Service Disruption Time Research Articles

Multiple Criteria-Based Intelligent Techniques for Efficient Handover in Next-Generation Networks

Background: Several wireless technologies are assumed to be operating in cooperation for next-generation networks. These networks offer various services to mobile users; however, efficient handover between different networks is the most challenging task in high mobility scenarios. The traditional signal strength-based handover algorithms are not able to cope with mobile users' high-quality requirements. Methods: In this paper, multiple criteria-based intelligent techniques are proposed to deal with inefficiencies related to handover. This technique makes use of artificial neural networks that take multiple parameters as inputs in order to predict the degradation of parameters. These predictions are further used to design rules for initiating handover procedures prior to service quality degradation. Results: Based on the prediction results obtained by deep neural networks, the handover decisions are recommended according to the type of application: conversational or streaming. Conclusion: The simulation results demonstrate the efficacy of the proposed method as there is an improvement of up to 40% and 25% in terms of handover rate and service disruption time, respectively, with an acceptable prediction error of 0.05.

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

AbstractMillimeter wave (mmWave) technology, with its abundant spectrum resources and ultra‐high bandwidth, plays a crucial role in meeting the ultra‐high throughput requirements of future 6G communication and supporting a large number of device connections. However, mmWave signals are highly susceptible to congestion, and the channel quality in mmWave links can be extremely unstable, posing a significant challenge to mobility management in mmWave networks. In this article, we present a deep reinforcement learning‐based dual connectivity (DC) handover strategy for mmWave cellular networks. The DC architecture ensures a continuous connection to the core network, reducing the likelihood of frequent service interruptions. Additionally, the proposed handover strategy employs deep reinforcement learning to select the optimal handover target node, further enhancing system performance. Compared to traditional single connectivity (SC) handover strategies, DC involves more intricate handover trigger conditions and processes. We extend several typical SC handover algorithms for DC and compare them with the proposed algorithms in this study. Through numerical simulations, we evaluate the performance of these algorithms. The results demonstrate that the proposed algorithm minimizes unnecessary handovers, reduces service interruption time, and achieves the highest throughput.

Fast Resource Allocation for Resilient Service Coordination in an NFV-Enabled Internet-of-Things System

Network Functions Virtualization (NFV) is a new way of leveraging an Internet-of-Things (IoT) system to provide real-time and highly flexible service creation. In an NFV-enabled Internet-of-Things (NIoT) system, several IoT functions implemented as Virtual Network Functions can be linked as a service function chain to build a customized IoT service quickly. It is important for an IoT service to be able to recover from a failure. However, the supply of a resilient IoT service in an NIoT system is challenging due to the coordination of distributed VNF instances. In this paper, we formulate the problem of resilient service coordination in an NIoT system as a mixed-integer linear programming model, namely RSOd. The model offers the optimal resource allocation for minimizing service disruption when a failure happens at a node of an NIoT system. We also develop two modified versions of RSOd for different use cases required by an IoT provider. Further, two approximation algorithms are proposed to provide a resilient service for a large-scale NIoT system. The evaluation results show that RSOd and its modified versions produce the optimal resource allocation in significantly reduced time compared to previous work. The results suggest that an IoT provider should carefully select an appropriate resource allocation strategy as it has to pay a resource cost to minimize the service disruption. The results also show that our proposed priority-based heuristic algorithm outperforms an approximation algorithm based on Simulated Annealingin terms of the service disruption and computation time.

Comparing Machine Learning Algorithms for Improving the Maintenance of LTE Networks Based on Alarms Analysis

Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided. To achieve this goal, technological progress and scientific advances offer good opportunities for efficiency in the management of faults occurring in a mobile network. Machine learning techniques allow systems to learn from past experiences and can predict, solutions to be applied to correct the root cause of a failure. This paper evaluates machine learning techniques and identifies the decision tree as a learning model that provides the most optimal error rate in predicting outages that may occur in a mobile network. Three machine learning techniques are presented in this study and compared with regard to accuracy. This study demonstrates that the appropriate machine learning technique improves the accuracy of the model. By using the decision tree as a machine learning model, it was possible to predict solutions to network failures, with an error rate less than 2%. In addition, the use of Machine Learning makes it possible to eliminate steps in the network failure processing chain; resulting in reduced service disruption time and improved the network availability which is a key network performance index.

