TY - JOUR
T1 - User-oriented cloud resource scheduling with feedback integration
AU - Ding, Ding
AU - Fan, Xiaocong
AU - Luo, Siwei
N1 - Funding Information:
This work is supported by National Nature Science Foundation of China (61300176), Fundamental Research Funds for the Central Universities (2013JBM019), Special Fund for Fast Sharing of Science Paper in Net Era by CSTD (2013113), Beijing Higher Education Young Elite Teacher Project (YETP0546) and China Scholarship Council (201307095016).
Publisher Copyright:
© 2015, Springer Science+Business Media New York.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - Resource scheduling has been one of the key challenges facing both academia and industry ever since the inauguration of cloud computing. Most of the existing research and practices have been focused on the maximization of the profits of cloud providers, whereas attention to the real needs of cloud users has largely been neglected. In this research, we propose a resource scheduling mechanism empowered with a relevance feedback network, which can be employed by a cloud provider to better meet a user’s resource needs. Our approach is a continuous refinement process that involves three stages: resource matching, resource selection, and feedback integration, where the feedback integration stage allows the resource scheduling history of a user to be considered to update the user’s resource demands and preference. The feedback information integrated in one cycle will effectively adjust the resource matching and selection in the next cycle. Incrementally, this mechanism will produce resource selections that are closer and closer to the user’s real needs. Simulation results indicated that this relevance feedback scheduling mechanism is very effective in satisfying users’ diverse requirements, and it also performs well in terms of the resource utilization rate from the cloud provider’s perspective.
AB - Resource scheduling has been one of the key challenges facing both academia and industry ever since the inauguration of cloud computing. Most of the existing research and practices have been focused on the maximization of the profits of cloud providers, whereas attention to the real needs of cloud users has largely been neglected. In this research, we propose a resource scheduling mechanism empowered with a relevance feedback network, which can be employed by a cloud provider to better meet a user’s resource needs. Our approach is a continuous refinement process that involves three stages: resource matching, resource selection, and feedback integration, where the feedback integration stage allows the resource scheduling history of a user to be considered to update the user’s resource demands and preference. The feedback information integrated in one cycle will effectively adjust the resource matching and selection in the next cycle. Incrementally, this mechanism will produce resource selections that are closer and closer to the user’s real needs. Simulation results indicated that this relevance feedback scheduling mechanism is very effective in satisfying users’ diverse requirements, and it also performs well in terms of the resource utilization rate from the cloud provider’s perspective.
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U2 - 10.1007/s11227-015-1530-9
DO - 10.1007/s11227-015-1530-9
M3 - Article
AN - SCOPUS:84946781162
VL - 72
SP - 3114
EP - 3135
JO - Journal of Supercomputing
JF - Journal of Supercomputing
SN - 0920-8542
IS - 8
ER -