Workload Aware Load Balancing For Cloud Data Center SrividhyaR 1, Uma Maheswari K 2 and Rajkumar Rajavel 3 1,2,3 Associate Professor-IT, B-Tech- Information Technology, KCG college of Technology Abstract Cloud computing is emerging as a new standard for manipulating, configuring, and accessing large scale distributed computing applications over the network. Load balancing is the main Challenges in cloud computing which is required to distribute the workload evenly across all the nodes. Load is a measure of the work that a computation system performs which can be classified as CPU load and memory.the concept of load balancing used in cloud computing is to evenly distribute data to underutilized virtual machines in the data center for effectively scheduling. The challenge faced during the load balancing is to balance the load in single Data Center which allocates the resources overloaded in each Virtual Machines and the algorithm used two parameter namely processing power and computer loading, which is less efficient when user login multiple times. The proposed system is to perform load balancing in cloud based on the workload of the cloud data center and to create multiple data centers for single server so that when a particular Virtual Machines overloads tasks will automatically allocated to other available data center (which contains Virtual Machines) by using three parameters - number of task/jobs, workload of each Virtual Machines or threshold and computing power, which balances both data center and Virtual Machines efficiently, thereby increasing the performance.this technique that helps resources by providing a Maximum throughput with minimum response time. So that there will less time for allocating workload, thereby increases the performance. Index Terms Cloud computing, cloud data center, load balancing, processing power, virtual machines. I. INTRODUCTION Cloud computing is a type of Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. Workload is an abstraction of the actual work that instance or a set of instances are going to perform. Cloud load balancing is the process of distributing workloads and computing resources in a cloud computing environment. As cloud is composed of huge servers, load balancing techniques are used to balance load over multiple servers. Existing load balancing algorithm used two parameter namely processing power and computer loading, which is less efficient when user login multiple times. It Need to search for Candidate Virtual Machine from Data Centers[2]. While the scheduling algorithm implemented resource can execute only one job at a time which leads toincrease in execution time [3]. When the different users logged in the system in different time the scheduling and the load balancing algorithm cannot be efficiently used [1]. II. RELATED WORK The cloud balance algorithm-the cloud load balancing algorithm is designed for monitoring platform in order to obtain each server loading, computing power and the priority service value. When user sends request to the cloud server demanding for services which enters into the cloud load balancing distribution platforms, obtain the first half of the servers sorted by the priority service value from the service priority database, and it then uses a polling method to dispatch the user s request. Thus above cloud load balance architecture and cloud load balance algorithm can be used to calculate the server processing power and can also load and obtain priority service values. This can also be applied to the applications in the cloud, thus allowing smoother system operation. The experimental result for CLB enabled physical servers and virtual servers shows that cloud performance based on the architecture can balance the loading performance when the users logged in at the same time [3]. In autonomous agent based load balancing algorithm(a2lb) which provides dynamic loadbalancingforcloudenvironment.a2lbmechanism comprises of three agents.(1) Load agent controls information policies and maintains all the details of the data center. The major work of the load agent is to calculate the load on every available virtual machine after allocating new job in the data center. (2) Channel agent- It controls the transfer, selection IJIRT 144332 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 200
and location policy. After receiving the request form the load agent, the load agent will initiate some migration agent to other data center for searching the virtual machines having single configuration. (3) Migration agent- These agents are initiated by channel agent. It will move to other data center and communicate with load agent of that data center to enquire the status of the virtual machines present there, looking for desired configuration. The status of the migration of the agent may be live or destroyed based on its applicability. Load and channel agent are static and migration agent is an ant, which is a special category of mobile agent (has the ability to choose shortest or best path to their destination) The implementation is done by two ways considering the parameters number of data center, number of virtual machines, number of instances, memory unit, cost, wait time, number of runs.(1) When the requested virtual machine is found with normal status and allocation takes place.(2) When virtual machine is in critical state, then load balancing takes place. During the critical state A2LB algorithm is implemented. This mechanism provides proactive load calculation of virtual machines present in a data center and whenever the load of a virtual machine reaches near threshold value, load agent initiates search for a candidate virtual machine from the neighboring data centers. Keeping information of candidate virtual machines beforehand reduces service time but implementation has proved that this algorithm works satisfactorily [1]. Load balancing is method which distributes the workload equally among all available resources. The main focus is to allocating the load dynamically among the nodes in order to satisfy the user requirements and provide maximum resource utilization by assorting the available load to resources. In this approach Batch mode scheduling is used where the tasks are collected based on the arrival and mapped by prescheduled times. It uses two algorithms called Min-Min load balancing and Max- Min load balancing. (1)Min-Min algorithm is simple and fast providing improved performance for small set task only.min-min start with the set of all unassigned tasks in the make span. This algorithm works in two phases. First, the minimum expected completion time for all the tasks is calculated. The completion time for all the tasks is calculated on all the machines. In the second phase, the task with the minimum expected completion time from make span is selected and that tasks assigned to the corresponding resource. Then the task which is completed that is removed from the make span and this process is repeated until all tasks are completed.