Proximal Virtual Network Embedding based on Multi-dimensional Load Balancing in Data Centers

Aiming at the virtual network embedding problem under dynamic service request, Service Request Aware-based Dynamic Virtual Network Embedding (SRAD) algorithm is proposed. We first present virtual network embedding model, and then define virtual network construction cost according to the dynamic characteristic of service request. Our goal is to find the optimal reconfiguration policies by awaring service request type and changing trends, which can minimize the overall construction cost using substrate resource. Experimental results show that the proposed algorithm satisfies the service request, achieves higher success ratio and gains higher revenue/cost ratio for substrate network comparing with the existing algorithms.

Virtual network embedding (VNE) refers to allocating reasonable substrate network resources for virtual network (VN) requests that include computing resources and network resources, so as to obtain optimal income from leasing virtual resources. Such a way of providing virtual resources is the key technology of cloud computing and can greatly save the operating cost of enterprises and provide flexibility of application deployment. However, the existing VNE algorithms are mostly oriented to traditional stochastic network topologies. Due to the high connectivity and server density of data centers and the complexity of the user’s resource requirements, the traditional VNE algorithms suffer from low resource utilization rate and revenues in the VNE on the data centers. Different from the existing algorithms which are often based on heuristic algorithms, this paper proposes a VNE algorithm for data center topology based on the Q-learning algorithm which is a typical reinforcement learning method. The algorithm an agent for each VN designs a reward function related to the effect of virtual link embedding, which is used to update the Q-matrix through unsupervised learning process. Then, the agent can find the optimal embedding strategy based on the Q-table from each learning. Simulation results demonstrate that the proposed algorithm can improve the resource utilization ratio and obtain a better revenue/cost ratio of the substrate network compared with the traditional heuristic algorithms.

Efficient network resource utilization is crucial in Virtual Network Embedding (VNE) problem. The diversity of virtual topologies belong to various services providers (SPs) severely affects the efficiency of VNE algorithms and fairness between SPs. This paper proposes a fair VNE algorithm with a topological transformation mechanism. Such mechanism will transform virtual topology to reduce complexity. Then the algorithm will felicitously map the virtual network onto substrate network through a Disperse Particle Swarm Optimization (DPSO) based process. Simulation results show that due to the topological transformation procedure the algorithm can achieve more fairness than traditional VNE algorithms.

A novel evaluation function for higher acceptance rates and more profitable metaheuristic-based online virtual network embedding

In a network virtualization-enabled software-defined networking (SDN), the problem of virtual network embedding (VNE) is a major concern. Although a number of VNE algorithms have been proposed, they fail to consider the efficient utilization of substrate resources or the network load extensively, thus resulting in less efficient utilization of substrate resources or higher blocking ratio of the virtual networks. In this paper, we study the problem of mapping a number of virtual networks in SDN and formulate the VNE problem as a network load minimization problem. Since the formulated optimization problem is NP-hard and it cannot be solved conveniently, we propose a two-stage VNE algorithm consisting of node mapping stage and link mapping stage. Numerical results demonstrate that the effectiveness of our proposed algorithm.

The massive energy usage of data centers may be traced in part to the growing number of data centers and workstations because of economies of scale for cloud computing. It does, though, indicate wasteful power usage. Consequently, researching ways to improve the energy efficiency of datacenter equipment is now a crucial aspect in reducing datacenter power consumption. In this study, we describe methodologies and algorithms for flexible, energy-efficient, and effective load balancing in data centers, resulting in lower energy consumption by systems. Because of difficulties related with energy consumption, such as capital expenditure, operational costs, and environmental effect, renewable energy is becoming an increasing major consideration in data centers. With the rise in environmental consequences around the world, data centers must consider energy efficiency as one of the most critical factors. Cloud computing techniques have made a huge impact all over the world. Green DC is a concept that has been tested. We suggested an algorithm in this paper based on certain current algorithm limitations that will help to reduce environmental effect. We investigated the efficiency of our program further and used a load balancing method to improve its performance.

A major challenge in virtual network embedding is to find an efficient mapping of each virtual network (VN) to the nodes and links in the substrate network (SN) so that the residual SN can host as many VN requests as possible. This network optimization problem has been shown to be NP hard. In this paper we propose a “border matching” mechanism in VN embedding, which is shown to outperform a number of existing VN embedding algorithms. In particular, we devise an iterative algorithm called “VNE-B”. Typical VN embedding algorithms may cause fragmentations of the substrate network, i.e. the substrate resources are fragmented into small isolated pieces, making it unable to meet larger future VN requests. VNE-B starts the mapping from the “border” nodes of both VNs and the SN for reducing fragmentation of SN resources. Our experimental evaluations show that VN embedding algorithms with VNE-B result in more efficient mappings and thus fewer rejections for VN requests. We also visualize the residual SN to demonstrate the effectiveness of VNE-B.

Distributed randomized in-network localised load-balancing (DRILL) is one of the most commonly used protocols and relies on the “power of two choices” algorithm. However, this is a sub-optimal load balancing solution due to under-sampling and high number of candidate ports. Along with per-packet granularity, this sub-optimality increases the occurrence of out-of-order packets. Therefore, in this paper, we proposes a novel power of two choices-based load balancing protocol to overcome these shortcomings. Designated as state aware multiple-choice routing (SAMCR) for load balancing in data centers, it builds a Kalman filtering algorithm; to approximate queue length states using the developed processes and measurement models. Furthermore, it uses the estimated queue length states in an adaptive threshold-based covariance matrix to track the most optimal decision in the system. Mathematical simulations as well as an NS-3 discrete event network simulator were used to evaluate the superior performance of SAMCR over other state-of the art approaches in terms of system load, flow completion time and throughput for most types of flows.

