Dynamic Traffic Management for SD-WAN Inter-Cloud Communication

Duliński, Z.; Stankiewicz, Rafał; Rzym, Grzegorz; Wydrych, Piotr

doi:10.1109/jsac.2020.2986957

Cited by 36 publications

(18 citation statements)

References 23 publications

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“…As in [4], the authors focus on network resiliency by introducing traffic re-routing according to link failures, flows' priorities and QoS requirements. Authors in [7] propose a Dynamic Traffic Management (DTM) as a generic concept to tackle the problem of minimizing traffic transit expenses. It refers to different monetary cost of interdomain traffic, such as the one related to the network energy consumption, and other kind of costs related to the volume of traffic.…”

Section: Related Workmentioning

confidence: 99%

On Deep Reinforcement Learning for Traffic Engineering in SD-WAN

Troia

Sapienza

Vare

et al. 2021

IEEE J. Select. Areas Commun.

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Section: Related Workmentioning

confidence: 99%

On Deep Reinforcement Learning for Traffic Engineering in SD-WAN

Troia

Sapienza

Vare

et al. 2021

IEEE J. Select. Areas Commun.

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“…The service providers may provide the services at an increased bandwidth and minimize latency with a price costing less per bit than today. The parallel adoption of 5G and SD-WAN [27] makes it essential that organizations understand what these technologies can do for companies. :  To achieve the agility, costs, and flexibility necessary for the digital age, all techniques are guided and applied with a disassembled model that separates hardware and software, and the data plane from the control plane.…”

Section: Ai-enabled Sdn In I Andomentioning

confidence: 99%

Impact of Artificial Intelligence-enabled Software-defined Networks in Infrastructure and Operations: Trends and Challenges

Belgaum¹,

Alansari²,

Musa³

et al. 2021

IJACSA

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The emerging technologies trending up in information and communication technology are tuning the enterprises for betterment. The existing infrastructure and operations (I&O) are supporting enterprises with their services and functionalities, considering the diverse requirements of the end-users. However, they are not free of the challenges and issues to address as the technology has advanced. This paper explains the impact of artificial intelligence (AI) in the enterprises using software-defined networking (SDN) in I&O. The fusion of artificial intelligence with software-defined networking in infrastructure and operations enables to automate the process based on experience and provides opportunities to the management to make quick decisions. But this fusion has many challenges to be addressed. This research aimed to discuss the trends and challenges impacting infrastructure and operations, and the role of AI-enabled SDN in I&O and discusses the benefits it provides that influence the directional path. Furthermore, the challenges to be addressed in implementing the AI-enabled SDN in I&O shows future directions to explore.

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“…With the development of cloud computing, many enterprises and organizations choose to outsource their data to the cloud because of favorable cloud features of convenience, economy, reliability, scalable, flexible data sharing, and so forth. The demand for collaboration, notably information sharing in inter‐cloud scenario 1,2 has proliferated. An example is the case of data sharing between a research center and a hospital.…”

Section: Introductionmentioning

confidence: 99%

“…And the length of ciphertext used in ACE is too long to be applied practice 31 . The other inter‐cloud related works in the literature mainly focus on authentication, 32 authorization, 33 federated identity management, 34 meta‐scheduling, 1 data migration, 35 data integrity auditing, 36 communication traffic management, 2 and so forth.…”

Section: Introductionmentioning

confidence: 99%

Inter‐cloud secure data sharing and its formal verification

Zhang

Yang

et al. 2021

Trans Emerging Tel Tech

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With the development of cloud computing, data sharing and collaboration have become increasing among cloud‐oriented organizations. In addition to trivial management work, data providers face many difficulties in managing access control policies in cloud data sharing, to prevent permission abuse and information leakage. This article mainly answers the following question: is it possible to achieve verifiably secure inter‐cloud data sharing? This article proposes an inter‐cloud data sharing (ICDS) approach based on ciphertext‐policy attribute‐based encryption (CP‐ABE) to ensure the security of ICDS. The security properties that ICDS holds are formally verified, where the information flow rules are defined and used in the verification. The information flow of the proposed ICDS is firstly modeled by use of high‐level Petri nets (HLPN) formally, then being represented in Z language, and at last the security properties of ICDS are verified by the Z3 solver. The formal analysis proves that the ICDS holds the security properties of confidentiality, authenticity, and collusion‐resistance.

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Dynamic Traffic Management for SD-WAN Inter-Cloud Communication

Cited by 36 publications

References 23 publications

On Deep Reinforcement Learning for Traffic Engineering in SD-WAN

On Deep Reinforcement Learning for Traffic Engineering in SD-WAN

Impact of Artificial Intelligence-enabled Software-defined Networks in Infrastructure and Operations: Trends and Challenges

Inter‐cloud secure data sharing and its formal verification

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