A survey on QoS-based service composition in Cloud system environment

Ekie, Jesus; Gueye, Bassirou; Niang, Ibrahima; Ekie, Tresor

doi:10.1109/icsess52187.2021.9522246

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…QoS plays a pivotal role by categorizing packets into various traffic classes, enabling the allocation of appropriate resources based on distinct criteria. An extensively adopted mechanism for this purpose is Diffserv, which facilitates traffic management and QoS provision in contemporary IP networks [19,20]. In this landscape, addressing the issue of "unfairness" is paramount.…”

Section: Quality Of Service (Qos) Modelmentioning

confidence: 99%

Optimizing bandwidth utilization and traffic control in ISP networks for enhanced smart agriculture

Hassan,

Abdelaal,

Oshaba

et al. 2024

PLoS ONE

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As the demand for high-bandwidth Internet connections continues to surge, industries are exploring innovative ways to harness this connectivity, and smart agriculture stands at the forefront of this evolution. In this paper, we delve into the challenges faced by Internet Service Providers (ISPs) in efficiently managing bandwidth and traffic within their networks. We propose a synergy between two pivotal technologies, Multi-Protocol Label Switching—Traffic Engineering (MPLS-TE) and Diffserv Quality of Service (Diffserv-QoS), which have implications beyond traditional networks and resonate strongly with the realm of smart agriculture. The increasing adoption of technology in agriculture relies heavily on real-time data, remote monitoring, and automated processes. This dynamic nature requires robust and reliable high-bandwidth connections to facilitate data flow between sensors, devices, and central management systems. By optimizing bandwidth utilization through MPLS-TE and implementing traffic control mechanisms with Diffserv-QoS, ISPs can create a resilient network foundation for smart agriculture applications. The integration of MPLS-TE and Diffserv-QoS has resulted in significant enhancements in throughput and a considerable reduction in Jitter. Employment of the IPv4 header has demonstrated impressive outcomes, achieving a throughput of 5.83 Mbps and reducing Jitter to 3 msec.

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Section: Quality Of Service (Qos) Modelmentioning

confidence: 99%

Optimizing bandwidth utilization and traffic control in ISP networks for enhanced smart agriculture

Hassan,

Abdelaal,

Oshaba

et al. 2024

PLoS ONE

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“…Service composition methods have been widely researched in recent decades. Most of the methods try to meet the QoS requirement [4]. S. Sefati et al [6] used a hidden Markov model (HMM) and an ant colony optimization (ACO) for the service composition that targets enhancing the QoS.…”

Section: Related Workmentioning

confidence: 99%

“…With the development of the Internet and cloud computing, increasingly more services are provided on the Internet [1][2][3][4][5]. As a method of utilizing services, service composition has been widely used in different fields [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Service composition considering energy consumption of users and transferring files in a multicloud environment

Zhu

2023

J Cloud Comp

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In the internet and cloud environment, service composition is always used to enhance the function and processing ability of clouds. Those clouds work together for a user and provide different functions. A service request may involve multiple clouds. The past work focuses on the method of service composition and ignores the energy composition when files are transferred between clouds, including the energy consumption for transferring files (sending files from the user to the cloud and receiving files from the cloud to the user) of the user. The paper models the service composition in a multicloud environment. Based on those models, we use the GA (genetic algorithm) algorithm (GA-C) to solve the service composition problem with multiple targets in a multicloud environment. Simulation results show that the GA-C can: (1) reduce the average number of involved clouds and the energy consumption between clouds, and (2) reduce the energy consumption of the user and the failure rate of service composition.

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