SCM: A method to improve network service layout efficiency with network evolution

Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of “software defined network + network function virtualization” (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently.

[1]  Yaochu Jin,et al.  Pattern Recommendation in Task-oriented Applications: A Multi-Objective Perspective [Application Notes] , 2017, IEEE Computational Intelligence Magazine.

[2]  Nick Feamster,et al.  A slick control plane for network middleboxes , 2013, HotSDN '13.

[3]  Gang Xiong,et al.  A Mechanism for Configurable Network Service Chaining and Its Implementation , 2016, KSII Trans. Internet Inf. Syst..

[4]  Yi Wang,et al.  Virtual routers on the move: live router migration as a network-management primitive , 2008, SIGCOMM '08.

[5]  Ananth Balashankar,et al.  Software Defined Networking , 2019, 2019 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).

[6]  Minlan Yu,et al.  Enforcing Network-Wide Policies in the Presence of Dynamic Middlebox Actions using FlowTags , 2014, NSDI.

[7]  Nerea Toledo,et al.  Toward an SDN-enabled NFV architecture , 2015, IEEE Communications Magazine.

[8]  Gang Xiong,et al.  A virtual service placement approach based on improved quantum genetic algorithm , 2016, Frontiers of Information Technology & Electronic Engineering.

[9]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[10]  Yu Xue,et al.  A self-adaptive artificial bee colony algorithm based on global best for global optimization , 2017, Soft Computing.

[11]  Lucas Chaufournier,et al.  CloudNet: Dynamic Pooling of Cloud Resources by Live WAN Migration of Virtual Machines , 2011, IEEE/ACM Transactions on Networking.

[12]  Aditya Akella,et al.  OpenNF , 2014, SIGCOMM.

[13]  Meral Shirazipour,et al.  StEERING: A software-defined networking for inline service chaining , 2013, 2013 21st IEEE International Conference on Network Protocols (ICNP).

[14]  Meng Wang,et al.  Consolidating virtual machines with dynamic bandwidth demand in data centers , 2011, 2011 Proceedings IEEE INFOCOM.

[15]  Vijay Mann,et al.  Remedy: Network-Aware Steady State VM Management for Data Centers , 2012, Networking.

[16]  Xingyi Zhang,et al.  A Mixed Representation-Based Multiobjective Evolutionary Algorithm for Overlapping Community Detection , 2017, IEEE Transactions on Cybernetics.

[17]  Lisandro Zambenedetti Granville,et al.  Software-defined networking: management requirements and challenges , 2015, IEEE Communications Magazine.

[18]  Kim-Kwang Raymond Choo,et al.  Distributed controller clustering in software defined networks , 2017, PloS one.

[19]  Qingfu Zhang,et al.  A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[20]  Ibrahim Matta,et al.  BRITE: A Flexible Generator of Internet Topologies , 2000 .

[21]  Seungjoon Lee,et al.  Network function virtualization: Challenges and opportunities for innovations , 2015, IEEE Communications Magazine.

[22]  Aameek Singh,et al.  Server-storage virtualization: Integration and load balancing in data centers , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[23]  Gaochao Xu,et al.  A Heuristic Placement Selection of Live Virtual Machine Migration for Energy-Saving in Cloud Computing Environment , 2014, PloS one.

[24]  Fernando M. V. Ramos,et al.  Software-Defined Networking: A Comprehensive Survey , 2014, Proceedings of the IEEE.

[25]  Vyas Sekar,et al.  Stratos: A Network-Aware Orchestration Layer for Middleboxes in the Cloud , 2013, ArXiv.

[26]  Minlan Yu,et al.  SIMPLE-fying middlebox policy enforcement using SDN , 2013, SIGCOMM.

[27]  Filip De Turck,et al.  Network Function Virtualization: State-of-the-Art and Research Challenges , 2015, IEEE Communications Surveys & Tutorials.

[28]  Zhiming Wang,et al.  Enabling network function combination via service chain instantiation , 2015, Comput. Networks.

[29]  Vyas Sekar,et al.  Design and Implementation of a Consolidated Middlebox Architecture , 2012, NSDI.

[30]  Inderveer Chana,et al.  Energy-aware Virtual Machine Migration for Cloud Computing - A Firefly Optimization Approach , 2016, Journal of Grid Computing.

[31]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[32]  James J. Buckley,et al.  Evolutionary algorithm solution to fuzzy problems: Fuzzy linear programming , 2000, Fuzzy Sets Syst..

[33]  Xiang Long,et al.  Adaptive Controller for Dynamic Power and Performance Management in the Virtualized Computing Systems , 2013, PloS one.

[34]  Ye Tian,et al.  An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility , 2018, IEEE Transactions on Evolutionary Computation.