# Virtual Machine Placement via Bin Packing in Cloud Data Centers

^{1}

^{2}

^{3}

^{4}

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. Limitations of Research and Contributions

- An improved levy-based PSO algorithm is proposed to solve the VM-placement problem
- Variable-sized bin packing is used to minimize the utilization rate of the running PMs
- The best-fit strategy is used to achieve an optimal solution without wasting any space of a running PM
- Efficient use of cloud data center resources, i.e., packing a PM to its capacity without wasting any resource

#### 1.2. Implementation Practice Guidelines

- Random distribution of population
- Evaluation of all particle fitness value
- Finding the personal best and global best values

## 2. Related Work

## 3. Particle Swarm Optimization Algorithm

#### 3.1. Update Position

#### 3.2. Update Velocity

- Previous velocity
- A velocity component that drives a particle towards the location in search space where it previously found the best solution
- A velocity through which the best solution found by neighbor particles in search space

## 4. Levy Flight Algorithm

- The selection of random direction
- The production of new steps

#### 4.1. Simple Levy Distribution

#### 4.2. Fourier Transform

## 5. Proposed Levy Flight Particle Swarm Optimization with Variable Sized Bin Packing

Algorithm 1 Pseudocode of the PSOLF |

## 6. Problem Formulation

## 7. Bin Packing Problem

#### 7.1. Lower Bound for the Problem

- Add all items
- Then divide them with total capacity of a bin

#### 7.2. First Fit Algorithm

#### 7.3. Best Fit Algorithm

## 8. Simulation Results

#### 8.1. Benchmark Functions

#### 8.2. Comparison of Algorithms

#### 8.2.1. Discussion of Convergence Progress

#### 8.2.2. Unimodal Functions

#### 8.2.3. Multimodal Functions

#### 8.3. PSOLBP

## 9. Conclusions

#### Future Studies

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Kong, Y.; Zhang, M.; Ye, D. A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl.-Based Syst.
**2017**, 115, 123–132. [Google Scholar] [CrossRef] - Guo, Y.; Stolyar, A.; Walid, A. Online VM Auto-Scaling Algorithms for Application Hosting in a Cloud. IEEE Trans. Cloud Comput.
**2018**. [Google Scholar] [CrossRef] - Fu, X.; Chen, J.; Deng, S.; Wang, J.; Zhang, L. Layered virtual machine migration algorithm for network resource balancing in cloud computing. Front. Comput. Sci.
**2018**, 12, 75–85. [Google Scholar] [CrossRef] - Abdel-Basset, M.; Abdle-Fatah, L.; Sangaiah, A.K. An improved Levy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Clust. Comput.
**2018**, 1–16. [Google Scholar] [CrossRef] - Aisha Fatima, N.J.; Sultana, T.; Butt, A.A.; Shabbir, S. An Efficient Virtual Machine Placement via Bin Packing in Cloud Data Centers. In Proceedings of the 33rd International Conference on Advanced Information Networking and Applications (AINA), Matsue, Japan, 27–29 March 2019. [Google Scholar]
- Jensi, R.; Wiselin, G. An enhanced particle swarm optimization with levy flight for global optimization. Appl. Soft Comput.
