Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method
Selecting the QoS-optimized software-as-a-service (SaaS) from a large number of services with the same functionality and different Quality of Service (QoS) is still a hot issue. Massive QoS feedback forms big QoS data, which exhibits ambiguity and randomness increasing the uncertainty of service sel...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10403878/ |
| _version_ | 1827375825597497344 |
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| author | Longchang Zhang Jing Bai |
| author_facet | Longchang Zhang Jing Bai |
| author_sort | Longchang Zhang |
| collection | DOAJ |
| description | Selecting the QoS-optimized software-as-a-service (SaaS) from a large number of services with the same functionality and different Quality of Service (QoS) is still a hot issue. Massive QoS feedback forms big QoS data, which exhibits ambiguity and randomness increasing the uncertainty of service selection. Starting from the characterization of big QoS data, the Uncertain Big QoS data-driven Efficient SaaS Decision Making Method (UBQoS_ESDM) is proposed. The method firstly utilizes cloud model to portray QoS in order to solve the problem of inaccurate description of uncertain big QoS data; then draws on the idea of Skyline query to establish uncertain service Skyline set, which reduces the search space and improves the efficiency of QoS-optimal SaaS selection; and draws on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to design two decision-making algorithms to evaluate alternative SaaSs, and to obtain the QoS-optimal SaaS that reflects user requirements. In addition, two types of backward QoS cloud generators are introduced to convert big QoS data to QoS cloud models, and the QoS cloud model adaptive adjustment mechanism is introduced too, which can adapt to the dynamic changes of QoS. Finally, theoretical proofs and experiments verify the superiority and efficiency of the method. |
| first_indexed | 2024-03-08T11:57:45Z |
| format | Article |
| id | doaj.art-a4a7fe1063ba40788190d78c00735449 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T11:57:45Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a4a7fe1063ba40788190d78c007354492024-01-24T00:00:29ZengIEEEIEEE Access2169-35362024-01-0112111961121610.1109/ACCESS.2024.335546910403878Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making MethodLongchang Zhang0https://orcid.org/0000-0002-4085-2676Jing Bai1School of Information Engineering, Suqian University, Suqian, ChinaSchool of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, ChinaSelecting the QoS-optimized software-as-a-service (SaaS) from a large number of services with the same functionality and different Quality of Service (QoS) is still a hot issue. Massive QoS feedback forms big QoS data, which exhibits ambiguity and randomness increasing the uncertainty of service selection. Starting from the characterization of big QoS data, the Uncertain Big QoS data-driven Efficient SaaS Decision Making Method (UBQoS_ESDM) is proposed. The method firstly utilizes cloud model to portray QoS in order to solve the problem of inaccurate description of uncertain big QoS data; then draws on the idea of Skyline query to establish uncertain service Skyline set, which reduces the search space and improves the efficiency of QoS-optimal SaaS selection; and draws on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to design two decision-making algorithms to evaluate alternative SaaSs, and to obtain the QoS-optimal SaaS that reflects user requirements. In addition, two types of backward QoS cloud generators are introduced to convert big QoS data to QoS cloud models, and the QoS cloud model adaptive adjustment mechanism is introduced too, which can adapt to the dynamic changes of QoS. Finally, theoretical proofs and experiments verify the superiority and efficiency of the method.https://ieeexplore.ieee.org/document/10403878/SaaSbig QoS datacloud modelSkylineTOPSIS |
| spellingShingle | Longchang Zhang Jing Bai Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method IEEE Access SaaS big QoS data cloud model Skyline TOPSIS |
| title | Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method |
| title_full | Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method |
| title_fullStr | Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method |
| title_full_unstemmed | Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method |
| title_short | Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method |
| title_sort | uncertain big qos data driven efficient saas decision making method |
| topic | SaaS big QoS data cloud model Skyline TOPSIS |
| url | https://ieeexplore.ieee.org/document/10403878/ |
| work_keys_str_mv | AT longchangzhang uncertainbigqosdatadrivenefficientsaasdecisionmakingmethod AT jingbai uncertainbigqosdatadrivenefficientsaasdecisionmakingmethod |