Multicast Session Research Articles

Discrete Artificial Fish Swarm Algorithm-Based One-Off Optimization Method for Multiple Co-Existing Application Layer Multicast Routing Trees

As an effective multicast application mechanism, the application layer multicast (ALM) determines the path of data transmission through a routing tree. In practical applications, multiple multicast sessions often occur simultaneously; however, few studies have considered this situation. A feasible solution is to sequentially optimize each co-existing ALM routing tree. However, this approach can lead to node congestion, and, even if the node out-degree reservation strategy is adopted, an optimal solution may not be obtained. In this study, to solve the problem of routing tree construction for multiple co-existing application layer multicast sessions, an optimization model that minimizes the overall delay and instability is constructed, and a one-off optimization method based on the discrete artificial fish swarm algorithm (DAFSA) is proposed. First, Steiner node sets corresponding to the multicast sessions are selected. Then, the routing trees for each multicast session are obtained through the improved spanning tree algorithm based on the complete graph composed of Steiner node sets. The experimental results show that the proposed method can simultaneously obtain multiple co-existing ALM routing trees with a low total delay and low instability. Even if the input is a single multicast session, it can lead to ALM routing trees with a lower delay and less instability than other algorithms, and the introduction of a penalty function can effectively avoid the problem of excessive replication and forwarding loads on some end-hosts. In addition, the proposed algorithm is insensitive to parameter changes and exhibits good stability and convergence properties for networks of different sizes.

TABDeep: A two-level action branch architecture-based deep reinforcement learning for distributed sub-tree scheduling of online multicast sessions in EON

The surge in demand for multimedia applications has brought a great pressure on network bandwidth resources. In order to improve the efficiency of bandwidth resource usage and ensure the transmission quality of multicast sessions, it is very urgent to effectively schedule online multicast sessions. To do so, in this paper, we study the distributed sub-tree scheduling problem of online multicast sessions in elastic optical network (EON). First, we model the problem as a Markov Decision Process (MDP) by defining state, action and reward. Second, a two-level action branch architecture-based deep reinforcement learning (TABDeep) algorithm is proposed to realize the destination node grouping, source node selection, route establishment and spectrum assignment of multicast sessions. Finally, to test the superiority of TABDeep, we construct a series of simulation experiments to compare TABDeep with a link-aware distributed steiner sub-tree benchmark algorithm (LA-DSST). The experimental results confirm that TABDeep performs better than LA-DSST in reducing the blocking probability of the session.

DRL assisted multi-objective algorithm for multicast scheduling in elastic optical network

Multicasting, as a main transmission mode for most of the current applications in elastic optical network, has attracted more and more research attention. In this paper, we investigate multicast scheduling model and solving algorithm. First, we model the multicast scheduling problem as a multi-objective optimization problem (MOP) by minimizing the bandwidth resources and maximizing the user service quality, and then, we propose a deep reinforcement learning assisted multi-objective algorithm for the model (DRL-MM), in which we design source node selection strategy, routing scheme, modulation scheme and spectrum assignment scheme for each multicast session. To identify the superiority of the proposed DRL-MM, we conduct the experiments and compare DRL-MM with an approximation based Steiner tree algorithm (STA-RSA) and a load-balancing routing tree-based algorithm (LD-RSA) through the experiments. The results show that DRL-MM outperforms STA-RSA and LD-RSA in terms of both bandwidth resource usage and user service quality.

Energy Efficient Multicast Communication in Cognitive Radio Wireless Mesh Network.

Multicasting is a basic networking primitive used in a wide variety of applications that is also true for cognitive radio-based networks. Although cognitive radio technology is considered to be the most promising technology to deal with spectrum scarcity, it relates to completely different aspects of networking and presents new challenges. For cognitive radio-based multicast sessions, it is important to use the spectrum efficiently by reducing the number of channels used as well as engaging fewer nodes in data relaying. This will benefit the network in three ways. First, it will decrease the number of transmissions. Second, it will help to reduce energy usage. Third, it will spare more channels and relay nodes for simultaneous multicast sessions. To achieve these advantages, efficient channel selection and relay nodes are required based on hop-to-hop communication. In this paper an algorithm has been developed that attempts to minimize energy consumption by selecting the minimum possible number of relay nodes and channels for a multicast session, taking into account the sporadic availability of the spectrum. The proposed method performs effectively compared to the flooding method in terms of energy consumption for the provided examples in multicasting.

