CECT: computationally efficient congestion-avoidance and traffic engineering in software-defined cloud data centers

The proliferation of cloud data center applications and network function virtualization (NFV) boosts dynamic and QoS dependent traffic into the data centers network. Currently, lots of network routing protocols are requirement agnostic, while other QoS-aware protocols are computationally complex and inefficient for small flows. In this paper, a computationally efficient congestion avoidance scheme, called CECT, for software-defined cloud data centers is proposed. The proposed algorithm, CECT, not only minimizes network congestion but also reallocates the resources based on the flow requirements. To this end, we use a routing architecture to reconfigure the network resources triggered by two events: (1) the elapsing of a predefined time interval, or, (2) the occurrence of congestion. Moreover, a forwarding table entries compression technique is used to reduce the computational complexity of CECT. In this way, we mathematically formulate an optimization problem and define a genetic algorithm to solve the proposed optimization problem. We test the proposed algorithm on real-world network traffic. Our results show that CECT is computationally fast and the solution is feasible in all cases. In order to evaluate our algorithm in term of throughput, CECT is compared with ECMP (where the shortest path algorithm is used as the cost function). Simulation results confirm that the throughput obtained by running CECT is improved up to 3× compared to ECMP while packet loss is decreased up to 2×.     

Energy-aware routing considering load balancing for SDN: a minimum graph-based Ant Colony Optimization

Software-Defined Network (SDN) technology is a network management approach that facilitates a high level of programmability and centralized manageability. By leveraging the control and data plane separation, an energy-aware routing model could be easily implemented in the networks. In the present paper, we propose a two-phase SDN-based routing mechanism that aims at minimizing energy consumption while providing a certain level of QoS for the users’ flows and realizing the link load balancing. To reduce the network energy consumption, a minimum graph-based Ant Colony Optimization (ACO) approach is used in the first phase. It prunes and optimizes the network tree by turning unnecessary switches off and providing an energy-minimized sub-graph that is responsible for the network existing flows. In the second phase, an innovative weighted routing approach is developed that guarantees the QoS requirements of the incoming flows and routes them so that to balance the loads on the links. We validated our proposed approach by conducting extensive simulations on different traffic patterns and scenarios with different thresholds. The results indicate that the proposed routing method considerably minimizes the network energy consumption, especially for congested traffics with mice-type flows. It can provide effective link load balancing while satisfying the users’ QoS requirements.

     

Traffic-aware routing protocol for wireless sensor networks

In wireless sensor networks, when a sensor node detects events in the surrounding environment, the sensing period for learning detailed information is likely to be short. However, the short sensing cycle increases the data traffic of the sensor nodes in a routing path. Since the high traffic load causes a data queue overflow in the sensor nodes, important information about urgent events could be lost. In addition, since the battery energy of the sensor nodes is quickly exhausted, the entire lifetime of wireless sensor networks would be shortened. In this paper, to address these problem issues, a new routing protocol is proposed based on a lightweight genetic algorithm. In the proposed method, the sensor nodes are aware of the data traffic rate to monitor the network congestion. In addition, the fitness function is designed from both the average and the standard deviation of the traffic rates of sensor nodes. Based on dominant gene sets in a genetic algorithm, the proposed method selects suitable data forwarding sensor nodes to avoid heavy traffic congestion. In experiments, the proposed method demonstrates efficient data transmission due to much less queue overflow and supports fair data transmission for all sensor nodes. From the results, it is evident that the proposed method not only enhances the reliability of data transmission but also distributes the energy consumption across wireless sensor networks.     

Efficient load balancing on biswapped networks

This paper focuses on devising an efficient algorithm for load balancing on the promising biswapped interconnection networks which were recently proposed as a better architecture over the well-known OTIS networks. The proposed algorithm is called GPM which reduces the number of load balancing steps of the existed algorithms obviously. GPM algorithm first schedules load flows on inter-groups links to achieve the balanced status among groups. Then a general load balancing strategy is executed in each of all groups to balance processor loads. The analytical model proves that GPM algorithm is efficient and results of computer simulation experiment indicate that GPM can implement load balancing in biswapped network interconnected environments efficiently, in terms of various parameters.     

