HyScaler: A Dynamic, Hybrid VNF Scaling System for Building Elastic Service Function Chains Across Multiple Servers

Chen, Zhenke; Li, He; Ota, Kaoru; Dong, Mianxiong

doi:10.1109/tnsm.2023.3277552

Cited by 2 publications

(4 citation statements)

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“…The VNF placement problem has also been tackled from different perspectives from availability. [25][26][27][28][29][30][31][32] A VNF placement and chaining model that minimizes resource allocation while guaranteeing end-to-end latency requirements was introduced in Alleg et al 25 The work in Hyodo et al 26 presented a VNF placement model for SFC to minimize the cost of using computing resources while guaranteeing recovery for server failures within a prespecified recovery time objective. The work in Huin et al 27 addressed the SFC placement problem by jointly considering VNF placement and routing between VNFs; its goal is to provide an optimal compromise between bandwidth minimization and the number of VNFs.…”

Section: Vnf Placement Without Consideration Of Availabilitymentioning

confidence: 99%

“…Dynamic VNF placement is being addressed in response to changing requests and network traffic over time. [29][30][31][32] The work in Li et al 29 utilized a correlation-based greedy algorithm to solve the VNF placement problem of optimizing resource utilization in a cloud data center with the time-varying workloads. The work in Taghavian et al 30 introduced a deterministic solution to the VNF placement problem using a branch and bound structure and artificial intelligence search strategies.…”

Section: Vnf Placement Without Consideration Of Availabilitymentioning

confidence: 99%

“…It includes a time-efficient approximation algorithm based on dynamic programming for VNF placement and Pareto-optimal solutions for VNF migration. The work in Chen et al 32 presented HyScaler, a system that dynamically scales VNF in response to real-world network demands, improving adaptability via elastic SFCs and scaling across multiple servers. By monitoring network load changes, adjusting overloaded VNF instances, and applying a global VNF placement and chaining algorithm, it optimizes the placement and chaining of new VNF instances.…”

Section: Vnf Placement Without Consideration Of Availabilitymentioning

confidence: 99%

“…In contrast to earlier studies, [25][26][27][28][29][30][31][32] this paper addresses the VNF placement problem of making the SFC deployment cost as low as possible while meeting the availability requirements, with separately setting deployment costs of servers and VNF instances.…”

Section: Vnf Placement Without Consideration Of Availabilitymentioning

confidence: 99%

See 3 more Smart Citations

Availability‐aware virtual network function placement based on multidimensional universal generating functions

Arakawa,

Oki

2023

Int J Network Mgmt

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Network function virtualization (NFV) implements network functions as software, which enables flexible, resource‐efficient, and rapid provision of network services. In NFV, network services are realized by the service function chain (SFC), which is a chain of virtual network functions (VNFs) linked in the proper order. Both availability and deployment cost are key concerns for network operators providing network services as SFC. This paper proposes a flexible VNF placement model on a per‐VNF instance basis that minimizes deployment costs while satisfying availability requirements that may be placed on SFC. This paper uses a multidimensional universal generating function (MUGF) method, which is a multistate system analysis method, to compute the availability of a multistate SFC system with multiple VNFs coexisting on a server. The MUGF method calculates the performance of the entire SFC by combining the performance of servers as determined by applying a continuous‐time Markov chain. To reduce the time to compute the SFC availability, we introduce operators to be applied to MUGF and develop an availability computing method. In addition, a heuristic algorithm for determining VNF placement targeting the lowest deployment cost possible while meeting availability requirements is presented. Numerical results show that the proposed model obtains VNF placement with lower cost than the conventional model in all examined cases. The proposed model achieves VNF placement at 58.5%–75.0% of the deployment cost of the conventional model for the same SFC availability requirements.

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