Federated 3GPP Mobile Edge Computing Systems: A Transparent Proxy for Third Party Authentication With Application Mobility Support

Multi-Access or Mobile Edge Computing (MEC) is being deployed by 4G/5G operators to provide computational services at lower latencies. Federating MECs across operators expands capability, capacity, and coverage but gives rise to two issues for continuous service during roaming without re-authentication—third-party authentication and application mobility. In this work, we propose a Federated State transfer and 3rd-party Authentication (FS3A) mechanism that uses a transparent proxy to transfer the information of both authentication and application state across operators to resolve these issues. The FS3A proxy is kept transparent with virtual counterparts to avoid any changes to existing MEC and cellular architectures. FS3A provides users with a token which, when authenticated by an MEC, can be reused across operators for faster authentication. Prefetching of subscription and state is also proposed to further reduce authentication and application mobility latencies. We evaluated FS3A on an OpenAirInterface (OAI)-based testbed and the results show that token reuse and subscription prefetching reduce the authentication latency by 53–65%, compared to complete re-authentication, while state prefetching reduces application mobility latency by 51–91%, compared to no prefetching. Overall, FS3A reduces the service interruption time by 33%, compared to no token reuse and prefetching.

Container-Based Fast Service Migration Method for Mobile Edge Computing

Mobile edge computing (MEC) provides users with low-latency, high-bandwidth, and high-reliability services by migrating the computing power of the cloud computing center to the edge of the network. It is thus being considered an effective solution for the contradiction between the limited computing capabilities of Internet of Things (IoT) devices and the rapid development of delay-sensitive real-time applications. In this study, we propose and design a container union file system based on the differencing hard disk and dynamic loading strategy to address the excessively long migration time caused by the bundling transmission of the file system and container images during container-based service migration. The proposed method involves designing a mechanism based on a remote dynamic loading strategy to avoid the downloading of all container images, thereby reducing the long preparation time before which stateless migration can begin. Furthermore, in view of the excessive latency of the edge service during the stateful migration process, a strategy for avoiding the transmission of the underlying file system and container images is designed to optimize the service interruption time and service quality degradation time. Experiments show that the proposed method and strategy can effectively reduce the migration time of container-based services.

A Dynamic Adjustment Method of Service Function Chain Resource Configuration

In the network function virtualization environment, dynamic changes in network traffic will lead to the dynamic changes of service function chain resource demand, which entails timely dynamic adjustment of service function chain resource configuration. At present, most researches solve this problem through virtual network function migration and link rerouting, and there exist some problems such as long service interruption time, excessive network operation cost and high penalty. This paper proposes a dynamic adjustment method of service function chain resource configuration for the dynamic changes of network traffic. First, a dynamic adjustment request of service function chain is generated according to the prediction of network traffic. Second, a dynamic adjustment strategy of service function chain resource configuration is determined according to substrate network resources. Finally, the resource configuration of a service function chain is pre-adjusted according to the dynamic adjustment strategy. Virtual network functions combination and virtual machine reusing are fully considered in this process. The experimental results show that this method can reduce the influence of service function chain resource configuration dynamic adjustment on quality of service, reduce network operation cost and improve the revenue of service providers.

Availability and service disruption of network services: From high-level requirements to low-level configuration constraints

In the context of Network Function Virtualization (NFV), a Network Service (NS) is defined as a composition of Network Functions (NFs), which can be Virtual Network Functions (VNFs) or Physical Network Functions (PNFs). The availability of an NS is among its important characteristics. Moreover, when there is a service outage there is often a service disruption. We define the Service Disruption Time for an NS functionality as the amount of time for which the service state is lost due to service outages for a given period. The Service Data Disruption for an NS functionality is defined as the maximum amount of data lost due to a service outage. To have a comprehensive design solution considering service availability and service disruption, we propose a novel method which enhances an NS design by mapping service-level availability and disruption requirements to constraints on low-level configuration parameters, and calculates the number of required standby instances for each VNF so that the requirements can be met. Our method guarantees the availability and disruption requirements at any scaling level of the NS while minimizing the resource cost. To reduce the complexity of the complete search for the optimal configuration we also propose a heuristic algorithm and perform experiments to compare it with the complete search.

