulation results show that our proposed method decreases the localization error significantly by jointly considering the energy consumption of sensors and is overall 44% more accurate than DV-Hop.

In time-sensitive applications within industry 4.0, e.g. anomaly detection and human-in-the-loop, the data generated by multiple sources should be quickly separated to give the applications more time to make decisions and ultimately improve production performance. In this paper, we propose a Component-dependent Independent Component Analysis (CdICA) method that can separate multiple randomly mixed signals into independent source signals faster, for further data analysis in time-sensitive applications. Based on the Independent Component Analysis (ICA) algorithm, we first generate an initial separation matrix relying on the known mixture components, so that the separation speed of the traditional ICA can be increased. Our simulative results show that the CdICA method reduces the separation time by 55% to 83% compared to the most notable related work called FastICA and meanwhile it does not diminish the accuracy of the separation.

Data transmission in wireless networks is vulnerable to errors, due to the nature of wireless characteristics. As a result, corrupted packets are a common case in wireless data transmissions. Many techniques have been proposed to tackle these issues and one of the recommended techniques is the partial packet recovery (PPR) scheme. There are various existing PPR methods that have been proposed in wireless networks over the past two decades. In addition, the recent works have shown the possibility of network coding along with its capabilities in recovering the partial packets. A review study of PPR approaches in wireless networks is presented in this study. The authors classify the approaches into several groups based on the soft information in the PHY layer. Furthermore, they describe the PPR techniques that have used in each of these groups. Their studies found that there are 24 protocols have been proposed using PPR to improve the performance in wireless networks.

5G ecosystems will benefit significantly from Multi-access Edge Computing (MEC) and Network Function Virtualization (NFV). While NFV allows for dynamical deployment of virtualized network functions, MEC allows applications to be deployed close to mobile users, thus reducing latency. When used together, NFV and MEC bring flexibility and enhanced performance to meet the user's demand. However, to increase service availability and maximize the convenience of MEC users, an MEC framework has to interface with multiple NFV orchestrators to utilize running network functions in an efficient manner. We introduce in this demonstration APMEC, a framework addressing the above two challenges. Via a combination of an interactive GUI, KPI views and a live-demo setup, we will showcase the advanced features of the framework.

Virtualization of services in factory production allows achieving higher service reliability and smaller cost of industrial equipment. The demonstrator shows Mobile Edge Cloud (MEC) implementation for services such as path planning and movement control of a robot arm, collaboration between two robot arms, and remote control of a robot arm. The audience can participate in the demonstrator through guiding a robot arm remotely using a wireless controller. The MEC resources required for such virtualized services are shown in terms of traffic (throughput and inter-arrival time) and server load.

This proposal for demonstration focuses on network slicing as an enabler for flexible and efficient Industrial IoT (IIoT) networks. We show that by using network slicing in a novel three-layer architecture, self-organization and flexibility, as well as maximization of network efficiency can be achieved in IIoT networks. A practical scenario of conditional monitoring is demonstrated by means of distributing sensor devices on site to engage the audience.

This demonstration shows both the utility of Mobile Edge Cloud (MEC) for 5G connected cars, as well as the impact of MEC server selection (i.e. migration) strategy on latency. The demonstration simulates cars moving according to a realistic model inside the city of Munich, and implements different MEC server selection strategies. A player can steer an ambulance using a controller, and can also choose one among four server selection strategies while driving. With each strategy, the player will experience a different latency.

Conditional monitoring for industrial IoT often uses acoustic signals for non-invasive anomaly detection. The acoustic sensors capture the mixed sound of several working machines, which should be separated in per-machine components for further analysis. The accuracy can be in part improved by installing redundant acoustic sensors. However, this would increase the amount of the transmitted data. In this paper, we propose a joint application of (i) Blind Source Separation (BSS) to separate the mixed sound of several working machines, and (ii) Compressed Sensing (CS) for reducing the amount of data transmitted over the network for partially correlated data sources. We also propose a set of key performance indicators to evaluate the whole system. Our simulation results, performed using the FastICA and CVXPY libraries, show that our solution provides a well balance between the amount of transmitted data and the separation quality. In other words, it optimizes the network throughput for the given value of desired separation quality.

Responding to network security incidents requires interference with ongoing attacks to restore the security of services running on production systems. This approach prevents damage, but drastically impedes the collection of threat intelligence and the analysis of vulnerabilities, exploits, and attack strategies. We propose the live confinement of suspicious microservices into a sandbox network that allows to monitor and analyze ongoing attacks under quarantine and that retains an image of the vulnerable and open production network. A successful sandboxing requires that it happens completely transparent to and cannot be detected by an attacker. Therefore, we introduce a novel metric to measure the Quality of Deception (QoD) and use it to evaluate three proposed network deception mechanisms. Our evaluation results indicate that in our evaluation scenario in best case, an optimal QoD is achieved. In worst case, only a small downtime of approx. 3s per microservice (MS) occurs and thus a momentary drop in QoD to 70.26% before it converges back to optimum as the quarantined services are restored.

