TY - GEN
T1 - Optimal Resource Allocation Through Joint VM Selection and Placement in Private Clouds
AU - Chen, Hongkun
AU - Tang, Feilong
AU - Kong, Linghe
AU - Xu, Wenchao
AU - Zhang, Xingjun
AU - Yang, Yanqin
N1 - Publisher Copyright:
© 2019, IFIP International Federation for Information Processing.
PY - 2019
Y1 - 2019
N2 - It is the goal of private cloud platforms to optimize the resource allocation process and minimize the expense to process tasks. Essentially, resource allocation in clouds involves two phases: virtual machine selection (VMS) and virtual machine placement (VMP), and they can be jointly considered. However, existing solutions separate VMS and VMP, therefore, they can only get local optimal resource utilization. In this paper, we explore how to optimize the resource allocation globally through considering VMS and VMP jointly. Firstly, we formulate the joint virtual machine selection and placement (JVMSP) problem, and prove its NP hardness. Then, we propose the Resource-Decoupling algorithm that converts the JVMSP problem into two independent sub-problems: Max-Capability and Min-Cost. We prove that the optimal solutions of the two sub-problems guarantees the optimal solution of the JVMSP problem. Furthermore, we design the efficient Max-Balanced-Utility and Extent-Greedy heuristic algorithms to solve Max-Capability and Min-Cost, respectively. We evaluate our proposed algorithms on datasets with different distributions of resources, and the results demonstrate that our algorithms significantly improve the resource utilization efficiency compared with traditional solutions and existing algorithms.
AB - It is the goal of private cloud platforms to optimize the resource allocation process and minimize the expense to process tasks. Essentially, resource allocation in clouds involves two phases: virtual machine selection (VMS) and virtual machine placement (VMP), and they can be jointly considered. However, existing solutions separate VMS and VMP, therefore, they can only get local optimal resource utilization. In this paper, we explore how to optimize the resource allocation globally through considering VMS and VMP jointly. Firstly, we formulate the joint virtual machine selection and placement (JVMSP) problem, and prove its NP hardness. Then, we propose the Resource-Decoupling algorithm that converts the JVMSP problem into two independent sub-problems: Max-Capability and Min-Cost. We prove that the optimal solutions of the two sub-problems guarantees the optimal solution of the JVMSP problem. Furthermore, we design the efficient Max-Balanced-Utility and Extent-Greedy heuristic algorithms to solve Max-Capability and Min-Cost, respectively. We evaluate our proposed algorithms on datasets with different distributions of resources, and the results demonstrate that our algorithms significantly improve the resource utilization efficiency compared with traditional solutions and existing algorithms.
KW - Private clouds
KW - Resource allocation
KW - Resource utilization efficiency
KW - VM placement
KW - VM selection
UR - https://www.scopus.com/pages/publications/85076108105
U2 - 10.1007/978-3-030-30709-7_13
DO - 10.1007/978-3-030-30709-7_13
M3 - 会议稿件
AN - SCOPUS:85076108105
SN - 9783030307080
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 156
EP - 168
BT - Network and Parallel Computing - 16th IFIP WG 10.3 International Conference, NPC 2019, Proceedings
A2 - Tang, Xiaoxin
A2 - Chen, Quan
A2 - Bose, Pradip
A2 - Zheng, Weiming
A2 - Gaudiot, Jean-Luc
PB - Springer
T2 - 16th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2019
Y2 - 23 August 2019 through 24 August 2019
ER -