Network Resource Consumption Research Articles

Multi-Party WebRTC Services Using Delay and Bandwidth Aware SDN-Assisted IP Multicasting of Scalable Video Over 5G Networks

At present, multi-party WebRTC videoconferencing between peers with heterogenous network resources and terminals is enabled over the best-effort Internet using a central selective forwarding unit (SFU), where each peer sends a scalable encoded video stream to the SFU. This connection model avoids the upload bandwidth bottleneck associated with mesh connections; however, it increases peer delay and overall network load (resource consumption) in addition to requiring investment in servers since all video traffic must go through SFU servers. To this effect, we propose a new multi-party WebRTC service model over future 5G networks, where a video service provider (VSP) collaborates with a network service providers (NSP) to offer an NSP-managed service to stream scalable video layers using software-defined networking (SDN)-assisted Internet protocol (IP) multicasting between peers using NSP infrastructure. In the proposed service model, each peer sends a scalable coded video upstream, which is selectively duplicated and forwarded as layer streams at SDN switches in the network, instead of at a central SFU, in a multi-party WebRTC session managed by multicast trees maintained by the SDN controller. Experimental results show that the proposed SDN-assisted IP multicast service architecture is more efficient than the SFU model in terms of end-to-end service delay and overall network resource consumption, while avoiding peer upload bandwidth bottleneck and distributing traffic more evenly across the network. The proposed architecture enables efficient provisioning of premium managed WebRTC services over bandwidth-reserved SDN slices to provide videoconferencing experience with guaranteed video quality over 5G networks.

Parametric Optimization of Packet Transmission with Resending Packets Mechanism

The data transmission process is modelled by a Markov closed queuing network, which consists of two stations. The primary station describes the process of sending packets over a lossy channel by means of a finite and single-channel queue. The auxiliary station, being a multichannel queuing system, accumulates packets lost by the primary station and forwards them back for retrial. The transmission rate at the primary station and the retrial rate at the auxiliary station are in the specified ranges and are subject to optimization in order to minimize the time of successful delivery and the amount of network resources used. The explicit expressions for these characteristics are derived in the steady-state mode in order to formulate the problem of bi-criterion optimization. The optimal policies are established in two scenarios: the first problem is to minimize the average time of successful transmission with limited resources; the second problem is to minimize the consumption of network resources under the constraint on the time for successful transmission. The set of Pareto-optimal policies is obtained by solving the problem of minimization of the augmented functional. The quality characteristics of approximate solutions that do not take into account the service rate in the auxiliary system are analyzed.

Convolutional Neural Networks for On-Board Cloud Screening

A cloud screening unit on a satellite platform for Earth observation can play an important role in optimizing communication resources by selecting images with interesting content while skipping those that are highly contaminated by clouds. In this study, we address the cloud screening problem by investigating an encoder–decoder convolutional neural network (CNN). CNNs usually employ millions of parameters to provide high accuracy; on the other hand, the satellite platform imposes hardware constraints on the processing unit. Hence, to allow an onboard implementation, we investigate experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy. We experimentally explore approaches such as halving the computation precision, using fewer spectral bands, reducing the input size, decreasing the number of network filters and also making use of shallower networks, with the constraint that the resulting CNN must have sufficiently small memory footprint to fit the memory of a low-power accelerator for embedded systems. The trade-off between the network performance and resource consumption has been studied over the publicly available SPARCS dataset. Finally, we show that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art.

Connectedness-Aware Copy-Adaptive Routing Protocol in Intermittently Connected Networks

In the intermittently connected networks where the complete paths from a source to a destination do not always exist, multi-copy forwarding is considered to be capable of providing more reliable transmission than single-copy forwarding. However, injecting too many copies into a network can cause unnecessary network resource consumption that degrades the network performance, especially if the network nodes are low-cost and resource-constrained. Therefore, copy control is a critical efficiency mechanism for the forwarding protocols in such situations. Using a preset threshold is a common copy-control mechanism used by many multi-copy forwarding protocols. The preset threshold is the maximum allowed number of copies of a packet in a network, calculated before running these protocols. It can effectively reduce the unnecessary copies and provide better performance than the protocols without using copy control. However, preset threshold makes the protocol less flexible with respect to the variations of network characteristics such as density or connectedness caused by node failures or new nodes joining. In this paper, we propose a multi-copy routing algorithm, Connectedness-Aware Copy-Adaptive Routing (CACAR) protocol, which adopts a novel copy-control mechanism. CACAR estimates the network connectedness locally and conducts the copy control and the next-hop selection based on the measured connectedness. This proposed protocol can achieve a higher delivery rate and lower latency without sacrificing the energy efficiency compared with other benchmark multi-copy routing protocols.

