In the present decade, the development of cloud computing framework is witnessed for providing computational resources by dynamic service providing methods. There are many problems in load balancing in cloud, when there is a huge demand for resources. The objective of load balancing is to equilibrate the cloud server computations for avoiding overloading problems. On addressing the issue, this paper develops a new model called Evolutionary Algorithm based Adaptive Load Balancing (EA-ALB) for enhancing the efficacy and user satisfaction of cloud services. Efficient Scheduling Scheme for the virtual machines using machine learning algorithm is proposed in this work. Initially, process of K-means clustering is used for computing optimal min-max rates and then, local search capability for solving the load balancing problems in cloud model is determined with the incorporation of Evolutionary Algorithm. The results show that the proposed model achieves better results in terms of load balancing factors, Virtual Machine (VM) migration, energy consumption and so on, when compared to the existing model.

In the present scenario, cloud computing is very much popular due to the capability to offer seamless computing services with the on-demand based model [

For handling this problem, it is significant for the cloud providers to use the cloud resources effectively. For attaining that, load balancing operations are performed in cloud by migrating virtual machines from overloaded physical machines to idle machine. The load balancing models [

Server Workload Forecasting

Selection of VMs

Selection of Target Server

And the contributions of the proposed Evolutionary Algorithm based Adaptive Load Balancing (EA-ALB) are listed as follows

The objective of the work is to determine the accurate CPU utilization of Cloud servers based on the aforementioned factors

Incorporated K-Means Clustering (KMC) for finding the VMs with minimal migration, performance interferences and traffic.

The Local Search Capability in enhanced with the Modified Evolutionary Algorithm (MEA). And the VM determined by KMC is migrated to the destination server to provide efficient load balancing.

The remainder of this work is organized as follows, Section 2 explains about the related works developed previously for solving load balancing problems in cloud. The complete work process with the flow diagram is explained in Section 3. The results and discussions with comparisons are provided in Section 4. Finally, the work is concluded in the Section 5 with some ideas for future work.

Myriad works are proposed in recent times for handling cloud resources effectively by performing dynamic load balancing. The authors of [

Centralized Load Balancing

Distributed Load Balancing

In Centralized Load Balancing, the central node involves in the process of resource allocation and de-allocation. On the other hand, in distributed load balancing, multiple machines act as the coordinator and perform the process of resource allocation. Furthermore, several scheduling methodologies such as, First Come First Serve (FCFS), Round Robin and other load balancing techniques such as Ant Colony Optimization (ACO), Max-Min, etc., are involved in solving the problems on cloud resource management and provisioning [

Load balancing model provided in [

Load balancing methodology for distributed cloud models is provided in [

An algorithm for dynamic resource provisioning in data center for efficient resource utilization is proposed in [

In the cloud model, the load balancing of servers is processed based on the number of Virtual Machines, VM Migration, memory, traffic flow and the server capacity. The server load condition is non-linear and periodic to certain level. In the proposed model, K-means clustering algorithm which is integrated with evolutionary algorithm for efficient load balancing between machines in cloud environment. In typical KMC algorithm, the following issues are noted.

The process of k-value selection using KMC is hard to determine

In clustering, time and iterations are increased because of various reasons.

When there is a huge dataset, time complexity may cause

Considering the issues, this paper derives an efficient KMC based on the cluster-center and the k-value for determining the appropriate min-max.

The operations of KMC in determining the Min-Max includes k-value selection and Cluster-Center (CC) selection.

