TY - JOUR
T1 - Cost-Effective and Latency-minimized Data Placement Strategy for Spatial crowdsourcing in Multi-Cloud Environment
AU - Wang, Pengwei
AU - Chen, Zhen
AU - Zhou, Meng Chu
AU - Zhang, Zhaohui
AU - Abdullah, Abusorrah
AU - Chiheb Ammari, Ahmed
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - As an increasingly mature business model, crowdsourcing, especially spatial crowdsourcing, has played an important role in data collection, disaster response, urban planning and other fields. However, the rapid growth of user scale and massive data collected inevitably brings serious challenges to computing and storage resources. The emergence of cloud computing provides an opportunity to handle such challenges. Its nearly unlimited resource provision capability can provide reliable services for different crowdsourcing applications. Nevertheless, considering the risks of privacy leakage and vendor lock-in using only a single cloud, as well as the additional restrictions caused by the wide geographical distribution of data and associations among workers, the use of multi-cloud seems to be a better choice. In this article, we define a problem to find an effective data placement scheme for spatial crowdsourcing in multi-cloud environment to achieve the cost-effectiveness and minimal latency. We take full account of the interval pricing strategy. Then we analyze the geographical distribution characteristics of data centers through a clustering algorithm, and propose an effective data initialization strategy. Finally, we use a genetic algorithm to further optimize the results. Through experiments on real-world data from cloud providers, the efficiency and effectiveness of our proposed method is verified. Compared with some existing algorithms, the proposed method can significantly reduce the system cost and latency, among which the cost reduction is up to 150 times and the latency reduction is up to twice.
AB - As an increasingly mature business model, crowdsourcing, especially spatial crowdsourcing, has played an important role in data collection, disaster response, urban planning and other fields. However, the rapid growth of user scale and massive data collected inevitably brings serious challenges to computing and storage resources. The emergence of cloud computing provides an opportunity to handle such challenges. Its nearly unlimited resource provision capability can provide reliable services for different crowdsourcing applications. Nevertheless, considering the risks of privacy leakage and vendor lock-in using only a single cloud, as well as the additional restrictions caused by the wide geographical distribution of data and associations among workers, the use of multi-cloud seems to be a better choice. In this article, we define a problem to find an effective data placement scheme for spatial crowdsourcing in multi-cloud environment to achieve the cost-effectiveness and minimal latency. We take full account of the interval pricing strategy. Then we analyze the geographical distribution characteristics of data centers through a clustering algorithm, and propose an effective data initialization strategy. Finally, we use a genetic algorithm to further optimize the results. Through experiments on real-world data from cloud providers, the efficiency and effectiveness of our proposed method is verified. Compared with some existing algorithms, the proposed method can significantly reduce the system cost and latency, among which the cost reduction is up to 150 times and the latency reduction is up to twice.
KW - Spatial crowdsourcing
KW - data placement
KW - density clustering
KW - interval pricing
KW - multi-cloud
UR - http://www.scopus.com/inward/record.url?scp=85117830120&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85117830120&partnerID=8YFLogxK
U2 - 10.1109/TCC.2021.3119862
DO - 10.1109/TCC.2021.3119862
M3 - Article
SN - 2168-7161
VL - 11
SP - 868
EP - 878
JO - IEEE Transactions on Cloud Computing
JF - IEEE Transactions on Cloud Computing
IS - 1
ER -