Resource Optimization and Delay Guarantee Virtual Network Function Placement for Mapping SFC Requests in Cloud Networks

Abstract : Since the advent of network function virtualization (NFV), cloud service providers (CSPs) can implement traditional dedicated network devices as software and flexibly instantiate network functions (NFs) on common off-the-shelf servers. NFV technology enables CSPs to deploy their NFs to a cloud data center in the form of virtual network functions (VNFs) without costly capital expenditures and operating expenses. However, it is an essential but intractable issue for CSPs to devise a suitable VNF placement scheme to optimize network resource consumption and improve network performance. In this article, we focus on the VNF placement problem for mapping users’ service function chain requests (SFCRs) in cloud networks. To enhance network resource utilization, we consider the fundamental resource overheads and implementation method of VNFs. The VNF placement problem is formulated as an integer linear programming model with the aim of minimizing the total network resource consumption while guaranteeing the delay requirements of SFCRs. We devise a two-phase optimization solution (TPOS) to solve the problem. TPOS contains a mapping phase to map SFCRs on servers and an adjustment phase to optimize the placement of VNFs and VNF requests. Evaluation results demonstrate that TPOS can derive near-optimal server resource consumption and significantly enhance network resource utilization. TPOS can guarantee the delay requirements of SFCRs and outperform contrastive schemes in terms of activated servers, SFCR acceptance ratio, and average VNF utilization.

 EXISTING SYSTEM :

 ? On the other hand, the optimized consolidation policies outlined in some existing studies are used to manage computing resources in the cloud. ? In such environments, dedicated physical hardware and virtual network functions coexist, depending on the demand. ? We developed a baseline model that represents existing approaches based on a Strict Resource Allocation approach (SRAM) which ignores the aforementioned dependency. ? We proposed a MixedInteger Quadratically Constrained program (MIQCP) formulation called Flexible Resources Allocation Model (FRAM) that takes into account the Linear Dependency that exists between the amount of resources allocated to a VNF and its processing delay.

 DISADVANTAGE :

 ? In this paper, we study the VNF placement problem considering users’ SFC requests (SFCr) in NFV and EC enabled networks. ? In VNF placement problem that considers SFCs, an SFC is inclined to be placed in one MDC, so that most of flows between the VNFs do not go through the network links when chaining the VNFs of the SFC, then the bandwidth consumptions are reduced. ? We consider the instantiation method and chaining problem of VNFs, and reveal the need of tradeoff between node resource consumption and bandwidth consumption. ? We make a complete formulation of the problem mathematically, which is modeled as an ILP, and propose an efficient polynomial time heuristic to solve it.

 PROPOSED SYSTEM :

 • Many heuristic and metaheuristic algorithms have been proposed to reduce the computational complexity and NP-hardness of the VNF placement problem. • The study in proposed a resource allocation algorithm for VNFs based on genetic algorithms. • A genetic algorithm for the placement of VNF chains was proposed in to satisfy the SLA and QoS objectives. • A rapid and scalable polynomial algorithm was proposed to solve the designed ILP model. However, the proposed algorithm was designed for static traffic scenarios. • The study in proposed a migration-based reliability method for the service chain to ensure network service continuity via a replication strategy.

 ADVANTAGE :

 ? We evaluate the performance of PG and make a thorough analysis to the VNF placement problem in NFV and EC enabled networks. ? The relationship between the size of BRCs when instantiating an VNF and the resource demand by each VNFr may have influences on the performance of different solutions. ? In each group of results, the final BRCs, bandwidth consumptions and number of activated MDCs are used to evaluate the performance of different solutions. ? Compared with the single-tenant architecture, in which each tenant gets his own instance of application, the multi-tenant architecture can lead to higher resource utilization, lower service price, and more efficient management for the cloud service providers.