PRIORITY BASED NON-PREEMPTIVE SHORTEST JOB FIRST RESOURCE ALLOCATION TECHNIQUE IN CLOUD COMPUTING

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1 International Journal of Computer Engineering & Technology (IJCET) Volume 9, Issue 2, March-April 2018, pp , Article ID: IJCET_09_02_014 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication PRIORITY BASED NON-PREEMPTIVE SHORTEST JOB FIRST RESOURCE ALLOCATION TECHNIQUE IN CLOUD COMPUTING Rishi Aluri, Shriya Mehra, Apoorva Sawant, Pankti Agrawal, Mayank Sohani Department of Computer Engineering, SVKM NMIMS, Mukesh Patel School of Technology Management and Engineering ABSTRACT In this world where technology changes so rapidly, cloud computing field has managed to make a huge impact towards customer satisfaction in a real-time environment. Cloud computing has made a huge reduction in cost of storage and has led to efficient management and processing of data. Cloud consumers request various services based on their dynamically changing needs. Due to the reason that resources are often scarcely available it becomes a burden for the cloud providers to fulfill the demands of the users. Therefore, it is necessary to find a resource allocation technique that meets the dynamic changing needs of people through optimum utilization of resources. The aim is to propose an improved load-balancing algorithm, which will dynamically allocate resources by strategically forwarding the traffic onto the servers in a real-time cloud environment. Cite this Article: Rishi Aluri, Shriya Mehra, Apoorva Sawant, Pankti Agrawal and Mayank Sohani, Priority based Non-Preemptive Shortest Job First Resource Allocation Technique in Cloud Computing. International Journal of Computer Engineering & Technology, 9(2), 2018, pp INTRODUCTION Cloud computing is considered as the most important and advanced technology. The cloud computing is based on the simple and effective principle i.e. resource sharing. In cloud computing we basically share resources over the internet i.e. we store the data on the cloud and the group of organizations access it over the internet. Cloud computing represents a group of systems, which are connected in a network to each other to provide a scalable infrastructure so that the software systems and data can take use of it [6]. Cloud computing performs load balancing to distribute the load and reduce the traffic from the nodes editor@iaeme.com

2 Priority based Non-Preemptive Shortest Job First Resource Allocation Technique in Cloud Computing 1.1. Load balancing [2][8] Load balancing is a technique, which involves the distribution of the load from the heavy loaded ones to the lightly loaded nodes to improve the performance of the system Resource allocation techniques Round robin is the pre-emptive scheduling algorithm. Round robin algorithm consists of a queue and a quantum. The quantum is predefined, as that is the amount of time given to each process for execution [7]. The process in the queue is executed for the particular quantum and if it completed executed then the process is removed from the queue else, the remaining process that needs to be executed is added at the back of the queue. In the shortest job first algorithm, the processes are arranged according to their execution time in the ascending order and the executed in the same order. The processes with the least execution time are executed first completely and then the CPU is assigned to other processes. The logic behind the same is to reduce the waiting time. In the priority based resource allocation technique, the priorities are assigned to each of the processes waiting in the queue to be executed [9]. The processes are executed based on their priorities the one with the highest priority are executed first as compare to the lower priority ones. The priority of the processes can be decided based on many factors such as memory, time or any other requirements Cloud sim Cloud sim is a tool which provides the groundwork for the simulation of cloud computing services. Cloud sim is developed in the CLOUDS laboratory, at the computer science and software department of the university of Melbourne [10]. 2. RELATED WORKS Pandaba Pradhan, Prafullu Ku. Behera & BNB ray [3] have proposed a modified roundrobin algorithm for allocation of resources in a real-time cloud environment. This algorithm has made use of two registers that are: SR - It is used for storing the burst time calculated by adding the burst time of all the processes left in the queue AR - To calculate AR we must need to do following steps: 1. Realise the value present in the SR 2. Find the total number of requests present in the queue. 3. Now, calculate and store the avg. value of burst time by dividing the value found from Step 1 by value found from Step 2. After execution of a process, it is popped out from the ready queue if its burst time is finished, else it will be added at the end of the queue and accordingly, the SR & AR will be updated. Sagar D. Girase, Mayank Sohani, Suraj Patil [1] proposed Dynamic Resource Provisioning using Priority based Virtual Machine's in Cloud Computing Environment. The resources are allocated to the processes based on their priority. The proposed system is built with a preemption mechanism, a load balancer and a VM allocation policy. If the priority comes out to be equal then it was done on the basis of First Come First serve. Saraswati. AT, Kalaashri. Y R A, Dr. S. Padmavathi [4] proposed Dynamic Resource Allocation technique in a real-time Cloud environment editor@iaeme.com