Modeling,analysis and simulation of ant-based network routing protocols

Using the metaphor of swarm intelligence, ant-based routing protocols deploy control packets that behave like ants to discover and optimize routes between pairs of nodes. These ant-based routing protocols provide an elegant, scalable solution to the routing problem for both wired and mobile ad hoc networks. The routing problem is highly nonlinear because the control packets alter the local routing tables as they are routed through the network. We mathematically map the local rules by which the routing tables are altered to the dynamics of the entire networks. Using dynamical systems theory, we map local protocol rules to full network performance, which helps us understand the impact of protocol parameters on network performance. In this paper, we systematically derive and analyze global models for simple ant-based routing protocols using both pheromone deposition and evaporation. In particular, we develop a stochastic model by modeling the probability density of ants over the network. The model is validated by comparing equilibrium pheromone levels produced by the global analysis to results obtained from simulation studies. We use both a Matlab simulation with ideal communications and a QualNet simulation with realistic communication models. Using these analytic and computational methods, we map out a complete phase diagram of network behavior over a small multipath network. We show the existence of both stable and unstable (inaccessible) routing solutions having varying properties of efficiency and redundancy depending upon the routing parameters. Finally, we apply these techniques to a larger 50-node network and show that the design principles acquired from studying the small model network extend to larger networks.     

Effectiveness of flexible routing control

Flexibility in part process representation and in highly adaptive routing algorithms are two major sources for improvement in the control of flexible manufacturing systems (FMSs). This article reports the investigation of the impact of these two kinds of flexibilities on the performance of the system. We argue that, when feasible, the choices of operations and sequencing of the part process plans should be deferred until detailed knowledge about the real-time factory state is available. To test our ideas, a flexible routing control simulation system (FRCS) was constructed and a programming language for modeling FMS part process plans, control strategies, and environments of the FMS was designed and implemented. In addition, a scheme for implementing flexible process routing called data flow dispatching rule (DFDR) was derived. The simulation results indicate that flexible processing can reduce mean flow time while increasing system throughput and machine utilization. We observed that this form of flexibility makes automatic load balancing of the machines possible. On the other hand, it also makes the control and scheduling process more complicated and calls for new control algorithms.     

Performance Evaluation of Flexible Manufacturing Systems with Finite Local Buffers: Fixed and Dynamic Routings

This article evaluates the performance of flexible manufacturing systems with finite local buffers and fixed or dynamic routing rules, and addresses the optimal design or system configuration problem of maximizing the system throughput. The costs include machine cost, part (or pallet) cost, and local buffers cost. First, the system throughputs and their behaviors are considered with both queueing network analysis and simulation, and it is shown for a fixed routing model that the system throughput in the case of finite local buffers is greater than in the case of infinite local buffers. For a fixed versus dynamic routing rule, it is also found that the throughput in the former case can be close to the one in the latter case by changing the setting parameters. Next, the design problems of maximizing the system throughput are considered numerically for fixed and dynamic routing cases. Then, it is seen that better combination of design variables is a class of the monotonicity in local buffers, service rates, and routing probabilities.     

A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks

This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.     

Contention-Aware Communication Schedule for High-Speed Communication

A lot of efforts have been devoted to address the software overhead problem in the past decade, which is known as the major hindrance on high-speed communication. However, this paper shows that having a low-latency communication system does not guarantee to achieve high performance, as there are other communication issues that have not been fully addressed by the use of low-latency communication, such as contention and scheduling of communication events. In this paper, we use the complete exchange operation as a case study to show that with careful design of communication schedules, we can achieve efficient communication as well as prevent congestion. We have developed a complete exchange algorithm, the Synchronous Shuffle Exchange, which is an optimal algorithm on the non-blocking network. To avoid congestion loss caused by the non-deterministic delays in communication events, a global congestion control scheme is introduced. This scheme coordinates all participating nodes to monitor and regulate the traffic load, which effectively avoids congestion loss and maintains sufficient throughput to maximize the performance. To improve the effectiveness of the congestion control scheme when working on the hierarchical network, we incorporate information on the network topology to devise a contention-aware permutation. This permutation scheme generates a communication schedule, which is both node and switch contention-free as well as distributing the network loads more evenly across the hierarchy. This relieves the congestion build-up at the uplink ports and improves the synchronism of the traffic information exchange between cluster nodes. Performance results of our implementation on a 32-node cluster with various network configurations are examined and reported in this paper.     