In the proposed work, the VMs in the cloud model is categorized based on three factors, such as,

Load of the Server Machine

VM migration cost

Performance Interference

Moreover, the virtual machines clustering are provided as, three dimensional structure. Cost can be further considered as, low and high and further, the performance also considered as low and high, respectively. The server load is noted as, over load, under load and mediate load and their corresponding descriptions are given in

VM samples | Server load | Cost | Performance interference |
---|---|---|---|

1 | Under | Low | Low |

2 | Mediate | Low | Low |

3 | Over | Low | Low |

4 | Under | Low | High |

5 | Mediate | Low | High |

6 | Over | Low | High |

7 | Under | High | Low |

8 | Mediate | High | Low |

9 | Over | High | Low |

10 | Under | High | High |

11 | Mediate | High | High |

12 | Over | High | High |

In KMC, initial cluster center (CC) is selected in random manner from the k-number of samples. When the selection process is instable, the derived solution is not optimal. Hence, the proposed model derives the optimal solution by CC selection to reduce the iteration numbers by increasing the distance between the initial CC. In the process of min-max selection of CC, initial CC is selected in random manner and given as CC_{0}. Following, the distance between CC_{0} and each other sample point is calculated, in which the sample at shortest distance is taken as CC_{1} and the sample at longer distance is noted as CC_{2}. The algorithm is presented in

In the proposed model, the conventional differential evolution model is enhanced for improving the search ability. The process is employed to VM migration to make it more effective with respect to the aforementioned factors along with energy efficiency. For deriving optimal solutions, the fitness function is derived based on the following steps.

i) The traffic flow generated in the process of VM migration is based on the routing and memory. Here, the fitness function (FF) based on the traffic flow is given as,_{i}’ denotes the size of data transmission of VM, during migration, ‘len_{r(i)}’ denotes the length of routes between VMs based on their topology, between the source and target and the formula is given as,

ii) Secondly, the FF based migration cost is derived based on the memory of the machine and the network bandwidth. The optimal solution is considered one which derives with minimal migration cost which is given as,_{0}’ denotes the initiation time of VM migration, ‘

iii) Further, the FF is computed based on the performance interference after the completion of VM migration. And the determined value is considered to be minimal, which is calculated as,

From the above equation, ‘T_{i}’ denotes the running time of VM of ‘i’ th server.

iv) Energy Efficiency based FF derivation is processed in the fourth step, where optimal solution is considered as,

Here ‘v_{i}(t_{j})’ denotes the CPU utilization and the power consumption is given as, ‘P(v_{i}(t_{j}))’ and the formula is given below.

Hence, the energy efficient optimized solution is derived with the following formula, presented in

And in _{1}, m_{2}, m_{3} and m_{4} are the balancing parameters on each derivation and their summation results unit value. Those factors can be adjusted to impact the other factors in determining the fitness function.

In this process, the population_size is defined as, ‘M’ and the number of VM migrations is given as ‘N’ and the servers are given as ‘S’ and the inbetween links are in ‘l’ length. The placement of VM is provided as [1, S] with the maximal number of ‘r’ iterations. Further, the factor of mutation rate is given as, δ∈[0, 2] and the factor for crossover probability is provided as C_{p}∈[0, 1]. Hence, the 1st generation of ith VM is computed as,

In the above equation, _{r}(0), (^{th} VM of 0^{th} generation is required to be migrated and ^{th} placement. And the computation is given as,

From the above equation, the minimal and maximal vectors rates are provided with the mapping of [1, S]. Further, based on the evolutionary algorithm, the different VM patterns with randomly generated population ‘e’ are given as

In the above equation,

Here, the newly produced individual is given as, ‘_{t}(_{t}(

The formula for cross over is given as,

Here, _{t}(

In the above equation,

This section presents the results and discussions to prove the efficacy of the proposed model. The model is evaluated using the simulation software called CloudSim. And the results are compared with the existing models such as, First Come First Serve (FCFS), Ant Colony Optimization (ACO) and PROTEUS. The simulation parameters and the domain values are presented in the following

