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This LB algorithm (Chen et al. 2017) is the most advance algorithm for load balancing. It will select only lowest number of server for transaction. This algorithm is also a dynamic algorithms, the main function of this algorithm is that it can able to count the number of connections for each server dynamically and estimate the load condition. It is used to evaluate the workloads condition. The LC method takes the present workload servers into process. The server

Figure 4 This LB algorithm (Chen et al. 2017) is the most advance algorithm for load balancing. It will select only lowest number of server for transaction. This algorithm is also a dynamic algorithms, the main function of this algorithm is that it can able to count the number of connections for each server dynamically and estimate the load condition. It is used to evaluate the workloads condition. The LC method takes the present workload servers into process. The server

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Many types of research have been done in the field of CC. Some of the general challenges includes load balanc- ing, resource scheduling, service broker policies, resource
Many types of research have been done in the field of CC. Some of the general challenges includes load balanc- ing, resource scheduling, service broker policies, resource
Fig. 2 Types of services model using different clouds
Fig. 2 Types of services model using different clouds
The Weighted Least-Connection (WLC) (Diaz et al. 2016) gorithm is a superset of the least-connection algorithm, n which you can assign a performance weight to each real erver. The servers with a higher weight value will receive i larger percentage of active connections at any one time. The default server weight is one, and the IP Virtual Server IPVS) administrator or monitoring program can assign any
The Weighted Least-Connection (WLC) (Diaz et al. 2016) gorithm is a superset of the least-connection algorithm, n which you can assign a performance weight to each real erver. The servers with a higher weight value will receive i larger percentage of active connections at any one time. The default server weight is one, and the IP Virtual Server IPVS) administrator or monitoring program can assign any
weight to real server. In the WLC method, a low number of server is connected to a new network. Here, large number of weighted value is connected to each and every server, this makes the performance of algorithm very fast. Figure 8 shows the server 2 is selected by the load bal- ancer which has a fewest number of transaction with low assigned weight. Here the algorithm connect the request according to the Least Connection provided in the balancer according to the weight.
weight to real server. In the WLC method, a low number of server is connected to a new network. Here, large number of weighted value is connected to each and every server, this makes the performance of algorithm very fast. Figure 8 shows the server 2 is selected by the load bal- ancer which has a fewest number of transaction with low assigned weight. Here the algorithm connect the request according to the Least Connection provided in the balancer according to the weight.
4.3.1.3. Max-Min LB algorithm The performance of Max— Min (Ghumman and Kaur 2015) is nearly similar to the pre- vious algorithm, the only difference is that extreme values are selected after finding the finishing time of the jobs. Then the node’s waiting time is updated after having mapped the
4.3.1.3. Max-Min LB algorithm The performance of Max— Min (Ghumman and Kaur 2015) is nearly similar to the pre- vious algorithm, the only difference is that extreme values are selected after finding the finishing time of the jobs. Then the node’s waiting time is updated after having mapped the
Fig. 10 Flow diagram for Genetic Algorithm
Fig. 10 Flow diagram for Genetic Algorithm
Fig. 11 Flow structure for Honey Bee Foraging Technique
Fig. 11 Flow structure for Honey Bee Foraging Technique
where 7, represent the pheromone trail of VM, 7; 1s a heu- ristic value, and, a and f determines the effect of the phero- mone track. The function of n' is based on the distance between physical node and VM is shown in Ea. (3). between physical node and VM is shown in Eq. (3).
where 7, represent the pheromone trail of VM, 7; 1s a heu- ristic value, and, a and f determines the effect of the phero- mone track. The function of n' is based on the distance between physical node and VM is shown in Ea. (3). between physical node and VM is shown in Eq. (3).