**2016**, 43, 248–261. [Google Scholar] [CrossRef] - Mirjalili, S.; Saremi, S.; Mirjalili, S.M.; Coelho, L.d.S. Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Syst. Appl.
**2016**, 47, 106–119. [Google Scholar] [CrossRef] - Khosravi, A.; Andrew, L.L.H.; Buyya, R. Dynamic vm placement method for minimizing energy and carbon cost in geographically distributed cloud data centers. IEEE Trans. Sustain. Comput.
**2017**, 2, 183–196. [Google Scholar] [CrossRef] - Chekired, D.A.; Khoukhi, L. Smart grid solution for charging and discharging services based on cloud computing scheduling. IEEE Trans. Ind. Inform.
**2017**, 13, 3312–3321. [Google Scholar] [CrossRef] - Cao, Z.; Lin, J.; Wan, C.; Song, Y.; Zhang, Y.; Wang, X. Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Trans. Smart Grid
**2017**, 8, 1943–1955. [Google Scholar] - Wang, H.; Tianfield, H. Energy-Aware Dynamic Virtual Machine Consolidation for Cloud Datacenters. IEEE Access
**2018**, 6, 15259–15273. [Google Scholar] [CrossRef] - Javaid, N.; Javaid, S.; Abdul, W.; Ahmed, I.; Almogren, A.; Alamri, A.; Niaz, I.A. A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies
**2017**, 10, 319. [Google Scholar] [CrossRef] - Zhou, A.; Wang, S.; Cheng, B.; Zheng, Z.; Yang, F.; Chang, R.N.; Lyu, M.R.; Buyya, R. Cloud service reliability enhancement via virtual machine placement optimization. IEEE Trans. Serv. Comput.
**2017**, 10, 902–913. [Google Scholar] [CrossRef] - Moreno-Vozmediano, R.; Montero, R.S.; Huedo, E.; Llorente, I.M. Orchestrating the Deployment of High Availability Services on Multi-zone and Multi-cloud Scenarios. J. Grid Comput.
**2018**, 16, 39–53. [Google Scholar] [CrossRef] - Vakilinia, S. Energy efficient temporal load aware resource allocation in cloud computing datacenters. J. Cloud Comput.
**2018**, 7, 2. [Google Scholar] [CrossRef][Green Version] - Zahoor, S.; Javaid, S.; Javaid, N.; Ashraf, M.; Ishmanov, F.; Afzal, M. Cloud Fog Based Smart Grid Model for Efficient Resource Management. Sustainability
**2018**, 10, 2079. [Google Scholar] [CrossRef] - Naz, M.; Iqbal, Z.; Javaid, N.; Khan, Z.A.; Abdul, W.; Almogren, A.; Alamri, A. Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes. Energies
**2018**, 11, 384. [Google Scholar] [CrossRef] - Rahim, M.; Khalid, A.; Javaid, N.; Ashraf, M.; Aurangzeb, K.; Altamrah, A. Exploiting Game Theoretic Based Coordination Among Appliances in Smart Homes for Efficient Energy Utilization. Energies
**2018**, 11, 1426. [Google Scholar] [CrossRef] - Duong-Ba, T.H.; Nguyen, T.; Bose, B.; Tran, T.T. A Dynamic virtual machine placement and migration scheme for data centers. IEEE Trans. Serv. Comput.
**2018**. [Google Scholar] [CrossRef] - Khalid, A.; Javaid, N.; Guizani, M.; Alhussein, M.; Aurangzeb, K.; Ilahi, M. Towards dynamic coordination among home appliances using multi-objective energy optimization for demand side management in smart buildings. IEEE Access
**2018**, 6, 19509–19529. [Google Scholar] [CrossRef] - Khan, A.; Javaid, N.; Khan, M.I. Time and device based priority induced comfort management in smart home within the consumer budget limitation. Sustain. Cities Soc.
**2018**, 41, 538–555. [Google Scholar] [CrossRef] - Mohiuddin, I.; Almogren, A.; al Qurishi, M.; Hassan, M.M.; al Rassan, I.; Fortino, G. Secure distributed adaptive bin packing algorithm for cloud storage. Future Gener. Comput. Syst.
**2019**, 90, 307–316. [Google Scholar] [CrossRef] - Alam, M.G.R.; Hassan, M.M.; Uddin, M.Z.I.; Almogren, A.; Fortino, G. Autonomic computation offloading in mobile edge for IoT applications. Future Gener. Comput. Syst.