This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods. Traditional heuristic solutions like Weighted-Cost Multi-Path (WCMP) and Local Shortest Queue (LSQ) are less flexible to the changing workload distributions and arrival rates, with a poor balance among multiple load balancers. The cooperative network load balancing task is formulated as a Dec-POMDP problem, which naturally induces the MARL methods. To bridge the reality gap for applying learning-based methods, all methods are directly trained and evaluated on an emulation system from moderate-to large-scale. Experiments on realistic testbeds show that the independent and "selfish" load balancing strategies are not necessarily the globally optimal ones, while the proposed MARL solution has a superior performance over different realistic settings. Additionally, the potential difficulties of MARL methods for network load balancing are analysed, which helps to draw the attention of the learning and network communities to such challenges.

Due to growing interest in network cost optimization through resource sharing, virtual network embedding has significantly attracted the attention of researchers. Recently, various virtual network embedding algorithms have been suggested. The mapping of these algorithms is not reliable due to the absence of fault tolerance capability. Therefore, this paper proposes a technique for maximizing fault tolerance and minimizing delay in Virtual Network Embedding (VNE) using Non-dominated Sorting Genetic Algorithm (NSGA-II). The multi-objective optimization problem is mathematically formulated. An adapted NSGA-II is proposed for solving the optimization problem. The major components of adapted NSGA-II are representation of chromosome, computation of fault tolerance and delay, sorting using non-domination, and crossover and mutation operation. Two novel mathematical functions for computing fault tolerance and delay are developed. The analysis of simulation results clearly indicates that the proposed technique effectively optimizes both the considered objectives in VNE.

Recently network virtualization has been proposed as a promising way to overcome the current ossification of the Internet by allowing multiple heterogeneous virtual networks (VNs) to coexist on a shared infrastructure. A major challenge in this respect is the VN embedding problem that deals with efficient mapping of virtual nodes and virtual links onto the substrate network resources. Since this problem is known to be NP-hard, previous research focused on designing heuristic-based algorithms which had clear separation between the node mapping and the link mapping phases. This paper proposes VN embedding algorithms with better coordination between the two phases. We formulate the VN embedding problem as a mixed integer program through substrate network augmentation. We then relax the integer constraints to obtain a linear program, and devise two VN embedding algorithms D-ViNE and R-ViNE using deterministic and randomized rounding techniques, respectively. Simulation experiments show that the proposed algorithms increase the acceptance ratio and the revenue while decreasing the cost incurred by the substrate network in the long run.

Emerging network applications (augmented reality, industrial Internet, etc.) introduce stringent new requirements on the performance, dependability, and adaptability of communication networks. Programmable data planes (e.g., based on P4) provide new opportunities to meet these requirements, by enabling adaptive network reconfigurations. However, ensuring consistency during such reconfigurations remains challenging. This paper makes a first step toward a more automated state management of adaptive data planes. In particular, we present an efficient P4 state management framework, P4State, which allows to quickly identify the network states from the source code that are critical for data plane reconfigurations (e.g., due to scaling, failure recovery). We report on first promising evaluation results of our prototype implementation in terms of correctness and efficiency, also considering two case studies using HULA (load balancing in data center) and HashPipe (line-rate measurement in data plane).

With both cloud applications and infrastructure technologies becoming more advanced, it is generally expected that cloud based applications are going to experience improved end-to-end performance assurance while maintaining a competitive cost of service. In this paper, two policies are considered for joint allocation of network link and data center resources in the presence of dynamic application requests: the Steady State Probability (SPP) policy and the Network State (NS) policy. The SSP policy is based on pre-computed probability to select data center and network resources and the NS policy is based on the knowledge of the network and data center state. An algorithm for optimally computing the probabilities for the SSP policy is presented. Obtained simulation results demonstrate and numerically assess the trade-off between the system performance in terms of blocking probability and the signaling overhead.

To improve the low acceptance ratio and revenue to cost ratio caused by the poor match between the virtual nodes and the physical nodes in the existing virtual network embedding (VNE) algorithms, we established a multi-objective optimization integer linear programming model for the VNE problem, and proposed a novel two-stage virtual network embedding algorithm based on topology potential (VNE-TP). In the node embedding stage, the field theory once used for data clustering was introduced and a node embedding function designed to find the optimal physical node. In the link embedding stage, both the available bandwidth and hops of the candidate paths were considered, and a path embedding function designed to find the optimal path. Extensive simulation results show that the proposed algorithm outperforms other existing algorithms in terms of acceptance ratio and revenue to cost ratio.

The recent advances in virtualization technology have enabled the sharing of computing and networking resources of cloud data centers among multiple users. Virtual Network Embedding (VNE) is highly important and is an integral part of the cloud resource management. The lack of historical knowledge on cloud functioning and inability to foresee the future resource demand are two fundamental shortcomings of the traditional VNE approaches. The consequence of those shortcomings is the inefficient embedding of virtual resources on Substrate Nodes (SNs). On the contrary, application of Artificial Intelligence (AI) in VNE is still in the premature stage and needs further investigation. Considering the underlying complexity of VNE that includes numerous parameters, intelligent solutions are required to utilize the cloud resources efficiently via careful selection of appropriate SNs for the VNE. In this paper, Reinforcement Learning based prediction model is designed for the efficient Multi-stage Virtual Network Embedding (MUVINE) among the cloud data centers. The proposed MUVINE scheme is extensively simulated and evaluated against the recent state-of-the-art schemes. The simulation outcomes show that the proposed MUVINE scheme consistently outperforms over the existing schemes and provides the promising results.