Impacts Evaluation of DoS Attacks Over IPv6 Neighbor Discovery Protocol

The Neighbor Discovery Protocol (NDP) is one of the main protocols in the Internet Protocol version 6 (IPv6) suite. It provides many basic functions for the normal operations of IPv6 in a Local Area Network (LAN), such as address auto-configuration and address resolution. However, NDP has several vulnerabilities that can be used by malicious nodes to launch attacks, because NDP messages are easily spoofed. Surrounding this problem many solutions have been proposed for securing NDP but these solutions either proposed new protocols that need to be supported by all nodes or built mechanisms that require the cooperation of all nodes. In this paper we overview NDP vulnerabilities and available solutions to overcome their impacts on IPv6 network. In addition a research test bed setup to implement these vulnerabilities was introduced. Moreover attacks that prove these vulnerabilities are implemented on different types of operating systems, Windows and Linux platforms. Three network metrics throughput, delay and resources consumption have been chosen to investigate, analyze and evaluate the impacts of NDP related attacks on IPv6 link-local communication. Overall, the results had shown that performance of Linux based operating system is better than Windows based operating system.

Minimum-cost forest for uncertain multicast with delay constraints

The use of multicast transmission can efficiently reduce the consumption of network resources by jointly serving multiple destinations with a single source node. Currently, many multicast applications impose the constraint wherein multicast flows must be processed by a series of Virtual Network Functions (VNFs) before reaching their destinations. Given a multicast transmission, there are usually multiple server nodes, each of which is able to host all the required VNFs. Thus, the multicast flow should be initially steered to one or a few selected server nodes that act as pseudo sources, and the destinations will then retrieve new flow from any of these pseudo sources. In this paper, we model this kind of multicast as an uncertain multicast with multiple pseudo sources, whose routing structure is usually a forest consisting of multiple isolated trees. We then characterize and construct the Delay-guaranteed Minimum Cost Forest (D-MCF) such that each path from the source to the destination satisfies the end-to-end delay constraint. To tackle this NP-hard problem, we design two efficient methods, the Partition Algorithm (PA) and the Combination Algorithm (CA), to approximate the optimal solution. Theoretical analyses and evaluations indicate that these two methods can generate the desired routing forest for any multicast transfer. Moreover, the PA method achieves a better balance between performance and time consumption than the CA method. The evaluation results show that PA-(Ω+ 20) can reduce total cost by 49:02% while consuming 12:59% more time, thus significantly outperforming the CA-(Ω+ 20) method.

LoCO: Local Cooperative Data Offloading System Based on Location Information

The development of high speed mobile networks and the widespread use of smartphones have enabled users to easily obtain large data volumes via the Internet. This causes a heavy consumption of network resources, a burden on the available bandwidth. To solve such problems, a data offloading method with a wireless LAN access point has been used to distribute traffic from mobile to fixed networks. However, the method using wireless LAN access points can only change the communication paths but cannot reduce the overall traffic. This paper proposes a local cooperative data offloading system (LoCO) that reduces the overall traffic by sharing data, with direct communication between neighbors based on their location-related information. Moreover, the authors implemented the LoCO system on Android smartphones and clarified its performance in comparison with a traditional client/server system through experiments to download data in a real-world environment.

Distributed filtering for time-varying systems over sensor networks with randomly switching topologies under the Round-Robin protocol

This paper focuses on the filtering problem for nonlinear systems over sensor networks whose topologies are changeable subject to Round-Robin (RR) protocol in the finite horizon case. The phenomenon of switching topologies for sensor networks is considered due to the weak connections between the node and its neighbors. The Round-Robin communication strategy is employed to save the limited bandwidth and reduce the network resource consumption. It is our target to design distributed filters so that the dynamics of augmented error system satisfies the given level of average H∞ performance. We get the sufficient conditions in order to guarantee the existence of the desired distributed filters whose parameter matrices would be attained by a recursive method in terms of a set of matrix inequalities. In the end, a numerical simulation is exploited to describe the validity of the raised filter design technique.