**2019**, 90, 149–157. [Google Scholar] [CrossRef] - Habibi, M.; Fazli, M.; Movaghar, A. Efficient distribution of requests in federated cloud computing environments utilizing statistical multiplexing. Future Gener. Comput. Syst.
**2019**, 90, 451–460. [Google Scholar] [CrossRef] - Mao, B.; Yang, Y.; Wu, S.; Jiang, H.; Li, K.-C. IOFollow: Improving the performance of VM live storage migration with IO following in the cloud. Future Gener. Comput. Syst.
**2019**, 91, 167–176. [Google Scholar] [CrossRef] - Ziafat, H.; Babamir, S.M. A hierarchical structure for optimal resource allocation in geographically distributed clouds. Future Gener. Comput. Syst.
**2019**, 90, 539–568. [Google Scholar] [CrossRef] - Liu, X.-F.; Zhan, Z.-H.; Deng, J.D.; Li, Y.; Gu, T.; Zhang, J. An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans. Evol. Comput.
**2018**, 22, 113–128. [Google Scholar] [CrossRef] - Riahi, M.; Krichen, S. A multi-objective decision support framework for virtual machine placement in cloud data centers: A real case study. J. Supercomput.
**2018**, 74, 2984–3015. [Google Scholar] [CrossRef] - Rayati, M.; Ranjbar, A.M. Resilient transactive control for systems with high wind penetration based on cloud computing. IEEE Trans. Ind. Inform.
**2018**, 14, 1286–1296. [Google Scholar] [CrossRef] - Lopez, J.; Rubio, J.E.; Alcaraz, C. A Resilient Architecture for the Smart Grid. IEEE Trans. Ind. Inform.
**2018**, 14, 3745–3753. [Google Scholar] [CrossRef] - Liu, J.; Zhang, N.; Kang, C.; Kirschen, D.S.; Xia, Q. Decision-Making Models for the Participants in Cloud Energy Storage. IEEE Trans. Smart Grid
**2018**, 9, 5512–5521. [Google Scholar] [CrossRef] - Yang, T.; Lee, Y.; Zomaya, A. Collective energy-efficiency approach to data center networks planning. IEEE Trans. Cloud Comput.
**2018**, 6, 656–666. [Google Scholar] [CrossRef] - Munshi, A.A.; Mohamed, Y.A.-R.I. Data Lake Lambda Architecture for Smart Grids Big Data Analytics. IEEE Access
**2018**, 6, 40463–40471. [Google Scholar] [CrossRef] - Wu, L.; Zhang, Y.; Choo, K.-K.R.; He, D. Efficient Identity-Based Encryption Scheme with Equality Test in Smart City. IEEE Trans. Sustain. Comput.
**2018**, 3, 44–55. [Google Scholar] [CrossRef] - Wu, Q.; Ishikawa, F.; Zhu, Q.; Xia, Y. Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters. IEEE Trans. Serv. Comput.
**2016**. [Google Scholar] [CrossRef] - Ferdaus, M.H.; Murshed, M.; Calheiros, R.N.; Buyya, R. Virtual machine consolidation in cloud data centers using ACO metaheuristic. In Proceedings of the European Conference on Parallel Processing, Porto, Portugal, 25–29 August 2014; Springer: Cham, Switzerland, 2014; pp. 306–317. [Google Scholar]
- Challita, S.; Paraiso, F.; Merle, P. A Study of Virtual Machine Placement Optimization in Data Centers. In Proceedings of the 7th International Conference on Cloud Computing and Services Science, CLOSER 2017, Porto, Portugal, 24–26 April 2017; pp. 343–350. [Google Scholar]
- Comi, A.; Fotia, L.; Messina, F.; Pappalardo, G.; Rosaci, D.; Sarné, G.M.L. A reputation-based approach to improve qos in cloud service composition. In Proceedings of the 2015 IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), Larnaca, Cyprus, 15–17 June 2015; pp. 108–113. [Google Scholar]