3 Rishi Aluri, Shriya Mehra, Apoorva Sawant, Pankti Agrawal and Mayank Sohani In this paper, priority of jobs is given based on the deadline of the task. All the tasks that have a low priority are assigned to the existing VMs. When a higher priority job comes in, a lower priority job has to preempt its resources allowing the higher priority job to work in its resources. When no VM is free for executing a task this algorithm finds a low priority job causing it to pause and allocating its resources to the requesting higher priority job. Mokhtar A. Alworafi, Atyaf Dhari, Asma A. Al-Hashmi, A. Basit Darem, Suresha [5] proposed an improved SJF allocation policy in a real-time cloud computing environment. This paper aims to improve the shortest job first scheduling algorithm in the cloud computing. In tasks scheduling (TS), the most important parameters are makespan and response time. Therefore, we have proposed a Modified Shortest Job First algorithm (MSJF) to minimize the completion time of the last task (Makespan) and minimize the average response time with maximizing the resources utilization. MSJF has two functions, one is calculating the average of tasks length and the other one is Load-Balancing between the virtual machines. One of the important advantages of MSJF is sending the longest tasks to the fastest Machine. We compared the result of our proposed algorithm MSJF with SJF and FCFS. The performance of MSJF is better compared to SJF and FCFS. 3. SCHEDULING AND LOAD BALANCING Figure 1 Figure gives us an understanding about the components of cloud sim. The components of cloud sim play an important role for balancing the load of tasks that the cloud receives during runtime of cloud environment. The cloud sim uses the logic from the proposed algorithm to effectively balance the loads that is being subjected on the VM s. Use of central component to schedule VM efficiently, by assigning VM s based on the arrival time or job time (depending on the proposed algorithm), helps in further balancing the load [10]. Load balancing helps in migrating tasks from a heavily loaded VM to VM that is less loaded. VM provisioning is done by the hosts in the datacenter. Hosts manage the VM by enabling the VM provisioning algorithm to select the host with less PEs in use. Host is responsible to creation of VM, allocating the VM with PE (Processing Elements) and memory, migrating the VM, and even destroying a VM running in the host. VM are made up of certain characteristics that host needs to manage and provide before running the VM. Storage, Bandwidth, Memory, Cpus, Priority are the characteristics of each VM editor@iaeme.com

4 Priority based Non-Preemptive Shortest Job First Resource Allocation Technique in Cloud Computing Task requirements are based on the user. User that wants to use Cloud will provide the task details. Each task is treated as a cloudlet. Cloudlets provide the datacenter about important details necessary for running the tasks. Details provide datacenter with information about the task length and the scheduling policy that manages the cloudlet. The scheduling policy can be a proposed algorithm or any of the already provided policies such as round robin, throttled load balancing or active load balancing. When job requests are sent by users, the jobs or tasks are termed as cloudlets. Cloud sim defines its computational requirements based on the properties that cloudlets show and datacenter has the scheduling policy that manages the tasks. The Datacenter are comprised of hosts which as explained have the role to provisioning VM.VM requests are obtained to the hosts of the datacenter and hosts create VM. Each datacenter has different properties or characteristics to represent their hosts. The datacenter has a resource monitor that monitors the usage of VM from its hosts. This monitoring of usage gives the datacenter a correct estimate on the amount of time, storage and memory used by users through the VM and hence determines the cost that has to be charged to the user for using that datacenter s resources. A Datacenter Broker receives the tasks or cloudlets and maintains a list known as the cloud list. After this, the datacenter broker first submits a VM provisioning request to the datacenter and then it follows up with a request to submit tasks to VM. It receives a VM list that is used to link cloudlet s to appropriate VMs. Thus, Datacenter Broker assigns tasks to VM on behalf of users by hiding VM management which includes creation of VM, submission of cloudlets to VM and destruction of VM. The most frequently used scheduling principle is the Round Robin policy which is the default policy in cloud sim. Shortest Job with Priority is the proposed algorithm. In round robin algorithm, the tasks are allocated the next VM in the list without considering the load on that VM. Round Robin doesn t consider the priority, task length or other characteristics of the task and hence can produce high response times depending on certain situations. In the proposed algorithm, in a nutshell, considers the task length and priority of the task and based on these two factors assigns a VM to the high priority shortest jobs first. 4. SYSTEM MODEL In this section, we will give a detailed explanation about the policy that we are going to propose for allocating VM. The VM allocation policy we have proposed will be used for effective utilization of resources and will try to reduce the overall response time. The VM allocation policy focuses on three main mechanisms. The first, Datacenter controller monitors a datacenter and captures the policy selected by the user in the interface. Then the datacenter controller uses the selected policy and follows the algorithm to decide the VM to be allocated to the cloudlet. Finally, VM is allocated to the cloudlet and based on the policy it is decided if further migration of cloudlet to a different VM is needed or not. In our proposed algorithm, we first fetch the list of tasks. As soon the list of cloudlets is obtained, based on the length of each task, the task which has the shortest length is selected. This is how SJF algorithm is applied. Then the selected task is compared to any task from the list that has the same length. If we get any task having similar length as that of the selected task, the cloudlet having a higher priority among the two is selected. This process is done until we get a job that is shortest among the list and has the highest priority. As soon as we get the cloudlet, we allocate VM to it. In this process we allocate VM to all the tasks thus executing jobs with the shortest jobs first and also considering priority at the same time editor@iaeme.com