(2)max-min algorithm is used for large task set and small task will get executed concurrently. Max-Min start with the set of all unassigned tasks in the make span. This algorithm also works in two phases. First, the maximum expected completion time for all the tasks is calculated. The completion time for all the tasks is calculated on all the machines. In the second phase, the task with the maximum expected completion time from make span is selected and that tasks assigned to the corresponding resource. Then the task which is completed that is removed represents the modified max min algorithm.[2] III. ARCHITECTURE OF WORKLOAD AWARE SCHEDULER The method used in the project is workload balance scheduling. It helps to estimate the job related specification, processing power and load of each virtual machines. It distributes the workload evenly to all virtual machines. The objective of this project is to distribute the task/cloudlets among virtual machines and to calculate the workload of the data centers.using multiple data centers in a single server,tasks are executed efficiently. In this architecture the cloud user submits the tasks to the cloud environment were the cloud broker is present. The cloud broker does the estimation like job related estimation, estimation of workload and the processing power can be estimated. All the estimation is given to the scheduler and with the help of the estimation the scheduler distributes the loads efficiently to the datacenter present in the single server. IJIRT 144332 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 201
Else Assign the weight to the entire DC dc id Proportional DC selection (list size) dc Name load list. get(dc id); end if end if return dc Name end function. Submission of Cloudlets Job related estimation Estimation of workload Figure 1: Architecture diagram Algorithm: Input : Number of Cloudlets/Job. Number of Resource. Output: Balanced workload in all virtual machines. Estimation of Load in virtual machines in Data center, L= Executing jobs cloudlet Capacity of the vm Load per unit Capacity, LPC= L Ci n i=0 Where, n is number of nodes. ThresholdTi = LPC Ci Datacenter Selection Input : Load Output: Dc Name Load list Regional datacenter (region) If Load list is Not NULL then List size load list.size () If list size == 1 then Dc Name load list. get (0) IV. Estimation of processing power Schedule the Cloudlets Figure 2:System flow diagram. EXPERIMENTAL SETUP CloudSim version 3.0.3 is used as framework for stimulation and modeling for cloud computing environments. The CloudSim is written in java programming, for creating data center, scheduling and allocating the cloudlets.cloudsim package is imported in the Eclipse Integrated Development Environment and it is most widely used for simulating the cloud environment Java programming. In this experimental setup, job size of 50, 100, and 150 are simulated using round robin, Fist Come First Served and Workload Aware Scheduling algorithms. Experimental results are observed with respect to IJIRT 144332 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 202
waiting time response time average waiting time and turnaround time as shown in Table 1. Table 1 Experimental Results Jobs Scheduler Average Turnaround time Average Waiting time Avg turnaround time 16 14 12 50 Workload 8.32 5.32 9.002 5.878 10 8 6 Workload 100 Workload 150 Workload 9.342 5.999 12.415 8.799 12.985 8.9 13.023 9.22 16.071 12.796 16.895 13.02 4 2 0 50 100 150 waiting time Figure 3:Performance of scheduling algorithms with respect to Turnaround Time. 16 14 12 Avg waiting time 17.02 13.45 The proposed system results are compared with the existing scheduler Robbin and first come first serve with respect to average turnaround time and average waiting time as shown in Figure 2 and Figure 4respectively. 10 8 6 4 2 0 50 100 150 Workload no of jobs Figure 4: Performance of scheduling algorithms with respect to Waiting Time. IJIRT 144332 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 203
It is evident from the performance evaluation graph; the proposed workload scheduling algorithm evenly distributes the loads among all the available datacenter based the estimations of job workload, processing power and execution cost. There by job workloads are distributed evenly to all the virtual machines in each datacenters. Due to even distribution of workload the proposed algorithm minimizes the turnaround time and waiting time of all the user jobs. V. CONCLUSION AND FUTURE WORK The load balancing is one of the greatest issues in Cloud Computing environment. To solve this issue various techniques are used in the existing system. In this research work, a novel workload load balancing algorithm is proposed which gives a better performance and resources utilization with the different login time. In future this research work can be extended to the real time scheduling scenario of Amazon EC2 cloud environment. REFERENCES [1] AartiSingh, Dimple Juneja, Manisha Malhotra (2015), Autonomous Agent Based Load Balancing Algorithm In Cloud Computing, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015). [2] Geethu Gopinath P P, Shriram K Vasudevan, An in-depth analysis and study of Load Balancing techniques in the cloud Computing environment,2 nd International Symposium on Big Data and Cloud Computing(ISBCC 15). [3] Shang-Liang Chen, Yun-Yao Chen, Suang-Hong Kuo (2016), CLB: A novel load balancing architecture and algorithm for cloud services, Computer and Electrical Engineering on ELSEVIER. IJIRT 144332 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY 204