QoS-VNS-CS: QoS constraints Core Selection Algorithm based on Variable Neighborhood Search Algorithm

Within the development of network multimedia technology, more and more real-time multimedia applications arrive with the need to transmit information using multicast communication. Multicast IP routing is an important topic, covering both theoretical and practical interest in different networks layers. In network layer, there are several multicast routing protocols using multicast routing trees different in the literature. However PIM-SM and CBT protocols remains the most used multicast routing protocols; they propose using a shared Core-based Tree CBT. This kind of tree provides efficient management of multicast path in changing group memberships, scalability and performance. The prime problem concerning construction of a shared tree is to determine the best position of the core. QoS-CS’s problem (QoS constraints core Selection) consists in choosing an optimal multicast router in the network as core of the Shared multicast Tree (CBT) within specified QoS constraints associated. The choice of this specific router, called RP in PIM-SM protocol and core in CBT protocol, affects the structure of multicast routing tree, and therefore influences performances of both multicast session and routing scheme. QoS-CS is an NP complete problem need to be solved through a heuristic algorithm, in this paper, we propose a new core Selection algorithm based on Variable Neighborhood Search algorithm and new CMP fitness function. Simulation results show that good performance is achieved in multicast cost, end-to-end delay, tree construction delay and others metrics.

Addressing Challenges of Distance Learning in the Pandemic with Edge Intelligence Enabled Multicast and Caching Solution.

The COVID-19 pandemic has affected the world socially and economically changing behaviors towards medical facilities, public gatherings, workplaces, and education. Educational institutes have been shutdown sporadically across the globe forcing teachers and students to adopt distance learning techniques. Due to the closure of educational institutes, work and learn from home methods have burdened the network resources and considerably decreased a viewer’s Quality of Experience (QoE). The situation calls for innovative techniques to handle the surging load of video traffic on cellular networks. In the scenario of distance learning, there is ample opportunity to realize multi-cast delivery instead of a conventional unicast. However, the existing 5G architecture does not support service-less multi-cast. In this article, we advance the case of Virtual Network Function (VNF) based service-less architecture for video multicast. Multicasting a video session for distance learning significantly lowers the burden on core and Radio Access Networks (RAN) as demonstrated by evaluation over a real-world dataset. We debate the role of Edge Intelligence (EI) for enabling multicast and edge caching for distance learning to complement the performance of the proposed VNF architecture. EI offers the determination of users that are part of a multicast session based on location, session, and cell information. Moreover, user preferences and network’s contextual information can differentiate between live and cached access patterns optimizing edge caching decisions. While exploring the opportunities of EI-enabled distance learning, we demonstrate a significant reduction in network operator resource utilization and an increase in user QoE for VNF based multicast transmission.

A Low-Complexity Multicast Scheduling for D2D-Aided F-RANs Using Network Coding

Traditional studies on Instantly Decodable Network Coding (IDNC) focus on optimizing IDNC metrics, i.e., completion time or decoding delay, in broadcast scenarios in which all user-devices (UDs) want the same files. The problem of joint optimization over two interdependent parameters, i.e., transmitting user-devices (UDs) and IDNC was traditionally tackled by finding the set of feasible cooperation between UDs. This, however, requires high computational complexity, which is not feasible. This work, instead, develops low complexity, yet optimal, solution to the problem using graph theory in practical multicast scenarios where UDs are interested in different subsets of files. Specifically, the proposed method studies the condition for generating important UDs’ feasible cooperation that can minimize the IDNC metric under investigation while considering the effect of unwanted files on the performance of IDNC schemes for multicast sessions. Numerical results show that the proposed method outperforms the benchmark method in terms of completion time and decoding delay with significant reduction in the computational complexity.

Reconfiguring multicast sessions in elastic optical networks adaptively with graph-aware deep reinforcement learning

With the fast deployment of datacenters (DCs), bandwidth-intensive multicast services are becoming more and more popular in metro and wide-area networks, to support dynamic applications such as DC synchronization and backup. Hence, this work studies the problem of how to formulate and reconfigure multicast sessions in an elastic optical network (EON) dynamically. We propose a deep reinforcement learning (DRL) model based on graph neural networks to solve the sub-problem of multicast session selection in a more universal and adaptive manner. The DRL model abstracts topology information of the EON and the current provisioning scheme of a multicast session as graph-structured data, and analyzes the data to intelligently determine whether the session should be selected for reconfiguration. We evaluate our proposal with extensive simulations that consider different EON topologies, and the results confirm its effectiveness and universality. Specifically, the results show that it can balance the trade-off between the number of reconfiguration operations and blocking performance much better than existing algorithms, and the DRL model trained in one EON topology can easily adapt to solve the problem of dynamic multicast session reconfiguration in other topologies, without being redesigned or retrained.

