Research Article
Dynamic Q-Learning-Based Optimized Load Balancing Technique in Cloud
Algorithm 1
Dynamic programming algorithm to load utilization corresponding to bandwidth and network availability.
(1) | Data centre = ∑ Load, Let VMid = the VM which will start | (2) | for every data in PT = load in to DC | | Capacity in DC | (3) | Let Thres_bottom = the btbottom threshold for the load of VM | (4) | Let Thres_stop = the tttop threshold for a load of VMMC | (5) | Let n = the amount of hosts that the VM might be running on | (6) | Input: VMid, bt, tt, n | (7) | Output: Nill | (8) | End for Loop | (9) | For {Get the t hours load predictions of the starting VM} | (10) | VMPreload < -Get-LpLoadPrediction (VMid) | (11) | {Get load prediction of each VM on host} | (12) | HRes < -Get_ResFromLoad (VMs, PreLoads, eachhost) | (13) | endFor | (14) | For: each server PM in datacenter | (15) | PM.Tcpu > β | (16) | workloadBalance in Data center() | (17) | End Function |
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