01122019  Research  Issue 1/2019 Open Access
Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing
Introduction
Related work

Proposing an alternative optimization approach that uses the multiobjective optimization concept to enhance the properties of SSA based on SCA as a local search.

Applying the MOSSASCA method to solve the problem of VMP and allocating resources in cloud computing platforms.
Background
Problem formulation of VMP
Physical machine
Virtual machine
Objective functions

Energy consumption: The servers consume a large volume of power when they are in an idle state.where \(f^{Energy}\) represents the total power consumption of the PMs, while \(PM_{i}^{pmin}=PM_{i}^{pmax} \times 0.6\) [62, 63] defines the minimum power consumption of \(PM_{i}\). \(U_{CPU_{i}}(g)\) represents the utilization ratio of resource utilized by \(PM_i\) at instant t, while \(Y_i(g)\in [0,1]\) is equal to 1 if the \(PM_i\) is turned on; otherwise, \(Y_i(g)=0\).$$\begin{aligned} \begin{aligned} f^{Energy} =\sum _{i=1}^{N_V}((PM_{i}^{pmax}PM_{i}^{pmin}) \times U_{CPU_{i}}(g)+ PM_{i}^{pmin})\times Y_i(g), \end{aligned} \end{aligned}$$(5)

SLAV: The infrastructure of cloud computing and hosts try to meet QoS requirements, which are modeled in the SLAV form to maximum response time or minimum throughput. SLAV can be caused by a host, as defined in the following equations.where SLAO represents the average ratio of the period in the case the host experiences CPU utilization of 100% and is defined as follows:$$\begin{aligned} f^{SLAV} = SLAO \times SLAM, \end{aligned}$$(6)where \(T_{ai}\) is the active time of ith host, and \(T_{si}\) represents the total time when the ith host experiences 100% SLAV utilization. Moreover, the SLAM represents the degradation in the performance due to the migration of VMs and is defined as follows:$$\begin{aligned} SLAO= \frac{1}{N_P}\sum \limits _{i=1}^{N_P}\frac{T_{si}}{T_{ai}}, \end{aligned}$$(7)where \(C_{rj}\) indicates the total CPU utilization needed by the \(VM_j\), and \(C_{dj}\) represents the degradation in the performance that results from VM migration.$$\begin{aligned} SLAM= \frac{1}{N_V}\sum \limits _{j=1}^{N_V}\frac{C_{dj}}{C_{rj}}, \end{aligned}$$(8)

Mean time before a host shutdown (MTBHS): This time is measured in seconds, and the average is calculated as follows.where \(h_i\) represents the host shutdown time.$$\begin{aligned} \ f^{MTBHS}_{j} = \frac{1}{N_P}{\sum _{i=1 }^{N_P}{h}_{i}}, \end{aligned}$$(9)
Constraints:
Multiobjective optimization
Salp swarm algorithm
Sinecosine algorithm
Proposed MOSSASCA for VMP method
Initialization
Update the population using SSASCA
Update the archive
Full description of MOSSASCA method
Complexity of MOSSASCA method
Results and discussion
Environment description
Parameter  Value 

VM types  2, 4 
VM RAM  870, 1740 MB 
VM bandwidth  100 to 200 Mbit/s 
VM MIPS  2500, 100 
Number of VMs  50, 100, 150, 200 
VM PES  1,1 
Data  Standard deviation (SD) (%)  Mean (%)  The number of VMs 

03/03/2011  17.09  12.31  1052 
06/03/2011  16.83  11.44  898 
09/03/2011  15.57  10.70  1061 
22/03/2011  12.78  9.26  1516 
25/03/2011  14.14  10.56  1078 
03/04/2011  16.55  12.39  1463 
09/04/2011  15.09  11.12  1358 
11/04/2011  15.07  11.56  1233 
12/04/2011  15.15  11.54  1054 
20/04/2011  15.21  10.43  1033 
Performance measures
Experimental results analysis
Comparison with other algorithms
MOSSASCA  MOSCA  MOPSO  MOEAD  NSGAII  

Power consumption  
VM50PM25  
Min  4.22  4.24  4.77  4.77  4.41 
Max  6.25  8.25  6.78  6.78  7.68 
Std  0.55  0.97  0.62  0.66  0.73 
VM150PM75  
Min  11.06  11.98  14.25  13.27  12.20 
Max  17.73  14.25  23.85  24.58  19.91 
STD  1.75  0.66  1.96  2.93  2.46 
VM200PM100  
Min  13.03  14.24  14.78  15.77  13.37 
Max  18.92  19.77  22.04  19.77  19.49 
STD  1.73  1.99  2.26  1.35  1.82 
SLAV  
VM50PM25  
Min  10.00  10.00  10.00  10.00  10.30 
Max  14.94  16.10  18.50  17.00  14.90 
STD  1.63  1.75  2.58  2.24  1.50 
VM150PM75  
Min  9.67  9.67  9.67  9.67  10.21 
Max  13.29  14.16  14.07  14.07  13.62 
STD  1.60  0.97  1.76  1.98  1.25 
VM200PM100  
Min  9.60  10.00  10.00  10.00  10.00 
Max  12.13  13.33  14.00  14.00  15.00 
STD  1.15  1.00  1.72  1.45  1.96 
MTBHS  
VM50PM25  
Min  8845.46  7397.86  8538.01  8547.97  7942.10 
Max  10860.11  10773.76  8739.08  8724.04  8566.20 
STD  868.70  856.34  45.33  64.88  192.77 
VM150PM75  
Min  7630.12  7613.31  8513.32  7570.60  7524.73 
Max  9229.50  9165.59  8723.81  8441.30  8529.89 
STD  305.74  603.68  95.95  314.67  337.52 
VM200PM100  
Min  8236.32  7397.86  8111.96  7614.16  7516.34 
Max  8995.71  8773.76  8731.38  8715.56  8756.34 
STD  205.42  472.11  244.68  275.83  401.91 
Performance evaluation based on MOP indicators
MOSSASCA  MOSCA  NSGAII  MOEAD  MOPSO  