An [formula omitted] time version of Wang and Shroff’s characterization for the networks with two unit-rate multicast sessions

Wang and Shroff (2010) proposed a graph-theoretic characterization of pairwise intersession network coding based on paths with controlled edge-overlap. They showed that the solvability of networks with two unit-rate multicast sessions can be decided in polynomial time. Wang and Shroff’s characterization generalizes the edge-disjoint path characterization of noncoded network communication, which is a very important property of their characterization. Then, Song et al. (2013) presented a characterization to the problem based region decomposition method, which determines the solvability in O(|E|) time, where |E| is the number of links. This paper presents a new version of Wang and Shroff’s characterization based region decomposition. We prove that this version of their characterization decides the solvability in O(|E|) time. This version of the characterization concludes a very easy algorithm to diagnose the solvability such that it is only based on the parents of sink regions, which yields a fast method to diagnose solvability or non-solvability of networks with two unit-rate multicast sessions.

The concept of multicast protocol based on network coding

Research subject. The paper presents the concept of a multicast session protocol based on the net-work coding method. Method. Development of a data transfer protocol based on the analysis of existing principles and models of network coding. Core results. The proposed NCDP multicast protocol is de-signed to work over a datagram transport protocol. The basic structure of the NCDP packet header has been developed, taking into account the variability of the protocols used in conjunction with NCDP. Prac-tical relevance. The proposed protocol is intended for use in content delivery systems based on mul-ticast data transmission from several sources.

Solving the Delay-Constrained Least-Cost routing problem using Tabu Search with Edge Betweenness

Multicast routing consists of concurrently sending the same information from a source to a subset of all possible destinations in a computer network thus becomes an important technology communication. To solve the problem, a current approach for efficiently supporting a multicast session in a network consists of establishing a multicast tree that covers the source and all terminal nodes. This problem can be reduced to a minimal Steiner tree problem (MST) which aims to look for a tree that covers a set of nodes with a minimum total cost, the problem is NP-hard. In this paper, we investigate metaheuristics approaches for the Delay-Constrained Least-Cost (DCLC) problem, we propose a novel algorithm based on Tabu Search procedure with the Edge Betweenness (EB). The EB heuristic used first to improve KMB heuristic, able to measure the edge value to being included in a given path. The obtained solution improved using the tabu search method. The performance of the proposed algorithm is evaluated by experiments on a number of benchmark instances from the Steiner library. Experimental results show that the proposed metaheuristic gives competitive results in terms of cost and delay compared to the optimal results in Steiner library and other existing algorithms in the literature.

Improving acceptance rate of QoS-guaranteed point-to-multipoint traffic flows in elastic optical networks

Abstract This paper studies the load-balanced routing and admission control for point-to-multipoint traffic flows in elastic optical networks (EONs). EONs have emerged as a promising technology to provide high capacity for next-generation networks. Elastic bandwidth allocation promotes the spectrum utilization efficiency. We consider the problem of constructing bandwidth-guaranteed trees for on-demand point-to-multipoint session requests. A request is accepted, if a multicast tree with sufficient bandwidth on each link can be established. The main objective is to increase the multicast acceptance rate in the network. For this purpose, we propose an efficient scheme, namely optical load-balanced multicast routing with admission control (OLMR-AC), to improve the wavelength resources utilization in the network. As an advantage, OLMR-AC jointly considers both distance and congestion factors to create a multicast session. For each admitted session, a bandwidth–guaranteed steiner tree is provided in which not only the source node has a minimum-cost path to each receiver, but also its overall cost is minimal. The proposed scheme distributes the on-demand traffic flows over the available links fairly. Simulation results show the efficiency of the OLMR-AC algorithm in terms of multicast acceptance rate, average length of lightpaths, throughput, number of transmissions, and load variance.

