Stochastic virtual network embedding via accelerated Benders decomposition

Farkiani, Behrooz; Bakhshi, Bahador; MirHassani, S. A.

doi:10.1016/j.future.2018.11.048

Cited by 11 publications

(5 citation statements)

References 33 publications

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“…[10]- [17]). In addition, the effects of performance uncertainty have been studied to a different extent in the framework of provisioning and scheduling in cloud environments (see, e.g., [18]- [23]) as well as in other environments, such as virtualized networks in the framework of placement and embedding (see, e.g., [24], [25]).…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Optimization of Deadline and Budget-Aware Workflow Scheduling in Uncertain Clouds

et al. 2021

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Cloud technologies are being used nowadays to cope with the increased computing and storage requirements of services and applications. Nevertheless, decisions about resources to be provisioned and the corresponding scheduling plans are far from being easily made especially because of the variability and uncertainty affecting workload demands as well as technological infrastructure performance. In this paper we address these issues by formulating a multi-objective constrained optimization problem aimed at identifying the optimal scheduling plans for scientific workflows to be deployed in uncertain cloud environments. In particular, we focus on minimizing the expected workflow execution time and monetary cost under probabilistic constraints on deadline and budget. According to the proposed approach, this problem is solved offline, that is, prior to workflow execution, with the intention of allowing cloud users to choose the plan of the Pareto optimal set satisfying their requirements and preferences. The analysis of the combined effects of cloud uncertainty and probabilistic constraints has shown that the solutions of the optimization problem are strongly affected by uncertainty. Hence, to properly provision cloud resources, it is compelling to precisely quantify uncertainty and take explicitly into account its effects in the decision process.

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Section: Related Workmentioning

confidence: 99%

Multi-Objective Optimization of Deadline and Budget-Aware Workflow Scheduling in Uncertain Clouds

et al. 2021

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“…We utilize the idea of the Benders decomposition method [32] and the variation used in [33] to decompose the PSFCD into two subproblems: PSFCD-RMP and PSFCD-SP. Then we use our proposed algorithm to solve the problems iteratively to reach the optimal solution.…”

Section: B Exact Solving Algorithmmentioning

confidence: 99%

“…For the AEA, we set the time limit of the PSFCD-RMP to 50 seconds and the termination condition to reaching 500 iterations or exceeding 400 seconds or achieving an optimality gap below 0.0001. In our implementation of Benders, we calculate the difference between lower and upper bound values [14], [33], and if this value becomes less than 0.01 or the algorithm reaches 500 iterations or exceeds 400 seconds of execution, the algorithm will be terminated. Consequently, the execution of the algorithms may be terminated before reaching the global optimal solution.…”

Section: A Evaluation Setupmentioning

confidence: 99%

Prioritized Deployment of Dynamic Service Function Chains

Farkiani

Bakhshi

MirHassani

et al. 2021

IEEE/ACM Trans. Networking

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Service Function Chaining and Network Function Virtualization are enabling technologies that provide dynamic network services with diverse QoS requirements. Regarding the limited infrastructure resources, service providers may prioritize some service requests over others and even reject some of the low-priority requests to satisfy the requirements of high-priority services. In this paper, we study the problem of deployment and reconfiguration of a set of chains with different priorities with the objective of maximizing the service provider's profit; wherein, we also consider management concerns including the ability to control the migration of virtual functions. We show the problem is more practical and comprehensive than the previous studies, and propose an MILP formulation of it along with two solving algorithms. The first algorithm is a fast polynomial-time heuristic that calculates an initial feasible solution to the problem. The second algorithm is an exact method that utilizes the initial feasible solution to achieve the optimal solution quickly. Using extensive simulations, we evaluate our algorithms and show the proposed heuristic can find a feasible solution in at least 83% of the simulation runs in less than 7 seconds, and the exact algorithm can achieve 25% more profit 8 times faster than the state-of-the-art MILP solving method.

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“…Farkiani et al [27] used the stochastic programming framework to formulate the stochastic VNE problem for the first time.…”

Section: Exact and Exact-like Vne Algorithmsmentioning

confidence: 99%

Novel Node-Ranking Approach for SDN-Based Virtual Network Embedding

Shi

Meng

Kang

et al. 2020

Mathematical Problems in Engineering

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Network virtualization is considered as a key technology for the future network. The emergence of software-defined network (SDN) provides a platform for the research and development of network virtualization. One of the key challenges in network virtualization is virtual network embedding (VNE). Some of the previous VNE algorithms perform virtual node embedding, which combines the nodes’ resource attributes and local topology attributes by arithmetic operations. On the one hand, it is not easy to distinguish the topological differences between SN and VN only by simple topology metrics. On the other hand, it is easy to ignore the different weight impacts of different metrics using only arithmetic operations, which will lead to an unbalanced embedding solution. To deal with these issues, we propose a novel node-ranking approach based on topology-differentiating (VNE-NRTD) for SDN-based virtual network embedding. Owing to the topological difference between SN and VN, different node metrics are used to quantify the substrate nodes and virtual nodes, respectively. Then, the nodes are ranked using the modified set pair analysis (SPA) method to avoid the unbalanced embedding solution. On this basis, we introduce the global bandwidth of the network topology into node-ranking to further improve the efficiency of node embedding. The simulation results show that the VNE-NRTD algorithm proposed in this paper outperforms other latest heuristic algorithms in terms of the VNR acceptance ratio, long-term average R/C ratio, substrate node utilization, and substrate link utilization.

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Stochastic virtual network embedding via accelerated Benders decomposition

Cited by 11 publications

References 33 publications

Multi-Objective Optimization of Deadline and Budget-Aware Workflow Scheduling in Uncertain Clouds

Multi-Objective Optimization of Deadline and Budget-Aware Workflow Scheduling in Uncertain Clouds

Prioritized Deployment of Dynamic Service Function Chains

Novel Node-Ranking Approach for SDN-Based Virtual Network Embedding

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