VNF-EQ: dynamic placement of virtual network functions for energy efficiency and QoS guarantee in NFV

With the advances of network function virtualization and cloud computing technologies, a number of network services are implemented across data centers by creating a service chain using different virtual network functions (VNFs) running on virtual machines. Due to the complexity of network infrastructure, creating a service chain requires high operational cost especially in carrier-grade network service providers and supporting stringent QoS requirements from users is also a complicated task. There have been various research efforts to address these problems that only focus on one aspect of optimization goal either from users such as latency minimization and QoS based optimization, or from service providers such as resource optimization and cost minimization. However, meeting the requirements both from users and service providers efficiently is still a challenging issue. This paper proposes a VNF placement algorithm called VNF-EQ that allows users to meet their service latency requirements, while minimizing the energy consumption at the same time. The proposed algorithm is dynamic in a sense that the locations or the service chains of VNFs are reconfigured to minimize the energy consumption when the traffic passing through the chain falls below a pre-defined threshold. We use genetic algorithm to formulate this problem because it is a variation of the multi-constrained path selection problem known as NP-complete. The benchmarking results show that the proposed approach outperforms other heuristic algorithms by as much as 49% and reduces the energy consumptions by rearranging VNFs.     

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.

     

Incorporating service and user information and latent features to predict QoS for selecting and recommending cloud service compositions

The rapid growth of published cloud services in the Internet makes the service selection and recommendation a challenging task for both users and service providers. In cloud environments, software re services collaborate with other complementary services to provide complete solutions to end users. The service selection is performed based on QoS requirements submitted by end users. Software providers alone cannot guarantee users’ QoS requirements. These requirements must be end-to-end, representing all collaborating services in a cloud solution. In this paper, we propose a prediction model to compute end-to-end QoS values for vertically composed services which are composed of three types of cloud services: software (SaaS), infrastructure (IaaS) and data (DaaS) services. These values can be used during the service selection and recommendation process. Our model exploits historical QoS values and cloud service and user information to predict unknown end-to-end QoS values of composite services. The experiments demonstrate that our proposed model outperforms other prediction models in terms of the prediction accuracy. We also study the impact of different parameters on the prediction results. In the experiments, we used real cloud services’ QoS data collected using our developed QoS monitoring and collecting system.     

Internal Node and Shortcut Based Routing with Guaranteed Delivery in Wireless Networks

Several localized position based routing algorithms for wireless networks were described recently. In greedy routing algorithm (that has close performance to the shortest path algorithm, if successful), sender or node S currently holding the message m forwards m to one of its neighbors that is the closest to destination. The algorithm fails if S does not have any neighbor that is closer to destination than S. FACE algorithm guarantees the delivery of m if the network, modeled by unit graph, is connected. GFG algorithm combines greedy and FACE algorithms. Greedy algorithm is applied as long as possible, until delivery or a failure. In case of failure, the algorithm switches to FACE algorithm until a node closer to destination than last failure node is found, at which point greedy algorithm is applied again. Past traffic does not need to be memorized at nodes. In this paper we further improve the performance of GFG algorithm, by reducing its average hop count. First we improve the FACE algorithm by adding a sooner-back procedure for earlier escape from FACE mode. Then we perform a shortcut procedure at each forwarding node S. Node S uses the local information available to calculate as many hops as possible and forwards the packet to the last known hop directly instead of forwarding it to the next hop. The second improvement is based on the concept of dominating sets. Each node in the network is classified as internal or not, based on geographic position of its neighboring nodes. The network of internal nodes defines a connected dominating set, i.e., and each node must be either internal or directly connected to an internal node. In addition, internal nodes are connected. We apply several existing definitions of internal nodes, namely the concepts of intermediate, inter-gateway and gateway nodes. We propose to run GFG routing, enhanced by shortcut procedure, on the dominating set, except possibly the first and last hops. The performance of proposed algorithms is measured by comparing its average hop count with hop count of the basic GFG algorithm and the benchmark shortest path algorithm, and very significant improvements were obtained for low degree graphs. More precisely, we obtained localized routing algorithm that guarantees delivery and has very low excess in terms of hop count compared to the shortest path algorithm. The experimental data show that the length of additional path (in excess of shortest path length) can be reduced to about half of that of existing GFG algorithm.     