Research on Key Technologies of Student Electronic Depository System Based on Blockchain

Aiming at the problem that a large number of students’ electronic deposit data is easy to forge and destroy, a student electronic deposit application architecture based on blockchain is proposed. There are some features on blockchain, such as decentralized, the non-tamperable of data, the consensus mechanism and data encryption features, and the electronic depository network. All these features provide a secure and credible storage environment, ensures the traceable of the student’s electronic deposit information, the trust of the entire network, and the security of the data. At the same time, an improved PBFT consensus algorithm is proposed, which eliminates the certificate elimination phase to reduce network resource consumption, and constructs a decentralized electronic depository platform in combination with the blockchain to realize the authenticity, integrity and reliability of the electronic certificate data. The further development and application of blockchain technology is prospected, hoping to provide inspiration and reference for future related research.

Consumption Behavior Analysis of Over the Top Services: Incremental Learning or Traditional Methods?

Network monitoring and analysis of consumption behavior are important aspects for network operators. The information obtained about consumption trends allows to offer new data plans aimed at specific users and obtain an adequate perspective of the network. Over The Top applications are known by their large consumption of network resources. Service degradation is a common mechanism that applies limits to the amount of information that can be transferred and it is usually applied in a generalized way, affecting the performance of applications consumed by users while leaving aside their behavior and preferences. With this in mind, a proposal of personalizing service degradation policies applied to users has been considered through data mining and traditional machine learning. However, such approach is incapable of considering the swift changes a user can present in their consumption behavior over time. In order to observe which approach is capable of a continuous model adaptation while maintaining their usefulness over time, this paper introduces a performance comparison of traditional and incremental machine learning algorithms applied to information about users’ Over The Top consumption behavior. Two datasets are implemented for the tests: the first one is built through a real network experiment holding 1,581 instances, and the second one holds 150,000 instances generated in a synthetic way. After analyzing the obtained results, the best algorithm from the traditional approach was a Support Vector Machine while the best classifier from the incremental approach was an ensemble method composed by Oza Bagging and the K-Nearest Neighbor algorithm.

Using IoT and smart monitoring devices to optimize the efficiency of large-scale distributed solar farms

This paper presents a novel IoT-based architecture that utilizes IoT hardware, software, and communication technologies to enable real-time monitoring and management of solar photovoltaic systems at large scales. The system enables stakeholders to remotely control and monitor the photovoltaic systems and evaluate the effect of various environmental factors such as weather, air quality, and soiling. The system was implemented and evaluated in terms of network delay and resource consumption. Message Queueing Telemetry Transport (MQTT) was used to facilitate wide-scale real-time communication. The average network delay was found to be less than 1 s, proving the architecture to be ideal for solar and smart grid monitoring systems. As for resource consumption, the evaluation showed the hardware to consume about 3% of the panel’s output, while the application also utilized a very small percentage of the CPU. This led to the conclusion that the proposed architecture is best deployed using low-cost constrained edge devices where a combination of IoT-based paradigm, efficient MQTT communication, and low resources consumption makes the system cost-effective and scalable.

Opportunistic protocol based on social probability and resources efficiency for the intelligent and connected transportation system

In recent years, Intelligent and Connected Transportation Systems (ICTS) have become a practical and valuable alternative for wide variety of novel applications in road traffic safety. It can be utilized to guarantee road safety and create new forms of inter-vehicle communications. However, due to the high speed of vehicles, the topology of the network is highly dynamic and the network may be disconnected frequently, which will lead to a decline in communication performance. Delay Tolerant Networks (DTNs) follow the approach to store and forward the message. DTNs can adapt to the highly dynamic scenario, envisioned for communication in ICTS suffering from intermittent connection. In this paper, we propose the Social Probability And Resource Effective (SPARE) protocol to improve delivery ratio and minimize the consumption of network resources. In SPARE, we focus on considering four factors that include the nodal resources effective consumption, encounter probability, nodal historical encounter information and the number of messages carried by nodes. We use the nodes’ resources efficiency and encounter probability similarity to improve the delivery ratio of SPARE algorithm. In addition, SPARE applies the mechanism of dynamically managing messages to reduce network overhead. Finally, the simulation results show that SPARE achieves a higher delivery ratio and lower overhead ratio, compared to other protocols within resource constrained network situations.

