Automatically Design Heuristics for Multi-Objective Location-Aware Service Brokering in Multi-Cloud

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Chen:2022:SCC,

• author = "Yuheng Chen and Tao Shi and Hui Ma and Gang Chen2",
• title = "Automatically Design Heuristics for Multi-Objective Location-Aware Service Brokering in Multi-Cloud",
• booktitle = "2022 IEEE International Conference on Services Computing (SCC)",
• year = "2022",
• pages = "206--214",
• month = "10-16 " # jul,
• address = "Barcelona, Spain",
• keywords = "genetic algorithms, genetic programming, Costs, Service computing, Quality of service, QoS, Dynamic scheduling, Dynamic programming, Task analysis, Multi-objective optimization, multi-cloud, location-aware, service brokering, GPHH",
• ISSN = "2474-2473",
• DOI = "doi:10.1109/SCC55611.2022.00039",
• size = "9 pages",
• abstract = "Multi-cloud provides cloud services at distributed locations. As the number of cloud services from multi-cloud providers growing, how to select proper cloud services to optimize multiple potentially conflicting objectives simultaneously has become a challenging task. Multi-objective location-aware service brokering (MOLSB) aims to provide a set of trade-off solutions to minimize cost and latency. To handle dynamic resource requirements, various heuristics have been proposed to efficiently find suitable cloud services. However, these heuristics cannot achieve consistently good performance on a wide range of problem instances. Additionally, instead of replying on a single heuristic, it is desirable to design a set of effective heuristics that can balance different objectives with varied trade-offs. Genetic Programming hyper-heuristics (GPHH) have been applied to automatically design heuristics for many multi-objective dynamic optimization problems, e.g., workflow scheduling. In this pa-per, we propose a new GPHH-based approach, named GPHH-MOLSB, to automatically generate a group of Pareto-optimal heuristics that can be used to satisfy varied QoS preferences. GPHH-MOLSB can significantly outperform several existing approaches based on evaluation on real-world datasets.",
• notes = "Also known as \cite{9860215}",

}

Genetic Programming entries for Yuheng Chen Tao Shi Hui Ma Aaron Chen

Citations