5 Rishi Aluri, Shriya Mehra, Apoorva Sawant, Pankti Agrawal and Mayank Sohani 4.1. Proposed Mechanism Step 1 Users: Users, by means of an interface, setup up a request to the environment for getting the resources to complete the task. Step 2 Datacenter: Datacenter forms a list from the requested tasks and determines their length and priority. Step 3 Host: Host manages VM by creating, allocating, monitoring and destroying them. Based on the allocation policy the host gets to manipulate its VM in different ways. Datacenter comprises of these hosts and uses the proposed algorithm to allocate tasks to the VM Algorithm for VM allocation 1. Create list of cloudlets. 2. Get the task with the shortest length. 3. Compare the selected task with tasks of same length based on priority where higher priority is selected. 4. Allocate the task with the VM which is the shortest and at the same time has the highest priority. 5. Release VM after task is completed. 5. RESULTS 5.1. Experimental Setup Cloud sim is the basis used for setting up a cloud based environment. Cloud sim provides a varied range of features that help to properly simulate a real-time cloud environment. Not only we can specify our proposed algorithm, we can also utilize the interface provided to simulate using configuration simulation. In the configure simulation tab, we simulate a set of 90 user-bases keeping the parameters for each user-base. The parameters are Parameters Table 1 Value Requests per User per hr 60 Data size per Request 100 Peak hours Start 3 Peak hours End 9 Avg Peak users 1000 Avg off-peak users 100 And the only difference that separates every user-base from one another is the Region parameter. The 90 user-bases are separated over 6 regions in the following sequence R0, R1, R2, R3, R4, R5 respectively from the first user-base and after R5 region, R0 region is given i.e. this sequence is repeated till 90 th user-base. The Datacenters used are 6 Datacenter. Each datacenter are having similar parameters with each datacenter differing only from their region in which they are placed. The 6 datacenters are separated over the 6 regions in the following sequence -R0, R1, R2, R3, R4 and R5 respectively. The parameters for datacenter are editor@iaeme.com

6 Priority based Non-Preemptive Shortest Job First Resource Allocation Technique in Cloud Computing Parameter Table 2 Value Architecture x86 OS Linux VM manager XEN Cost per VM ($/HR) 0.1 Memory Cost ($/s) 0.05 Storage Cost ($/s) 0.1 Data Transfer Cost ($/Gb) 0.1 Physical HW units 2 In Cloud sim, the datacenter broker uses different service policies to provide the cloudlet with a VM. The different service broker policies are Closet data center, optimize response time and reconfigure dynamically with load. The service broker policy used by us for testing is optimizing response time policy Result analysis Overall Response Time Axis Title Minimum Average Maximum Proposed Allocation Policy Existing Allocation Policy (Round- Robin) Figure editor@iaeme.com

