
-
Previous Article
Inverse quadratic programming problem with $ l_1 $ norm measure
- JIMO Home
- This Issue
-
Next Article
A dynamic lot sizing model with production-or-outsourcing decision under minimum production quantities
Performance evaluation and Nash equilibrium of a cloud architecture with a sleeping mechanism and an enrollment service
1. | School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China |
2. | Department of Intelligence and Informatics, Konan University, Kobe 658-8501, Japan |
3. | Graduate School of Informatics, Kyoto University, Kyoto 606-8225, Japan |
Cloud computing makes it possible for application providers to provide services seamlessly and application users to receive services adaptively. By offering services that give users an initial experience, application providers can usually attract more users. This research proposes a type of sleeping mechanism-based cloud architecture where an experience service and an enrollment service are provided on one virtual machine (VM). Accordingly, we model the cloud architecture as a queue with an asynchronous multi-vacation and a selectable extra service. We also analyze the queueing model in the steady state by constructing a three-dimensional Markov chain. Following this, we evaluate the system performance of the proposed cloud architecture based on the energy conservation level of the system and the mean delay of the visitors who select the enrollment service. Moreover, we study the Nash equilibrium strategy of visitors by building an individual welfare function, and develop an improved intelligent search algorithm to investigate the socially optimal strategy of visitors. Aiming to achieve a social optimum, we formulate a pricing policy with a reasonable enrollment fee.
References:
[1] |
S. Ahn, J. Lee, S. Park, S. Newaz and J. Choi,
Competitive partial computation offloading for maximizing energy efficiency in mobile cloud computing, IEEE Access, 6 (2018), 899-912.
doi: 10.1109/ACCESS.2017.2776323. |
[2] |
R. Buyya, A. Beloglazov and J. Abawajy,
Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges, Eprint arXiv, 12 (2010), 6-17.
|
[3] |
R. Dhanwate and V. Bhagat,
Improving energy efficiency on android using cloud based services, International Journal of Advance Research in Computer Science and Management Studies, 3 (2015), 75-79.
|
[4] |
B. Doshi,
Queueing systems with vacations–-A survey, Queueing Systems, 1 (1986), 29-66.
doi: 10.1007/BF01149327. |
[5] |
W. Gary, P. Wang and M. Scott,
A vacation queueing model with service breakdowns, Applied Mathematical Modelling, 24 (2000), 391-400.
doi: 10.1016/S0307-904X(99)00048-7. |
[6] |
M. Ghorbani-Mandolakani and M. Rad,
ML and Bayes estimation in a Two-Phase tandem queue with a second optional service and random feedback, Communications in Statistics-Theory and Methods, 45 (2016), 2576-2591.
doi: 10.1080/03610926.2014.887107. |
[7] |
Z. Gui, J. Xia, N. Zhou and Q. Huang, How to Choose Cloud Services: Toward a Cloud Computing Cost Model, CRC Press, 2013.
![]() |
[8] |
Z. Guo, M. Song and Q. Wang, Policy-based market-oriented cloud service management architecture, Proc. of the International Conference on Information and Management Engineering, Wuhan, China, (2011), 284–291.
doi: 10.1007/978-3-642-24010-2_39. |
[9] |
J. Hu, J. Deng and J. Wu,
A green private cloud architecture with global collaboration, Telecommunication Systems, 52 (2013), 1269-1279.
doi: 10.1007/s11235-011-9639-5. |
[10] |
S. Hussein, Y. Alkabani and H. Mohamed, Green cloud computing: Datacenters power management policies and algorithms, Proc. of the 9th IEEE International Conference on Computer Engineering and Systems, Cairo, Egypt, (2015), 421–426.
doi: 10.1109/ICCES.2014.7030998. |
[11] |
A. Jain and M. Jain,
Multi-server machine repair problem with unreliable server and two types of spares under asynchronous vacation policy, International Journal of Mathematics in Operational Research, 10 (2017), 286-315.
doi: 10.1504/IJMOR.2017.083187. |
[12] |
S. Jin, H. Wu and W. Yue,
Pricing policy for a cloud registration service with a novel cloud architecture, Cluster Computing, 22 (2019), 271-283.
