Journal of Applied Engineering and Technological Science Vol 4(2) 2023: 711-721 711 TOWARDS IMPROVING 5G QUALITY OF EXPERIENCE: FUZZY AS A MATHEMATICAL MODEL TO MIGRATE VIRTUAL MACHINE SERVER IN THE DEFINED TIME FRAME Taufik Hidayat 1 , Kalamullah Ramli 2* , R. Deiny Mardian 3 , Rahutomo Mahardiko 4 Department of Electrical Engineering, Universitas Indonesia, Depok, Indonesia1 2 3 Department of Data Management, PT. BFI Finance Indonesia, Tbk, Tangsel, Indonesia4 kalamullah.ramli@ui.ac.id Received : 06 February 2023, Revised: 17 March 2023, Accepted : 26 March 2023 *Corresponding Author 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. Keywords: Virtual Machine Server, Fuzzy Model, Live Migration, Comparison Research 1. Introduction The development of VMs in the 4.0 era has significantly progressed well. VMs are widely used in data center (DC) to enhance business and non-business applications. Many companies utilize this server to optimize the physical server in their DC. VMs are outstanding because they are primary application resources (Haris et al., 2022a; Hossain et al., 2020). In its application, VMs are needed to serve and manage the operating system (OS), to run the application system, and many more. For many years, VMs have been operated and researched in many ways to see the better way of future VMs. To see the better way of VMs, we can review the issues or find other better ways. This research will see through the issue that appeared on VMs. Many problems arising to the production must be solved well. The appeared matters can make the OS dying. Moreover, the existing problems are the high stressing of the CPU and full of memory, which reduces the server performance to support the application. Providing a high data rate to all users without considering the network access duration and location is identified as the primary requirement in 5G technology. Many past researches have already been done for VMs migration. A classic VMs migration is manually moving from a host to another host once the issue occurs (Guo et al., 2022; Moura et al., 2022; Satpathy et al., 2021). In the earliest studies, some researchers attempted to reduce the risk that will be occurred soon. Research (Y. Kumar et al., 2022) said that Live migration of VMs targeting (1) reallocate resources between VMs in balancing data center workloads, (2) cluster-based genetic algorithms are able to display problematic VMs estimates, (3) genetic algorithms aim in selecting migration destinations Front VMs load on VMs. Research (Shao et al., 2020) said that managing the migration of VMs by scheduling aims to (1) place VMs that have hosts that are not too busy, and (2) schedule algorithms in placing VMs in live migration to reduce energy consumption in the Hidayat et al … Vol 4(2) 2023 : 711-721 712 DC. Furthermore, an analysis on the VMs taking the workload of the VMs has been conducted. The migration of VMs is based on high Quality of Service (QoS). In fact, VM migration uses fuzzy to predict the workload of network traffic. The optimizing for problem resources (CPU, RAM, Bandwidth, etc.) is the primary problem using Dependability-based Distributed Virtual Machine Placement (D2VMP). Therefore, fuzzy to determine replication of VM has been conducted (Elsaid et al., 2021; Hu et al., 2013; Jin et al., 2009). This study focuses on detecting problems in VMs so problems that often occur can be identified early and predicting the problem VMs with fuzzy analysis. Incident prediction using fuzzy was the primary research on VMs. In fact, the movement of a VM from one host to another host based on CPU, memory, network, and the disk has been done without a time frame definition. The early study highlighted no specific research on the time frame defining migrating the VMs (Silva Filho et al., 2018). This study also extends the result of the conducted previous research. In reality, the previous research was focused on something other than the time of the migration process. A new 5G network with wide coverage needs to be developed to achieve wide coverage during communication. Thus, to overcome the problem, this study focuses on the migrating issue of VMs in a defined time frame (Ahmad et al., 2015). We, expect that the result of the study will improve the awareness of the problems that happen in VMs so that the application will run well every time (Mugisha & Zhang, 2017). We have several sections for this paper. The following section is our literature study about VMs. The next section outlines our method and research result and discusses the impact. The last is the conclusion and future work on VM migration (Gilesh et al., 2020; Kaur et al., 2018). 