**Figure 9.**Multimodal Functions. (

**a**) SCHWEFEL2.26; (

**b**) RASTRIGIN; (

**c**) ACKLEY; (

**d**) GRIEWANK; (

**e**) PENALIZED1; (

**f**) PENALIZED2.

Techniques | Objectives | Achievements | Limitations |
---|---|---|---|

Decentralized belief propagation based method (PD-LBP) [1] | Performance of task allocation | Best allocation of big tasks and efficient performance in dynamic cloud | An agent can execute only one subtask |

Shadow routing based approach [2] | VM auto-scaling and VM-to-PM packing | Save energies and minimize operational cost | Chance of congestion in cloud data center |

Layered VM-migration algorithm [3] | Migration of VMs | Efficiently balanced the network resources | Migration of one task at a time may increase delay |

Improved levy-based whale optimization algorithm [4] | VM-placement | Efficiently balanced the load of a network | Initial placement of VMs is not an efficient way to balance the load of a whole network |

Particle swarm optimization with levy flight (PSOLF) [6] | Increase convergence efficiency | Enhanced global search | Only proposed for linear problems |

Multi-objective grey wolf optimization algorithm [7] | Multi-objective problem solving | Fixed size archive is integrated along with leader selection method | The proposed algorithm cannot handle uncertainties |

VM-placement approach [8] | Cost minimization | Reduced energy consumption | There is no guarantee of renewable energy availability |

Priority assignment algorithm [9] | Charging and discharging of electric vehicle | Stabled grid during on-peak hours | Disposal of batteries results in global warming |

Cost-oriented model [10] | On-peak hours grid stability | Efficient tariff policies for users | User has to give upfront payment for reserved instance |

Energy aware VM-consolidation and space aware best decreasing algorithm [11] | Save energy | Saved energy and assured SLA | Migration cost is great when VMs migrate too many times |

Hybrid genetic wind-driven (GWD) algorithm [12] | Load flattening in grid area network | Scheduled the load of a single home as well as multiple homes | Chance of delay whenever the request rate is high |

Network-topology-aware redundant VM-placement optimization algorithm [13] | Minimize the network resources consumption | Optimal VM-placement | Does not work for complex cloud |

VM-placement algorithm [14] | Global load balancing of a cloud | Efficient cloud scheduling | Cost is increased |

Secure distributed adaptive bin packing algorithm [22] | Efficient usage of resources and minimize number of active servers | Improved energy efficiency and minimized number of running servers | Initial placement cannot balance the load of a network |

Deep Q-learning based code offloading method [23] | Reduce network delay | Efficient energy consumption | Cost is increased |

Hierarchical state space model [29] | Manage fluctuation of wind | Provides robustness, optimality and flexibility | Energy cost is increased |

Novel architecture for cloud computing platform [30] | Distribute the load of cloud using time series forecasting | Efficiently balanced the load of a cloud | A mechanism is needed to ensure the continuity of the network |

Cloud energy storage pool [31] | Provide energy storage resources to consumers at cheap cost | Minimized the storage cost | Extra power loss to implement energy storage pool |

S. No. | Function Name | Formula | Dimension | Search Range |
---|---|---|---|---|

1 | $SPHER{E}^{\ast}$ | ${f}_{1}(x)={\sum}_{i=1}^{d}{x}_{i}^{2}$ | 30 | [$-100$,100] |

2 | $SCHWEFEL{2.22}^{\ast}$ | ${f}_{2}(x)={\sum}_{i=1}^{n}|{x}_{i}|+{\prod}_{i=1}^{n}\left|{x}_{i}\right|$ | 30 | [−100,100] |

3 | $ROSENBROC{K}^{\ast}$ | ${f}_{3}(x)={\sum}_{i=1}^{d-1}[(1-{x}_{i}^{2})+100{({x}_{i+1}-{x}_{i}^{2})}^{2}]$ | 30 | [−100,100] |

4 | $NOIS{E}^{\ast}$ | ${f}_{4}(x)={\sum}_{i=1}^{n}i{x}_{i}^{4}+random()(0,1)$ | 30 | [−100,100] |

5 | $SCHWEFEL{2.26}^{+}$ | ${f}_{5}(x)=418.9828\ast d-{\sum}_{i=1}^{d}[{x}_{i}sin(\sqrt{|{x}_{i}|})]$ | 30 | [−100,100] |