Stochastic virtual network embedding via accelerated Benders decomposition

The interest in network virtualization as a solution to the current ossification of the Internet has been recently revived. Virtual Network Embedding (VNE) is the central problem in network virtualization that determines mapping of Virtual Networks (VN) on the physical substrate network while satisfying VN requirements and the substrate network resource constraints. Much research has been conducted on the VNE problem with the assumption that the VN requirements are known beforehand. Nevertheless, the precise amounts of the requirements are uncertain in practice. The limited research that has considered the uncertainty focused on obtaining conservative solutions that increase embedding cost in terms of substrate network resource consumption. In this paper, we examine the VNE problem with the objective of minimizing embedding cost via obtaining a non-conservative solution in the case of the bandwidth requirements of virtual links are expressed as random variables with known distributions. For the first time, we formulate the problem using the stochastic programming framework and utilize the sample average approximation technique to solve it. To tackle its complexity, we decompose the problem based on Benders method which is accelerated by four proposed techniques. Extensive simulation results show that the proposed approach outperforms the conservative solution by about 40%–45%, and it is up to 7.5 times faster than the branch and bound method.

A Dynamic Placement Mechanism of Service Function Chaining Based on Software-defined Networking

To cope with the explosive growth of Internet services, Service Function Chaining (SFC) based on Software-defined Networking (SDN) is an emerging and promising technology that has been suggested to meet this challenge. Determining the placement of Virtual Network Functions (VNFs) and routing paths that optimize the network utilization and resource consumption is a challenging problem, particularly without violating service level agreements (SLAs). This problem is called the optimal SFC placement problem and an Integer Linear Programming (ILP) formulation is provided. A greedy heuristic solution is also provided based on an improved two-step mapping algorithm. The obtained experimental results show that the proposed algorithm can automatically place VNFs at the optimal locations and find the optimal routing paths for each online request. This algorithm can increase the average request acceptance rate by about 17.6% and provide more than 20-fold reduction of the computational complexity compared to the Greedy algorithm. The feasibility of this approach is demonstrated via NetFPGA-10G prototype implementation.

Using Proactive Fault-Tolerance Approach to Enhance Cloud Service Reliability

The large-scale utilization of cloud computing services for hosting industrial/enterprise applications has led to the emergence of cloud service reliability as an important issue for both cloud service providers and users. To enhance cloud service reliability, two types of fault tolerance schemes, reactive and proactive, have been proposed. Existing schemes rarely consider the problem of coordination among multiple virtual machines (VMs) that jointly complete a parallel application. Without VM coordination, the parallel application execution results will be incorrect. To overcome this problem, we first propose an initial virtual cluster allocation algorithm according to the VM characteristics to reduce the total network resource consumption and total energy consumption in the data center. Then, we model CPU temperature to anticipate a deteriorating physical machine (PM). We migrate VMs from a detected deteriorating PM to some optimal PMs. Finally, the selection of the optimal target PMs is modeled as an optimization problem that is solved using an improved particle swarm optimization algorithm. We evaluate our approach against five related approaches in terms of the overall transmission overhead, overall network resource consumption, and total execution time while executing a set of parallel applications. Experimental results demonstrate the efficiency and effectiveness of our approach.

Bandwidth Provisioning for Virtual Machine Migration in Cloud: Strategy and Application

Physical resources are highly virtualized in today's datacenter-based cloud-computing networks. Servers, for example, are virtualized as Virtual Machines (VMs). Through abstraction of physical resources, server virtualization enables migration of VMs over the interconnecting network. VM migration can be used for load balancing, energy conservation, disaster protection, etc. Migration of a VM involves iterative memory copy and network re-configuration. Memory states are transferred in multiple phases to keep the VM alive during the migration process, with a small downtime for switchover. Significant network resources are consumed during this process. Migration also results in undesirable performance impacts. Suboptimal network bandwidth assignment, inaccurate pre-copy iterations, and high end-to-end network delay in wide-area networks (WAN) can exacerbate the performance degradation. In this study, we devise strategies to find suitable bandwidth and pre-copy iteration count to optimize different performance metrics of VM migration over a WAN. First, we formulate models to measure network resource consumption, migration duration, and migration downtime. Then, we propose a strategy to determine appropriate migration bandwidth and number of pre-copy iterations, and perform numerical experiments in multiple cloud environments with large number of migration requests. Results show that our approach consumes less network resources when compared with maximum and minimum-bandwidth provisioning strategies while using an order of magnitude less bandwidth than maximum-bandwidth strategy. It also achieves significantly lower migration duration than minimum-bandwidth scheme.