2. Literature Review This section will analyze the literature to migrate the VMs from one host to another host. The workload analysis of VMs will focus on memory and CPU because they are required to allocate the VMs within the defined time frame. The first decision, the problems are full of memory, full load of CPU and high traffic. The second decision, the study on the migration time of VMs and the searched aspect for enhancing performance using the fuzzy Model, can boost the performance of VMs (Alharbe et al., 2022; Guo et al., 2023; Rukmini & Shridevi, 2023). Migration of VMs Based on the previous study on VM migration, they are determined that specific tasks should be completed. So we investigate VM migration and find that certain polls explain the challenges inherent to VM migration (Jin et al., 2011). VM allocation may be categorized depending on survey results. In fact, some requirements must be satisfied to migrate virtual machines, such as a high memory and CPU load. This research includes extra factors, such as a large disk load, a heavy network load, and a predetermined amount of time. The added time parameter will measure the incident in a time-based way, optimizing VM performance (Haris et al., 2022b; Hu et al., 2011; Ramanathan et al., 2021). Priority during VM migration is timely migration and placement on an appropriate host. Calculating VM migration using the fuzzy Model in line with CPU-SLA and RAM-SLA (Farzai et al., 2020; Karmakar et al., 2022; Singh & Singh, 2020; Yin & Zhang, 2022) is helpful. The purpose of VM placement is to promote VM performance. CPU, RAM, disk, and network are used to determine VM migration, but QoS is the determining factor. The primary factor in VM migration utilizing the fuzzy Model is the network due to network host overload. Past research was demonstrated that network overload can affect CPU and memory (K. Kumar et al., 2022; Seddiki et al., 2022). Figure 2 describes VM migration utilizing factors such as CPU, RAM, disk, and network, where these variables indicated VM host high load (Le, 2020; Li et al., 2017; Svard et al., 2011; Tao et al., 2019). Hidayat et al … Vol 4(2) 2023 : 711-721 713 Fig. 1. Model of VM Migration (Hidayat & Alaydrus, 2019) Classification Live Migration VMs Live migration VMs is a way to optimize application performance during live migration VMs in terms of bandwidth usage. Migration VMs can be classified into three parts pre-copy, post-copy and hybrid-copy. We describe the classification in the next paragraph (Bhardwaj & Rama Krishna, 2019). The pre-copy approach first duplicates all memory pages to the goal have, whereas the VM proceeds to run on the source have in a warm-up stage. After that, whereas the VM runs on the source have, the pre-copy strategy iteratively duplicates the adjusted memory pages of the VM to the goal have and stops after coming to a restrain that's characterized by cycles number edge or having a steady memory state of the VM at the goal have. The profoundly adjusted memory pages are replicated to the destination have alongside the VMs CPU state after ceasing the VM at the source have in a stage called halt and duplicate stage and then continuing the VM to proceed running at the goal have (Le, 2020). Post-copy operates by (1) stopping the VM on the source host, (2) copying its CPU state and non-pageable memory pages, and (3) restarting on the destination host. After that, the source starts to push the memory pages to the destination during the memory transfer. If the VM applications need a particular memory page that has not been transferred yet, an on-demand memory page request will request the page from the source as apage fault trap. Then it copies that page to the destination host. This method has less downtime (Rukmini & Shridevi, 2023). However, requesting too many page faults degrade the performance of the applications that run on the VM due to requesting the pages. It copies them through the network, which increases migration downtime and decreases