Evolutionary algorithm for provisioning VPN trees based on pipe and hose workload models

Chapter 59 - Virtual Private Networks

Virtual private networks (VPN) and IPsec are discussed in this chapter. A VPN emulates a private wide area network (WAN) facility using IP networks, such as the public Internet or private IP backbones. When VPNs are used, the Internet offers the appearance, functionality, and usefulness of a dedicated private network. One of the problems in using the Internet as a WAN is that the Internet is a public network and has relatively little security. IPsec provides the following security services to VPNs: data origin authentication, access control, confidentiality (encryption), connectionless integrity, rejection of replayed packets (a form of partial sequence integrity), and limited traffic flow confidentiality.

Virtual private networks (VPNs) can provide customers with cost-effective, reliable, and secure services. Currently there are two resource management models for provisioning VPN services, the pipe model and the hose model. The hose model only requires customers to specify an aggregate ingress and egress bandwidth capacities per-endpoint, instead of an exact traffic demand per endpoint pair as with the pipe model. This thesis investigates hose model VPN provisioning algorithms on the following important but neglected aspects.

The integration of Ethernet Passive Optical Networks (EPONs) and IEEE 802.16 (WiMAX) has been lately presented as a promising fiber-wireless (FiWi) broadband access network. Conversely, lightweight layer-2 virtual private networks (VPNs) over FiWi, which can provide bandwidth guarantee to the respective users, were only recently addressed by Dhaini et. al. In this paper, WiMAX-VPON, the framework proposed by Dhaini et. al to support layer-2 VPNs over EPON-WiMAX, is improved to take into account the polling control overhead when distributing the VPN bandwidth. A new generic analytical model is also presented to evaluate the performance of each registered VPN service. Our proposed model, which can also be used to analyze any polling-based FiWi network, applies for wireless and optical domains and provides performance measurements such as packet queuing delay, end-to-end (from wireless user to optical server) packet delay and average queue size. Numerical results are compared with simulation experiments, and show consistency between both outcomes.

We study the provisioning of virtual private network (VPN) service over WDM optical networks. For this purpose, we investigate the blocking performance of the hose model versus the pipe model for the provisioning. Two techniques are presented: an analytical queuing model and a discrete event simulation. The queuing model is developed from the multirate reduced-load approximation technique. The simulation is done with the OPNET simulator. Several experimental situations were used. The blocking probabilities calculated from the two approaches show a close match, indicating that the multirate reduced-load approximation technique is capable of predicting the blocking performance for the pipe model and the hose model in WDM networks. A comparison of the blocking behavior of the two models shows that the hose model has superior blocking performance as compared with pipe model. By and large, the blocking probability of the hose model is better than that of the pipe model by a few orders of magnitude, particularly at low load regions. The flexibility of the hose model allowing for the sharing of resources on a link among all connections accounts for its superior performance.

Virtual private networks (VPN) and link sharing are cost-effective ways of realizing corporate intranets. Corporate intranets will increasingly have to provide integrated services for voice and multimedia traffic. Although packet scheduling algorithms can be used to implement integrated services in link sharing and virtual private networks, their statistical multiplexing gains are limited. We propose a new scheme called stochastic fair sharing (SFS) to carry out fair link sharing and fair sharing among virtual leased links (VLL). In the link sharing environment, capacities allocated to different classes are adjusted dynamically as sessions arrive (or depart). The SFS admission control algorithm decides which sessions to accept and which to reject depending upon the current utilizations and provisioned capacities of the classes. SFS gives protection to classes with low session arrival rate against classes with high session arrival rates by ensuring them a low blocking probability. In the case of multi-hop VLL, capacity resizing requests are sent in the service providers's network which are admission-controlled using SFS. Our simulations indicate that using SFS, the equivalent capacity of virtual links converge to their max-min fair capacity, with a fairness index of 0.97 in extreme situations. The average signaling load of the protocol was found to be reasonable. The scheme is simple to implement, efficient, and robust. The potential applications of SFS are fair and efficient resource sharing in telecommunication networks, ATM networks, virtual private networks (VPN) and integrated services or differentiated services-based IP networks.

