Cost-Efficient VNF Placement and Scheduling in Public Cloud Networks

Gao, Tao; Li, Xin; Wu, Yu; Zou, Weixia; Huang, Shanguo; Tornatore, Massimo; Mukherjee, Biswanath

doi:10.1109/tcomm.2020.2992504

Cited by 60 publications

(24 citation statements)

References 32 publications

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“…The VNF allocation problem in DC/cloud environments has also been addressed in several works while orchestrating network and computational resources to set-up network slices (i.e., VNF placement problem). VNF placement and scheduling in public cloud networks is addressed in [19] where a cost-efficient algorithm is proposed to achieve a trade-off between computing resource consumption and latency performance. In [20], authors consider a multi-DC environment where they aim at maximizing the number of accepted VNF placement requests while differentiating them in priority levels and focusing on business related objectives.…”

Section: Related Workmentioning

confidence: 99%

Resource Orchestration Strategies With Retrials for Latency-Sensitive Network Slicing Over Distributed Telco Clouds

Gharbaoui¹,

Martini

Cecchetti

et al. 2021

IEEE Access

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The new radio technologies (i.e. 5G and beyond) will allow a new generation of innovative services operated by vertical industries (e.g. robotic cloud, autonomous vehicles, etc.) with more stringent QoS requirements, especially in terms of end-to-end latency. Other technological changes, such as Network Function Virtualization (NFV) and Software-Defined Networking (SDN), will bring unique service capabilities to networks by enabling flexible network slicing that can be tailored to the needs of vertical services. However, effective orchestration strategies need to be put in place to offer latency minimization while also maximizing resource utilization for telco providers to address vertical requirements and increase their revenue. Looking at this objective, this paper addresses a latency-sensitive orchestration problem by proposing different strategies for the coordinated selection of virtual resources (network, computational, and storage resources) in distributed DCs while meeting vertical requirements (e.g., bandwidth demand) for network slicing. Three orchestration strategies are presented to minimize latency or the blocking probability through effective resource utilization. To further reduce the slice request blocking, orchestration strategies also encompass a retrial mechanism applied to rejected slice requests. Regarding latency, two components were considered, namely processing and network latency. An extensive set of simulations was carried out over a wide and composite telco cloud infrastructure in which different types of data centers coexist characterized by a different network location, size, and processing capacity. The results compare the behavior of the strategies in addressing latency minimization and service request fulfillment, also considering the impact of the retrial mechanism.

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Section: Related Workmentioning

confidence: 99%

Resource Orchestration Strategies With Retrials for Latency-Sensitive Network Slicing Over Distributed Telco Clouds

Gharbaoui¹,

Martini

Cecchetti

et al. 2021

IEEE Access

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“…The limitation of this approach is that it is based on greedy approach to solve the problem and all the test conducted on simulation using random cloud data. Gao et al [8] have focused on heat recirculation for energy and resource optimization. A simulated annealing-based algorithm named SABA has been proposed to lower overall energy consumption in the cloud.…”

Section: Literature Studymentioning

confidence: 99%

A Novel Local Search-Based Approximation Algorithm to Optimize Virtual Machine Placement With Resource Constraints

Shah

Murthi

Kumar³

2021

MATEC Web Conf.

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Many problems in cloud computing are not solvable in polynomial time and only option left is to choose approximate solution instead of optimum. Virtual Machine placement is one of such problem with resource constraints in which overall objective is to optimize multiple resources of hosts during placement process. In this paper we have addressed this problem with large size NP-Hard instances and proposed novel local search-based approximation algorithm. This problem is not yet studied in the research community with NP hard instances. A new proposed algorithm is empirically evaluated with state-of-the-art techniques. and our algorithm has improved placement result by 18% in CPU utilization, 21% in resource contention and 26% in overall resource utilization for benchmark instances collected from azure private cloud data center.

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“…Combinatorial optimization theory for NFV: The VNF-PR problem has been studied for different objectives [27], such as cost minimization [28], [29], performance improvement [30], [31], and utility maximization [32]- [34]. In most studies, VNF placement, either alone or jointly with traffic routing, is investigated by using combinatorial optimization theory (e.g., primal-dual, rounding, Markov approximation).…”

Section: Related Workmentioning

confidence: 99%

“…However, the aforementioned studies do not consider QoS, such as the delay of the data flow in SFC. Gao et al [28] propose a cost-efficient scheme to address the VNFs placement problem in public cloud networks with the goal of low cost and latency. Cziva et al [39] study how to use the optimal stopping theory to place VNFs under the edge cloud to minimize the total delay expectation.…”

Section: Related Workmentioning

confidence: 99%

A-DDPG: Attention Mechanism-based Deep Reinforcement Learning for NFV

Yang

Liu

et al. 2021

2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS)

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The efficacy of Network Function Virtualization (NFV) depends critically on (1) where the virtual network functions (VNFs) are placed and (2) how the traffic is routed. Unfortunately, these aspects are not easily optimized, especially under time-varying network states with different quality of service (QoS) requirements. Given the importance of NFV, many approaches have been proposed to solve the VNF placement and traffic routing problem. However, those prior approaches mainly assume that the state of the network is static and known, disregarding real-time network variations. To bridge that gap, in this paper, we formulate the VNF placement and traffic routing problem as a Markov Decision Process model to capture the dynamic network state transitions. In order to jointly minimize the delay and cost of NFV providers and maximize the revenue, we devise a customized Deep Reinforcement Learning (DRL) algorithm, called A-DDPG, for VNF placement and traffic routing in a real-time network. A-DDPG uses the attention mechanism to ascertain smooth network behavior within the general framework of network utility maximization (NUM). The simulation results show that A-DDPG outperforms the state-ofthe-art in terms of network utility, delay, and cost.

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Cost-Efficient VNF Placement and Scheduling in Public Cloud Networks

Cited by 60 publications

References 32 publications

Resource Orchestration Strategies With Retrials for Latency-Sensitive Network Slicing Over Distributed Telco Clouds

Resource Orchestration Strategies With Retrials for Latency-Sensitive Network Slicing Over Distributed Telco Clouds

A Novel Local Search-Based Approximation Algorithm to Optimize Virtual Machine Placement With Resource Constraints

A-DDPG: Attention Mechanism-based Deep Reinforcement Learning for NFV

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