application performance. A comparison of various migration methods including Pre-copy (Wang et al., 2019). In, a hybrid method that combines the Pre-Copy and Post-Copy methods is introduced. This method begins with a single Pre-copy migration iteration that copies the VM's memory to the destination host while the VM is still running on the source host. Following that iteration, the Post-copy method operates by pausing the VM, copying the CPU status to the destination host, and then restarting the VM's operation on the destination host. Following that, it begins to retrieve the remaining memory from the source host (Bhardwaj & Rama Krishna, 2019; Jamali et al., 2016). See figure 2 on live migration pre-copy and post-copy VMs. Hidayat et al … Vol 4(2) 2023 : 711-721 714 Fig. 2. Classification Live migration VMs Model (Kokkinos et al., 2016) From this method, we, the authors, had an idea to improve the design using Fuzzy rule model. Hence, it will increase the possibility of moving VMs in a critical time or even better time. 3. Research Methods Framework Data Study In DC, the workload datasets were obtained via VM. We collected data using data mining in a month and have 7800 records. We collected data based on the operating hours of VM services using data mining. Observations were held at 9:00, 11:00, 13:00, 15:00, and 17:00. These hours served as filters for the data and performance of VMs. We forecasted which VMs should be transferred to another host based on a fuzzy model analysis of the total number of records (Katal et al., 2021; Rajakumari et al., 2022). Our analytical approach with datasets and fuzzy model processing is depicted in Figure 3. Fig. 3. Dataset Processing of VM Migration From the fig. 3, we describe the methodology to collect the data for Fuzzy Model. We must have the workload data of VMs, such as: CPU load, Memory load, Network load, Disk load. If we use some VM software, it has a count on each variable. Then we have a full dataset of these workloads. The collection of workload data will then be analyzed using Fuzzy Model. Next, we have the value of prediction. Then we report it. Data Workload Fuzzy Model Fuzzy Model translates input to output as a fuzzy set. A fuzzy set is a group of membership function variables. MIN-MAX is another definition of Fuzzy Model used to assess migration status (small, medium, large). There were four steps: (1) formation of fuzzy sets, (2) implication, (3) rule component, and (4) confirmation. Figure 4 depicts the unclear fuzzy model process. Fig. 4. Fuzzy Model Processing Data This study used centroid and affirmation methods. The method was a crips solution using fuzzy area concentration. The data sources were datasets retrieved for problem analysis in VM. Hidayat et al … Vol 4(2) 2023 : 711-721 715 We collected it for a month. Table 1 shows each variable's workload category (CPU, memory, network and disk). These four aspects were essential parts of the VM which interrelated with their functions. In this case, the authors would analyze the workload to achieve load development with the chosen method. Table 1 - Dataset of VM Workload Variable Term Set Workload percentage (%) CPU Small [0– 40] Medium [40– 60] Large [60– 80] Memory Small [0– 40] Medium [40– 60] Large [60– 80] Disk Small [0– 40] Medium [40– 60] Large [60– 80] Network Small [0– 40] Medium [40– 60] Large [60– 80] Migration No Migration VM [0– 60] Migration VM [60– 100] Table 1 shows the unprocessed data and only the defined category. We create the migration science with CPU, memory, network and disk-based on type to improve the VM performance. The function of the fuzzy set was a curve showing intervals 0 – 1 on data input mapping. The formula of the fuzzy set CPU, Memory, Disk, and Network is shown in Equation 1. [𝐶, 𝑀, 𝐷, 𝑁] = { 0; ( 𝑥−𝑎) ( 𝑏−𝑎) 1; ; 𝑥 ≤ 𝑎 𝑎 ≤ 𝑥 ≤ 𝑏 𝑥 ≥ 𝑏 This research followed the min implication function (Rukmini & Shridevi, 2023) . The process had a group of premises and one conclusion. We used the process to understand the premise's and conclusion's relationship (K. Kumar et al., 2022). The formula of min implication is shown in Equation 2 where i is fuzzy rule 𝑖-th. ∝ = 𝑃𝑟𝑒𝑑𝑖𝑐𝑎𝑡𝑒𝑖=𝜇𝐴1 [ 𝑥1 ] ∩ … ∩𝐴𝑛 [𝑥𝑛 ] = 𝑀𝑖𝑛 (𝜇𝐴1 [ 𝑥1 ], … , 𝜇𝐴𝑛 [ 𝑥𝑛 ] ) Rule Fuzzy Model We defined four parameters (disk, network, CPU and memory) and three classifications (small, medium, and large). We had 81 rule combinations. Chosen by the researcher. Table 2 - Role Fuzzy VM Migration No. Migration Rule of VM [R1] IF (CPU is Small) and (Memory is Small) and (Disk is Small) and (Network is Small), then (No Migration VM) [R2] IF (CPU is Large) and (Memory is Small) and (Disk is Large) and (Network is Large), then (Migration VM) [R3] IF (CPU is Small) and (Memory is Small) and (Disk is Large) and (Network is Medium), then (Migration VM) [R4] IF (CPU is Medium) and (Memory is Medium) and (Disk is Large) and (Network is Small), then (No Migration VM) … ... Hidayat et al … Vol 4(2) 2023 : 711-721 716 … … [R80] IF (CPU is Large) and (Memory is Large) and (Disk is Medium) and (Network is Large), then (Migration VM) [R81] IF (CPU is Medium) and (Memory is Large) and (Disk is Medium) and (Network is Medium), then (Migration VM) Table 2 shows some of the Fuzzy model algorithm logic applied to share the load on the VM. This logic function rule would produce a condition that was the desired load balance in which the CPU, memory, disk, and network aspects will be balanced in each process. Defuzzifier We utilized this stage to interpret an ambiguous membership into a conclusion. We must return a crips value and transform the fuzzy output into a crips output depending on the membership function we had provided. Defuzzifier was required because undefined decision variables must be turned into crisp variables. The fuzzy set was an input for the defuzzifier received from the fuzzy rule. Meanwhile, the output is the fuzzy set's domain. This concept will yield precise results. Equation 3 depicts the defuzzifier formula. 10 0 min{ ( ), ( ), ( ), ( )} Defuzzifier min{ ( ), ( ), ( ), ( )} i i i i i i i i n cv mv dv nvt n cv mv dv nvt c C M D N C M D N              Membership function performance depends on minimum Availability, CPU, Memory, Disk and Network membership values. Mathematical equations can be seen in the equation. ( ) ( ( ), ( ), ( ), ( ) i i i iper cv mv dv nv Y C and M and D and N     Eq. (3) can also be written as ( ) ( ( ), ( ), ( ), ( ) i i i iper cv mv dv nv P C and M and D and N     Similarly, Eq (4) can be written as shown in Eq (5). 𝜇𝑝𝑒𝑟 (𝑃) = (𝜇(𝑐𝑣∩𝑚𝑣∩𝑑𝑣∩𝑛𝑣∩(𝑌) 4. Results and Discussions The information was collected throughout a month of VM workload. The datasets consist of three hosts, including several VMs. These datasets consist of data mining on the workload of VMs using the specified parameter. The data were analyzed using Fuzzy Model. Table 3 summarizes the findings of the workload % experiment. Table 3 - The Experiment of Fuzzy in Workload VM CPU (%) Memory (%) Disk (%) Network (%) Defuzzification Result (%) 35.5 60.7 60.3 70.5 83.1 40.5 40.1 64.5 60.2 49 57.9 35.4 4.6 57.9 80.6 54.1 48.5 57.5 16.2 71.9 66.3 45.2 52.9 19 53.7 Hidayat et al … Vol 4(2) 2023 : 711-721 717 Fig. 5. Result Workload Data in Fuzzy Model Table 3 is visually moved. A month was divided into five weeks. The defuzzifier result revealed that the highest impact, 83.1, is for the first week. The third week's performance of 80.6 is the next highest. This result indicated that a dynamic network (1st week) or the combination of an active network and a high CPU processing rate (3rd week) results in the most defuzzifier. To comprehensively understand VMs migration, we examine 7800 data and show them in Table 4. Table 4 - Result Processing Dataset with Fuzzy Model Time Allocation Host Status Total Migration Normal 08:00 AM–8:00 PM Host A 1500 201 1299 08:00 AM–8:00 PM Host B 2400 574 1826 08:00 AM–8:00 PM Host C 3900 225 3675 Fig 6. Workload VM in Processing Fuzzy Model Figure 6 showed that host B contains more VMs migration than hosts A and C (24%). Other result showed that though host C has more VMs, it has less migration status of VMs (6%). Meanwhile, host A had 13% of the migration VMs. From the result, host B could be moved to host C from 8:00 AM to 20:00 AM to reduce VMs workload. Research Findings Referring to the results above, the author found some evidence that results are better than past research (Badem et al., 2017). The formulas that the author uses to get accuracy, precision and recall get the following results. 