Scaling Group Communication Services with Self-adaptive and Utility-driven Message Routing

Current group communication services have mostly been implemented on a homogeneous, distributed computing environment. This limits their applicability because most modern distributed computing environment are heterogeneous in nature. This paper describes the design, implementation, and performance evaluation of a CORBA group communication service. Using CORBA to implement a group communication service enables that group communication service to operate in a heterogeneous, distributed computing environment. To evaluate the effect of CORBA on the performance of a group communication service, this paper provides a detailed comparison of the performance measured from three implementations of an atomic broadcast protocol and a group membership protocol. Two of these implementations use CORBA, while the third uses UDP sockets for interprocess communication. The main conclusion is that heterogeneity can be achieved in group communication services by implementing them using CORBA, but there is a substantial performance cost. This performance cost can be reduced to a certain extent by carefully choosing a design and tuning various protocol parameters such as buffer sizes and timer values.

The performance of routing strategies on complex networks can be characterized by two measurements, i.e., the traffic capacity and the average packets travel time. By efficiently synthesizing the degree and the dynamic queue length of nodes, we propose the global hybrid (GH) routing strategy. It can achieve higher traffic capacity, as well as shorter average packets, travel time compared with the state-of-the-art global dynamic (GD) routing strategy and efficient routing (ER) strategy. Moreover, such superiority can be maintained through the queue length information and the corresponding routing paths are updated periodically. The simulation results show that our GH routing strategy can provide the same traffic capacity as the GD routing strategy does, which is more than twice as high as the ER strategy. At the same time, the average packets travel time of the GH routing strategy is more than 20% smaller than that of the GD routing strategy. It is worth noted that longer updating delay makes our GH routing strategy have a greater decline in the average packets travel time. With the updating delay equals 100, the decline can be up to 40%. To illustrate the practicability of our GH routing strategy, we also applied it to a scale-free network-based data center network. The simulation results reveal that it is practical, effective, and can be used in real scenarios to improve network performance.

We discuss a group protocol which supports applications with group communication service when QoS supported by networks or required by applications is changed. An autonomic group protocol is realised by cooperation of multiple autonomous agents. Each agent autonomously takes a class of each protocol function so as to support only and all QoS required. A group is composed of views for scalability. Each view is a subset of the agents where the agents autonomously take protocol classes consistent with each other. We make clear what combination of classes can be autonomously taken by agents in a view.

In distributed systems, high service availability can be achieved by letting a group of servers replicate the service state; if some servers fail, the surviving ones know the service state and can continue to provide the service. Group communication services, such as membership and atomic broadcast, have been proposed to solve the problem of maintaining server state replica consistency. Group membership achieves agreement on the history of server groups that provide the service over time, while atomic broadcast achieves agreement on the history of state updates performed in each group.

In this paper, we propose the design of an extension of the Java distributed object model based on the group communication paradigm for building replicated fault-tolerant servers. Maintaining the correctness and integrity of replicated servers is supported by the group manager object instantiated with each replica to form a logical group and the group membership algorithms to maintain a consistent group view and a reliable multicast mechanism to communicate with other group managers. Group manager object enables the creation of groups of remote objects that cooperate towards some common goal using a join/leave group communication service. Remote object groups simulate the behavior of standard remote objects by implementing a set of remote interfaces and by enabling clients to remotely invoke the methods defined in these interfaces through the standard Java RMI mechanism. For the first time, communication among senders forming a group is based on group method invocations as well, offering a completely object-oriented framework for programmers. And then, it is completed by a state merging service, which simplifies the development of state merging mechanism needed to re-establish a consistent state after the disappearing of join/leave properties

This paper focuses on the routing algorithm for the communications between vehicles and places in urban vehicular ad hoc networks. As one of the basic transportation facilities in an urban setting, buses periodically run along their fixed routes and cover many city streets. The trajectory of bus lines can be seen as a submap of a city. Based on the characters of bus networks, we propose a bus-trajectory-based street-centric (BTSC) routing algorithm, which uses buses as the main relay to deliver messages. In BTSC, we build a routing graph based on the trajectories of bus lines by analyzing the probability of bus appearing on every street. We propose two novel concepts, i.e., the probability of street consistency and the probability of path consistency, which are used as metrics to determine routing paths for message delivery. This aims to choose the best path with higher density of busses and lower probability of transmission direction deviating from the routing path. In order to improve the bus forwarding opportunity, we design a bus-based forwarding strategy with ant colony optimization to find a reliable and a steady multihop link between two relay buses in order to decrease the end-to-end delay. The BTSC makes improvements in the selection of routing paths and a strategy of message forwarding. Simulation results show that our proposed routing algorithm has a better performance in terms of the transmission ratio, transmission delay, and adaptability to different networks.