6 | $RASTRIGI{N}^{+}$ | ${f}_{6}(x)=10d+{\sum}_{i=1}^{d}[{x}_{i}^{2}-10cos(2\pi {x}_{i})]$ | 30 | [−100,100] |

7 | $ACKLE{Y}^{+}$ | ${f}_{7}(x)=-20exp[-0.2\sqrt{1/d{\sum}_{i=1}^{d}{x}_{i}^{2}}]$ | 30 | [−100,100] |

8 | $GRIEWAN{K}^{+}$ | ${f}_{8}(x)=1/4000{\sum}_{i=1}^{d}{x}_{i}^{2}-{\prod}_{i=1}^{d}cos({x}_{i}/\sqrt{i})+1$ | 30 | [−100,100] |

9 | $PENALIZED{1}^{+}$ | ${f}_{9}(x)=\pi /n(10{sin}^{2}(\pi {y}_{1}))+{\sum}_{i=1}^{n-1}{({y}_{i}-1)}^{2}\left[(i+10{sin}^{2}(\pi {y}_{(i+1)}))\right]+{({y}_{n}-1)}^{2}+{\sum}_{i=1}^{n}u({x}_{i},10,100,4)$ | 30 | [−100,100] |

${y}_{i}=1+1/4({x}_{i}+1),{u}_{{x}_{i}},a,k,m=\left\{\begin{array}{cc}k{({x}_{i}-a)}^{m},\hfill & \hfill \mathrm{for}\phantom{\rule{4.pt}{0ex}}{x}_{i}\le a\\ 0,\hfill & \hfill \mathrm{for}\phantom{\rule{4.pt}{0ex}}-a\le {x}_{i}\le a\\ k{({x}_{i}-1)}^{m},\mathrm{for}\phantom{\rule{4.pt}{0ex}}{x}_{i}<-a\hfill \end{array}\right\}$ | ||||

10 | $PENALIZED{2}^{+}$ | ${f}_{10}(x)=1/10({sin}^{2}(3\pi {y}_{1})+{\sum}_{i=1}^{n-1}{({x}_{i}-1)}^{2}\left[(1+{sin}^{2}(3\pi {x}_{(i+1)}))\right]+{({x}_{n}-1)}^{2}[1+{sin}^{2}(2\pi {x}_{n})]+{\sum}_{i=1}^{n}u({x}_{i},5,100,4)$ | 30 | [−100,100] |

Parameters | PSO | LFPSO | PSOLBP |
---|---|---|---|

Population size (NP) | 20 | 20 | 20 |

Maximum Fes | 200,000 | 200,000 | 200,000 |

c1, c2 | 1.1931 | 1.1931 | 1.1931 |

Inertia weight $(\omega )$ | 0.7213 | $\omega =(Maxiter-it/Maxit)$ | $\omega =0.1+0.8\times (1-it/Maxit)$ |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Fatima, A.; Javaid, N.; Sultana, T.; Hussain, W.; Bilal, M.; Shabbir, S.; Asim, Y.; Akbar, M.; Ilahi, M. Virtual Machine Placement via Bin Packing in Cloud Data Centers. *Electronics* **2018**, *7*, 389.
https://doi.org/10.3390/electronics7120389

**AMA Style**

Fatima A, Javaid N, Sultana T, Hussain W, Bilal M, Shabbir S, Asim Y, Akbar M, Ilahi M. Virtual Machine Placement via Bin Packing in Cloud Data Centers. *Electronics*. 2018; 7(12):389.
https://doi.org/10.3390/electronics7120389

**Chicago/Turabian Style**

Fatima, Aisha, Nadeem Javaid, Tanzeela Sultana, Waqar Hussain, Muhammad Bilal, Shaista Shabbir, Yousra Asim, Mariam Akbar, and Manzoor Ilahi. 2018. "Virtual Machine Placement via Bin Packing in Cloud Data Centers" *Electronics* 7, no. 12: 389.
https://doi.org/10.3390/electronics7120389