Application Research of Internet of Things Technology in the Causes of Dragon Boat Sports Injury

In recent years, the Internet of Things technology can effectively innovate applications and services. The Internet of Things technology has become more and more popular. It provides an effective and direct bridge between the physical world and virtual objects in cyberspace. With the increase in the intensity of dragon boat training and the increasingly fierce competition, the possibility of injury is increasing. Dragon boat racing is a noncontact team sport based on strength and technology. The purpose of this paper is to solve the problem of people's lack of understanding of the sports injuries and causes of dragon boat athletes. We used the data fusion algorithm and cluster maintenance optimization algorithm to study the application of Internet of Things technology in the cause of dragon boat sports injury. In order to save energy, extend the network life cycle, shorten service interruption time, and increase data packet transmission, the cluster maintenance optimization algorithm in this paper mainly improves and optimizes the startup time of cluster maintenance, which depends on the maintenance cost. The experiment result shows that the etiological detection system proposed in this paper matches the actual sports injury results well. The experiment result shows that the research on the cause of injury in dragon boat sports based on Internet of Things technology can detect the damage law well and can have a more comprehensive understanding for the cause of injury, which helps to prevent injuries better and take effective treatments. In the analysis part, it can be concluded that the detection system is very accurate in detecting the cause, and the accuracy rate is basically 100%.

A better estimation of wave arrival time in water distribution networks using WAvelet kNEe (WANE)

A transient pressure wave is a sudden pressure change that occurs in a short time, which can be induced by sudden changes in valve and pump operation, and pipe bursts in a Water Distribution Network (WDN). An accurate estimation of a transient wave arrival time is crucial because it facilitates pipe condition assessment, hydraulic model calibration, and accurate localization of pipe burst events. Due to the noisy and highly fluctuating nature of the pressure signals, estimating an accurate transient pressure wave arrival time is not a trivial task. Among many methodologies proposed for detecting abrupt pressure changes, Discrete Wavelet Transform (DWT) and Cumulative Sum (CUSUM) were the two most popular approaches. However, several limitations involved with these two approaches can easily lead to unsatisfactory results. Moreover, some of the existing methodologies were only tested on either a single pipeline, engineered events, or a small sample size of events, making these methodologies suitable and accurate only for a limited number of scenarios. Driven by these limitations, a novel approach is proposed to estimate the wave arrival time in water distribution networks (WDNs). The backbone of this approach is the integration of wavelet decomposition and a knee point detection algorithm, thus gaining the name WAvelet kNEe (WANE). Through a comparative study against the other methodologies using 90 recorded transient events detected in a real WDN, WANE is found to provide the best wave arrival time estimation, with a Root Mean Square Error (RMSE) of 0.4 s. Based on the result, our estimation error is at least 15 s lesser than the other methodologies. With an improved wave arrival time estimation, WANE has the potential to minimize the response time of repair crews, service disruption time, as well as the associated water losses due to a pipe break.

HTTP Adaptive Streaming on Improved LISP-MN Architecture

The increasing consumption of video content using mobile devices calls for a more efficient techniques of handling the enormous data sent and received across wireless Internet. The Loc/ID split architecture is a group of proposals geared towards enhancing the mobility of nodes on the Internet. Although Loc/ID protocols guarantee handover, they suffer excessive packet loss and long service disruption time during the process. In this paper, we posit that redirecting incoming packets to a nearby buffer node, while a handover is taking place, and forwarding the packets to the mobile device after it reconnects will reduce the negative impact of packet loss on user experience. Improvement in the performance of video services is one manifestation that could be observed. In line with this, we propose an improved architecture for one of the most popular Loc/ID protocols, Locator-Identifier Separation Protocol Mobile Node (LISP-MN) by adding a new node, termed loc-server, to LISP-MN components. Loc-server caches and forwards packets to a mobile device during its handover process. To evaluate the performance of the improved architecture, we implemented the system on a laboratory tested and developed a number of MPEG-DASH compliant players. The performance of the players streaming over the proposed architecture is then compared against vanilla LISP-MN architecture. Results of the evaluation show improved performance when running the players on top of the new architecture.

