ClusPR: Balancing Multiple Objectives at Scale for NFV Resource Allocation

Woldeyohannes, Yordanos Tibebu; Mohammadkhan, Ali; Ramakrishnan, K. K.; Jiang, Yuming

doi:10.1109/tnsm.2018.2870733

Cited by 60 publications

(79 citation statements)

References 28 publications

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“…NFs of a chain are randomly chosen out of the set of five services, which are Firewall, DPI, IDS, Proxy, NAT. Flows are assigned primary chains by using ClusPR algorithm [2]. The availability requirement of a flow is selected from the set {0.999, 0.9999, 0.99999}.…”

Section: Resultsmentioning

confidence: 99%

“…Network Function Virtualization (NFV) alters this inflexible architecture by decoupling the software of NFs from the hardware and run the NFs on virtualized environment such as virtual machines (VMs) or containers. NF instances can then be created on the fly depending on the traffic and the network state [2]. The VMs and containers of the NFs are usually hosted on commercial off-the-shelf (COTS) hardware, which are comparatively less expensive than the purpose-built hardware.…”

Section: Introductionmentioning

confidence: 99%

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Towards Carrier-Grade Service Provisioning in NFV

Woldeyohannes

Tola

Jiang

2019

2019 15th International Conference on the Design of Reliable Communication Networks (DRCN)

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Network Function Virtualization (NFV) is an emerging technology that reduces cost and brings flexibility in the provisioning of services. NFV-based networks are expected to be able to provide carrier-grade services, which require high availability. One of the challenges for achieving high availability is that the commodity servers used in NFV are more error prone than the purpose-built hardware. The "de-facto" technique for fault tolerance is redundancy. However, unless planned carefully, structural dependencies among network nodes could result in correlated node unavailabilities that undermine the effect of redundancy. In this paper, we address the challenge of developing a redundancy resource allocation scheme that takes into account correlated unavailabilities caused by network structural dependencies. The proposed scheme consist of two parts. In the first part, we propose an algorithm to identify nodes that can be highly affected by a node failure because of their network structural dependency with this node. The algorithm analyzes such dependencies using a recently proposed centrality measure called dependency index. In the second part, a redundancy resource allocation scheme that places backup network functions on nodes considering their dependency nature and assigns the instances to flows optimally is proposed. The results show that not considering the network structural dependency in backup placement may significantly affect the service availability to flows. The results also give insights into the trade-off between cost and performance.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards Carrier-Grade Service Provisioning in NFV

Woldeyohannes

Tola

Jiang

2019

2019 15th International Conference on the Design of Reliable Communication Networks (DRCN)

Self Cite

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“…However, this approach has two main drawbacks, i.e., 1) one time and randomly choosing the clusters' threshold, and 2) the arbitrarily initial center selection, not being able to guarantee the end-to-end latency to be shortened. Similarly, the MST-based clustering algorithm, as described in [27], is also very dependent on the proximity threshold, which utilized to remove network edges from the MST cluster, whose lengths are greater than the threshold value. Hence, several successive values are required to be generated [28].…”

Section: Existing Solutionsmentioning

confidence: 99%

“…Second, a prediction of NIS usage-ratio is employed to develop a dynamic resource allocation scheme, which optimizes further latency minimization in the corresponding clusters. 5) We evaluate our proposed method along with the recent state-of-the-art approaches, i.e., the simulated annealing [22], the basic sequential algorithmic scheme (BSAS) [26], the minimum spanning tree (MST) [27], and the modified BSAS [28]. The simulation-based experimental comparison is carried out on two network topologies, i.e., FatTree [29] and Abilene [30].…”

