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Self-adaptive model based on goal-oriented requirements engineering for handling service variability
Aradea A.a, Supriana I.a, Surendro K.a
a School of Electrical Engineering and Informatics, Bandung Institute of Technology, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 Universiti Utara Malaysia Press.Service system is currently facing environmental complexity problems, such as the need of a distributed, heterogeneous, decentralized, and interdependent system which operates dynamically and unpredictably. This condition requires the service system to have an ability to adapt in order to realize sustainable functions. The success of service adaptation is determined by its ability to handle variability at runtime. The purpose of this research is to realize service flexibility through variability modeling, which is an extension of previous work to enrich the adaptability view. The methodology was developed through the monitor-analyse-plan-execute-knowledge control loops approach integrated into the adaptive service (service level) element within the adaptive enterprise service system metamodel based on goal-oriented requirements engineering. Service adaptation scenario was prepared through proactive and reactive adaptation mechanisms. For evaluation, the model was applied to the case of a configuration management system. The experimental results showed that the model is able to adapt to runtime variability and accomodates the growth of the service component items shown by the description of the system scalability. The proposed model has a better alternative design in analyzing variability with a total response that can be applicable in normal operations and overload. It also meets the expected level (level-5: adapting) of the adaptive capability maturity model as a standard for assessment of a service system adaptation.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Goal-based,MAPE-K,Rule-based systems,Self-adaptive systems,Service variability[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]The work conducted in this study was supported by the Ministry of Research, Technology and Higher Education of the Republic of Indonesia (No. 181.A/ ADD/UN58.21/LT/2017).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]