Multitier Service Migration Framework Based on Mobility Prediction in Mobile Edge Computing

Mobile edge computing (MEC) pushes computing resources to the edge of the network and distributes them at the edge of the mobile network. Offloading computing tasks to the edge instead of the cloud can reduce computing latency and backhaul load simultaneously. However, new challenges incurred by user mobility and limited coverage of MEC server service arise. Services should be dynamically migrated between multiple MEC servers to maintain service performance due to user movement. Tackling this problem is nontrivial because it is arduous to predict user movement, and service migration will generate service interruptions and redundant network traffic. Service interruption time must be minimized, and redundant network traffic should be reduced to ensure service quality. In this paper, the container live migration technology based on prediction is studied, and an online prediction method based on map data that does not rely on prior knowledge such as user trajectories is proposed to address this challenge in terms of mobility prediction accuracy. A multitier framework and scheduling algorithm are designed to select MEC servers according to moving speeds of users and latency requirements of offloading tasks to reduce redundant network traffic. Based on the map of Beijing, extensive experiments are conducted using simulation platforms and real‐world data trace. Experimental results show that our online prediction methods perform better than the common strategy. Our system reduces network traffic by 65% while meeting task delay requirements. Moreover, it can flexibly respond to changes in the user’s moving speed and environment to ensure the stability of offload service.

Click-UP: Toward the Software Upgrade of Click-Based Modular Network Function

A Click-based network function (NF) has significant advantages for pipeline development, including modularity, extensibility, and programmability. Despite these features, its internal architecture has unfortunately not kept up with some specific problems of the software upgrade. To motivate our article, we analyzed a series of use cases to identify the limitations of native Click. These limitations include the inefficiencies in modifying modules, integrating modules, and recovering states. To bridge the gap, we present three novel enhancements in our Click upgrade (Click-UP) system: 1) modular state abstraction refines each type of stateful operations as an atom operation and decouples it from the pipeline, letting separately managing logics for stateless operations and stateful operations become practical; 2) essential module integration manages dependencies between modules, avoiding shipping unnecessary modules with neutral functionalities to the target NF; and 3) local state migration migrates needed states seamlessly from the old NF to the target NF at local memory. Our evaluation demonstrates that Click-UP reduces the context code required for module modification by 12–81%, cutting down the NF integration time by 78–96% and the service disruption time by 76–93%, as compared to the software upgrade performance represented by native Click.

Dynamic Hybrid-copy Live Virtual Machine Migration: Analysis and Comparison

Abstract Live VM migration has significantly gained attention as one of the most key strategies of improving data center efficiency through better resources allocation that leads to efficient services. The data center efficiency includes less power consumption, less unutilized resources, and less service interruption time in cases of maintenance or failures. The key point of the live VM migration is to copy the VM’s state, memory, and disk with the minimal downtime to guarantee less interruption in the services the VM provides. Pre-copy, Post-copy, and Hybrid-copy are examples of Live VM migration methods that copy the VM’s memory states to the destination host, while the VM still runs at the source host, as in the Pre-copy, or at the destination host, as in Post-copy. Each of these classical live migration methods has different characteristics and performance. Dynamic Hybrid-copy combines Pre-copy iterations with the Post-copy to have the best of both methods. In this paper, we present the mathematical model for the Dynamic Hybrid-copy live migration method for the total migration time, downtime, and total transferred data during the migration process. We compare the Dynamic Hybrid-copy with other classical live migration methods. We implement the models on JAVA and evaluate the performance of the Dynamic Hybrid-copy and compare it with other methods for different experimental parameters. Based on the performance results, we show how choosing the effective number of Pre-copy iterations leads to better migration performance. Mathematical models were also implemented in CloudSim and a simulation scenario was tested for the four migration methods.

