A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions <s> 2) NFV <s> The scientific study of networks, including computer networks, social networks, and biological networks, has received an enormous amount of interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas. Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks. <s> BIB001 </s> A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions <s> 2) NFV <s> This letter suggests an approach for decomposing a multiobjective optimization problem (MOP) into a set of simple multiobjective optimization subproblems. Using this approach, it proposes MOEA/D-M2M, a new version of multiobjective optimization evolutionary algorithm-based decomposition. This proposed algorithm solves these subproblems in a collaborative way. Each subproblem has its own population and receives computational effort at each generation. In such a way, population diversity can be maintained, which is critical for solving some MOPs. Experimental studies have been conducted to compare MOEA/D-M2M with classic MOEA/D and NSGA-II. This letter argues that population diversity is more important than convergence in multiobjective evolutionary algorithms for dealing with some MOPs. It also explains why MOEA/D-M2M performs better. <s> BIB002 </s> A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions <s> 2) NFV <s> Network functions virtualization (NFV) is a new network architecture framework where network function that traditionally used dedicated hardware (middleboxes or network appliances) are now implemented in software that runs on top of general purpose hardware such as high volume server. NFV emerges as an initiative from the industry (network operators, carriers, and manufacturers) in order to increase the deployment flexibility and integration of new network services with increased agility within operator’s networks and to obtain significant reductions in operating expenditures and capital expenditures. NFV promotes virtualizing network functions such as transcoders, firewalls, and load balancers, among others, which were carried out by specialized hardware devices and migrating them to software-based appliances. One of the main challenges for the deployment of NFV is the resource allocation of demanded network services in NFV-based network infrastructures. This challenge has been called the NFV resource allocation (NFV-RA) problem. This paper presents a comprehensive state of the art of NFV-RA by introducing a novel classification of the main approaches that pose solutions to solve it. This paper also presents the research challenges that are still subject of future investigation in the NFV-RA realm. <s> BIB003 </s> A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions <s> 2) NFV <s> Network function virtualization (NFV) and Service function chaining (SFC) can fulfill the traditional network functions by simply running special softwares on general-purpose computer servers and switches. This not only provides significantly more agility and flexibility in network service deployment, but can also greatly reduce the capital and operating cost of networks. In this paper, a comprehensive survey on the motivations and state of the art efforts towards implementing the NFV and SFC is provided. In particular, the paper first presents the main concepts of these new emerging technologies; then discusses in details various stages of SFC, including the description, composition, placement and scheduling of service chains. Afterwards, existing approaches to SFC are reviewed according to their application environments, parameters used, and solution strategies. Finally, the paper points out a number of future research directions. <s> BIB004 </s> A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions <s> 2) NFV <s> For efficiently solving the problem of the special resource allocation in network function virtualization (NFV) scenarios with virtualized network functions (VNFs) precedence constraints, we formulate such a problem as an integer linear programming (ILP) model and propose a novel mechanism to both build and map the service function chain (SFC) with a coordinated method in inter-datacenter networks over elastic optical infrastructure. We design the VNFs reusing and deploying mechanism that is based on the bandwidth change ratio of each request. In the coordinated chaining and mapping algorithm (CCMA) proposed, both reducing the bandwidth consumption and reusing the deployed VNF instances are considered jointly. Simulation results show that the proposed CCMA could reduce the average cost significantly when compared with the existing uncoordinated methods. <s> BIB005 </s> A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions <s> 2) NFV <s> Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this paper, we propose a service function chain deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing. <s> BIB006 </s> A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions <s> 2) NFV <s> In recent years, much attention has been focused on deploying service function chains (SFCs), each of which is composed of a set of virtual network functions (VNFs) in a specified order. This is a promising approach for enabling cloud service providers to deploy user service requests more flexibly while saving costs. However, less effort has been directed toward meeting heterogeneous needs, such as high throughput or low latency of user service requests with heterogeneous bandwidth demands, especially in data center networks (DCNs). In this paper, we propose an efficient orchestration algorithm for online SFC requests. It first splits a large flow into a number of subflows and replicates the same number of sub-SFCs. Each subflow is redirected to one of these “parallelized” sub-SFCs, which is termed a sub-user request. Then, each sub-user request is deployed based on a worst-fit strategy, and VNFs in the same SFC are instantiated on the same server to the greatest possible extent. Our algorithm is expected to enable network load balancing, reducing the delay experienced by small flows while improving the acceptance ratio for user requests. Finally, the simulation results show that the proposed algorithm outperforms other comparable algorithms. <s> BIB007
In traditional networks, network functions are implemented by the dedicated hardware (e.g., video coder/decoder). However, in NFV scenarios, these functions are implemented in software that runs on top of general-purpose hardware like commercial servers BIB003 , thus flexibly and efficiently utilizing network resources while reducing Capex and Opex. One of the important issues is the resource allocation in NFV (RA-NFV) scenarios. Each user request is described by a service function chain (SFC), and the RA-NFV procedure consists of three stages of SFC constructing, mapping, and scheduling BIB004 , each of which could work independently or collaboratively with up to two others. Much attention has been focused on SFC deployments. Sun et al. BIB006 designed a heuristic algorithm to realize low-latency and resourceefficient SFC orchestration. Similar to the idea of decomposition in MOEA/D, Sun et al. BIB007 split a large flow into a number of subflows and each subflow was redirected to one of these ''parallelized'' sub-SFCs. Xu et al. BIB005 considered the requested resource quantity of each virtual network function (VNF) and VNFs precedence, which means that, for an SFC, its one VNF must be executed before the special VNF. To face the challenges resulting from three stages and additional constrains in the RA-NFV, we need to add some mechanisms to enhance MOEA/D so that it can globally optimize the RA-NFV even for the case of static SFC. The first one is to embed a heuristic algorithm in MOEA/D to check the feasibility of a solution by inspecting various constraints like VNFs precedence. In the second one, we could combine the physical characteristic of a network to help MOEA/D select a better path when co-evolving SFC mapping and scheduling jointly, such as several centralities of closeness, betweenness, and degree BIB001 . For the dynamic SFC case, the computational time is stringent, so we have to greatly reduce the objective space and/or search space by using decomposition BIB002 ,  .