35.5 60.7 60.3 70.5 83.1 40.5 40.1 64.5 60.2 49 57.9 35.4 4.6 57.9 80.6 54.1 48.5 57.5 16.2 71.9 66.3 45.2 52.9 19 53.7 0 10 20 30 40 50 60 70 80 90 CPU (%) Memory (%) Disk (%) Network (%) Defuzzification Result (%) Result Fuzzy Model 1500 201 1299 2400 574 1826 3900 225 3675 0 2000 4000 6000 Total Migration Normal Status Time Migration VMs 08:00 AM–8:00 PM Host A 08:00 AM–8:00 PM Host B 08:00 AM–8:00 PM Host C Hidayat et al … Vol 4(2) 2023 : 711-721 718 Table 5 - Comparison Method Algorithm Accuracy (%) Precession (%) Recall k-NN 75.73 73.39 64.54 DSS 86.26 90,08 69,50 Fuzzy 90.22 91.10 80.21 From these results, it can be seen that the value of fuzzy is better than k-NN and DSS. From these results, the combination of 4 variables that the author uses brings better accuracy. With these results, it is hoped that better future research will be held (Gümüşçü et al., 2020). Discussions In the tests that have been carried out, the author observes network traffic at certain hours, gets information on busy times in the data center at 9:00, 11:00, 13:00, 15:00, and 17:00, this pattern is used as a benchmark for three-time frames of a month. The workload dataset for three months on VMs such as CPU to Memory, is used as a migration limit with the criteria specified in table 1. The author also observes bandwidth patterns with the time frame, then this is used as the time to determine when the live migration of VMs is carried out. The purpose of this bandwidth pattern is a strategy in determining the right time to live migrate VMs. As a result, failure in live migration of VMs is low. In the tests carried out by the author, the network traffic is 83.1% defuzzifier with the combination in table 3. 5. Conclusion The result of VM migration with fuzzy showed the positive impacts. First, VMs migration can be conducted within the defined time frame to reduce VMs workload. Second, the undefined method's application was periodically determining the overload VMs. With these results, the excellent performance of VMs can be continuously maintained, and the activity would run as it should. The goal of live migrating VMs within a period of time is to reduce network traffic on each VMs, where this can reduce failures in carrying out live migration of VMs, and can provide decisions in carrying out live migration of VMs, from the results of the comparison of the KNN and DSS algorithm models the need for additional data more and combining other algorithms, so the results will be better. Future works are explained here. First future work, the use of several techniques can be applied to predict the VM overload and how to migrate to overcome this overload, one of which was the Markov chain method or other processes related to predicting a dominant problem. 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Mobile Networks and Applications, 1-10. https://doi.org/10.1016/j.jpdc.2018.06.009 TOWARDS IMPROVING 5G QUALITY OF EXPERIENCE: FUZZY AS A MATHEMATICAL MODEL TO MIGRATE VIRTUAL MACHINE SERVER IN THE DEFINED TIME FRAME Taufik Hidayat1, Kalamullah Ramli2*, R. Deiny Mardian3, Rahutomo Mahardiko4 Department of Electrical Engineering, Universitas Indonesia, Depok, Indonesia1 2 3 Department of Data Management, PT. BFI Finance Indonesia, Tbk, Tangsel, Indonesia4 kalamullah.ramli@ui.ac.id Received : 06 February 2023, Revised: 17 March 2023, Accepted : 26 March 2023 *Corresponding Author 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 ... Keywords: Virtual Machine Server, Fuzzy Model, Live Migration, Comparison Research 1. Introduction The development of VMs in the 4.0 era has significantly progressed well. VMs are widely used in data center (DC) to enhance business and non-business applications. Many companies utilize this server to optimize the physical server in their DC. VMs are... To see the better way of VMs, we can review the issues or find other better ways. This research will see through the issue that appeared on VMs. Many problems arising to the production must be solved well. The appeared matters can make the OS dying. M... In the earliest studies, some researchers attempted to reduce the risk that will be occurred soon. Research (Y. Kumar et al., 2022) said that Live migration of VMs targeting (1) reallocate resources between VMs in balancing data center workloads, (2) ... This study focuses on detecting problems in VMs so problems that often occur can be identified early and predicting the problem VMs with fuzzy analysis. Incident prediction using fuzzy