无线传感器网络中基于缠绕多路径的ReInforM路由

对缠绕多路径算法技术的基本构建方法和ReInforM路由算法技术的基本过程进行了讨论,分析了其在无线传感器网络应用中的发展方向,提出了基于缠绕多路径的ReInforM路由。     

Self Organized Terminode Routing

We consider the problem of routing in a wide area mobile ad hoc network called Terminode Network. Routing in this network is designed with the following objectives. First, it should scale well in terms of the number of nodes and geographical coverage; second, routing should have scalable mechanisms that cope with the dynamicity in the network due to mobility; and third, nodes need to be highly collaborative and redundant, but, most of all, cannot use complex algorithms or protocols. Our routing scheme is a combination of two protocols called Terminode Local Routing (TLR) and Terminode Remote Routing (TRR). TLR is used to route packets to close destinations. TRR is used to route to remote destinations. The combination of TLR and TRR has the following features: (1) it is highly scalable because every node relies only on itself and a small number of other nodes for packet forwarding; (2) it acts and reacts well to the dynamicity of the network because as a rule multipath routing is considered; and (3) it can be implemented and run in very simple devices because the algorithms and protocols are very simple and based on high collaboration. We performed simulations of the TLR and TRR protocols using the GloMoSim simulator. The simulation results for a large, highly mobile ad hoc environment demonstrate benefits of the combination of TLR and TRR over an existing protocol that uses geographical information for packet forwarding.     

Promoting Wired Links in Wireless Mesh Networks: An Efficient Engineering Solution

Wireless Mesh Networks (WMNs) cannot completely guarantee good performance of traffic sources such as video streaming. To improve the network performance, this study proposes an efficient engineering solution named Wireless-to-Ethernet-Mesh-Portal-Passageway (WEMPP) that allows effective use of wired communication in WMNs. WEMPP permits transmitting data through wired and stable paths even when the destination is in the same network as the source (Intra-traffic). Tested with four popular routing protocols (Optimized Link State Routing or OLSR as a proactive protocol, Dynamic MANET On-demand or DYMO as a reactive protocol, DYMO with spanning tree ability and HWMP), WEMPP considerably decreases the end-to-end delay, jitter, contentions and interferences on nodes, even when the network size or density varies. WEMPP is also cost-effective and increases the network throughput. Moreover, in contrast to solutions proposed by previous studies, WEMPP is easily implemented by modifying the firmware of the actual Ethernet hardware without altering the routing protocols and/or the functionality of the IP/MAC/Upper layers. In fact, there is no need for modifying the functionalities of other mesh components in order to work with WEMPPs. The results of this study show that WEMPP significantly increases the performance of all routing protocols, thus leading to better video quality on nodes.     

A decentralized clustering scheme for transparent mode devices

Clustering multiple devices to form a single powerful device is a common method for improving performance. Most designs of the clustering schemes in the current literature are deploying a traffic splitter in front of devices in the cluster which acts as a centralized job dispatcher splitting workloads to backend devices. In this paper, we propose a decentralized clustering scheme, with no traffic splitter deployed, as an alternative solution on building a cluster system for those devices configured in transparent mode, such as bandwidth controllers, NIPSs, and traffic monitors. Devices in the cluster process the network traffic in parallel in a decentralized manner to scale the throughput. A device can also migrate its workload to others for the purpose of load balance or fault tolerance. Experiment results suggest that the proposed scheme can effectively improve performance of transparent mode devices in terms of throughput, load balance, and fault tolerance.     

