A survey on quality of service in cloud computing

Jelassi, Mariem; Ghazel, Cherif; Saı̈dane, Leı̈la Azouz

doi:10.1109/icfsp.2017.8097142

Cited by 29 publications

(15 citation statements)

References 16 publications

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“…4) Latency: A mass of novel mobile applications is emerging, most of them are latency-sensitive. Typically, latency is defined as the delay between a user's request and response of a service provider [51]. It has a high effect on the usability and enjoyability of end devices.…”

Section: B Metricsmentioning

confidence: 99%

“…Therefore, many researchers focused on makespan in CC only. 6) Cost: Since users and service providers usually belong to different entities, users have to pay for availing specific resources [51] such as computing or communication resources. The cost can consist of computing cost, running cost, and setup cost.…”

Section: B Metricsmentioning

confidence: 99%

“…7) Throughput: Throughput measures the rate at which data is successfully transferred between two endpoints. It concerns the ability of a service provider to handle the demands of users as it cannot directly track users' experience [51]. It is affected by latency and limitations existing at users and central/edge clouds.…”

Section: B Metricsmentioning

confidence: 99%

See 2 more Smart Citations

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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Section: B Metricsmentioning

confidence: 99%

Section: B Metricsmentioning

confidence: 99%

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A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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show abstract

“…Due to this complex nature, right now there is no dedicated framework for automatically assigning the indexing and ranking of cloud services (Ghahramani et al, 2017). Furthermore, there are different levels of quality of service in cloud computing (Jelassi et al, 2017). When the indexing procedure is going on in cloud services, the key factor is that the requirement of the user should be satisfied.…”

Section: Cloud Indexingmentioning

confidence: 99%

Cloud Services Ranking by measuring Multiple Parameters using AFIS

Abbas

Alyas

Athar

et al. 2019

ICST Transactions on Scalable Information Systems

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Assigning a level to a number of choices is referred to as ranking. The concept of ranking is applied in many situations, wherein, team rankings, player rankings, university rankings, and country rankings are commonly used these days. Similarly, in cloud standardization, ranking the web services is a principal concern, as it is a relatively new approach, assigning ranks to cloud facilities has gained significant attention from researchers across the globe. Furthermore, cloud services standardization is an important idea as it is necessary if it is required to assign ranking for cloud services. There are few limitations in cloud standardization as there is no technique to check valid services and its classifications, wherein, the standardization of cloud services will play a major role in controlling the redundancy of cloud services. In this article, a new cloud service ranking method is proposed using an Adaptive Fuzzy Inference System (AFIS).

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“…Hence, process scheduling is an indispensable issue in cloud computing. In order to avoid these nodes with less load and those at the shortest distance are identified and thereby the Quality of Service(QoS) metrics [6] [7]: throughput and latency can be improved and decreased respectively. In the literature review, these research-related different algorithms have been reviewed.…”

Section: Fig2 Architecture Of Cloud Services and Applicationsmentioning

confidence: 99%

Process Optimization of Big-Data Cloud Centre Using Nature Inspired Firefly Algorithm and K-Means Clustering

T*¹,

Samath²

2019

IJITEE

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During the last decade, the growth of big data is immeasurable in information technology. Big data has the potential to take all the decisions necessary for a company or business. But it has many challenges as well. As its size and volume are immeasurably ample it is a very challenging task to store, process and mines it. At the same time as a boon to it cloud computing has a large capacity to store this big data and provides tremendous processing power. It is a challenging task to process large amount of data frequently in the big-data cloud center through the thousands of interconnected servers. Due to the day by day growth of the big-data, big-data cloud center is forced to improve its Quality of Service (QoS) metrics like throughput, latency and response time. Hence, to develop an optimal data processing optimization method is a current research problem that has to be solved. The major intention of this paper is to develop an application that provides maximum throughput, minimum latency and reduce the response time. Toward this, we have developed an optimization technique using nature-inspired firefly optimization algorithm and k-means clustering (FA-KMeans). The developed optimization method has been evaluated with state of art algorithms. Its experimental result elucidates that our proposed method provides good throughput, reduces latency and response time.

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A survey on quality of service in cloud computing

Cited by 29 publications

References 16 publications

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Cloud Services Ranking by measuring Multiple Parameters using AFIS

Process Optimization of Big-Data Cloud Centre Using Nature Inspired Firefly Algorithm and K-Means Clustering

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