Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Table 3 The comparison of results among the algorithms in each tested VMP problem

	MOSSASCA	MOSCA	MOPSO	MOEAD	NSGAII
Power consumption
VM50-PM25
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
VM150-PM75
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
VM200-PM100
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
VM50-PM25
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
VM150-PM75
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
VM200-PM100
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
VM50-PM25
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
VM150-PM75
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
VM200-PM100
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