Load monitoring and system-traffic-aware live VM migration-based load balancing in cloud data center using graph theoretic solutions

This paper proposes solutions to monitor the load and to balance the load of cloud data center. The proposed solutions work in two phases and graph theoretical concepts are applied in both phases. In the first phase, cloud data center is modeled as a network graph. This network graph is augmented with minimum dominating set concept of graph theory for monitoring its load. For constructing minimum dominating set, this paper proposes a new variant of minimum dominating set (V-MDS) algorithm and is compared with existing construction algorithms proposed by Rooji and Fomin. The V-MDS approach of querying cloud data center load information is compared with Central monitor approach. The second phase focuses on system and network-aware live virtual machine migration for load balancing cloud data center. For this, a new system and traffic-aware live VM migration for load balancing (ST-LVM-LB) algorithm is proposed and is compared with existing benchmarked algorithms dynamic management algorithm (DMA) and Sandpiper. To study the performance of the proposed algorithms, CloudSim3.0.3 simulator is used. The experimental results show that, V-MDS algorithm takes quadratic time complexity, whereas Rooji and Fomin algorithms take exponential time complexity. Then the V-MDS approach for querying Cloud Data Center load information is compared with the Central monitor approach and the experimental result shows that the proposed approach reduces the number of message updates by half than the Central monitor approach. The experimental results show on load balancing that the developed ST-LVM-LB algorithm triggers lesser Virtual Machine migrations, takes lesser time and migration cost to migrate with minimum network overhead. Thus the proposed algorithms improve the service delivery performance of cloud data center by incorporating graph theoretical solutions in monitoring and balancing the load.     

Using Trust to Secure Geographic and Energy Aware Routing against Multiple Attacks

To address the vulnerability of geographic routing to multiple security threats such as false routing information, selective forwarding and the Sybil attack in wireless sensor networks, this paper proposes a trust-based defending model against above-mentioned multiple attacks. Considering the characteristics of resource-constrained sensor nodes, trust values of neighboring nodes on the routing path can be calculated through the Dirichlet distribution function, which is based on data packets'' acknowledgements in a certain period instead of energy-consuming monitoring. Trust is combined with the cost of geographic and energy aware routing for selecting the next hop of routing. At the same time, the initial trust is dynamically determined, service requests are restricted for malicious nodes in accordance with trust values, and the impact of node mobility is weakened by the trust evolution. The simulation results and analysis show that the proposed model under multiple attacks has advantages in packet delivery ratio and network lifetime over the existing models.     

Improved Neural Networks with Random Weights for Short-Term Load Forecasting

An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Eight factors are selected as the inputs. A mutual information weighting algorithm is then used to allocate different weights to the inputs. The neural networks with random weights and kernels (KNNRW) is applied to approximate the nonlinear function between the selected inputs and the daily maximum load due to the fast learning speed and good generalization performance. In the application of the daily load in Dalian, the result of the proposed INNRW is compared with several previously developed forecasting models. The simulation experiment shows that the proposed model performs the best overall in short-term load forecasting.     

Effect of stable and unstable load carriage on walking gait variability,dynamic stability and muscle activity of older adults

Load carriage perturbs the neuromuscular system, which can be impaired due to ageing. The ability to counteract perturbations is an indicator of neuromuscular function but if the response is insufficient the risk of falls will increase. However, it is unknown how load carriage affects older adults. Fourteen older adults (65 ± 6 years) attended a single visit during which they performed 4 min of walking in 3 conditions, unloaded, stable backpack load and unstable backpack load. During each walking trial, 3-dimensional kinematics of the lower limb and trunk movements and electromyographic activity of 6 lower limb muscles were recorded. The local dynamic stability (local divergence exponents), joint angle variability and spatio-temporal variability were determined along with muscle activation magnitudes. Medio-lateral dynamic stability was lower (p = 0.018) and step width (p = 0.019) and step width variability (p = 0.015) were greater in unstable load walking and step width variability was greater in stable load walking (p = 0.009) compared to unloaded walking. However, there was no effect on joint angle variability. Unstable load carriage increased activity of the Rectus Femoris (p = 0.001) and Soleus (p = 0.043) and stable load carriage increased Rectus Femoris activity (p = 0.006). These results suggest that loaded walking alters the gait of older adults and that unstable load carriage reduces dynamic stability compared to unloaded walking. This can potentially increase the risk of falls, but also offers the potential to use unstable loads as part of fall prevention programmes.     

