Abstract
Cloud computing provides services and resources in the Internet, and many applications are self-service-supported, on-demand resource allocation-adapted. These dynamic networks allocated necessary resource to the users’ need and they require proper resource allocation scheme. Since various resources are consumed by users if resource allocation is not proper, this leads the system to load imbalance nature. Using Internet-connected devices for storage and computation not only communicates the cloud resources but also connects the devices to network through various protocols. These changes make the network into a complex, dense, heterogeneous system. In this paper, a green computing fair resource allocation through deep reinforcement learning model is proposed to provide efficient resource allocation scheme to the users in the network. Conventional Q-learning model fails in dimensionality problem when the state space increases exponentially. The proposed model is combined with fair resource allocation with deep reinforcement learning to provide better allocation schemes compared to the conventional model.
Similar content being viewed by others
References
Bermejo B, Guerrero C, Lera I, Juiz C (2016) Cloud resource management to improve energy efficiency based on local nodes optimizations. Procedia Comput Sci 83:878–885
Bhardwaj T, Sharma SC (2018) Fuzzy logic-based elasticity controller for autonomic resource provisioning in parallel scientific applications: a cloud computing perspective. Comput Electr Eng 70:1049–1073
Chang B-J, Lee Y-W, Liang Y-H (2018) Reward-based Markov chain analysis adaptive global resource management for inter-cloud computing. Future Gener Comput Syst 79:588–603
Chen J, Wang Y (2018) A resource demand prediction method based on EEMD in cloud computing. Procedia Comput Sci 131:116–123
Jararweh Y, Issa MB, Daraghmeh M, Al-Ayyoub M, Alsmirat MA (2018) Energy efficient dynamic resource management in cloud computing based on logistic regression model and median absolute deviation. Sustain Comput Inform Syst 19:262–274
Leontiou N, Dechouniotis D, Denazis S, Papavassiliou S (2018) A hierarchical control framework of load balancing and resource allocation of cloud computing services. Comput Electr Eng 67:235–251
Lin W, Siyao X, He L, Li J (2017) Multi-resource scheduling and power simulation for cloud computing. Inf Sci 397–398:168–186
Lin K, Pankaj S, Wang D (2018) Task offloading and resource allocation for edge-of-things computing on smart healthcare systems. Comput Electr Eng 72:348–360
Liu D, Sui X, Li L, Jiang Z, Zeng Y (2018) A cloud service adaptive framework based on reliable resource allocation. Future Gener Comput Syst 89:455–463
Lyazidi MY, Aitsaadi N, Langar R (2018) A dynamic resource allocation framework in LTE downlink for Cloud-Radio Access Network. Comput Netw 140:101–111
Mekala MS, Viswanathan P (2019) Energy-efficient virtual machine selection based on resource ranking and utilization factor approach in cloud computing for IoT. Comput Electr Eng 73:227–244
Mergenci C, Korpeoglu I (2019) Generic resource allocation metrics and methods for heterogeneous cloud infrastructures. J Netw Comput Appl 146:102413
Peng J, Zhi X, Xie X (2016) Application type-based resource allocation strategy in cloud environment. Microprocess Microsyst 47:385–391
Peng Y, Kang D-K, Al-Hazemi F, Youn C-H (2017) Energy and QoS aware resource allocation for heterogeneous sustainable cloud datacenters. Opt Switching Netw 23(3):225–240
Pradhan P, Behera PK, Ray BNB (2016) Modified round robin algorithm for resource allocation in cloud computing. Procedia Comput Sci 85:878–890
Samimi P, Teimouri Y, Mukhtar M (2016) A combinatorial double auction resource allocation model in cloud computing. Inf Sci 357:201–216
Tang H, Li C, Bai J, Tang JH, Luo Y (2019) Dynamic resource allocation strategy for latency-critical and computation-intensive applications in cloud–edge environment. Comput Commun 134:70–82
Wang Q, Guo S, Liu J, Yang Y (2019) Energy-efficient computation offloading and resource allocation for delay-sensitive mobile edge computing. Sustain Comput Inform Syst 21:154–164
Xiaoying T, Dan H, Yuchun G, Changjia C (2017) Dynamic resource allocation in cloud download service. J China Univ Posts Telecommun 24(5):53–59
Yang J, Lu Z, Wang N, Wu J, Hung PCK (2017) Multi-policy-aware MapReduce resource allocation and scheduling for smart computing cluster. J Syst Arch 80:17–29
Yuan X, Min G, Yang LT, Ding Y, Fang Q (2017) A game theory-based dynamic resource allocation strategy in geo-distributed datacenter clouds. Future Gener Comput Syst 76:63–72
Zhang X, Wu T, Chen M, Wei T, Buyya R (2019) Energy-aware virtual machine allocation for cloud with resource reservation. J Syst Softw 147:147–161
Zhu W, Zhuang Y, Zhang L (2017) A three-dimensional virtual resource scheduling method for energy saving in cloud computing. Future Gener Comput Syst 69:66–74
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
We do not have any conflict of interest.
Human and animal rights
Humans/animals are not involved in this work. We used our own data.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Karthiban, K., Raj, J.S. An efficient green computing fair resource allocation in cloud computing using modified deep reinforcement learning algorithm. Soft Comput 24, 14933–14942 (2020). https://doi.org/10.1007/s00500-020-04846-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-04846-3