A network optimization model (NOM) that provides a tool for performance analysis and optimal design of a broad class of routing strategies is presented. It can evaluate and optimize both static and dynamic, local and global routing strategies. The tool is applicable to both packet-switched and circuit-switched networks. NOM software is based on original modeling methods and optimization algorithms. It is user-friendly and menu-driven, and has extensive 'what if' capabilities. NOM can be used for performance evaluation of existing routing strategy and for optimal routing design for a given performance criterion. As a performance evaluation tool, for a given network topology, link and node capacities, point-to-point demand matrix, and routing strategy, NOM models the routing and computes important characteristics of network performance. As a routing optimization tool, for a given network topology, link and node capacities, point-to-point demand matrix, and performance criterion, NOM generates the optimal routing, and computes important characteristics of network performance. The current Telenet local dynamic routing strategy is compared with a set of optimal routing strategies designed for various performance criteria. >

In wireless sensor networks (WSN), energy consumption is one of the crucial issues. It is very important to conserve energy at each sensor node to prolong a network lifetime. The main challenge in WSN is to develop an energy efficient algorithm to minimize energy consumption during transmitting information from deployed sensors up to the cloud resources. Many researches have been studied the designing of energy efficient technique based on one-layer stack model approach. In this study, we propose Energy-Efficient Cross-Layer (EECL) approach by using the interaction of MAC layer and physical layer information to be exploited by a network layer to achieve energy efficient communication. More precisely, network layer could utilize the MAC layer and physical layer information to establish an energy efficient route path to be used in forwarding data. The proposed EECL approach uses X-MAC protocol in support of duty cycle which introduces short preambles that switches to wake-up/sensing mode only for nodes belonging to routing path while the other nodes set to be in sleep mode. The distance between nodes that influences energy consumption and Bit Error Rate (BER) are set to be the parameters which they are help in indicating the required power for each hop during route path selection in WSN and avoid the rely-hops that suffering from high average BER and with farther distance. We conduct the experiment using Matlab to evaluate the effectiveness of our proposed approach in terms of power consumption and obtained data rate. The results show that our proposed EECL approach outperforms its representatives in the ability of tuning the power utilized in respect with required data rate that could satisfy the desired Quality-of-Service (QoS).

The design of wireless sensor networks for signal processing applications has to consider the limited battery power of the sensor nodes. In this paper, a combined routing path selection and power allocation strategy is presented that is especially designed for distributed detection in sensor networks with a serial topology. The objective is to minimize the global probability of detection error of the serial network under a total network power constraint. The cross-layer approach for the selection of a routing path through the network is based on the local observation SNR of the sensors as well as fading conditions and is efficiently implemented by a backward greedy algorithm. After the routing path is established, a subsequently performed power allocation algorithm aims to optimally distribute a total power budget with respect to the detection performance of the sensor network.

This paper investigates the impact of network structure on latency in complex networks under two different routing strategies. The performances of both scale-free networks and random networks are compared and evaluated under the shortest path routing strategy and least cost routing strategy. Our study shows that under the shortest path routing strategy, the scale-free network fares much worse compared to the random network. Conversely, under the least cost routing, the performance of the scale-free network far exceeds that of the random network because of its unique network structure.

The increasing demand for ultra-low-latency and high-throughput communication necessitates network architectures capable of ensuring reliable real-time connectivity. To address this requirement, integrated architectures combining terrestrial networks (TN) and non-terrestrial networks (NTN)—particularly hierarchical multi-layer satellite systems composed of geostationary Earth orbit (GEO), medium Earth orbit (MEO), and low Earth orbit (LEO) satellites—have attracted considerable attention. Such hierarchical systems offer global coverage, low latency, and high capacity. However, their highly dynamic topology, fast-moving satellites, and fluctuating link quality pose significant challenges to establishing efficient end-to-end routing paths. Traditional routing methods based on static paths or centralized control lack the adaptability required for such environments. To address these challenges, this paper introduces a neural hierarchical reinforcement learning (HRL)-based routing algorithm for satellite networks. The framework exploits the layered structure of GEO, MEO, and LEO satellites, enabling gateway-assisted routing and adaptive path selection. Control responsibilities are distributed across orbital layers: the GEO layer allocates hop-count budgets, the MEO layer selects feasible LEO paths, and the LEO layer forwards data packets. When inter-satellite links are unavailable, terrestrial gateways provide alternative routes. Routing decisions further account for satellite-to-satellite and satellite-to-ground link quality, with an end-to-end delay-based reward function capturing transmission, processing, and propagation effects. Simulation results show that the proposed neural hierarchical framework, supported by integrated terrestrial and non-terrestrial networking, achieves faster convergence, greater route stability, and better adaptability than conventional routing algorithms and HRL approaches.

