Optimized efficient job scheduling resource (OEJSR) approach using cuckoo and grey wolf job optimization to enhance resource search in cloud environment

Figures & data

Figure 1. Architecture of job scheduling in cloud.

Figure 2. Proposed diagram of OEJSR method.

Table 1. Time as well as resource quality samples for completing the task.

	R1	R2	R3	–	Rp
J1	(8,3)	–	(4,1)	–	–
J2	–	(6,1)	–	–	–
J3	–	–	–	–	–
J4	(3,1)	–	–	–	–
	–	–	–	–	–
	–	–	–	–	–
JM	(2,1)	–	–	–	–

Figure 3. Makespan analysis for OEJSR method with existing system.

Table 2. Makespan analysis using the current system using the OEJSR technique.

Iterations of the Jobs	BAT-Algorithm (Javadpour et al., 2023)	WBAT- Algorithm (Srimoyee et al., 2020)	Firefly-Algorithm (Tripathi et al., 2020)	Bees-Life-Algorithm (Masadeh et al., 2019)	OEJSR
20	111.48	107.49	103.46	99.26	95.03
40	114.86	108.39	102.87	100.45	96.26
60	113.89	107.16	103.97	99.57	95.73
80	115.75	108.95	104.54	101.57	94.16
100	119.65	109.48	104.97	101.89	94.72

Figure 4. Computation time analysis for OEJSR method with existing system.

Javadpour, A., Sangaiah, A. K., Pinto, P., Ja’fari, F., Zhang, W., Abadi, A. M. H., & Ahmadi, H. (2023). An energy-optimized embedded load balancing using DVFS computing in cloud data centers. Computer Communications, 197, 255–266. https://doi.org/10.1016/j.comcom.2022.10.019

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Data availability statement

Yes, on your request data will be provided.