A Greedy Routing Strategy Based on Euclidean Geometry for Vehicular Delay Tolerant Network

Delay/Disruption-Tolerant Networks (DTN) was initially used for Inter-Planetary Networks (IPN) and it was extended to many different scenarios characterized by the high mobility of nodes, the constant disconnection and high delays, such as Vehicular Networks (VANETs), Sensor Networks, Military Ad Hoc Networks and Networks in catastrophic scenarios, Pocket Switched Networks (PSN) and Under-Water Networks. A key challenge in an DTN-based environment is the message routing protocol that enables communication between any two nodes (at any time) because it must deal with possible unconnected end-to-end paths at the time of sending a message to a destination. This paper presents a Greedy DTN Routing Strategy Based on Euclidean Geometry (GREG) for Vehicular Delay Tolerant Network, where a mathematical model is employed to predict the position of the nodes. GREG algorithm computes, based on prediction positions computed, the distance between the node currently holding the message and the destination node. An Euclidean Geometry distance between two points is used. Results show that the GREG protocol has a good trade-off between routing efficacy and network resource consumption.

Design adaptive Subnetting Hybrid Gateway MANET Protocol on the basis of Dynamic TTL value adjustment

Mobile ad hoc Network is infrastructure less wireless network and decentralized way, and then for a large network number of nodes dynamically therefor the connection established between source node to destination node is really challenging. The challenge is interconnecting ad hoc network to the internet seems from the needs to inform ad hoc nodes about available gateways in an extremely challenging scenario while a making a minimum consumption of the source network resources. Then an efficient gateway discovery of an ad hoc network becomes one of the central factors to enable the economic consumption of hybrid ad hoc network in future mobile and wireless network. In mobile ad hoc network have multihop nature of MANET therefore several reachable gateways for mobile node at any period of time. If the mobile node receives gatways advertisement from more than one gateway. It has to determine which gateway to use for connecting to the net. Most existing protocol choose the gateway which is closer in terms of the number of physical hops. This paper has focused on design an efficient and adaptive subnetting hybrid gateway discovery mechanism on the basis of dynamic TTL value adjustment such that congestion and unnecessary overhead is reduced. Selecting the gateway on the basis of one and two parameters will increase the performance and throughput of the network. The main objective of adaptive gateway discovery to determine the optimal TTL value in terms of number of hops to determine the proactive area, nodes outside this area follow the reactive approach. Consequently, for achieving a good trade off between performanceand network operating expense.

Traffic Engineering and Manageability for Multicast Traffic in Hybrid SDN

Multicast communication can effectively reduce network resources consumption in contrast with unicast. With the advent of SDN, current researches on multicast traffic are mainly conducted in the SDN scenario, thus to mitigate the problems of IP multicast such as the unavoidable difficulty in traffic engineering and high security risk. However, migration to SDN cannot be achieved in one step, hybrid SDN emerges as a transitional networking form for ISP network. In hybrid SDN, for acquiring similar TE and security performance as in SDN multicast, we redirect every multicast traffic to an appropriate SDN node before reaching the destinations of the multicast group, thus to build up a core-based multicast tree substantially which is first introduced in CBT. Based on the core SDN node, it is possible to realize dynamic control over the routing paths to benefit traffic engineering (TE), while multicast traffic manageability can also be obtained, e.g., access control and middlebox-supported network services. On top of that, multiple core-based multicast trees are constructed for each multicast group by fully taking advantage of the routing flexibility of SDN nodes, in order to further enhance the TE performance. The multicast routing and splitting (MRS) algorithm is proposed whereby we jointly and efficiently determine an appropriate core SDN node for each group, as well as optimizing the traffic splitting fractions for the corresponding multiple core-based trees to minimize the maximum link utilization. We conduct simulations with different SDN deployment rate in real network topologies. The results indicate that, when 40% of the SDN switches are deployed in HSDN as well as calculating 2 trees for each group, HSDN multicast adopting MRS algorithm can obtain a comparable TE performance to SDN multicast.

Survivable Virtual Optical Network Coordinated Mapping with Local Backup

Abstract Survivable virtual optical network (VON) mapping is researched to overcome the ossification of current network architectures. In this paper, we study the survivable VON mapping problem in elastic optical network (EON) with the objective of minimizing the average resource consumption of VON request. We propose a protective algorithm, namely, coordinated mapping algorithm based on minimum spanning tree (CMST), to provide the dedicated protection against the single physical link failure. In CMST, for virtual node mapping, we not only consider the resources constraint but also the node degree constraint. For virtual link mapping, we adopt the coordinated mapping method for part of the virtual nodes and virtual links. And we provide the backup path for the minimum spanning tree link of virtual topology to reduce the resource consumption. Simulation results showed that CMST can reduce the VON request blocking probability and average network resource consumption. And it can increase the revenue of physical network.