was the primary research on VMs. In fact, the movement of a VM fro... This study also extends the result of the conducted previous research. In reality, the previous research was focused on something other than the time of the migration process. A new 5G network with wide coverage needs to be developed to achieve wide c... 2. Literature Review This section will analyze the literature to migrate the VMs from one host to another host. The workload analysis of VMs will focus on memory and CPU because they are required to allocate the VMs within the defined time frame. The first decision, the p... Based on the previous study on VM migration, they are determined that specific tasks should be completed. So we investigate VM migration and find that certain polls explain the challenges inherent to VM migration (Jin et al., 2011). VM allocation may ... Priority during VM migration is timely migration and placement on an appropriate host. Calculating VM migration using the fuzzy Model in line with CPU-SLA and RAM-SLA (Farzai et al., 2020; Karmakar et al., 2022; Singh & Singh, 2020; Yin & Zhang, 2022)... Live migration VMs is a way to optimize application performance during live migration VMs in terms of bandwidth usage. Migration VMs can be classified into three parts pre-copy, post-copy and hybrid-copy. We describe the classification in the next par... The pre-copy approach first duplicates all memory pages to the goal have, whereas the VM proceeds to run on the source have in a warm-up stage. After that, whereas the VM runs on the source have, the pre-copy strategy iteratively duplicates the adjust... Post-copy operates by (1) stopping the VM on the source host, (2) copying its CPU state and non-pageable memory pages, and (3) restarting on the destination host. After that, the source starts to push the memory pages to the destination during the mem... In, a hybrid method that combines the Pre-Copy and Post-Copy methods is introduced. This method begins with a single Pre-copy migration iteration that copies the VM's memory to the destination host while the VM is still running on the source host. Fol... From this method, we, the authors, had an idea to improve the design using Fuzzy rule model. Hence, it will increase the possibility of moving VMs in a critical time or even better time. 3. Research Methods In DC, the workload datasets were obtained via VM. We collected data using data mining in a month and have 7800 records. We collected data based on the operating hours of VM services using data mining. Observations were held at 9:00, 11:00, 13:00, 15:... From the fig. 3, we describe the methodology to collect the data for Fuzzy Model. We must have the workload data of VMs, such as: CPU load, Memory load, Network load, Disk load. If we use some VM software, it has a count on each variable. Then we have... Fuzzy Model translates input to output as a fuzzy set. A fuzzy set is a group of membership function variables. MIN-MAX is another definition of Fuzzy Model used to assess migration status (small, medium, large). There were four steps: (1) formation o... This study used centroid and affirmation methods. The method was a crips solution using fuzzy area concentration. The data sources were datasets retrieved for problem analysis in VM. We collected it for a month. Table 1 shows each variable's workload ... Table 1 - Dataset of VM Workload Table 1 shows the unprocessed data and only the defined category. We create the migration science with CPU, memory, network and disk-based on type to improve the VM performance. The function of the fuzzy set was a curve showing intervals 0 – 1 on data... This research followed the min implication function (Rukmini & Shridevi, 2023) . The process had a group of premises and one conclusion. We used the process to understand the premise's and conclusion's relationship (K. Kumar et al., 2022). The formula... We defined four parameters (disk, network, CPU and memory) and three classifications (small, medium, and large). We had 81 rule combinations. Chosen by the researcher. Table 2 - Role Fuzzy VM Migration Table 2 shows some of the Fuzzy model algorithm logic applied to share the load on the VM. This logic function rule would produce a condition that was the desired load balance in which the CPU, memory, disk, and network aspects will be balanced in eac... We utilized this stage to interpret an ambiguous membership into a conclusion. We must return a crips value and transform the fuzzy output into a crips output depending on the membership function we had provided. Defuzzifier was required because undef... Membership function performance depends on minimum Availability, CPU, Memory, Disk and Network membership values. Mathematical equations can be seen in the equation. Eq. (3) can also be written as Similarly, Eq (4) can be written as shown in Eq (5). 