Resource Aware Chaining and Adaptive Capacity Scaling for Service Function Chains in Distributed Cloud Network

Abstract

With the development of network technology such as software-defined network (SDN) and network function virtualization (NFV), Internet service providers (ISPs) are increasingly placing the virtual network function(VNF) instances at the network edge to provide network service. However, there are some issues to be tackled in the distributed SDN/NFV enabled cloud. Firstly, VNF instances require to be chained in predefined order to provide network services. It is a challenge to optimally select and chain VNF instances from the multi-instances. Moreover, due to the capacity limitation of the distributed edge nodes. The capacity of the Virtual Machines (VMs) that host VNFs should be proactively adjusted to cope with traffic demands. Since most existing works ignore the vertical capacity scaling problem in routing commodities with Service Function Chain (SFC) requests. In this paper, a fine-grained scheduling scheme at VM-level is proposed. Firstly, we formulate the SFC chaining problem as an Integer Linear Programming (ILP) model aiming to embed SFC requests with minimum estimated latency cost. Furthermore, we formulate the adaptive VNF resource allocation (VNF-AR) problem as a convex optimization. The theoretical optimal capacity for each VM can be derived from the Karush-Kuhn Tucker (KKT) conditions. At last, a novel joint optimization approach of VNF chaining and adaptive scaling (VNF-CAS) is proposed to efficiently embed the SFC requests. Performance evaluation shows that VNF-CAS can achieve better performance in SFC requests acceptance rate, average effective throughput, average load utilization and VM load balancing when it is compared with other algorithms in existing works. INDEX TERMS Network function virtualization, service function chain, distributed cloud network, resource optimization.

SARM: Service function chain active reconfiguration mechanism based on load and demand prediction

“…Six different VNF types were considered, namely, NAT, FW, TM, WOC, VOC, and IDPS. The SFC parameter settings in the experiments were set based on Table 3 40 . Further, according to Pham et al, 4 the resource requirements for one VNF instance under different traffic rates are given in Table 4.…”

“…The SFC parameter settings in the experiments were set based on Table 3. 40 Further, according to Pham et al, 4 the resource requirements for one VNF instance under different traffic rates are given in Table 4. Table 4 demonstrates that the memory resources required by VNF instances are related only to the VNF type and do not depend on the network traffic.…”

A Dynamic Adjustment Method of Service Function Chain Resource Configuration

“…Step (11)(12)(13)(14)(15)(16)(17)(18)(19) means when the demand for the i-th VNF at t2 is less than 70% of the existing resources and the duration is more than 1s, a VNF scale-down request is generated.…”

“…Constraint (9) denotes that the number of VNF types that each substrate node can host is m. The constraint (10) means that each VNF of an SFC can only be deployed on a server. Constraint (11) means that the residual available CPU resources cpu(nc l ) of the server node nc l cannot be less than the CPU resource demand cpu(vi) of vi, and Nc represents the set of server nodes. Constraint (12) indicates that the residual bandwidth resource of the substrate link es lm should be greater than the link resource demand of SFC, and Es represents the set of substrate links.…”

“…However, this method does not consider the changes of VNF demand caused by traffic fluctuations. The work in [11] jointly optimizes SFC deployment and dynamic resource configuration, but does not consider the service interruption time and network operation cost of SFC resource adjustment.…”