Mobile Edge Computing (MEC) and Network Function Virtualization (NFV) are widely considered to be key players in the 5G era. Whereas MEC enables to reduce latency significantly by allowing applications to be deployed close to end users, NFV allows for flexible deployment of virtualized network functions. The performance and flexibility can be improved further by combining MEC and NFV. Existing frameworks for managing and orchestrating MEC applications and NFV are either tightly coupled or completely separated. The former design is inflexible and increases the complexity of one framework, while the latter leads to inefficient use of computation resources. In this paper, we extend our Automated Provisioning Framework for MEC (APMEC), which combines each MEC application and its respective network service (comprising a chain of virtual network functions) in a MEC service. We propose a novel MEC service placement algorithm allowing to reuse a subset of yet underloaded network functions. Our evaluation results, obtained from a testbed implementation and simulations, show that our solution allows to remarkably increase the utilization of network functions and simultaneously reduce routing cost. Specifically, it allows to accept at least 60% more user requests, and at the same time lowering the routing cost by more than 30%, as compared to the baseline approach.

Implementing DASH, the most popular method for multimedia streaming, over NDN, a potential future Internet architecture, can substantially increase the network bandwidth utilization. However, inherent features of NDN can create new security risks for adaptive multimedia streaming. We propose a novel attack called Bitrate Oscillation Attack (BOA), which adversely exploits NDN's autonomous on-path caching and interest aggregation to unsettle DASH functionality. BOA forces the resolution and quality of video received by the attacked client to oscillate with high frequency and amplitude. Subsequently, we present CoMon-DAS, a framework for lightweight coordination that mitigates BOA and other attacks in NDN. Through extensive simulations, we demonstrate that BOA is very harmful for DAS over NDN, but can be significantly mitigated by CoMon-DAS.

A wide variety of applications ranging from distributed storage to Forward Error Correction (FEC) benefit from Random Linear Network Coding (RLNC). Recent research activities exploited Network Coding as a Service (NCaaS) as FEC for Software-Defined Networking (SDN). Based on the aforementioned research, in this paper we propose a more efficient approach to use RLNC in a reliability-centric network. By leveraging both, the knowledge possessed by the SDN switches on channel conditions and the dynamic flexibility provided by the virtualized coding instances, we developed a novel method to adapt the code rate of the underlying FEC, to support two different transport protocols with varying reliability and throughput requirements. We have implemented our approach in a realistic SDN emulator to evaluate it in a communication network with time-variant links for TCP and UDP flows comparing against existing fixed-code rate approaches. The results show that our method adapts the flows respectively to the channel conditions and thus delivers a good trade-off between reliability, bandwidth, and overhead. Specifically, our loss estimation algorithm can precisely estimate future losses with a deviation of up to 3%. Our approach is also able to precisely determine the delivery probability with a maximum deviation of 1.5%. In addition, we show that our adaptive redundancy enables TCP to achieve a stable throughput despite losses.

Increasing the traffic visibility, by monitoring network flow packets, provides valuable information for various network management tasks. The mirroring mode of flow packet monitoring requires the switches and routers to duplicate packets of interest, and to send them to flow monitors for in-depth analysis. A common practice to avoid the interference between the original and the mirrored flows is to transmit them separately, in two different planes (data plane and monitoring plane, respectively). In this paper, we aim at reducing the overall cost of transmitting both the original and mirrored flows. Towards that end, we present a generic monitoring framework called REMO. The key idea of REMO is twofold: (i) placing the flow monitors in central locations, and (ii) passing the original flows through the vicinity of the monitors. By doing so, REMO reduces the resources consumed in the monitoring plane, without unworthily increasing the resource consumption in the data plane. The results of extensive numerical simulations show that REMO effectively reduces the overall transmission cost, remarkably outperforming several baseline strategies, particularly when the transmission is more expensive in the monitoring plane.

P2P-based social networking services are severely challenged by churn and the lack of reliable service providers, especially considering the high frequency of posts and profile updates of their users. Improved consistency and data availability shall facilitate better acceptance, which in turn will enhance privacy, an inherent benefit of this class of systems. We present Lilliput, a P2P storage primitive designed with the characteristics of Online Social Network workloads in mind. Lilliput separates the storage of static bulk data (videos and photo albums) from the essential social glue (e.g. basic profile information, frequent updates, notifications, and personal messages): it provides the latter through agile, lightweight replica groups. Extensive simulations show that Lilliput ensures high data availability (99.07% to 99.64%) and consistency, with a small bandwidth usage under realistic usage and load models.

Defending against cache pollution attacks, highly detrimental attacks that are easy to implement in Named-Data Networking (NDN), currently suffers from the lack of coordination. Solving cache pollution attacks is a prerequisite for the deployment of NDN, which is widely considered to be the basis for the future Internet. We present CoMon++ to this end, a framework for lightweight coordination that protects from cache pollution and further attacks in NDN. Our simulation studies demonstrate that CoMon++ efficiently and effectively prevents cache pollution, remarkably outperforming a very notable state-of-the-art solution.

