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Bio‐inspired cost‐aware optimization for data‐intensive service provision
Author(s) -
Wang Lijuan,
Shen Jun,
Luo Junzhou
Publication year - 2015
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.3589
Subject(s) - computer science , scalability , cloud computing , adaptability , big data , service (business) , robustness (evolution) , scope (computer science) , negotiation , data science , risk analysis (engineering) , distributed computing , database , business , data mining , ecology , biochemistry , chemistry , marketing , gene , biology , programming language , operating system , political science , law
Summary The use of Big Data and the development of cloud computing have led to greater dependence on data‐intensive services. Each service may actually request or create a large amount of data sets. The scope, number, and complexity of data‐intensive services are all set to soar in the future. To compose these services will be more challenging. Issues of autonomy, scalability, adaptability, and robustness, become difficult to resolve. Bio‐inspired algorithms can overcome the new challenging requirements of data‐intensive service provision. It is useful for the provision of data‐intensive services to explore key features and mechanisms of biological systems and accordingly to add biological mechanisms to services. In this paper, we will discuss single‐objective and multi‐objective data‐intensive service provision problems based on bio‐inspired algorithms. Further, we will propose an ant‐inspired negotiation approach. Finally, this paper points out future research topics. Copyright © 2015 John Wiley & Sons, Ltd.