TY - JOUR
T1 - Slow Replica and Shared Protection
T2 - Energy-Efficient and Reliable Task Assignment in Cloud Data Centers
AU - Fan, Yuqi
AU - Wang, Chen
AU - Wu, Weili
AU - Znati, Taieb
AU - Du, Dingzhu
N1 - Funding Information:
Manuscript received January 22, 2019; revised April 30, 2019; accepted June 15, 2019. Date of publication July 10, 2019; date of current version August 31, 2021. This work was supported in part by the National Science Foundation under Grant 1747818, in part by the U.S. Department of Energy under Contract DESC0014376, and in part by the Anhui Provincial Natural Science Foundation under Grant 1608085MF142. Associate Editor: Y. Dai. (Corresponding author: Yuqi Fan.) Y. Fan and C. Wang are with the School of Computer Science and Information Engineering, Anhui Province Key Laboratory of Industry Safety and Emergency Technology, Hefei University of Technology, Hefei 230601, China (e-mail: yuqi.fan@hfut.edu.cn; chenw@mail.hfut.edu.cn).
Publisher Copyright:
© 1963-2012 IEEE.
PY - 2021/9
Y1 - 2021/9
N2 - With the explosive growth in the scale of cloud computing infrastructures, reliability and energy efficiency have become important concerns considering the great complexity of cloud data centers. There is an urgent need for efficient task assignment that can dispatch tasks to appropriate cloud data center servers, which is critical to achieve reliability and energy efficiency in current cloud data centers. Most of the research on task assignment focuses on only one of the objectives of reliability and energy efficiency, while the two objectives are intrinsically conflicting with each other. In this paper, we deal with the problem of task assignment in data centers, with the objective of minimizing the energy consumption while providing failure tolerance to task execution failure. We propose a reliability-aware and energy-efficient task replica assignment algorithm based on running task replicas at a low speed and enabling multiple task replicas to share the same server resources. Each task in a job processed by the cloud computing platform has two instances: main task and task replica (shadow). Each main task runs on an individual server, and the task replica associated with the main task is assigned on a different server. The main tasks run at the full server speed, while the task replicas run at a lower rate than the main tasks. The task replicas can be mapped onto dedicated backup servers or be assigned to the servers on which the main tasks are running. Multiple task replicas can share the same server resources to reduce the number of servers required. We conduct experiments through simulations. Experimental results demonstrate that the proposed algorithm can effectively reduce the energy consumption, while achieving a good balance between the number of servers used and job completion time.
AB - With the explosive growth in the scale of cloud computing infrastructures, reliability and energy efficiency have become important concerns considering the great complexity of cloud data centers. There is an urgent need for efficient task assignment that can dispatch tasks to appropriate cloud data center servers, which is critical to achieve reliability and energy efficiency in current cloud data centers. Most of the research on task assignment focuses on only one of the objectives of reliability and energy efficiency, while the two objectives are intrinsically conflicting with each other. In this paper, we deal with the problem of task assignment in data centers, with the objective of minimizing the energy consumption while providing failure tolerance to task execution failure. We propose a reliability-aware and energy-efficient task replica assignment algorithm based on running task replicas at a low speed and enabling multiple task replicas to share the same server resources. Each task in a job processed by the cloud computing platform has two instances: main task and task replica (shadow). Each main task runs on an individual server, and the task replica associated with the main task is assigned on a different server. The main tasks run at the full server speed, while the task replicas run at a lower rate than the main tasks. The task replicas can be mapped onto dedicated backup servers or be assigned to the servers on which the main tasks are running. Multiple task replicas can share the same server resources to reduce the number of servers required. We conduct experiments through simulations. Experimental results demonstrate that the proposed algorithm can effectively reduce the energy consumption, while achieving a good balance between the number of servers used and job completion time.
KW - Energy efficient
KW - reliability
KW - shadow
KW - task assignment
KW - task replication
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U2 - 10.1109/TR.2019.2923770
DO - 10.1109/TR.2019.2923770
M3 - Article
AN - SCOPUS:85069500169
SN - 0018-9529
VL - 70
SP - 931
EP - 943
JO - IEEE Transactions on Reliability
JF - IEEE Transactions on Reliability
IS - 3
M1 - 8759087
ER -