Towards Improving 5G Quality of Experience: Fuzzy as a Mathematical Model to Migrate Virtual Machine Server in The Defined Time Frame

Abstract

The industry and government have recently acknowledged and used virtual machines (VM) to promote their businesses. During the process of VM, some problems might occur. The issues, such as a heavy load of memory, a large load of CPU, a massive load of a disk, a high load of network and time-defined migration, might interrupt the business processes. This paper identifies the migration process among hosts for VM to overcome the problem within the defined time frame of migration. The introduction of VMs migration in a timely manner is to detect a problem earlier. There are workload parameters, such as network, CPU, disk and memory as our parameters. To overcome the issue, we have to follow the Model named Fuzzy rule. The rule follows the basic of tree model for decision-making. The application of the fuzzy Model for the study is to determine VMs allocation from busy VMs to vacant VMs for balancing purposes. The result of the study showed that the use of the fuzzy Model to forecast VMs migration based on the defined rule had 2 positive impacts. The positive impacts are (1) Time frame live migration of VMs can reduce workload by 80 %. This aims to reduce failures in performing a live migration of VMs to increase data center performance. (2) In testing, the fuzzy Model can provide results with an accuracy of 90 %, so this model can perform a live migration of VMs precisely in determining the execution time. Next, the workload could be balanced among VMs. This research could be used further to improve 5G Quality of Experience (QoE) shortly.

Systematic Cloud-Based Optimization: Twin-Fold Moth Flame Algorithm for VM Deployment and Load-Balancing

“…If the value of ran1 < 0.5, the program utilizes the moth flame algorithm (MFA) to adjust the placement of moths and flames using Eqs. ( 8)- (11).…”

“…In contrast, when ran2 > 0.5, modifications are applied to the positions of iterations using Eq. (11) to calculate the distance between moths and flames. rad = beatps(Flame) − class(pop) (11) Layer 4: Transitions and the likelihood…”

“…(11) to calculate the distance between moths and flames. rad = beatps(Flame) − class(pop) (11) Layer 4: Transitions and the likelihood…”

“…else 3 Utilizing Eqs. (10) and (11) to ascertain the radius within the moth and the flame: end if 3 end if 1 end Terminate…”

“…Although this strategy may quickly use up resources, it does not provide the best possible outcome when executing tasks [9,10]. Researchers commonly acknowledge the limitations of heuristic approaches, such as artificial bee colony (ABC) and ant colony optimization (ACO) [11]. We emphasize developing innovative and creative solutions to reduce energy consumption during the VM relocation process.…”