Backup Battery Analysis and Allocation against Power Outage for Cellular Base Stations

Base stations have been widely deployed to satisfy the service coverage and explosive demand increase in today's cellular networks. Their reliability and availability heavily depend on the electrical power supply. Battery groups are installed as backup power in most of the base stations in case of power outages due to severe weathers or human-driven accidents, particularly in remote areas. The limited numbers and capacities of batteries, however, can hardly sustain a long power outage without a well-designed allocation strategy. As a result, the service interruption occurs along with an increasing maintenance cost. Meanwhile, a deep discharge of a battery in such case can also accelerate the battery degradation and eventually contribute to a higher battery replacement cost. In this paper, we closely examine the base station features and backup battery features from a 1.5-year dataset of a major cellular service provider, including 4,206 base stations distributed across 8,400 square kilometers and more than 1.5 billion records on base stations and battery statuses. Through exploiting the correlations between the battery working conditions and battery statuses, we build up a deep learning based model to estimate the remaining lifetime of backup batteries. We then develop BatAlloc , a battery allocation framework to address the mismatch between the battery supporting ability and diverse power outage incidents. We present an effective solution that minimizes both the service interruption time and the overall cost. Our real trace-driven experiments show that BatAlloc cuts down the average service interruption time from 4.7 hours to nearly zero with only 85 percent of the overall cost compared to the current practical allocation.

Link Fault Tolerant Algorithms to Optimize the Blocking in Optical Burst Switching Networks

Abstract In this paper, link fault tolerant algorithms (LFTAs) are employed for optimization of blocking performance in optical burst switching (OBS) networks for dynamic establishment of wavelength-routed lightpaths. This paper presents two algorithms: link fault tolerance algorithm for least congestion (LFTALC) and link fault tolerance algorithm for optimized blocking (LFTAOB). In former algorithm, the path with least congestion is selected and in the latter algorithm the path with least blocking probability is selected and then both the algorithms check for the availability of fault on the links present in the selected path. The findings indicate that methods of developing link based fault tolerant algorithms keep good performance and fairness among all the connections. The key advantage of these proposed algorithms as compared to routing and wavelength assignment algorithm is that these are very simple in nature and require less service disruption time.

An Improved LISP Mobile Node Architecture

The increased use of mobile devices has prompted the need for efficient mobility management protocols to ensure continuity of communication sessions as users switch connection between available wireless access networks. Locator Identifier Separation Protocol Mobile Node (LISP-MN) was designed to enable such efficient mobility of nodes on the Internet. The protocol enables mobility by ensuring that the IP address used for creating data session is maintained throughout the lifetime of the communication session and the location of the mobile node (MN) is updated as the device moves. While session continuity is achieved during handover, we observed that LISP-MN records loss of packets in transit, long service disruption time, throughput degradation and increased rate of TCP retransmission as an MN conducts a handover from one access link to another. To mitigate the poor handover performance, we introduced a novel network node into the LISP-MN architecture, a loc-server that buffers the packets sent to an MN during handover and forwards to the device upon completion of the movement process. We analysed both qualitative features and quantitative measurements of vanilla LISP-MN against LISP-MN with loc-server support. Results show that the improved architecture significantly improved the performance of LISP-MN in all the investigated parameters.

Dual-Connectivity Prevenient Handover Scheme in Control/User-Plane Split Networks