doi: 10.1007/s10586-018-2854-z. |
[13] |
S. Jin, X. Ma and W. Yue, Energy-saving strategy for green cognitive radio networks with an LTE-advanced structure, Journal of Communications and Networks, 18 (2016), 610-618. |
[14] |
Z. Ma, P. Wang and W. Yue,
Performance analysis and optimization of a pseudo-fault Geo/Geo/1 repairable queueing system with $N$-policy, setup time and multiple working vacations, Journal of Industrial and Management Optimization, 13 (2017), 1467-1481.
doi: 10.3934/jimo.2017002. |
[15] |
K. Madan,
An M/G/1 queue with second optional service, Queueing Systems, 34 (2000), 37-46.
doi: 10.1023/A:1019144716929. |
[16] |
M. Neuts, Matrix-Geometric Solutions in Stochastic Models, Johns Hopkins University Press, 1981.
![]() ![]() |
[17] |
P. Shi, H. Wang, X. Yue, S. Yang, X. Fu and Y. Peng, Corporation architecture for multiple cloud service providers in jointcloud computing, Proc. of the 37th International Conference on Distributed Computing Systems Workshops, Atlanta, USA, (2017), 294–298.
doi: 10.1109/ICDCSW.2017.9. |
[18] |
C. Singh, M. Jain and B. Kumar,
Queueing model with state-dependent bulk arrival and second optional service, International Journal of Mathematics in Operational Research, 3 (2011), 322-340.
doi: 10.1504/IJMOR.2011.040029. |
[19] |
A. Tarabia,
Transient and steady state analysis of an M/M/1 queue with balking, catastrophes, server failures and repairs, Journal of Communications and Networks, 7 (2017), 811-823.
doi: 10.3934/jimo.2011.7.811. |
[20] |
C. Wei, L. Cai and J. Wang,
A discrete-time Geom/G/1 retrial queue with balking customers and second optional service, Opsearch, 53 (2016), 344-357.
doi: 10.1007/s12597-015-0232-7. |
[21] |
H. Wu, S. Jin, W. Yue and Y. Takahashi, Performance evaluation for a registration service with an energy efficient cloud architecture, Proc. of the International Conference on Queueing Theory and Network Applications, Tsukuba City, Japan, (2018), 133–141.
doi: 10.1007/978-3-319-93736-6_10. |
[22] |
K. Ye, D. Huang, X. Jiang, H. Chen and S. Wu, Virtual machine based energy-efficient data center architecture for cloud computing: A performance perspective, Proc. of the IEEE/ACM International Conference on Green Computing and Communications, Hangzhou, China, (2010), 171–178.
doi: 10.1109/GreenCom-CPSCom.2010.108. |
show all references
References:
[1] |
S. Ahn, J. Lee, S. Park, S. Newaz and J. Choi,
Competitive partial computation offloading for maximizing energy efficiency in mobile cloud computing, IEEE Access, 6 (2018), 899-912.
doi: 10.1109/ACCESS.2017.2776323. |
[2] |
R. Buyya, A. Beloglazov and J. Abawajy,
Energy-efficient management of data center resources for cloud computing: A vision, architectural elements, and open challenges, Eprint arXiv, 12 (2010), 6-17.
|
[3] |
R. Dhanwate and V. Bhagat,
Improving energy efficiency on android using cloud based services, International Journal of Advance Research in Computer Science and Management Studies, 3 (2015), 75-79.
|
[4] |
B. Doshi,
Queueing systems with vacations–-A survey, Queueing Systems, 1 (1986), 29-66.
doi: 10.1007/BF01149327. |
[5] |
W. Gary, P. Wang and M. Scott,
A vacation queueing model with service breakdowns, Applied Mathematical Modelling, 24 (2000), 391-400.
doi: 10.1016/S0307-904X(99)00048-7. |
[6] |
M. Ghorbani-Mandolakani and M. Rad,
ML and Bayes estimation in a Two-Phase tandem queue with a second optional service and random feedback, Communications in Statistics-Theory and Methods, 45 (2016), 2576-2591.
doi: 10.1080/03610926.2014.887107. |
[7] |
Z. Gui, J. Xia, N. Zhou and Q. Huang, How to Choose Cloud Services: Toward a Cloud Computing Cost Model, CRC Press, 2013.