This section introduces MultiProtocol Label Switching (MPLS) an alternative for regular IP forwarding and routing in IP networks. MPLS allows the setup of Label Switched Paths (LSPs) between IP routers to avoid the IP forwarding in the intermediate routers. This sections compares IP forwarding with MPLS forwarding. It gives an example of LSP setup. Then more details about the MPLS forwarding and signaling layers are given. The important MPLS signaling protocols: Label Distribution Protocol (LDP), Constraint‐Based LDP (CR‐LDP), RSVP extensions for LSP tunnels (RSVP‐TE) and carrying labels in BGP‐4 are described. Combing Quality of Service (QoS) (both integrated service and differentiated services) and MPLS is described. Finally, this section describes three important applications for MPLS: Traffic Engineering, Virtual Private Networks (VPN) and resilience.

Chapter 1 - An Introduction to Virtualization

The efficient utilization and management of bandwidth in broadband networks is of paramount importance since bandwidth costs are likely to dominate network operational costs, especially in the wide area scenario. However, relatively little attention has been given to this issue in developing the current schemes for the transport of Internet data traffic over ATM. In this paper we describe a bandwidth allocation and management approach for the transport of Internet traffic over ATM that uses Virtual Private Networks, exploits the ABR service and implements bandwidth guarantees. We also address the efficient sharing of bandwidth between Internet data traffic and other media traffic using ATM.KeywordsMultimedia networksbandwidth managementInternet over ATM

IPSec encrypts and authenticates the packets of IP protocol. Both sides of IP network layer communication use encryption authentication, data source authentication and integrity verification to ensure the integrity, confidentiality and anti replay of IP protocol packet transmission. In enterprise network management, we can usually use IPSec VPN technology to establish a secure channel to realize the secure communication between headquarters and branches.[1]

Nurse Rostering Problem (NRP) is one of NP - hard combinatorial optimization problems about the distribution of medical resources. In the past, there have been several proposed methods like heuristic algorithms and algorithms based on establishing rigorous mathematical models. Especially, the hybrid algorithm combined integer programming and evolutionary algorithm (IP+EA) have been proved to be effective for NRP. However, these methods are not efficient in dealing with large-scale NPR instances, like Chinese NRP. In order to overcome the premature convergence of IP+EA, we propose a hybrid evolutionary algorithm based on scout bee global search strategy. Inspired by the behavior of scouts in artificial bee colony algorithms, the global search is integrated into EA, which can lead the algorithm to escape from local optima. The experimental results indicate that, our proposed approach is more effective than several existing algorithms to solve the Chinese NRP.

Proteins are cellular macromolecules made up of linear chains of amino acids that adopt a unique three-dimensional structure to carry out specific biological functions. Amongst various computational approaches developed for Protein Structure Prediction (PSP), ab initio methods perform the prediction without prior knowledge of known structures. However, due to huge computational complexity incurred by these methods, simplified hydrophobic-polar (HP) models are often used for the investigations. Despite this simplicity, PSP continues to be a hard combinatorial optimization problem requiring development of efficient heuristic search techniques. Memetic algorithm (MA), with its flexible architecture, encompasses the strength of both local and global search and has shown promise in solving problems with complex search landscape, including PSP. This thesis is focused on developing an effective ab initio technique under a suitable MA framework for solving the PSP problem using low resolution HP models. To enhance the effectiveness of the MA, a mechanism is devised to include knowledge from the problem domain by exploiting the concept of hydrophobic-core (H-core) formation. The size of the maximal possible H-core is estimated and the approximation is validated by our proposed deterministic search technique. Further, a new knowledge-based initial population generation technique ensures generating conformations which are not only valid, but also maintain diversity in the population. This helps to commence the search with good seeds leading towards faster convergence. While solving the PSP problem using MA, the greatest intricacy arises from the complex nature of its multi-modal landscape. Like all the evolutionary algorithms (EAs), MAs have the tendency to get trapped into local minima due to selection pressure and genetic drift induced by the genetic operators, and techniques are necessary to surmount this difficulty. A novel parental selection technique called “Adaptive Strategy for Assortative Mating” is developed that dynamically distributes the re production opportunity among the individuals of the population to boost the effectiveness of cross-over. Further, a new survival selection strategy, namely “Sib-based Survival Selection”, prevents the rapid spread of genetic material from a particular individual to other members in the population, ensuring the concurrent preservation of several potential solutions, and restricting premature convergence. A new diversification mechanism, namely “Memory-based Diversification”, systematically diversifies the population by introducing new genetic material whenever it gets stuck at local peaks and allows it to proceed for further progress. An extensive experimental analysis is also reported on the influence of all three techniques proposed to maintain the diversity of population. Furthermore, in order to carry out the conformational search efficiently, especially with the increase in sequence length, the optimization technique is designed such that it provides an effective balance between exploration and exploitation. A robust Multimodal Memetic Framework has been developed where optimization is accomplished in hierarchical stages. The population of each stage is partitioned into three different states: an Exploratory state to locate promising peaks, an Exploitative state to fine-tune the solutions found in the Exploratory state of the previous stage, and finally, a Central state to carry out the formation of potential solutions to explore the undiscovered regions. Non-identical genetic operators have been implemented in each state to balance the exploration and exploitation by employing necessary selection pressure and diversity. A non-isomorphic encoding based technique has been developed to capture the sub-structures that are representing a particular region. Furthermore, a novel local search technique exploits already explored fit individuals by employing the knowledge of sub-structures. The efficacy of the proposed framework is demonstrated with an adequate number of distinctive data sets and comparison with state-of-the-art methods.