4. Results and Discussions The information was collected throughout a month of VM workload. The datasets consist of three hosts, including several VMs. These datasets consist of data mining on the workload of VMs using the specified parameter. The data were analyzed using Fuzzy... Table 3 - The Experiment of Fuzzy in Workload VM Table 3 is visually moved. A month was divided into five weeks. The defuzzifier result revealed that the highest impact, 83.1, is for the first week. The third week's performance of 80.6 is the next highest. This result indicated that a dynamic networ... Table 4 - Result Processing Dataset with Fuzzy Model Figure 6 showed that host B contains more VMs migration than hosts A and C (24%). Other result showed that though host C has more VMs, it has less migration status of VMs (6%). Meanwhile, host A had 13% of the migration VMs. From the result, host B co... Referring to the results above, the author found some evidence that results are better than past research (Badem et al., 2017). The formulas that the author uses to get accuracy, precision and recall get the following results. Table 5 - Comparison Method From these results, it can be seen that the value of fuzzy is better than k-NN and DSS. From these results, the combination of 4 variables that the author uses brings better accuracy. With these results, it is hoped that better future research will be... Discussions In the tests that have been carried out, the author observes network traffic at certain hours, gets information on busy times in the data center at 9:00, 11:00, 13:00, 15:00, and 17:00, this pattern is used as a benchmark for three-time frames of a mo... The author also observes bandwidth patterns with the time frame, then this is used as the time to determine when the live migration of VMs is carried out. The purpose of this bandwidth pattern is a strategy in determining the right time to live migrat... 5. Conclusion The result of VM migration with fuzzy showed the positive impacts. First, VMs migration can be conducted within the defined time frame to reduce VMs workload. Second, the undefined method's application was periodically determining the overload VMs. Wi... Future works are explained here. First future work, the use of several techniques can be applied to predict the VM overload and how to migrate to overcome this overload, one of which was the Markov chain method or other processes related to predicting... Acknowledgement This article’s publication is supported by the Penelitian Disertasi Doktor, Kementerian Riset Dan Teknologi/Badan Riset Dan Inovasi Nasional Tahun Anggaran 2022 Nomor: NKB-1011/UN2.RST/HKP.05.00/2022. References Ahmad, R. W., Gani, A., Shiraz, M., Xia, F., & Madani, S. A. (2015). 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A Dynamic Virtual Machine Resource Consolidation Strategy Based on a Gray Model and Improved Discrete Particle Swarm Optimization. IEEE Access, 8, 228639-228654. https://doi.org/10.1109... Silva Filho, M. C., Monteiro, C. C., Inácio, P. R. M., & Freire, M. M. (2018). Approaches for optimizing virtual machine placement and migration in cloud environments: A survey. Journal of Parallel and Distributed Computing, 111, 222-250. https://doi.... Singh, S., & Singh, D. (2020). Live virtual machine migration techniques in cloud computing. In Data Security in Internet of Things Based RFID and WSN Systems Applications (pp. 99-106). CRC Press. Svard, P., Tordsson, J., Hudzia, B., & Elmroth, E. (2011). High performance live migration through dynamic page transfer reordering and compression. 2011 IEEE Third International Conference on Cloud Computing Technology and Science, Tao, Z., Xia, Q., Hao, Z., Li, C., Ma, L., Yi, S., & Li, Q. (2019). A survey of virtual machine management in edge computing. 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