A control/user (C/U) plane-split network, where macro- and microevolved Node Bs (eNBs) are responsible for C/U-planes, respectively, is a potential 5G solution to address the problems experienced by high-speed railway communication systems such as frequent handovers (HOs), low system capacity, and low data rate. The C-plane transition occurs during an intermacro eNB HO to reduce the frequency of HOs. However, the intermacro-eNB HO in a C/U-plane-split network is prone to handover failures (HOFs) and long service interruption time similar to the traditional long-term evolution-advanced (LTE-A) HO without improvements because it is similar to an LTE-A HO between macro-eNBs from the perspective of C-plane transition. In this paper, we realize a C/U-plane-split network using the LTE-A dual-connectivity (DC) technique and propose a dc-based prevenient HO (PHO) scheme to improve the robustness of the intermacro-eNB HO. The proposed scheme specifically is an a priori method and uses the transmit diversity based on the dc technique to reduce the risk of HOFs and the long service interruption time. Further, we first develop the analytical expressions for HO performance for PHO and LTE-A HO, where HOFs are aligned with the LTE-A specifications such that they are defined as the association between the radio link monitor and HO processes. The analytical results are verified against simulation results. Through simulations, we compare PHO with LTE-A HO and the representative schemes of the previous literature in a variety of mobility performance metrics (such as the averages of the successful HO rate, the HOF rate, the radio link failure rate, the ping-pong rate, and the service interrupt time). The results confirm that the proposed scheme achieves superior performance in the key metrics of the successful HO rate and service interrupt time.

A Network-Based Seamless Handover Scheme for VANETs

Vehicular ad hoc network (VANET) standards, including IEEE 1609 WAVE and ETSI C-ITS, use IPv6 to interconnect VANET with the Internet. Considering the high vehicle speeds and short communication ranges of road side units (RSUs), such vehicles may suffer from frequent service interruptions. Several VANET IP mobility schemes have been proposed in order to mitigate this problem. However, these only support L3 handover, as the RSUs act as IPv6 access routers (ARs) for vehicles. Some VANET IP mobility schemes reduce the handover latency of the L3 handover but do not fundamentally mitigate the frequency of the L3 handover. As L3 handover indicates that the default router of the vehicle is changing, the vehicle must perform IPv6 configuration, including router discovery and neighbor unreachability detection. Since most L3 handover latency is caused by this IPv6 configuration, the L3 handover involves a higher signaling cost and longer handover latency than the L2 handover. We propose a network-based L2 extension handover scheme for VANET. It decouples the AR functions from the RSU. An AR connects several RSUs via L2 links within its coverage. In this configuration, most inter-AR handovers are replaced with intra-AR handovers, so that the frequency of the L3 handover is decreased substantially. Therefore, the service disruption time caused by default router switching is also reduced, and the deployment of VANET can be made more flexible by decoupling the RSU and the AR. Proxy Mobile IPv6 is adopted in order to support inter-AR handover in the proposed scheme. Furthermore, the scheme supports seamlessness in both the intra-AR and inter-AR handovers with buffering at the AR. The performance analysis and the simulation result reveal that the proposed scheme reduces the signaling cost and handover latency and also shows seamless packet delivery for vehicles.

A Novel Framework of Multi-Hop Wireless Charging for Sensor Networks Using Resonant Repeaters

Wireless charging has provided a convenient alternative to renew nodes' energy in wireless sensor networks. Due to physical limitations, previous works have only considered recharging a single node at a time, which has limited efficiency and scalability. Recent advances on multi-hop wireless charging is gaining momentum and provides fundamental support to address this problem. However, existing single-node charging designs do not consider and cannot take advantage of such opportunities. In this paper, we propose a new framework to enable multi-hop wireless charging using resonant repeaters. First, we present a realistic model that accounts for detailed physical factors to calculate charging efficiencies. Second, to achieve balance between energy efficiency and data latency, we propose a hybrid data gathering strategy that combines static and mobile data gathering to overcome their respective drawbacks and provide theoretical analysis. Then, we formulate multi-hop recharge schedule into a bi-objective NP-hard optimization problem. We propose a two-step approximation algorithm that first finds the minimum charging cost and then calculates the charging vehicles' moving costs with bounded approximation ratios. Finally, upon discovering more room to reduce the total system cost, we develop a post-optimization algorithm that iteratively adds more stopping locations for charging vehicles to further improve the results while ensuring none of the nodes will deplete battery energy. Our extensive simulations show that the proposed algorithms can handle dynamic energy demands effectively, and can cover at least three times of nodes and reduce service interruption time by an order of magnitude compared to the single-node charging scheme.