Deadline-Aware SFC Orchestration Under Demand Uncertainty

Nguyen, Minh-Hoang; Dolati, Mahdi; Ghaderi, Majid

doi:10.1109/tnsm.2020.3029749

Cited by 21 publications

(9 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The delay experienced by a chain may vary over time because (i) the PoA of the request, hence the network delay, has changed, or, (ii) there is a traffic surge/reduction, and the processing time of the chain VMs changes [26]. We assume that a monitoring system predicts the performance of the deployed services every T time units (hereinafter also referred to as the decision period), and it identifies the set of critical chains, whose experienced latency is expected to violate the delay constraints due to changes in the requests' attributes (e.g., PoA, or values of λ u k ).…”

Section: The Deployment and Migration Problemmentioning

confidence: 99%

Dynamic Service Provisioning in the Edge-cloud Continuum with Provable Guarantees

Cohen¹,

Chiasserini²,

Giaccone³

et al. 2022

Preprint

View full text Add to dashboard Cite

We consider a hierarchical edge-cloud architecture in which services are provided to mobile users as chains of virtual network functions. Each service has specific computation requirements and target delay performance, which require placing the corresponding chain properly and allocating a suitable amount of computing resources. Furthermore, chain migration may be necessary to meet the services' target delay, or convenient to keep the service provisioning cost low. We tackle such issues by formalizing the problem of optimal chain placement and resource allocation in the edge-cloud continuum, taking into account migration, bandwidth, and computation costs. Specifically, we first envision an algorithm that, leveraging resource augmentation, addresses the above problem and provides an upper bound to the amount of resources required to find a feasible solution. We use this algorithm as a building block to devise an efficient approach targeting the minimum-cost solution, while minimizing the required resource augmentation. Our results, obtained through trace-driven, large-scale simulations, show that our solution can provide a feasible solution by using half the amount of resources required by state-of-the-art alternatives.

show abstract

Section: The Deployment and Migration Problemmentioning

confidence: 99%

Dynamic Service Provisioning in the Edge-cloud Continuum with Provable Guarantees

Cohen¹,

Chiasserini²,

Giaccone³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Because a service function chain specifies a sequence of virtual network functions for user traffic to realize a network service, the problem of orchestration this chain is very crucial [ 17 ]. It can be formulated the deadline-aware co-located and geo-distributed orchestration with demand uncertainty as optimization issues with the consideration of end-to-end delay in service chains by modeling queueing and processing delays.…”

Section: Related Workmentioning

confidence: 99%

“…It can be formulated the deadline-aware co-located and geo-distributed orchestration with demand uncertainty as optimization issues with the consideration of end-to-end delay in service chains by modeling queueing and processing delays. The proposed algorithm improved the performance in terms of the ability to cope with demand fluctuations, scalability and relative performance against other recent algorithms [ 17 ].…”

Section: Related Workmentioning

confidence: 99%

Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud

Balicki

2021

Entropy

View full text Add to dashboard Cite

Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking for efficient alternatives. The quality of solutions was verified in the issue of assignment of resources in the computing cloud to improve the live migration of virtual machines. We consider the multi-criteria optimization problem of deep learning-based models embedded into virtual machines. Computing clouds with deep learning agents can support several areas of education, smart city or economy. Because deep learning agents require lots of computer resources, seven criteria are studied such as electric power of hosts, reliability of cloud, CPU workload of the bottleneck host, communication capacity of the critical node, a free RAM capacity of the most loaded memory, a free disc memory capacity of the most busy storage, and overall computer costs. Quantum gates modify an accepted position for the current location of a particle. To verify the above concept, various simulations have been carried out on the laboratory cloud based on the OpenStack platform. Numerical experiments have confirmed that multi-objective quantum-inspired particle swarm optimization algorithm provides better solutions than the other metaheuristics.

show abstract

“…Compared with VNE, the SFCO is more complicated on account of the VNFs order and flow direction constraints etc. In [17], the authors assumed that the traffic demand of each virtual link is different, formulated it into an exact robust optimization model, and then developed an approximate algorithm to solve the co‐located and geo‐distributed SFCO problem. Generally, the traffic between different virtual links in the same SFC is determined by the ingress traffic and the predecessor VNF, rather than being independent of each other.…”

Section: Introductionmentioning

confidence: 99%

“…However, stochastic optimization is very hard to solve in practice, because it needs to describe and analyze the probability distribution of the random variables, and even consider the auto-correlation and cross-correlation of each random variable [19]. Inspired by the Robust Optimization (RO) in [16,17], we propose a robust service provisioning of SFC (RSP) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Robust service provisioning of service function chain under demand uncertainty

et al. 2022

View full text Add to dashboard Cite

In network function virtualization, resource allocation is one of the key techniques to ensure the QoS of service requests in face of the uncertain traffic demand and traffic fluctuation of user services. The previous related works usually assume that the traffic demand is a deterministic value and then reallocate substrate resources to deal with the demand uncertainty and traffic fluctuation during operation. In response to performance degradation caused by demand uncertainty and traffic fluctuation, the paper models the service function chain orchestration under demand uncertainty as a robust optimization problem, where the parameter Γ is introduced to control the conservativeness of the solution. On this basis, the strong duality theory is used to transform the original problem into a mixed‐integer linear programming, and then devise an exact Robust Service Provisioning (RSP) algorithm. The simulation evaluation demonstrates that the proposed algorithm could achieve different levels of robustness and make a trade‐off between robustness and cost. The impact of Γ value on the realized robustness and price of robustness is also analysed. Thus, the algorithm proposed could get an effective service function chain orchestration scheme under uncertain traffic demands and provide a reference of the total cost.

show abstract

Deadline-Aware SFC Orchestration Under Demand Uncertainty

Cited by 21 publications

References 33 publications

Dynamic Service Provisioning in the Edge-cloud Continuum with Provable Guarantees

Dynamic Service Provisioning in the Edge-cloud Continuum with Provable Guarantees

Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud

Robust service provisioning of service function chain under demand uncertainty

Contact Info

Product

Resources

About