Energy-saving traffic scheduling in backbone networks with software-defined networks

Lei, Junru; Deng, Shuhua; Lu, Zebin; He, Yihao; Gao, Xieping

doi:10.1007/s10586-020-03102-5

Cited by 9 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For solving energy optimization problems, most approaches established ILP and then designed comparable energy routing algorithms. Majumder et al [21] and Lei et al [22] chose newly active paths with high utilization to save energy. A modified version of OSPF protocol that reduces the number of active links by utilizing shared shortest paths trees was shown in [23].…”

Section: Network Energy Aware Approaches and Methodsmentioning

confidence: 99%

On adaptive multi-objective optimization for greener wired networks

Yazbek

Liu

2020

SN Appl. Sci.

View full text Add to dashboard Cite

Section: Network Energy Aware Approaches and Methodsmentioning

confidence: 99%

On adaptive multi-objective optimization for greener wired networks

Yazbek

Liu

2020

SN Appl. Sci.

View full text Add to dashboard Cite

“…Although a variety of factors (distance, technology, redundancy, accessibility, etc.) contribute to this cost as a capital expenditure, the energy used by network devices to process the traffic carried by this link is one of the significant operating expenses affecting this cost and must be precisely addressed in order to realize future networks [24]. P = {p : (⊢ p , ⊣ p )|p ⊂ L} denotes the set of all paths in the network, where ⊢ p and ⊣ p are the head and tail nodes of path p, and δ p,l is a binary constant equal to 1 if path p contains link l. It should be noted that all paths are directed vectors of nodes with no loops.…”

Section: A Infrastructure Modelmentioning

confidence: 99%

Double Deep Q-Learning-Based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications

Shokrnezhad

Taleb

Dazzi

2024

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

Nowadays, as the need for capacity continues to grow, entirely novel services are emerging. A solid cloudnetwork integrated infrastructure is necessary to supply these services in a real-time responsive, and scalable way. Due to their diverse characteristics and limited capacity, communication and computing resources must be collaboratively managed to unleash their full potential. Although several innovative methods have been proposed to orchestrate the resources, most ignored network resources or relaxed the network as a simple graph, focusing only on cloud resources. This paper fills the gap by studying the joint problem of communication and computing resource allocation, dubbed CCRA, including function placement and assignment, traffic prioritization, and path selection considering capacity constraints and quality requirements, to minimize total cost. We formulate the problem as a non-linear programming model and propose two approaches, dubbed B&B-CCRA and WF-CCRA, based on the Branch & Bound and Water-Filling algorithms to solve it when the system is fully known. Then, for partially known systems, a Double Deep Q-Learning (DDQL) architecture is designed. Numerical simulations show that B&B-CCRA optimally solves the problem, whereas WF-CCRA delivers near-optimal solutions in a substantially shorter time. Furthermore, it is demonstrated that DDQL-CCRA obtains near-optimal solutions in the absence of request-specific information.

show abstract

“…Otherwise, the penalty function is updated by the following criteria: if entering a configuration choice when having a certain status has previously led to a network congestion or disconnection reported by an LSA message, the LSA time will be checked first if it occurs at the same time (same day of week, same day of month and month, and same hour) as in Figure 1. Otherwise, the penalty function is updated by a multiplicative value of δ as shown in (1). First of all, the penalty function for each possible configuration is initialized according to the criteria in (2) and it is updated by learning based on history and status.…”

Section: Approach Descriptionmentioning

confidence: 99%

“…Basis for comparison: we compare RedCon with the optimal solution computed using CPLEX tool and a well-known traffic based solutions [22], [23]. To measure the value of a carefully designed solution we also compare to a solution that simply removes links at random (R).…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Accordingly, we must try to save energy in every aspect we can, and this is the justification of this paper. In the past few decades power consumption has been a major worldwide issue taking too much of scientists and researchers time and effort to reduce the amount of consumed power due to its huge effects from global CO2 omissions to the great waste of energy [1]- [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Green distributed algorithm for energy saving in IP wired networks using sleep scheduling

Hussein

Alabbasi

Alsadeh

2021

IJECE

View full text Add to dashboard Cite

Energy saving has become a critical issue and a great challenge in the past few decades, and a great effort as well is being made to reduce consumed energy. The Internet forms a major source for energy consumption. Therefore, in this work we propose an algorithm for energy saving in distributed backbone networks, the reduced energy consumption (RedCon) algorithm. In this paper, we introduce a new version for saving energy on the Internet by switching off underutilized links and switching on idle links when the network is overloaded in a distributed manner over the network nodes based on LSA messages and without any knowledge of the traffic matrix. Our algorithm is more accurate and outperforms other algorithms with its time checks and advanced learning algorithm.

show abstract

Energy-saving traffic scheduling in backbone networks with software-defined networks

Cited by 9 publications

References 39 publications

On adaptive multi-objective optimization for greener wired networks

On adaptive multi-objective optimization for greener wired networks

Double Deep Q-Learning-Based Path Selection and Service Placement for Latency-Sensitive Beyond 5G Applications

Green distributed algorithm for energy saving in IP wired networks using sleep scheduling

Contact Info

Product

Resources

About