A Novel LTE Scheduling Algorithm for Green Technology in Smart Grid

Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application’s priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.     

Task offloading method of edge computing in internet of vehicles based on deep reinforcement learning

Compared with the traditional network tasks, the emerging Internet of Vehicles (IoV) technology has higher requirements for network bandwidth and delay. However, due to the limitation of computing resources and battery capacity of existing mobile devices, it is hard to meet the above requirements. How to complete task offloading and calculation with lower task delay and lower energy consumption is the most important issue. Aiming at the task offloading system of the IoV, this paper considers the situation of multiple MEC servers when modeling, and proposes a dynamic task offloading scheme based on deep reinforcement learning. It improves the traditional Q-Learning algorithm and combines deep learning with reinforcement learning to avoid dimensional disaster in the Q-Learning algorithm. Simulation results show that the proposed algorithm has better performance on delay, energy consumption, and total system overhead under the different number of tasks and wireless channel bandwidth.

     

Traffic load monitoring and load balancing for the Internet

This paper proposes a class-based multipath routing algorithm to support Quality of Service (QoS). The algorithm is called Two-level Class-based Routing with Prediction (TCRP). Since frequently flooding routing information is very expensive for dynamic routing, the TCRP is designed to have the traffic load information monitored in one stable period as a guide to control traffic forwarding in the next stable period. The monitoring function is implemented by adopting the leaky bucket mechanism. In TCRP, the path selection function can utilize resources on multipath to achieve load balancing, increase network throughput and reduce the queuing delay. The extensive simulation is conducted to analyze the performance of the TCRP algorithm. The simulation results show that the TCRP can reduce packet drops and increase network throughput in any size network topology. This revised version was published online in July 2006 with corrections to the Cover Date.     

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.     

On an improved clustering algorithm based on node density for WSN routing protocol

To better collect data in context to balance energy consumption, wireless sensor networks (WSN) need to be divided into clusters. The division of clusters makes the network become a hierarchical organizational structure, which plays the role of balancing the network load and prolonging the life cycle of the system. In clustering routing algorithm, the pros and cons of clustering algorithm directly affect the result of cluster division. In this paper, an algorithm for selecting cluster heads based on node distribution density and allocating remaining nodes is proposed for the defects of cluster head random election and uneven clustering in the traditional LEACH protocol clustering algorithm in WSN. Experiments show that the algorithm can realize the rapid selection of cluster heads and division of clusters, which is effective for node clustering and is conducive to equalizing energy consumption.

     

Bayes Node Energy Polynomial Distribution to Improve Routing in Wireless Sensor Network

Wireless Sensor Network monitor and control the physical world via large number of small, low-priced sensor nodes. Existing method on Wireless Sensor Network (WSN) presented sensed data communication through continuous data collection resulting in higher delay and energy consumption. To conquer the routing issue and reduce energy drain rate, Bayes Node Energy and Polynomial Distribution (BNEPD) technique is introduced with energy aware routing in the wireless sensor network. The Bayes Node Energy Distribution initially distributes the sensor nodes that detect an object of similar event (i.e., temperature, pressure, flow) into specific regions with the application of Bayes rule. The object detection of similar events is accomplished based on the bayes probabilities and is sent to the sink node resulting in minimizing the energy consumption. Next, the Polynomial Regression Function is applied to the target object of similar events considered for different sensors are combined. They are based on the minimum and maximum value of object events and are transferred to the sink node. Finally, the Poly Distribute algorithm effectively distributes the sensor nodes. The energy efficient routing path for each sensor nodes are created by data aggregation at the sink based on polynomial regression function which reduces the energy drain rate with minimum communication overhead. Experimental performance is evaluated using Dodgers Loop Sensor Data Set from UCI repository. Simulation results show that the proposed distribution algorithm significantly reduce the node energy drain rate and ensure fairness among different users reducing the communication overhead.     