Section: Introductionmentioning

confidence: 99%

Latency-Optimal Network Intelligence Services in SDN/NFV-Based Energy Internet Cyberinfrastructure

et al. 2020

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Energy internet (EI) is a very complex system with various applications that not only require a high-level of cyber-security but also need low-latency communication. Thus, cyberinfrastructure with latency-optimal network intelligence services (NIS), in which application data flows are deeply examined in real-time, is inevitable. In the future internet system, a set of NIS can flexibly be implemented in network function virtualization (NFV)-based middleboxes that overlay on software-defined networking (SDN) architecture, becoming an SDN/NFV-based cyberinfrastructure. However, how to deploy these middleboxes is a non-deterministic optimization problem, which is complicated and time-consuming. Hence, by focusing on latency minimization, we develop an artificial intelligence (AI)-powered solution consisted of two phases. First, middleboxes placement based on the graph cluster analysis, and second, NIS resource allocation based on the prediction of service usage-ratio in each corresponding cluster. The simulation-based experimental evaluation shows that our proposed strategy using an optimized K-means algorithm outperforms the recent state-of-the-art middleboxes placement approaches. The average end-to-end flow latencies are around 23.81%, 18.44%, and 11.49% lower compared with the simulated annealing method, the basic sequential algorithmic scheme, and the minimum spanning tree procedure, respectively. Besides, the proposed resource allocation scheme optimizes further the latency minimization around 4.24%. We believe that the work presented in this paper will aid the communication service providers (CSP) in providing a secure and low-latency SDN/NFV-based cyberinfrastructure for the EI ecosystem.

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“…In NFV, several studies (e.g., [4], [13], [14]) consider the placement of a minimum number of VNF instances to cover all flows. A single type of network functions is considered in [4], [13], [15], [16], and the case of multiple network functions is considered in [14], [17], [18], [19], [20]. However, these work neglects either the budget constraint or the multi-dimensional resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Placement and Allocation of Virtual Network Functions: Multi-dimensional Case

Sallam

Zheng

2019

2019 IEEE 27th International Conference on Network Protocols (ICNP)

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Network function virtualization (NFV) is an emerging design paradigm that replaces physical middlebox devices with software modules running on general purpose commodity servers. While gradually transitioning to NFV, Internet service providers face the problem of where to introduce NFV in order to make the most benefit of that; here, we measure the benefit by the amount of traffic that can be serviced through the NFV. This problem is non-trivial as it is composed of two challenging subproblems: 1) placement of nodes to support virtual network functions (referred to as VNF-nodes); and 2) allocation of the VNF-nodes resources to network flows; the two subproblems need to be considered jointly to satisfy the objective of serving the maximum amount of traffic. This problem has been studied recently but for the one-dimensional setting, where all network flows require one network function, which requires a unit of resource to process a unit of flow. In this work, we extend to the multi-dimensional setting, where flows can require multiple network functions, which can also require a different amount of each resource to process a unit of flow. The multi-dimensional setting introduces new challenges in addition to those of the onedimensional setting (e.g., NP-hardness and non-submodularity) and also makes the resource allocation a multi-dimensional generalization of the generalized assignment problem with assignment restrictions. To address these difficulties, we propose a novel two-level relaxation method and utilize the primal-dual technique to design two approximation algorithms that achieve an approximation ratio of (e−1)(Z−1) 2e 2 Z(kR) 1/(Z−1) and (e−1)(Z−1) 2e(Z−1+eZR 1/(Z−1) ) , where k (resp. R) is the number of VNF-nodes (resp. resources), and Z is a measure of the available resource compared to flow demand. Finally, we perform extensive trace-driven simulations to show the effectiveness of the proposed algorithms.

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ClusPR: Balancing Multiple Objectives at Scale for NFV Resource Allocation

Cited by 60 publications

References 28 publications

Towards Carrier-Grade Service Provisioning in NFV

Towards Carrier-Grade Service Provisioning in NFV

Latency-Optimal Network Intelligence Services in SDN/NFV-Based Energy Internet Cyberinfrastructure

Placement and Allocation of Virtual Network Functions: Multi-dimensional Case

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