Parameters | Values |
---|---|

Server based parameters | |

Server MIPS | 1.8 to 3.0 GHz |

Memory size | 4–16 GB |

Bandwidth | 1000 Mbit/s |

Hard disk size | 50–320 GB |

VM based parameters | |

VM MIPS | 0.5 to 2.5 GHz |

Memory size | 613–1740 MB |

Bandwidth | 100 Mbit/s |

Models | 0 | 50 | 100 | 150 | 200 | 250 | 300 |
---|---|---|---|---|---|---|---|

FCFS | 1,500 | 1,092 | 651 | 901 | 1,000 | 702 | 560 |

ACO | 1,358 | 921 | 768 | 603 | 1,140 | 647 | 506 |

PROTEUS | 1,600 | 1,140 | 669 | 669 | 863 | 549 | 396 |

EA-ALB | 1,259 | 745 | 427 | 450 | 461 | 329 | 210 |

Models | 0 | 500 | 1000 | 1500 | 2000 | 2500 | 3000 |
---|---|---|---|---|---|---|---|

FCFS | 46.7 | 21.0 | 26.7 | 21.0 | 22.0 | 19.6 | 10.7 |

ACO | 48.4 | 26.3 | 23.6 | 24.3 | 19.7 | 16.0 | 12.1 |

PROTEUS | 51.5 | 25.0 | 23.0 | 19.7 | 21.0 | 16.7 | 15.0 |

EA-ALB | 46.0 | 23.0 | 20.0 | 19.0 | 16.0 | 11.0 | 8.0 |

Traffic Flow based results are provided in

Models | 0 | 500 | 1000 | 1500 | 2000 | 2500 | 3000 |
---|---|---|---|---|---|---|---|

FCFS | 522 | 916 | 1,770 | 1,557 | 1,261 | 1,441 | 981 |

ACO | 816 | 881 | 1,721 | 2,294 | 1,539 | 1,622 | 1,212 |

PROTEUS | 522 | 1,031 | 1,999 | 2,361 | 1,721 | 1,409 | 1,490 |

EA-ALB | 455 | 851 | 1,589 | 1,439 | 1,261 | 999 | 768 |

Models | 0 | 50 | 100 | 150 | 200 | 250 | 300 |
---|---|---|---|---|---|---|---|

FCFS | 522 | 916 | 1,946 | 1,557 | 1,919 | 1,902 | 2,382 |

ACO | 816 | 1,866 | 2,663 | 2,294 | 2,131 | 2,213 | 2,610 |

PROTEUS | 522 | 1,031 | 2,459 | 2,796 | 2,358 | 2,461 | 3,001 |

EA-ALB | 455 | 544 | 843 | 872 | 891 | 999 | 1,074 |

Models | 20 | 50 | 100 | 150 | 200 | 250 | 300 |
---|---|---|---|---|---|---|---|

FCFS | 25.4 | 30.0 | 21.0 | 28.0 | 28.0 | 30.0 | 32.0 |

ACO | 34.0 | 29.0 | 34.0 | 33.0 | 31.0 | 42.0 | 41.0 |

PROTEUS | 38.0 | 37.0 | 34.0 | 27.0 | 27.0 | 53.0 | 46.0 |

EA-ALB | 67.0 | 74.0 | 77.0 | 78.0 | 78.0 | 79.0 | 85.0 |

For the process of efficient migration of virtual machines in Cloud computing process, this paper proposes a new model called EA-ALB. The model integrates the efficiency of KMC in determining best solution and the Evolutionary Algorithm for load balancing. The proposed model effectively predicts the resource utilization by machines, in which the min-max algorithm is used for finding the cluster centers. The model evaluation is carried out based on the factors such as cost effectiveness, CPU utilization, migration effectiveness and traffic flow. It is evidenced from the results that the proposed model achieves minimal cost, traffic flow and interference than other compared works. And the utilization is maximal, that is, the model effectively utilizes the machines about 95%, where load balancing is effectively achieved with the proposed model.

In Future, as the load balancing in cloud has a greater research scope, the potential applicability can be expanded for large scale cloud models. Methods can be developed to measure the algorithm's efficacy in applying it on a real life case to attain better results and routine.