Named-Data Networking (NDN) is a promising architecture for the future Internet. However, it is hampered by interest flooding, an NDN-tailored DDoS attack which has been shown to cause dropping majority of legitimate packets. While several defence mechanisms have been suggested against it, they cannot protect NDN against the Collusive Interest Flooding Attack (CIFA), a previously disregarded version of interest flooding. In CIFA, malicious clients issue interest packets that can be satisfied only by a malicious server. The server, in turn, responds with data packets just before expiration of the corresponding PIT entries. We study the effect of CIFA. Extensively simulating CIFA, we show that it affects the network and legitimate users almost as badly as an extensively researched version of interest flooding. Subsequently, we develop a generic defence mechanism against interest flooding attacks. The mechanism is based on CoMon, our framework for coordination in NDN. Thanks to CoMon, the attacks are detected and mitigated at an early stage by only a few routers. Via realistic simulations, we show that our defence decreases the amount of dropped legitimate packets remarkably, incurring a very low signalling overhead.

The autonomous cache management in Content-Centric Networking (CCN) results in suboptimal caching decisions and implies cache-ignorant routing. Cache coordination and similar improvements hence have been the subject of several recent studies. The proposed solutions, however, are either impractical due to their massive coordination overhead, or of limited benefit since they cannot realize perfect coordination. We present CoMon, an architecture for network-wide coordinated caching. CoMon realizes an affordable, yet highly effective, coordination by assigning monitoring and cache-aware (re)routing tasks to only a few nodes, through which the majority of traffic is expected or enforced to pass. CoMon, by design, maximizes the diversity of cached contents and reduces cache replacements. In addition, our simulation study using ISP topologies, shows that CoMon under several scenarios, when coordinates through a small ratio of the nodes, reduces the server hit ratio (i.e. the ratio of requests consumed by the origin content providers) of both CCN and notable related work, remarkably.

Named-Data Networking (NDN) is a promising architecture for future Internet. Its design, however, can be misused to perform a new DDoS attack known as the Interest Flooding Attack (IFA). In IFA, the attacker issues non-satisfiable interest packets, aiming to drop legitimate interest packets by overwhelming pending interest tables in NDN routers. Prior defence mechanisms are not highly effective, harm legitimate interest packets, and/or incur high overhead. We propose a coordinated defence mechanism against IFAs. We realize our solution by adapting CoMon, a framework that we developed previously to coordinate caching-related decisions in NDN, motivated by its effective, yet affordable, coordination. In our solution, IFAs are detected and mitigated by few routers based on aggregated knowledge of traffic and forwarding states. These routers are selected by a novel heuristic enabling them to observe the entire traffic at an early stage. Extensive simulations confirm the feasibility and effectiveness of our solution.

The widely used distributed hash table (DHT) in KAD is commonly analyzed and optimized based on partial measurements and simulation results, which are limited in scope and subject to simplification. An accurate characterization, however, is vital for a thorough understanding and effective enhancement. Analyzing and comparing complete real graphs collected from a large-scale measurement campaign as well as synthetic graphs generated by a novel simulation model, we study their degree distributions as well as resilience in face of random departure and targeted attacks. Our results show that the online KAD graph, although scale-free, is highly robust not only to random departure, but also to targeted attacks, making it suitable for distributed applications requiring a high resilience. Resilience to random departure and shape of degree distribution are well modelled by the simulations. However, due to a greatly increased ratio of stale routing information, the complete graph in the real system is much more vulnerable to targeted attacks compared to estimations based on simulative results.

Discovery of nodes and content in large-scale distributed systems is generally based on Kademlia, today. Understanding Kademlia-type systems to improve their performance is essential for maintaining a high service quality for an increased number of participants, particularly when those systems are adopted by latency-sensitive applications. This paper contributes to the understanding of Kademlia by studying the impact of diversifying neighbours' identifiers within each routing table bucket on the lookup performance. We propose a new, yet backward-compatible, neighbour selection scheme that attempts to maximize the aforementioned diversity. The scheme does not cause additional overhead except negligible computations for comparing the diversity of identifiers. We present a theoretical model for the actual impact of the new scheme on the lookup's hop count and validate it against simulations of three exemplary Kademlia-type systems. We also measure the performance gain enabled by a partial deployment for the scheme in the real KAD system. The results confirm the superiority of the systems that incorporate our scheme.

Motivated by the popularity of content distribution and file sharing applications that nowadays dominate Internet traffic, we focus on the incentive mechanism of a very popular, yet not very well studied, peer-to-peer application, eMule. In our work, we recognize that the incentive scheme of eMule is more sophisticated than current alternatives (e.g., BitTorrent) as it uses a general, priority-based, time-dependent queuing discipline to differentiate service among cooperative users and free-riders. In this paper, we describe a general model of such an incentive mechanism and analyze its properties in terms of application performance. We validate our model using both numerical simulations (when analytical techniques become prohibitive) and with a measurement campaign of the live eMule system. Our results, in addition to validating our model, indicate that the incentive scheme of eMule suffers from starvation. Therefore, we present an alternative scheme that mitigates this problem, and validate it through numerical simulations and a second measurement campaign.