Dynamic Quality of Service Model for Improving Performance of Multimedia Real-Time Transmission in Industrial Networks

Nowadays, quality of service (QoS) is very popular in various research areas like distributed systems, multimedia real-time applications and networking. The requirements of these systems are to satisfy reliability, uptime, security constraints and throughput as well as application specific requirements. The real-time multimedia applications are commonly distributed over the network and meet various time constraints across networks without creating any intervention over control flows. In particular, video compressors make variable bit-rate streams that mismatch the constant-bit-rate channels typically provided by classical real-time protocols, severely reducing the efficiency of network utilization. Thus, it is necessary to enlarge the communication bandwidth to transfer the compressed multimedia streams using Flexible Time Triggered- Enhanced Switched Ethernet (FTT-ESE) protocol. FTT-ESE provides automation to calculate the compression level and change the bandwidth of the stream. This paper focuses on low-latency multimedia transmission over Ethernet with dynamic quality-of-service (QoS) management. This proposed framework deals with a dynamic QoS for multimedia transmission over Ethernet with FTT-ESE protocol. This paper also presents distinct QoS metrics based both on the image quality and network features. Some experiments with recorded and live video streams show the advantages of the proposed framework. To validate the solution we have designed and implemented a simulator based on the Matlab/Simulink, which is a tool to evaluate different network architecture using Simulink blocks.     

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×.     

SOCCER: Self-Optimization of Energy-efficient Cloud Resources

Cloud data centers often schedule heterogeneous workloads without considering energy consumption and carbon emission aspects. Tremendous amount of energy consumption leads to high operational costs and reduces return on investment and contributes towards carbon footprints to the environment. Therefore, there is need of energy-aware cloud based system which schedules computing resources automatically by considering energy consumption as an important parameter. In this paper, energy efficient autonomic cloud system [Self-Optimization of Cloud Computing Energy-efficient Resources (SOCCER)] is proposed for energy efficient scheduling of cloud resources in data centers. The proposed work considers energy as a Quality of Service (QoS) parameter and automatically optimizes the efficiency of cloud resources by reducing energy consumption. The performance of the proposed system has been evaluated in real cloud environment and the experimental results show that the proposed system performs better in terms of energy consumption of cloud resources and utilizes these resources optimally.     

WHY FORAGING BIRDS IN TREES SHOULD CLIMB AND HOP UPWARDS RATHER THAN DOWNWARDS

This paper describes the energy cost of locomotion in birds foraging over vertical zones in trees. In particular, the energetically cheapest pattern for a bird flying among trees and moving within them is explored. For birds moving vertically by climbing and hopping (but not by flying) it should take less energy to climb and hop upwards in a tree and fly downwards to the next one, than to do the reverse. This is because part of the potential energy gained in climbing upwards may be used for subsequent horizontal progression to the next tree. For movements the other way, the potential energy is largely wasted during downward hopping and climbing within a tree. It is predicted that birds moving within trees by climbing and hopping (but not by flying) leave at a higher level than they arrive (whether the vertical movements within trees are along the trunk or among branches). These energetic considerations probably expose one selection pressure behind the morphology of the woodpecker—treecreeper type, which shows obvious adaptation for climbing upwards rather than downwards.     

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.     

Connection re–routing method for QoS guarantee in the ATM–based wired/wireless integrated network

On the ATM-based wired/wireless integrated network, we propose a connection re-routing method which reduces the inter-cluster handoff delay by reserving VPI/VCIs for possible inter-cluster handoff calls in advance. Additionally, we propose wired resource reservation methods, which are the auxiliary method and the split method, for handoff QoS guarantee of various expected services. The characteristics of these methods reserve wired connection resources based on the information on the possible inter-cluster handoff calls. With mathematical analysis, we also propose an algorithm and cost function for deciding the optimal amount in reserving resources. With numerical examples, we can see that the auxiliary method effectively reduces the cost in all cases (α > β, α ≔ β, and α < β). The split method, however, has good effects on cost reduction, only in case that the capacity of total resource C _T is relatively small and handoff calls have priority over new calls. The numerical results show that these reservation methods can flexibly cope with the time-variant environment and meet the QoS requirements on the inter–cluster handoff calls. This revised version was published online in August 2006 with corrections to the Cover Date.     

