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Stochastic Scheduling towards Cost Efficient Network Function Virtualization in Edge Cloud

Stochastic Scheduling towards Cost Efficient Network Function Virtualization in Edge Cloud. Deze Zeng, Jie Zhang, Lin Gu, and Song Guo. Contents. P 1. Background and Motivation. P 2. Problem Formulation. P 3. Algorithm Design. P 4. Performance Evaluation. Background.

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Stochastic Scheduling towards Cost Efficient Network Function Virtualization in Edge Cloud

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  1. Stochastic Scheduling towards Cost Efficient Network Function Virtualization in Edge Cloud Deze Zeng, Jie Zhang, Lin Gu, and Song Guo

  2. Contents P1 Background and Motivation P2 Problem Formulation P3 Algorithm Design P4 Performance Evaluation

  3. Background Classical Network Appliance Approach Network Function Virtualization Approach Tester/QoE Monitor Radio Network Controller Virtual Application Firewall Transition WAN Accelerator Carrier Grade NAT IDS Genetic High Volume Standard Server,Switch and Storage Session Border Controller DPI PE Router • Agile • Standard • Automated • Cumbersome • Proprietary • Manual

  4. Background The Hype Cycle in 2013（Garter） The Hype Cycle in 2012（Garter） NFV in 2013 OpenFlow in 2012

  5. Motivation • The recent development in cloud computing has resulted in a massive deployment of geo-distributed datacenters interconnected by the Internet. • This raises a new cloud computing comparable concept called edge cloud. • Edge cloud, with the advantage of user proximity, is an ideal platform to host the network functions so as to provide network services faster, better and cheaper. Fig. 1. A Network Service Chain and an Edge Cloud

  6. Motivation Network Function Management in NFV network function placement problem assume a preknown or predictable network traffic demand Task Distribution (e.g., flow balancing) the routing of network service chains fixed infrastructure and mainly focus on the real-time task distribution. Existing works fail to orchestrate the traffic flows and function management at runtime according to the real-time network status.

  7. System Model function 2 function 3 function 1 function 0 Fig. 2. Extended Network Service Chain Graph for Fig. 1 Each network function has N replicas in different servers. the locations of the front-end proxies (i.e., function 0) are reserved. Binary indicating whether function i is activated on node n at time t

  8. Problem Formulation Packet Scheduling and Resources Allocation The total number of packets that can be distributed is limited by the service rate. (1) Communication capacity constraints: (2) Computation capacity constraints: (3)

  9. Problem Formulation Queueing Model (4) Where (5) The system is stable: (6)

  10. Problem Formulation Total Cost Communication Cost（“pay-as-you-go” ）: (7) Objective: Computation Cost: (8) Total cost: (9)

  11. Algorithm Design Lyapunov Drift Bound Lyapunov function: one-slot conditional Lyapunov drift function: Where

  12. Algorithm Design Optimal Analysis Drift-plus-penalty expression

  13. Algorithm Design drift-plus-penalty expression

  14. Online Algorithm

  15. Performance Evaluation 取得效益 （a）Queue backlog at different time slots （b）Overall cost at different time slots

  16. Performance Evaluation 取得效益 （d）Overall cost under different computation capacities （c）Time-averaged backlog and cost under different values of V （e）Overall cost under different communication capacities

  17. Conclusion Optimizing the packet scheduling, network function management and resource allocation for NFV in the literature in real time. We explores the queueing information available in the system to make online control decisions. Making use of Lyapunov optimization to design a dynamic scheduling algorithm on task distribution, function management and resource allocation towards cost efficient NFV in edge cloud.

  18. THANKS

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