Virtual Machine Placement Using JAYA Optimization Algorithm

Figures & data

Figure 1. Datacenter energy consumption trends (Van Heddeghem et al. 2014).

Table 1. Summary of the literature.

Reference	Methodology	Remarks
Addya et al. (2017)	Integer Linear Programming	Does not consider the energy consumption
Buyya et al. (Beloglazov and Buyya 2012)	Adaptive threshold-based approach	Multiple resource types are not taken in to account and does not guarantee the optimal solution
Garg et al. (2014)	Non-Heuristic approach	Works better for non-interactive and transactional applications. Energy consumption and different resources are not considered.
Ripal et al. (Nathuji and Schwan 2007)	Policy based coordination	Multiple resource types are not taken in to account. This approach may reduce the server performance because of application specific VM consolidation.
Li et al. (2013)	Multi-dimensional space partition model	Energy consumption is considered. However SLA violations multiple resource types and VM migration cost are not taken into consideration.
Wang and Xia (2016)	Mixed integer programing (MIP)	Does not consider the energy consumption. This approach takes more time when there are many virtual machines requests.
Duan et al. (2017)	Improved ACO	Works Based on load trend prediction and multiple resource types are not taken in to account.
Wang et al. (2016)	Improved PSO	Multiple resource types are not taken in to account.
Kruekaew and Kimpan (2014)	Artificial Bee Colony (ABC)	Multiple resource types and energy consumption are not taken into consideration.
Wu, Tang, and Fraser (2012)	Simulated annealing	It is not suitable for dynamic virtual machine consolidation. Further multiple resource types are not taken in to account.
Goiri et al. (2012)	scoring matrix	This algorithm has a chance to enter into a periodic cycle without converging.
Dashti and Rahmani (2016)	Modified PSO	Multiple resource types are not taken in to account.
Dong et al. (2013)	minimum cut	Server overloading network congestion and energy consumption are not considered.

Table 2. Performance of MBFD algorithm.

	Energy Consumption (kWh)		SLA Violations		Migrations
Number 2of VMs	Env 1	Env 2	Env 1	Env 2	Env 1	Env 2
50	0.37	0.36	10	9.31	41	23
100	0.51	0.55	9.4	10.29	59	103
150	0.66	0.66	9.7	9	139	130
200	0.8	0.85	9.51	9.67	171	171
250	1	0.96	12.62	16.40	281	253
300	1.26	1.12	13.21	9.9	319	291

Table 3. Performance of JAYA algorithm.

	Energy (kWh)		SLA Violations		Migrations
Number of VMs	Env 1	Env 2	Env 1	Env 2	Env 1	Env 2
50	0.36	0.35	10	10	42	42
100	0.49	0.52	10	10.14	83	86
150	0.57	0.61	10.74	9.14	151	116
200	0.61	0.72	8.87	10	124	140
250	0.67	0.78	9.29	10	185	150
300	0.69	0.91	10.32	9.17	215	158

Table 4. Performance of PSO algorithm.

	Energy Consumption (kWh)		SLA Violations		Migrations
Number of VMs	Env 1	Env 2	Env 1	Env 2	Env 1	Env 2
50	0.36	0.35	10	10	42	42
100	0.55	0.49	9.78	10.72	60	121
150	0.67	0.71	10	10.26	145	192
200	0.75	0.78	10	10.03	218	214
250	0.95	0.87	10	9.65	308	298
300	1.1	1.02	8.93	8.56	305	340

Figure 2. Energy comparison in homogeneous environment for PSO, MBFD, and JAYA.

Figure 3. Energy consumption in heterogeneous environment for PSO, MBFD, and JAYA.

Figure 4. SLA Violation in homogeneous environment for PSO, MBFD, and JAYA.

Figure 5. SLA Violation in heterogeneous environment for PSO, MBFD, and JAYA.

Figure 6. VM Migration in homogeneous environment for PSO, MBFD, and JAYA.

Figure 7. VM Migration in a heterogeneous environment for PSO, MBFD, and JAYA.

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