@inproceedings{e64eeb27d9da4dae96337b5953a5a78b,
title = "Towards location-aware joint job and data assignment in cloud data centers with NVM",
abstract = "In this paper, we investigate the joint job and data assignment problem in cloud data centers with non-volatile memory (NVM) for makespan minimization. Through extensive analysis, we find that there is an indicator variable that characterizes the hardness of the problem. Depending on the value of the indicator variable, we classify our problem into three cases: inf-case, opt-case, and nph-case. We first show that there is no feasible assignment under the inf-case. For the opt-case, we present an optimal algorithm. We show that a mixed data assignment with diversified popularity achieves high memory utilization. For the nph-case, we first prove the problem's NP-hardness and then propose a heuristic algorithm and a 2-approximation algorithm to tackle it. We conduct extensive simulations, and we find that the performance of the heuristic algorithm is better than the 2-approximation algorithm and that it is nearly the same as the theoretical optimal solution.",
keywords = "cloud data center, Data replication, Job assignment, Makespan, Resource allocation",
author = "Xin Li and Jie Wu and Zhuzhong Qian and Shaojie Tang and Sanglu Lu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 36th IEEE International Performance Computing and Communications Conference, IPCCC 2017 ; Conference date: 10-12-2017 Through 12-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/PCCC.2017.8280434",
language = "English",
series = "2017 IEEE 36th International Performance Computing and Communications Conference, IPCCC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--8",
booktitle = "2017 IEEE 36th International Performance Computing and Communications Conference, IPCCC 2017",
address = "United States",
}