The Loss of Efficiency Caused by Agents’ Uncoordinated Routing in Transport Networks

Large-scale daily commuting data were combined with detailed geographical information system (GIS) data to analyze the loss of transport efficiency caused by drivers’ uncoordinated routing in urban road networks. We used Price of Anarchy (POA) to quantify the loss of transport efficiency and found that both volume and distribution of human mobility demand determine the POA. In order to reduce POA, a small number of highways require considerable decreases in traffic, and their neighboring arterial roads need to attract more traffic. The magnitude of the adjustment in traffic flow can be estimated using the fundamental measure traffic flow only, which is widely available and easy to collect. Surprisingly, the most congested roads or the roads with largest traffic flow were not those requiring the most reduction of traffic. This study can offer guidance for the optimal control of urban traffic and facilitate improvements in the efficiency of transport networks.     

Estimating Urban Traffic Patterns through Probabilistic Interconnectivity of Road Network Junctions

The emergence of large, fine-grained mobility datasets offers significant opportunities for the development and application of new methodologies for transportation analysis. In this paper, the link between routing behaviour and traffic patterns in urban areas is examined, introducing a method to derive estimates of traffic patterns from a large collection of fine-grained routing data. Using this dataset, the interconnectivity between road network junctions is extracted in the form of a Markov chain. This representation encodes the probability of the successive usage of adjacent road junctions, encoding routes as flows between decision points rather than flows along road segments. This network of functional interactions is then integrated within a modified Markov chain Monte Carlo (MCMC) framework, adapted for the estimation of urban traffic patterns. As part of this approach, the data-derived links between major junctions influence the movement of directed random walks executed across the network to model origin-destination journeys. The simulation process yields estimates of traffic distribution across the road network. The paper presents an implementation of the modified MCMC approach for London, United Kingdom, building an MCMC model based on a dataset of nearly 700000 minicab routes. Validation of the approach clarifies how each element of the MCMC framework contributes to junction prediction performance, and finds promising results in relation to the estimation of junction choice and minicab traffic distribution. The paper concludes by summarising the potential for the development and extension of this approach to the wider urban modelling domain.     

STACRP: a secure trusted auction oriented clustering based routing protocol for MANET

In mobile ad hoc network?(MANET) nodes have a tendency to drop others’ packet to conserve its own energy. If most of the nodes in a network start to behave in this way, either a portion of the network would be isolated or total network functionality would be hampered. This behavior is known as selfishness. Therefore, selfishness mitigation and enforcing cooperation between nodes is very important to increase the availability of nodes and overall throughput and to achieve the robustness of the network. Both credit and reputation based mechanisms are used to attract nodes to forward others’ packets. In light of this, we propose a game theoretic routing model, Secure Trusted Auction oriented Clustering based Routing Protocol (STACRP), to provide trusted framework for MANET. Two auction mechanisms procurement and Dutch are used to determine the forwarding cost-per-hop for intermediate nodes. Our model is lightweight in terms of computational and communication requirements, yet powerful in terms of flexibility in managing trust between nodes of heterogeneous deployments. It manages trust locally with minimal overhead in terms of extra messages. STACRP organizes the network into 1-hop disjoint clusters and elects the most qualified and trustworthy nodes as Clusterhead. The trust is quantified with carefully chosen parameters having deep impact on network functionality. The trust model is analyzed using Markov chain and is proven as continuous time Markov chain. The security analysis of the model is analyzed to guarantee that the proposed approach achieves a secure reliable routing solution for MANETs. The proposed model have been evaluated with a set of simulations that show STACRP detects selfish nodes and enforces cooperation between nodes and achieves better throughput and packet delivery ratio with lees routing overhead compare to AODV.