The ever advances in wireless communication and mobile networks have brought novel workflow-formed applications, such as virtual reality and live-streaming, to our daily life. Arousing a growing need for workflow execution efficiency. An edge network is widely considered a promising way of bridging the gap between intensive resource demand and the limited computation capabilities of mobile terminals. However, when an edge network is partially connected, ordinary workflow scheduling algorithms suffer degradations as the data transmission time is prolonged. In this paper, we address the challenge of workflow makespan minimization in a partially connected edge network. Contrary to the general assumptions of a fully connected edge network, the edge servers under discussion are partly interconnected but can be reached within limited hops by using different paths. Here, the placement of interdependent tasks and selection of routing paths are two major factors that influence the makespan. We first propose a critical path analysis based dynamic task sorting algorithm to determine the scheduling order of tasks. Then the path quality is introduced as a reflection of path availability and is employed as the major indicator in selecting disjoint subpaths. We further model the workflow scheduling process into a Markov decision process and propose a reinforcement learning–based workflow embedding (RLWE) scheme to minimize the makespan of the workflow. With the fine-trained agent, the proposed scheme can coordinate the demand of computing resources and routing paths of interdependent tasks and provide a near-optimal makespan of the workflow. Numerical results validate the feasibility of our proposed scheme as its performance exceeds existing baselines with an improved quality of service in terms of makespan.

It is crucial to use an efficient network-aware overlay network to enable Internet-scale multicast streaming service to adjust under the dynamic underlying network conditions and overlay node churn without extensive network measurements. We utilize our previously proposed network-aware SuperPeers-Peers geometric overlay network hierarchy which exploits the underlying network locality and proximity of the nodes, to build massive scale overlay multicast streaming network (Bos). We create source routed overlay multicast tree from the resulting optimal geometric routing paths based on the shortest geometric distances between SuperPeers at the SuperPeers layer. The overlay multicast tree is self-healed locally in a distributed manner using an ancestor-recovery protocol. The high bandwidth multicast backbone infrastructure at the SuperPeers layer will serve the Peers in the lower layer. We evaluate Bos on ten massive scale networks each consisting of 100,000 nodes and in the PlanetLab. Our massive scale overlay multicast performance results show high efficiency and good scalability.

Wireless sensor network (WSN) has key constraint on energy of sensor nodes i.e. each nodes have finite amount of energy. Seeing energy as key constraint WSN must has robust energy optimized routing algorithm. Mostly routing approach use shortest path and minimal energy depletion model. Network operate on these routing scheme early partitioned due to unbalanced energy consumption of sensor nodes i.e. a single shortest route may used most of the time leads to faster consumption of energy of sensor nodes coming in route path. In current paper attempts has made to develop energy efficient routing scheme based on optimization techniques "particle swarm optimization (PSO) and genetic algorithm (GA)". We implemented all four routing scheme i.e. shortest path approach, PSO approach, GA approach and hybrid PSO-GA approach for small size as well as large size sensor network. Simulation results depicts hybrid PSO-GA routing scheme raised 12% to 23%, 8% to 15% and 5% to 13% lifetime compared to shortest path approach, PSO approach and GA approach respectively for large scale network. For large scale network hybrid PSO-GA raised 9% to 16%, 6% to 11% and 5% to 9% packet delivery ratio compared to shortest path approach, PSO approach and GA approach respectively.

Wireless sensor network (WSN) contains a huge number of spatially distributed sensor nodes that are connected by wireless to monitor and record information from the environment. The WSN nodes are battery-powered, thus reducing energy after a certain period which affects the network lifetime. To overcome this issue, this research proposed a spiral exploration mechanism with honey badger optimization (SEM-HBO) for cluster head (CH) and route path selection in WSN. The objective of this research is to reduce energy consumption and enhance network lifespan in WSN. The distance, communication cost, residual energy and cluster density are considered as fitness functions for selecting CH and route path in WSN. Through the SEM-HBO search behavior, it explores different routes and recognizes best one for reducing energy consumption and delays thereby enhancing network lifetime. The SEM-HBO performance is calculated based on packet delivery ratio (PDR), delay, energy consumption (EC), network lifetime (NL), and throughput for 100-500 nodes. The SEM-HBO performance is efficient and it achieves 99.62% and 99.59% of PDR for 100 and 200 nodes when compared to harmony search algorithm and competitive swarm optimization (HSA-CSO).