VM50PM25  
EPS  1.54E−01  2.77E−01  7.14E−01  6.12E−1  6.12E−01 
2.2E−02  2.5E−02  2.3E−04  0.00  0.0E+00  
Spread  1.36E+00  4.10E−01  1.08E+00  1.30E+00  1.05E+00 
2.6E−02  1.3E−01  2.3E−04  6.0E−02  2.9E−03  
GD  4.29E−03  4.73E−02  1.58E−01  3.54E−02  3.48E−02 
1.4E−03  2.2E−02  7.1E−05  1.1E−04  1.1E−06  
HV  4.91E−01  4.16E−01  2.31E−01  3.36E−01  3.36E−01 
1.5E−02  1.9E−02  1.7E−04  1.3E−04  2.6E−08  
IGD  1.08E−03  2.77E−03  1.42E−02  1.33E−02  1.35E−02 
7.8E−05  4.6E−04  1.6E−06  1.6E−05  2.1E−05  
VM150PM75  
EPS  1.16E−01  1.95E−01  4.43E−01  3.40E−01  2.40E−01 
8.3E−02  5.1E−02  9.2E−05  2.2E−04  0.0E+00  
Spread  1.50E+00  7.79E−01  1.01E+00  1.27E+00  9.08E−01 
9.1E−02  8.2E−02  2.0E−02  5.7E−02  5.2E−02  
GD  1.93E−03  1.31E−02  1.22E−02  7.15E−03  9.99E−05 
3.1E−04  7.3E−03  1.9E−04  3.8E−04  1.4E−04  
HV  6.79E−01  6.15E−01  4.53E−01  5.40E−01  6.33E−01 
2.8E−03  4.8E−03  8.1E−05  9.9E−03  1.1E−07  
I GD  1.22E−03  2.46E−03  8.73E−03  7.44E−03  7.79E−03 
8.7E−04  7.2E−05  3.9E−05  1.6E−04  1.0E−04  
VM200PM100  
EPS  1.27E−01  4.58E−01  3.97E−01  3.46E−01  2.91E−01 
3.8E−03  5.8E−04  1.5E−04  9.4E−03  6.8E−02  
Spread  7.35E−01  1.17E+00  1.07E+00  1.16E+00  1.14E+00 
2.6E−02  1.2E−04  2.0E−02  9.9E−02  5.0E−03  
GD  3.90E−02  4.61E−02  6.65E−03  3.50E−03  1.08E−04 
2.8E−03  1.30E−03  3.6E−04  1.6E−03  9.8E−05  
HV  6.10E−01  6.68E−01  4.76E−01  5.34E−01  7.00E−01 
1.1E−03  8.1E−03  1.4E−04  1.7E−02  5.1E−02  
IGD  5.55E−03  7.1E−03  1.18E−02  1.10E−02  1.10E−02 
1.2E−04  2.1E−03  4.1E−05  1.90E−04  1.2E−04 
Influence the parameters of proposed method
\(a=1.5\)

\(a=0.5\)
 

EPS  1.40E−01  1.6E−01  EPS  1.63E−01  1.2E−01 
Spread  8.79E−01  2.5E−03  Spread  9.51E−01  1.5E−01 
GD  4.79E−03  7.9E−03  GD  5.18E−03  5.9E−03 
HV  6.62E−01  3.0E−03  HV  6.16E−01  5.3E−03 
IGD  8.38E−03  4.7E−03  IGD  2.04E−02  2.3E−02 
\(N_S =200\)

\(N_S =50\)
 

EPS  3.47E−02  2.9E−02  EPS  1.71E−01  7.1E−02 
Spread  7.89E−01  3.0E−02  Spread  9.37E−01  6.5E−02 
GD  1.32E−03  1.6E−03  GD  6.08E−03  8.6E−03 
HV  6.83E−01  2.3E−03  HV  6.04E−01  6.5E−02 
IGD  1.12E−03  4.2E−03  IGD  9.37E−03  2.9E−03 
g 
\(max =50\)
 g 
\(max =200\)
 

EPS  9.47E−01  5.7E−02  EPS  8.36E−02  5.6E−02 
Spread  9.14E−01  8.3E−02  Spread  8.44E−01  1.7E−02 
GD  1.54E−02  5.3E−03  GD  1.18E−03  4.7E−03 
HV  6.37E−01  7.3E−02  HV  7.45E−01  5.0E−03 
IGD  9.66E−03  5.7E−03  IGD  1.16E−03  4.5E−03 
Influences of VMs and PMs on the proposed method
Power consumption  SLVA  MTBHS  EPS  Spread  GD  HV  IGD  

VM500PM200  26.03  10.48  8371.35  7.43E−02  7.20E−01  3.01E−03  4.92E−01  2.61E−03 
VM600PM300  36.71  10.81  7318.01  4.56E−02  9.17E−01  4.85E−03  6.91E−01  1.39E−03 
VM1000PM400  59.24  11.44  6038.51  1.44E−01  7.23E−01  9.79E−03  2.17E−01  2.26E−03 