Characterizing Resource Allocation Trade-Offs in 5G NR Serving Multicast and Unicast Traffic

The use of highly directional antenna radiation patterns for both the access point (AP) and the user equipment (UE) in the emerging millimeter-wave (mmWave)-based New Radio (NR) systems is inherently beneficial for unicast transmissions by providing an extension of the coverage range and eventually resulting in lower required NR AP densities. On the other hand, efficient resource utilization for serving multicast sessions demands narrower antenna directivities, which yields a trade-off between these two types of traffic that eventually affects the system deployment choices. In this work, with the tools from queuing theory and stochastic geometry, we develop an analytical framework capturing both the distance- and traffic-related aspects of the NR AP serving a mixture of multicast and unicast traffic. Our numerical results indicate that the service process of unicast sessions is severely compromised when (i) the fraction of unicast sessions is significant, (ii) the spatial session arrival intensity is high, or (iii) the service time of the multicast sessions is longer than that of the unicast sessions. To balance the multicast and unicast session drop probabilities, an explicit prioritization is required. Furthermore, for a given fraction of multicast sessions, lower antenna directivity at the NR AP characterized by a smaller NR AP inter-site distance (ISD) leads to a better performance in terms of multicast and unicast session drop probabilities. Aiming to increase the ISD, while also maintaining the drop probability at the target level, the serving of multicast sessions is possible over the unicast mechanisms, but it results in worse performance for the practical NR AP antenna configurations. However, this approach may become feasible as arrays with higher numbers of antenna elements begin to be available. Our developed mathematical framework can be employed to estimate the parameters of the NR AP when handling a mixture of multicast and unicast sessions as well as drive a lower bound on the density of the NR APs, which is needed to serve a certain mixture of multicast and unicast traffic types with their target performance requirements.

EMSC: a joint multicast routing, scheduling, and call admission control in multi-radio multi-channel WMNs

This paper deals with the problem of joint multicast routing, scheduling, and call admission control in multiradio multi-channel wireless mesh networks. To heuristically solve this problem, we propose a cross-layer algorithm named “extended MIMCR with scheduling and call admission control phases (EMSC)”. Our model relies on the on-demand quality of service (QoS) multicast sessions, where each admitted session creates a unique tree with a required bandwidth. The proposed scheme extends the MIMCR algorithm to fairly schedule multiple non-interfering transmissions in the same time slot. It also exploits a call admission control mechanism to protect the QoS requirements of the multicast traffics. EMSC reduces the number of occupied time slots, with consideration of spatial reuse, both Intra-flow and Inter-flow interferences, and selecting the minimum-interference minimum-cost paths. This subsequently leads to better radio resource utilization and increases the network throughput. Simulation results show that the proposed algorithm outperforms the other algorithms and improves the network performance.

Performance of MAODV and ODMRP Routing Protocol for Group Communication in Mobile Ad Hoc Network

A MANET is a self-conFigureuring system of mobile hosts connected by wireless links. The routers are free to move randomly and organize themselves arbitrarily; thus, the network's wireless topology may change rapidly and unpredictably. Routing is the process of exchanging information from one station to the other stations of the network. Multicasting is a popular mechanism for supporting group communication. In a multicast session, the sender transmits only one copy of each message that is replicated within the network and delivered to multiple recipients. This multicast routing is highly deal with self-organized network in recent days due to its broadcast characteristics. However, devising multicast protocols to provide group communications in mobile ad-hoc networks is significantly more complicated, because of the wireless medium, changing topology, battery power and available bandwidth as well. This paper, evaluates two prominent on-demand multicast routing protocols for group communication, namely, Multicast Ad hoc On-Demand Distance Vector (MAODV) and On-Demand Multicast Routing Protocol(ODMRP) as increasing number of multicast sources and receivers in both single-active multicast group and multi-active multicast group in the network.

Delay-Sensitive Multicast in Inter-Datacenter WAN Using Compressive Latency Monitoring

Multicast routing in inter-datacenter wide area networks is to compute a multicast tree from a source datacenter to multiple destination datacenters. Software-defined networking enables optimal delay-sensitive routing for multicast sessions in inter-datacenter WANs. Delay-sensitive routing requires the controller to obtain the up-to-date latency of every link. However, the communication overhead and time for collecting the latency information of all links can be prohibitively high for the controller to perform real-time scheduling in a WAN. Inspired by the sparsity of link load information in WANs, we propose compressive latency monitoring, a real-time method for collecting a near-accurate latency information of all links. The controller only needs to collect the latency information of a small subset of significant links and then estimates the latency information of insignificant links. We use real-world network topologies to simulate multicast routing both under the compressive latency monitoring and global latency monitoring. Simulation results show that the compressive latency monitoring can achieve nearly good network throughput performance as the global monitoring with much less communication overhead and in a more timely fashion.