![]() |
[8] |
Z. Guo, M. Song and Q. Wang, Policy-based market-oriented cloud service management architecture, Proc. of the International Conference on Information and Management Engineering, Wuhan, China, (2011), 284–291.
doi: 10.1007/978-3-642-24010-2_39. |
[9] |
J. Hu, J. Deng and J. Wu,
A green private cloud architecture with global collaboration, Telecommunication Systems, 52 (2013), 1269-1279.
doi: 10.1007/s11235-011-9639-5. |
[10] |
S. Hussein, Y. Alkabani and H. Mohamed, Green cloud computing: Datacenters power management policies and algorithms, Proc. of the 9th IEEE International Conference on Computer Engineering and Systems, Cairo, Egypt, (2015), 421–426.
doi: 10.1109/ICCES.2014.7030998. |
[11] |
A. Jain and M. Jain,
Multi-server machine repair problem with unreliable server and two types of spares under asynchronous vacation policy, International Journal of Mathematics in Operational Research, 10 (2017), 286-315.
doi: 10.1504/IJMOR.2017.083187. |
[12] |
S. Jin, H. Wu and W. Yue,
Pricing policy for a cloud registration service with a novel cloud architecture, Cluster Computing, 22 (2019), 271-283.
doi: 10.1007/s10586-018-2854-z. |
[13] |
S. Jin, X. Ma and W. Yue, Energy-saving strategy for green cognitive radio networks with an LTE-advanced structure, Journal of Communications and Networks, 18 (2016), 610-618. |
[14] |
Z. Ma, P. Wang and W. Yue,
Performance analysis and optimization of a pseudo-fault Geo/Geo/1 repairable queueing system with $N$-policy, setup time and multiple working vacations, Journal of Industrial and Management Optimization, 13 (2017), 1467-1481.
doi: 10.3934/jimo.2017002. |
[15] |
K. Madan,
An M/G/1 queue with second optional service, Queueing Systems, 34 (2000), 37-46.
doi: 10.1023/A:1019144716929. |
[16] |
M. Neuts, Matrix-Geometric Solutions in Stochastic Models, Johns Hopkins University Press, 1981.
![]() ![]() |
[17] |
P. Shi, H. Wang, X. Yue, S. Yang, X. Fu and Y. Peng, Corporation architecture for multiple cloud service providers in jointcloud computing, Proc. of the 37th International Conference on Distributed Computing Systems Workshops, Atlanta, USA, (2017), 294–298.
doi: 10.1109/ICDCSW.2017.9. |
[18] |
C. Singh, M. Jain and B. Kumar,
Queueing model with state-dependent bulk arrival and second optional service, International Journal of Mathematics in Operational Research, 3 (2011), 322-340.
doi: 10.1504/IJMOR.2011.040029. |
[19] |
A. Tarabia,
Transient and steady state analysis of an M/M/1 queue with balking, catastrophes, server failures and repairs, Journal of Communications and Networks, 7 (2017), 811-823.
doi: 10.3934/jimo.2011.7.811. |
[20] |
C. Wei, L. Cai and J. Wang,
A discrete-time Geom/G/1 retrial queue with balking customers and second optional service, Opsearch, 53 (2016), 344-357.
doi: 10.1007/s12597-015-0232-7. |
[21] |
H. Wu, S. Jin, W. Yue and Y. Takahashi, Performance evaluation for a registration service with an energy efficient cloud architecture, Proc. of the International Conference on Queueing Theory and Network Applications, Tsukuba City, Japan, (2018), 133–141.
doi: 10.1007/978-3-319-93736-6_10. |
[22] |
K. Ye, D. Huang, X. Jiang, H. Chen and S. Wu, Virtual machine based energy-efficient data center architecture for cloud computing: A performance perspective, Proc. of the IEEE/ACM International Conference on Green Computing and Communications, Hangzhou, China, (2010), 171–178.