A biased random-key genetic algorithm (BRKGA) is a general search procedure for finding optimal or near-optimal solutions to hard combinatorial optimization problems. It is derived from the random-key genetic algorithm of Bean (1994), differing in the way solutions are combined to produce offspring. BRKGAs have three key features that specialize genetic algorithms: A fixed chromosome encoding using a vector of N random keys or alleles over the real interval [0, 1), where the value of N depends on the instance of the optimization problem; A well-defined evolutionary process adopting parameterized uniform crossover to generate offspring and thus evolve the population; The introduction of new chromosomes called mutants in place of the mutation operator usually found in evolutionary algorithms. Such features simplify and standardize the procedure with a set of self-contained tasks from which only one is problem-dependent: that of decoding a chromosome, i.e. using, the keys to construct a solution to the underlying optimization problem, from which the objective function value or fitness can be computed. BRKGAs have the additional characteristic that, under a weak assumption, crossover always produces feasible offspring and, therefore, a repair or healing procedure to recover feasibility is not required in a BRKGA. In this tutorial we review the basic components of a BRKGA and introduce an Application Programming Interface (API) for quick implementations of BRKGA heuristics. We then apply the framework to a number of hard combinatorial optimization problems, including 2-D and 3-D packing problems, network design problems, routing problems, scheduling problems, and data mining. We conclude with a brief review of other domains where BRKGA heuristics have been applied.

This paper proposes a session control method which provides a mechanism to achieve desirable user-level QoS (i.e., perceptual QoS) of audio-video transmission over IP networks. The proposed method, which is referred to as GPSQ (Guarantee of Psychologically Scaled Quality), utilizes a user-level QoS parameter of the interval scale in the psychometric analysis. GPSQ involves four kinds of components: media terminals, a SIP server, a QoS manager and bandwidth-controllable routers. From the media terminals, the SIP server obtains the information for this control, which is further delivered to the QoS manager. The QoS manager keeps a database of representative regression lines which express user-level QoS as a function of guaranteed bandwidth of audio and that of video. Using the regression line, the QoS manager calculates necessary guaranteed bandwidth according to user-level QoS specified by the user. Then, the QoS manager sets up routers to guarantee the calculated bandwidth. We implemented GPSQ in a simple experimental network and confirmed its effectiveness.

As general purpose optimisation methods, evolutionary algorithms have been efficiently used to solve multi-objective combinatorial optimisation problems. However, few theoretical investigations have been conducted to understand the efficiency of evolutionary algorithms on such problems, and even fewer theoretical investigations have been conducted on multi-objective combinatorial optimisation problems coming from real world. In this paper, we analyse the performance of a simple multi-objective evolutionary algorithm on two simplified instances of the bi-objective minimum weight minimum label spanning tree problem, which comes from real world. Though these two instances are similar, the analysis results show that the simple multi-objective evolutionary algorithm is efficient for one instance, but it may be inefficient for the other. According to the analysis on the second instance, we think that the restart strategy may be useful in making the multi-objective evolutionary algorithm more efficient for the bi-objective problem.