Robust Optimization Model for Primary and Backup Resource Allocation in Cloud Providers

He, Fujun; Sato, Tomomasa; Chatterjee, Bijoy Chand; Kurimoto, Takashi; Urushidani, Shigeo; Oki, Eiji

doi:10.1109/tcc.2021.3051018

“…To guarantee service delivery, Refs. [18], [22], [23] proposed heuristic and approximate approaches to handle single and mixed-type failure. Ref.…”

Section: Related Workmentioning

confidence: 99%

“…Ref. [18] proposed a primary and backup resource allocation model using probabilistic protection to protect the deployed services. Ref.…”

Section: Related Workmentioning

confidence: 99%

Service Function Chaining with Deterministic Fault Tolerance in Optical Edge Networks

Zheng¹,

Shen²,

Li³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

In the upcoming 5G-and-beyond era, ultra-reliable low-latency communication (URLLC) services will be ubiquitous in edge networks. To improve network performance and quality of service (QoS), URLLC services could be delivered via a sequence of software-based network functions, also known as service function chains (SFCs). Towards reliable SFC delivery, it is imperative to incorporate deterministic fault tolerance during SFC deployment. However, deploying an SFC with deterministic fault tolerance is challenging because the protection mechanism needs to consider protection against physical/virtual network failures and hardware/software failures jointly. Against multiple and diverse failures, this work investigates how to effectively deliver an SFC in optical edge networks with deterministic fault tolerance while minimizing wavelength resource consumption. We introduce a protection augmented graph, called k-connected service function slices layered graph (KC-SLG), protecting against k-1 fiber link failures and k-1 server failures. We formulate a novel problem called deterministic-fault-tolerant SFC embedding and propose an effective algorithm, called most candidate first SF slices layered graph embedding (MCF-SE). MCF-SE employs two proposed techniques: k-connected network slicing (KC-NS) and k-connected function slicing (KC-FS). Through thorough mathematical proof, we show that KC-NS is 2-approximate. For KC-FS, we demonstrate that k = 3 provides the best cost-efficiency. Our experimental results also show that the proposed MCF-SE achieves deterministic-fault-tolerant service delivery and performs better than the schemes directly extended from existing work regarding survivability and average cost-efficiency.

show abstract

“…To guarantee service delivery, Refs. [18], [22], [23] proposed heuristic and approximate approaches to handle single and mixed-type failure. Ref.…”

Section: Related Workmentioning

confidence: 99%

Service Function Chaining with Deterministic Fault Tolerance in Optical Edge Networks

Zheng¹,

Shen²,

Li³

et al. 2021

Preprint

0

View full text Add to dashboard Cite

In the upcoming 5G-and-beyond era, ultra-reliable low-latency communication (URLLC) services will be ubiquitous in edge networks. To improve network performance and quality of service (QoS), URLLC services could be delivered via a sequence of software-based network functions, also known as service function chains (SFCs). Towards reliable SFC delivery, it is imperative to incorporate deterministic fault tolerance during SFC deployment. However, deploying an SFC with deterministic fault tolerance is challenging because the protection mechanism needs to consider protection against physical/virtual network failures and hardware/software failures jointly. Against multiple and diverse failures, this work investigates how to effectively deliver an SFC in optical edge networks with deterministic fault tolerance while minimizing wavelength resource consumption. We introduce a protection augmented graph, called k-connected service function slices layered graph (KC-SLG), protecting against k-1 fiber link failures and k-1 server failures. We formulate a novel problem called deterministic-fault-tolerant SFC embedding and propose an effective algorithm, called most candidate first SF slices layered graph embedding (MCF-SE). MCF-SE employs two proposed techniques: k-connected network slicing (KC-NS) and k-connected function slicing (KC-FS). Through thorough mathematical proof, we show that KC-NS is 2-approximate. For KC-FS, we demonstrate that k = 3 provides the best cost-efficiency. Our experimental results also show that the proposed MCF-SE achieves deterministic-fault-tolerant service delivery and performs better than the schemes directly extended from existing work regarding survivability and average cost-efficiency.

show abstract

“…In [10], a constraint-based PV admittance capacity planning method is proposed, but using a single scenario lacks accounting for DG output uncertainty. In [11], a robust optimization model considering DG output uncertainty is proposed, but the calculation results based on the robust optimization method are too conservative. In [12], a distributed robust optimization model-based admittance capacity assessment method is proposed by transforming the model considering the conditional risk.…”

Section: Introductionmentioning

confidence: 99%

“…the nonlinear constraint Equation(11), the large M method is used to linearize and replace this constraint by introducing binary variables b and c, and the constant large M as shown in Eqs. (15)-(18).…”

mentioning

confidence: 99%

A Combing Data-Driven and Model-Driven Methods for Renewable Energy Acceptance Capacity Assessment in Distribution Networks

Xin¹,

Jiang²,

Yu³

et al. 2023

J. Phys.: Conf. Ser.

0

View full text Add to dashboard Cite

To accurately improve the balanced new energy distribution network capacity assessment, this paper proposes a combined data-driven and model-driven renewable energy capacity assessment method for distribution networks. Firstly, the distribution generations (DG) output data are pre-processed to construct a scenario training sample set and extract seven types of DG daily output characteristics indicators. Secondly, a scenario generation model based on a conditional generation adversarial network is proposed to realize the coupling of scenario reduction and scenario generation based on K-means++ through daily output characteristics indicators. Finally, the optimization model based on hybrid positive linear programming is proposed for distributed power supply acceptance capacity evaluation of the distribution network with the objective of maximum DG access capacity and full consideration of voltage deviation, branch current, tidal current return, and other operational constraints. It is validated by IEEE 33-bus simulation. The simulation analysis shows that the proposed method can effectively quantify the DG capacity uncertainty compared with the traditional method, and the proposed method has higher computational accuracy and efficiency than the traditional method.

show abstract

Robust Optimization Model for Primary and Backup Resource Allocation in Cloud Providers

Cited by 27 publications

References 32 publications

Service Function Chaining with Deterministic Fault Tolerance in Optical Edge Networks

Service Function Chaining with Deterministic Fault Tolerance in Optical Edge Networks

Service Function Chaining with Deterministic Fault Tolerance in Optical Edge Networks

A Combing Data-Driven and Model-Driven Methods for Renewable Energy Acceptance Capacity Assessment in Distribution Networks

Contact Info

Product

Resources

About