doi: 10.1109/GreenCom-CPSCom.2010.108. |





Step 1: Setting the error precision |
|
|
Step 2: Tackle |
and |
|
|
|
Step 3: Calculate |
Step 4: While{ |
% |
%elements in |
|
|
Step 5: |
Step 6: Output |
Step 1: Setting the error precision |
|
|
Step 2: Tackle |
and |
|
|
|
Step 3: Calculate |
Step 4: While{ |
% |
%elements in |
|
|
Step 5: |
Step 6: Output |
Step 1: Set the number |
search frequency |
bound |
volume attenuation coefficient |
Set the initial number of iterations as |
as |
Step 2: Initialize the position, the loudness and the pulse rate for each bat. |
For |
|
% |
% distribution. % |
|
|
Endfor |
Step 3: Calculate the fitness for each bat. |
|
|
Step 4: Calculate the position and the fitness for each bat. |
For |
|
|
|
If |
|
% |
Endif |
|
If |
|
|
|
Endif |
Endfor |
Step 5: Select the optimal position among all the bats. |
|
Step 6: Check iterations. |
If |
|
Endif |
Step 7: Output the optimal position |
Step 1: Set the number |
search frequency |
bound |
volume attenuation coefficient |
Set the initial number of iterations as |
as |
Step 2: Initialize the position, the loudness and the pulse rate for each bat. |
For |
|
% |
% distribution. % |
|
|
Endfor |
Step 3: Calculate the fitness for each bat. |
|
|
Step 4: Calculate the position and the fitness for each bat. |
For |
|
|
|
If |
|
% |
Endif |
|
If |
|
|
|
Endif |
Endfor |
Step 5: Select the optimal position among all the bats. |
|
Step 6: Check iterations. |
If |
|
Endif |
Step 7: Output the optimal position |
Sleeping parameter | Enrollment | Socially optimal | Maximum social | Enrollment |
probability |
arrival rate |
welfare |
fee |
|
no sleep | 0.3 | 2.1256 | 73.0759 | 114.5963 |
no sleep | 0.4 | 1.8489 | 68.6578 | 92.8360 |
no sleep | 0.5 | 1.6465 | 65.3641 | 79.3976 |
0.8 | 0.3 | 2.0560 | 65.4861 | 105.0306 |
0.8 | 0.4 | 1.7981 | 61.9026 | 85.2437 |
0.8 | 0.5 | 1.6020 | 59.2212 | 73.3388 |
0.2 | 0.3 | 1.8647 | 49.7374 | 78.4550 |
0.2 | 0.4 | 1.6420 | 47.9301 | 69.9957 |
0.2 | 0.5 | 1.4728 | 46.6180 | 60.8772 |
Sleeping parameter | Enrollment | Socially optimal | Maximum social | Enrollment |
probability |
arrival rate |
welfare |
fee |
|
no sleep | 0.3 | 2.1256 | 73.0759 | 114.5963 |
no sleep | 0.4 | 1.8489 | 68.6578 | 92.8360 |
no sleep | 0.5 | 1.6465 | 65.3641 | 79.3976 |
0.8 | 0.3 | 2.0560 | 65.4861 | 105.0306 |
0.8 | 0.4 | 1.7981 | 61.9026 | 85.2437 |
0.8 | 0.5 | 1.6020 | 59.2212 | 73.3388 |
0.2 | 0.3 | 1.8647 | 49.7374 | 78.4550 |
0.2 | 0.4 | 1.6420 | 47.9301 | 69.9957 |
0.2 | 0.5 | 1.4728 | 46.6180 | 60.8772 |
[1] |
Gábor Horváth, Zsolt Saffer, Miklós Telek. Queue length analysis of a Markov-modulated vacation queue with dependent arrival and service processes and exhaustive service policy. Journal of Industrial and Management Optimization, 2017, 13 (3) : 1365-1381. doi: 10.3934/jimo.2016077 |
[2] |
Jinsong Xu. Reversible hidden data access algorithm in cloud computing environment. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1219-1232. doi: 10.3934/dcdss.2019084 |
[3] |
Hao Song, Xiaonong Lu, Xu Zhang, Xiaoan Tang, Qiang Zhang. Collaborative optimization for energy saving and service composition in multi-granularity heavy-duty equipment cloud manufacturing environment. Journal of Industrial and Management Optimization, 2022 doi: 10.3934/jimo.2022063 |
[4] |
Xuena Yan, Shunfu Jin, Wuyi Yue, Yutaka Takahashi. Performance analysis and system optimization of an energy-saving mechanism in cloud computing with correlated traffic. Journal of Industrial and Management Optimization, 2021 doi: 10.3934/jimo.2021106 |
[5] |
Min Zhang, Gang Li. Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1413-1426. doi: 10.3934/dcdss.2019097 |