Adaptive resource utilization and remote access capabilities in high‐performance distributed systems: The Open HPC++ approach

Development of high-performance distributed applications, called metaapplications, is extremely challenging because of their complex runtime environment coupled with their requirements of high-performance and Quality of Service (QoS). Such applications typically run on a set of heterogeneous machines with dynamically varying loads, connected by heterogeneous networks possibly supporting a wide variety of communication protocols. In spite of the size and complexity of such applications, they must provide the high-performance and QoS mandated by their users. In order to achieve the goal of high-performance, they need to adaptively utilize their computational and communication resources. Apart from the requirements of adaptive resource utilization, such applications have a third kind of requirement related to remote access QoS. Different clients, although accessing a single server resource, may have differing QoS requirements from their remote connections. A single server resource may also need to provide different QoS for different clients, depending on various issues such as the amount of trust between the server and a given client. These QoS requirements can be encapsulated under the abstraction of remote access capabilities. Metaapplications need to address all the above three requirements in order to achieve the goal of high-performance and satisfy user expectations of QoS. This paper presents Open HPC++, a programming environment for high-performance applications running in a complex and heterogeneous run-time environment. Open HPC++ provides application level tools and mechanisms to satisfy application requirements of adaptive resource utilization and remote access capabilities. Open HPC++ is designed on the lines of CORBA and uses an Object Request Broker (ORB) to support seamless communication between distributed application components. In order to provide adaptive utilization of communication resources, it uses the principle of open implementation to open up the communication mechanisms of its ORB. By virtue of its open architecture, the ORB supports multiple, possibly custom, communication protocols, along with automatic and user controlled protocol selection at run-time. An extension of the same mechanism is used to support the concept of remote access capabilities. In order to support adaptive utilization of computational resources, Open HPC++ also provides a flexible yet powerful set of load-balancing mechanisms that can be used to implement custom load-balancing strategies. The paper also presents performance evaluations of Open HPC++ adaptivity and load-balancing mechanisms. This revised version was published online in July 2006 with corrections to the Cover Date.     

Lifetime extension based on residual energy for receiver-driven multi-hop wireless network

An important research topic in wireless sensor networking is the extension of operating time by controlling the power consumption of individual nodes. In a receiver-driven communication protocol, a receiver node periodically transmits its ID to the sender node, and in response the sender node sends an acknowledgment, after which data transmission starts. By applying such a receiver-driven protocol to wireless sensor networks, the average power consumption of the network can be controlled, but there still remains the problem of unbalanced load distribution among nodes. Therefore, part of the network shuts down when the battery of the node that consumes the most power is completely discharged. To extend the network lifetime, we propose a method where information about the residual energy level is exchanged through ID packets in order to balance power consumption. Simulation results show that the network lifetime can be extended by about 70–100 % while maintaining high network performance in terms of packet collection ratio and delay.     

linkcomm: an R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type

SUMMARY: An essential element when analysing the structure, function, and dynamics of biological networks is the identification of communities of related nodes. An algorithm proposed recently enhances this process by clustering the links between nodes, rather than the nodes themselves, thereby allowing each node to belong to multiple overlapping or nested communities. The R package 'linkcomm' implements this algorithm and extends it in several aspects: (i) the clustering algorithm handles networks that are weighted, directed, or both weighted and directed; (ii) several visualization methods are implemented that facilitate the representation of the link communities and their relationships; (iii) a suite of functions are included for the downstream analysis of the link communities including novel community-based measures of node centrality; (iv) the main algorithm is written in C++ and designed to handle networks of any size; and (v) several clustering methods are available for networks that can be handled in memory, and the number of communities can be adjusted by the user. AVAILABILITY: The program is freely available from the Comprehensive R Archive Network (http://cran.r-project.org/) under the terms of the GNU General Public License (version 2 or later).     

A Heuristic-Based Wormhole Routing Algorithm for Hypercube Multicomputer Networks

In this paper, we consider fault-tolerant routing algorithms in hypercube multicomputer networks. In particular, one of the most quoted adaptive fault-tolerant routing algorithm for hypercubes in the literature is studied in detail and its limited ability to route messages in the presence of some fault patterns (i.e., combination of node and link faults), is pointed out. A modified algorithm is proposed and its performance, using simulation, is compared to that of the above mentioned algorithm. It is shown that the proposed algorithm outperforms the existing one in terms of its ability to route routable messages around the hypercube in the presence of node and/or links faults. This improvement is achieved while using the same average path length or even improving it. Illustrative examples are shown in support of such improvement.