Optimizing co-existing multicast routing trees in IP network via discrete artificial fish school algorithm

Increasingly more internet-applications require the supports of multiple multicasts, while existing multicast routing algorithms only aim to establish a single multicast routing tree (MRT). Sequentially optimizing the co-existing MRTs each is a common practice of handling the situation of multiple multicast session being concurrent, but it results in link-congestion on low-cost routes, or leads to non-optimal solution even if bandwidth reservation strategy is adopted. This paper proposes to optimize multiple co-existing MRTs as a whole via one-off optimization instead of sequentially optimizing each in isolation. To carry out the one-off optimization, a discrete artificial fish school algorithm (DAFSA) is proposed. The simulation results show that the proposed DAFSA is capable of optimally packing co-existing MRTs and exhibits remarkably better ability than several the most representative state-of-the-art algorithms in the sense of avoiding the link-congestion and minimizing the overall tree cost. The running time of the proposed DAFSA fully meets the requirements of the practical IP multicasting. Besides, Monte Carlo test proves that the convergence of the proposed DAFSA is not sensitive to its parameters.

An Improved Weight Design for Unwanted Packets in Multicast Instantly Decodable Network Coding

Traditional studies on Instantly Decodable Network Coding (IDNC) focus on broadcast scenarios in which all users are interested in receiving the same packets. The problem is usually formulated as a maximum weight clique search in the IDNC graph for well-designed weights that reflect the metric under investigation, i.e., completion time or decoding delay. These schemes are allegedly extended to multicast sessions by introducing a two-layered IDNC graph wherein the first layer contains Wanted Packets (WPs), and the second one represents UnWanted Packets (UWPs). This letter studies the effect of UWPs on the performance of IDNC schemes for multicast sessions. In particular, the letter derives the condition under which the two-layer structure is optimal and concludes that the approach is the most appropriate in most, but not all, cases. Furthermore, the letter designs weights for UWPs that do not depend on the metric but rather represent their probability of offering future decoding opportunities for WPs. Numerical results reveal that the proposed solution outperforms existing methods for an identical computation complexity.

A Delay and Energy Efficient Multicast Routing Protocol using IWO and MOLO Algorithm for Vehicular Networks

Vehicular networks are significantly improving wireless communication network which provides an intelligent transportation system services among faster moving vehicles with internet and brings safety and comfort drive. From a single source vehicle the multicast routing protocols delivers multicast messages to all members of the multicast group by means of multi-hop communication. Increasing the density of vehicles results in channel overload, which increases the probability of data collision; hence reduction in successful received data will increase the delay. Therefore, delay and energy consumption are the major constraints that should affect the performance of routing. In this paper we suggest a delay and energy efficient multicast routing (DEMR) protocol for vehicular network using a hybrid machine learning algorithm. The DEMR protocol consists of four layers; are vehicle layer, fog layer, OpenFlow switch layer, and SDN controller layer. Moreover, to partition the vehicle layer, and select the optimal multicast path based on multiple constraints improved weed optimization (IWO) algorithm is proposed. IWO algorithm separates the multicast request into emergency, common and police requests. We design a multi-objective lion optimization (MOLO) algorithm among fog nodes for resource management, which increases the utilization of resources in fog layer and decrease the response time of multicast session request. MOLO algorithm removes the unnecessary flow table and session table entries in the controller. The DEMR protocol is implemented in Network Simulator (NS3) tool and simulation results are compared with the protocols as EEMSFV, MABC, and CVLMS. From the simulation results we conclude that the DEMR algorithm is better than EEMSFV, MABC and CVLMS in terms of transmission ratio, overhead load, average end to end delay, packet loss ratio and, energy consumption.

Integer programming formulations for the shared multicast tree problem

We study the shared multicast tree (SMT) problem in wireless networks. To support a multicast session between a set of network nodes, SMT aims to establish a wireless connection between them, such that the total energy consumption is minimized. All destinations of the multicast message must be connected, while non-destinations are optional nodes that can be used to relay messages. The objective function reflecting power consumption distinguishes SMT clearly from the traditional minimum Steiner tree problem. We develop two integer programming formulations for SMT. Both models are subsequently extended and strengthened. Theorems on relations between the LP bounds corresponding to the models are stated and proved. As the number of variables in the strongest formulations is a polynomial of degree four in the number of network nodes, the models are impractical for computing lower bounds in instances beyond a fairly small size, and therefore a constraint generation scheme is developed. Results from computational experiments with the models demonstrate good promise of the approaches taken.