[6] |
Yang Chen, Xiaoguang Xu, Yong Wang. Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 887-900. doi: 10.3934/dcdss.2019059 |
[7] |
Chandan Mahato, Gour Chandra Mahata. Optimal replenishment, pricing and preservation technology investment policies for non-instantaneous deteriorating items under two-level trade credit policy. Journal of Industrial and Management Optimization, 2021 doi: 10.3934/jimo.2021123 |
[8] |
Wei Fu, Jun Liu, Yirong Lai. Collaborative filtering recommendation algorithm towards intelligent community. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 811-822. doi: 10.3934/dcdss.2019054 |
[9] |
Liyuan Wang, Zhiping Chen, Peng Yang. Robust equilibrium control-measure policy for a DC pension plan with state-dependent risk aversion under mean-variance criterion. Journal of Industrial and Management Optimization, 2021, 17 (3) : 1203-1233. doi: 10.3934/jimo.2020018 |
[10] |
Bing-Bing Cao, Zai-Jing Gong, Tian-Hui You. Stackelberg pricing policy in dyadic capital-constrained supply chain considering bank's deposit and loan based on delay payment scheme. Journal of Industrial and Management Optimization, 2021, 17 (5) : 2855-2887. doi: 10.3934/jimo.2020098 |
[11] |
Kyosuke Hashimoto, Hiroyuki Masuyama, Shoji Kasahara, Yutaka Takahashi. Performance analysis of backup-task scheduling with deadline time in cloud computing. Journal of Industrial and Management Optimization, 2015, 11 (3) : 867-886. doi: 10.3934/jimo.2015.11.867 |
[12] |
Serap Ergün, Bariş Bülent Kırlar, Sırma Zeynep Alparslan Gök, Gerhard-Wilhelm Weber. An application of crypto cloud computing in social networks by cooperative game theory. Journal of Industrial and Management Optimization, 2020, 16 (4) : 1927-1941. doi: 10.3934/jimo.2019036 |
[13] |
Weidong Bao, Haoran Ji, Xiaomin Zhu, Ji Wang, Wenhua Xiao, Jianhong Wu. ACO-based solution for computation offloading in mobile cloud computing. Big Data & Information Analytics, 2016, 1 (1) : 1-13. doi: 10.3934/bdia.2016.1.1 |
[14] |
Jun Wu, Shouyang Wang, Wuyi Yue. Supply contract model with service level constraint. Journal of Industrial and Management Optimization, 2005, 1 (3) : 275-287. doi: 10.3934/jimo.2005.1.275 |
[15] |
Junichi Minagawa. On the uniqueness of Nash equilibrium in strategic-form games. Journal of Dynamics and Games, 2020, 7 (2) : 97-104. doi: 10.3934/jdg.2020006 |
[16] |
Jian Hou, Liwei Zhang. A barrier function method for generalized Nash equilibrium problems. Journal of Industrial and Management Optimization, 2014, 10 (4) : 1091-1108. doi: 10.3934/jimo.2014.10.1091 |
[17] |
Yanhong Yuan, Hongwei Zhang, Liwei Zhang. A penalty method for generalized Nash equilibrium problems. Journal of Industrial and Management Optimization, 2012, 8 (1) : 51-65. doi: 10.3934/jimo.2012.8.51 |
[18] |
Gopinath Panda, Veena Goswami, Abhijit Datta Banik, Dibyajyoti Guha. Equilibrium balking strategies in renewal input queue with Bernoulli-schedule controlled vacation and vacation interruption. Journal of Industrial and Management Optimization, 2016, 12 (3) : 851-878. doi: 10.3934/jimo.2016.12.851 |
[19] |
Weiping Li, Haiyan Wu, Jie Yang. Intelligent recognition algorithm for social network sensitive information based on classification technology. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1385-1398. doi: 10.3934/dcdss.2019095 |
[20] |
Dong-Sheng Ma, Hua-Ming Song. Behavior-based pricing in service differentiated industries. Journal of Dynamics and Games, 2020, 7 (4) : 351-364. doi: 10.3934/jdg.2020027 |
2020 Impact Factor: 1.801
Tools
Metrics
Other articles
by authors
[Back to Top]