7 Rishi Aluri, Shriya Mehra, Apoorva Sawant, Pankti Agrawal and Mayank Sohani Average Datacenter Request Servicing times DC1 DC2 DC3 DC4 DC5 DC6 Proposed Allocation Policy Excisting Allocation Policy Figure 3 After successfully carrying out the simulation using the above configuration details, the results are computed in the following figures. The above defined configurations are used oneby-one to the existing VM allocation policy Round Robin and then to the proposed VM allocation policy and then the results obtained for overall response time and datacenter processing time is shown in figure 1 and figure 2. In figure 1, the proposed allocation policy and the existing allocation policy are compared on the basis of overall response time. In figure 2, the proposed allocation policy and the existing allocation policy are compared on the basis of each datacenter request servicing times (average). From the performance analysis, we can interpret that our proposed allocation policy provides an improvement over the existing allocation policy by decreasing the processing time applied in the datacenter. Further improvements to the proposed algorithm will give even more improved results. 6. CONCLUSION Based on the analysis of several load balancing algorithms we have been able to develop and simulate a working algorithm. We have implemented the load balancing technique by integrating two algorithms- priority and shortest job first for the resource allocation in cloud computing environment. And the integration of these two algorithms provides us with the improvised results editor@iaeme.com

8 Priority based Non-Preemptive Shortest Job First Resource Allocation Technique in Cloud Computing REFERENCES [1] Sagar D.Girse,Mayank Sohani,Suraj patil, Dynamic Resource Provisioning in cloud computing environment using priority based virtual machine s. Advanced Communication Control and Computing Technologies (ICACCCT), 2014, IEEE International Conference [2] Pooja S. Kshirsagar, Prof Anita M. Pujar, Resource Allocation Strategy with Lease Policy and Dynamic Load Balancing. [3] Pandaba Pradhan, Prafulla Ku. Behera, B.N.B. Ray, Modified Round Robin Algorithm for Resource Allocation in Cloud Computing, In Procedia Computer Science, Volume 85, 2016, Pages , ISSN [4] A.T. Saraswathi, Y.R.A. Kalaashri, S. Padmavathi, Dynamic Resource Allocation Scheme in Cloud Computing, In Procedia Computer Science, Volume 47, 2015, Pages 30-36, ISSN [5] M. A. Alworafi, A. Dhari, A. A. Al-Hashmi, A. B. Darem and Suresha, "An improved SJF scheduling algorithm in cloud computing environment," 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), Mysuru, 2016, pp [6] Kamboj, Sheenam and Navtej Ghumman. A survey on cloud computing and its types rd International Conference on Computing for Sustainable Global Development (INDIACom). [7] S. M. Parikh, "A survey on cloud computing resource allocation techniques," 2013 Nirma University International Conference on Engineering (NUiCONE), Ahmedabad, 2013 [8] Palak Shrivastava, Sudheer Kumar Arya, Dr. Priyanka Tripathi Various Issues and Challenges of Load Balancing over Cloud : A survey International Journal of Engineering and Computer Science ISSN : Volume 5 Issues 8 Aug 2016, Page No [9] S. M. Parikh, "A survey on cloud computing resource allocation techniques," 2013 Nirma University International Conference on Engineering (NUiCONE), Ahmedabad, 2013 [10] [11] Yadav, Rakesh Kumar Yadav & Mishra, Abhishek & Prakash, Navin & Sharma, Himanshu. (2010). An Improved Round Robin Scheduling Algorithm for CPU scheduling. International Journal on Computer Science and Engineering. [12] Gouda, K C & V Radhika, T & Akshatha, M. (2013). Priority based resource allocation model for cloud computing. International Journal of Science, Engineering and Technology Research [13] Shahzad, Basit & Afzal, Muhammad. (2005). Optimized solution to shortest job first by eliminating the starvation. International Journal of Science, Engineering and Technology Research [14] Deepika Saxena and Dr. R.K. Chauhan. Shortest-Job First With Fair Priority and Energy Awareness Scheduling In Green Cloud Computing International Journal of Trend in Research and Development, Volume 3(6), ISSN: [15] Chitra Devi and V. Rhymend Uthariaraj, Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Non preemptive Dependent Tasks, The Scientific World Journal, vol. 2016, Article ID , 14 pages, doi: /2016/ editor@iaeme.com