Cloud service recommender system using clustering

Zain, T.; Aslam, Muhammad; Imran, Muhammad; Martinez-Enriquez, A. M.

doi:10.1109/iceee.2014.6978334

Cited by 16 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Online resources provide rich information about services, but a typical user can be unable to find precise information on urgent basis [22]. Many recommended systems are categorized in [30], based on users' requirements and expectations, employed for certain scenarios where more than one option exists. Business intelligence (E-business applications) becomes crucial to know the end user requirements.…”

Section: Related Workmentioning

confidence: 99%

“…For instance, Google provides storage services (Google Drive), software applications (Play Store Apps), and platforms (Google Apps Engine) [6]. Similarly, Microsoft provides Azure, Intune, Cloud Platform; Amazon deals with EC2, AWS, and many others which are listed-in [30]. The other prominent vendors are flexiscale.com, salesforce.com Rackspace, and RightScale.…”

Section: Introductionmentioning

confidence: 99%

“…Giving this scenario, it is difficult to make a best decision service [3,6,18]. The existing platforms use a feedback from respective users in the form of company value [14], applying k-mean clustering on the feedback input by fixing the number of clusters [30], filtration of company value with quality of attributes on user-based constraints [3], and KNN-Fuzzy classification technique [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Internet Service Selection Using Service Association Factor (SAF)

Rabbani

Aslam

2019

ITC

View full text Add to dashboard Cite

The modern organizations are equipping themselves with modern current regime of online application infrastructures through cloud, fog, and edge computing. In the presence of several opportunities, the selection criterion for such Internet-based services becomes vital, especially, when there is no supporting information available. Existing recommender systems provide services by evaluating the quality of service parameters, k-mean clustering, and fuzzy logic techniques on customer's feedback. However, these schemes typically rely on customers' feedback and do not provide any information on the interrelationship between the services. Feedback may be self-generated or biased and leading to improper recommendation to service seekers. To resolve the issue, we propose an innovative service association factor method that calculates the value of interrelationships among services appearing together as a package. This technique is implemented based on an intelligent agent that evaluates the values of the associations on standards and quality attributes. It enables the users to select the best services on their preferences. The proposed agent works on fog environment near to the customers. The technique is tested on leading cloud vendors. The results show that the system meets the desires of service seekers in all service models in an efficient manner.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Internet Service Selection Using Service Association Factor (SAF)

Rabbani

Aslam

2019

ITC

View full text Add to dashboard Cite

show abstract

“…In [7], authors focused on QoS in cloud service selection method, allowing users to specify their perception of quality criteria. The proposed work is based on data mining technique, clustering.…”

Section: Related Workmentioning

confidence: 99%

“…This technique has been reported by several researchers. other researchers aim to help a user to select best services from different cloud providers , based on the Quality of Services (QoS) and the user's feedback [2], [7]. These approaches are based on the K-means clustering techniques.…”

Section: Introductionmentioning

confidence: 99%

A Novel Cloud Services Recommendation System Based on Automatic Learning Techniques

Djiroun¹,

Guesssoum²,

Boukhalfa

et al. 2017

2017 International Conference on New Trends in Computing Sciences (ICTCS)

View full text Add to dashboard Cite

Abstract-The Cloud Computing technology is evolving constantly but essence remains the same that is to offer distinct cost saving opportunities by consolidating and restructuring information technology as a service. With the continuously increasing cloud provisions, cloud consumers start to have difficulties to find the best relevant services that suit their requirements. Therefore, selecting best services by cloud users is becoming a greater challenge. In this paper, we present a framework of services' recommendation system in a Cloud environment, using automatic learning techniques. The system aims at finding the services that suit the interests and preferences of cloud consumers by combining content based and behaviour based recommendations. In this paper, we present, USTHB-CLOUD, a cloud services recommendation prototype evaluated with an experimental study.

show abstract