Planning of virtual machines with balancing of work load in the CC is used to assign the cloud servers and sched- ule the resources to each servers based on priority order. When planning in a CC system, it is essential to consider the performance of the various processor parts. Because the Scheduling Algorithm (SA) is a long-lasting task and the scheduler can perform parallel job planning on a core. In a cloud network with high computational requirements, it is possible to balance the workload and evenly distribute the load on all nodes, using planning algorithms appropriately (Smanchat and Viriyapant 2015). This way of planning the load will create problems because during the allocation of jobs a node can be heavily loaded and one can be slightly loaded regardless of their capacity. This resource manage- ment nodes are responsible for planning the process and assigning all resources owned by the data center. In addi- tion, resource management nodes manage and monitor all resources, at the time they can implement the LB of the cloud datacenter. This type of scheduling used to reduce the
Planning of virtual machines with balancing of work load in the CC is used to assign the cloud servers and sched- ule the resources to each servers based on priority order. When planning in a CC system, it is essential to consider the performance of the various processor parts. Because the Scheduling Algorithm (SA) is a long-lasting task and the scheduler can perform parallel job planning on a core. In a cloud network with high computational requirements, it is possible to balance the workload and evenly distribute the load on all nodes, using planning algorithms appropriately (Smanchat and Viriyapant 2015). This way of planning the load will create problems because during the allocation of jobs a node can be heavily loaded and one can be slightly loaded regardless of their capacity. This resource manage- ment nodes are responsible for planning the process and assigning all resources owned by the data center. In addi- tion, resource management nodes manage and monitor all resources, at the time they can implement the LB of the cloud datacenter. This type of scheduling used to reduce the
energy feeding and resource costs in the data center. The scheduling algorithm for heterogeneous VM in CC environ- ments used to manage all workloads before a deadline (Zuo et al. 2018). Scheduling model for the CC environment, are as shown in Fig. 12. of resources among different users based on certain rules and regulating the use of resources in a specified cloud environment. Examine algorithms to increase performance, involving dynamic planning based on threshold, Genetic Algorithm (GA) optimized with dual suitability and Ant Colony Optimization Algorithm (ACA) optimized for plan- ning (Madni et al. 2016; Bansal et al. 2016).
energy feeding and resource costs in the data center. The scheduling algorithm for heterogeneous VM in CC environ- ments used to manage all workloads before a deadline (Zuo et al. 2018). Scheduling model for the CC environment, are as shown in Fig. 12. of resources among different users based on certain rules and regulating the use of resources in a specified cloud environment. Examine algorithms to increase performance, involving dynamic planning based on threshold, Genetic Algorithm (GA) optimized with dual suitability and Ant Colony Optimization Algorithm (ACA) optimized for plan- ning (Madni et al. 2016; Bansal et al. 2016).
Fig. 14 Classification of CSBP The cloud service brokering contain types of service bro- kering policies, which is used to provide the service to user request based on their demand. The service broker routing policy has some basic mechanisms that are responsible for the proper allocation of datacenters to meet different user requests. Unfortunately, standard SPBR are established on the cost, location and processing time of the datacenters in use. Analyzing these problems, a pricing routing policy for cloud providers to CSB are presented. Cloud brokering
Fig. 14 Classification of CSBP The cloud service brokering contain types of service bro- kering policies, which is used to provide the service to user request based on their demand. The service broker routing policy has some basic mechanisms that are responsible for the proper allocation of datacenters to meet different user requests. Unfortunately, standard SPBR are established on the cost, location and processing time of the datacenters in use. Analyzing these problems, a pricing routing policy for cloud providers to CSB are presented. Cloud brokering
Fig. 15 CSB services in three roles
Fig. 15 CSB services in three roles
Table 3 Comparative analysis of most predominant techniques in LB
Table 3 Comparative analysis of most predominant techniques in LB
Table 4 Survey of static and dynamic LB algorithms Table 5 gives some basic ideas about the classification of the LB algorithm, and represents what types of metrics are present for each LB algorithm. In Table 6, the complexity and statistical analysis among various load balancing algo- rithms are presented.
Table 4 Survey of static and dynamic LB algorithms Table 5 gives some basic ideas about the classification of the LB algorithm, and represents what types of metrics are present for each LB algorithm. In Table 6, the complexity and statistical analysis among various load balancing algo- rithms are presented.
Table 5 Comparison of LB algorithms based on various metrics Table 6 Comparison of complexity and statistical analysis of various scheduling LB algorithms
Table 5 Comparison of LB algorithms based on various metrics Table 6 Comparison of complexity and statistical analysis of various scheduling LB algorithms
Ground sim, and Green cloud. The comparison of some tools is given in Table 7.
Ground sim, and Green cloud. The comparison of some tools is given in Table 7.
Fig. 16 Pie chart of parameter comparison
Fig. 16 Pie chart of parameter comparison
Table 8 Comparative analysis of CSBP
Table 8 Comparative analysis of CSBP
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