EVALUATION OF SCHEDULING ALGORITHMS USING VIDEO AND VOICE APPLICATIONS IN CLOUD COMPUTING

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1 EVALUATION OF SCHEDULING ALGORITHMS USING VIDEO AND VOICE APPLICATIONS IN CLOUD COMPUTING 1 SUNNY KUMAR, 2 SHIVANI KHURANA Department Of Computer Science,CT College of Engineering and Technology Shahpur, Jalandhar Abstract- Cloud computing provides a service on network that works like pay and get service and accessible at any place, at any time. Cloud computing contains lot of component for example central processing unit (CPU), server, datacenter, router and software. Cloud provides communication among these resources. As in cloud, resources are shared to get better performance and get fast speed as well as resource availability at any time. To achieve quality of services in cloud proper scheduling mechanism need to be adopted for better output. In a cloud, packet loss in delivery and delay occur and it cannot be tolerated in case of real time application. Real time applications such that in video conferencing and voice, delay and packet loss can change value of data. Adopting proper scheduling, CPU utilization can be more. So proper scheduling method is required to get maximum throughput and CPU utilization. In this paper different scheduling methods are evaluated on the basis of throughput, CPU utilization and End-To-End delay using cloud computing environment. Keywords- Cloud Computing, CPU, Scheduling, Resource, Job, Task. I. INTRODUCTION Cloud computing is a term used where several resource like computers are connected with each other to share data and their application through real time connection. Internet provides a connection to connect computers with each other and cloud computing is used as an expression to determine network connectivity. Using cloud computing application running on one machine is applicable for all machines which are connected to that particular machine i.e. a concept of distributed system. Cloud computing share resources like grid computing to run applications on machines. For multithreading one machine have two or more virtual machines. Virtual machine execute task assigned to them. Cloud computing is provided by several organization for rent. As much you pay you get more service, paid service. If any user wants to access service, he just needs to pay to the cloud service provider.to use cloud computing services efficiently requires a good scheduling technique. In any type of cloud, scheduling is an important issue to schedule jobs. Several jobs executes in a cloud at a same time. To keep jobs on track scheduling need to be handled carefully. If not, job will not execute and errors occur. As cloud is a payable service user need to get better performance. Several resources are shared using cloud computing. Which user get which facility and when depends all on scheduling. But scheduling task is not simple in case of cloud computing because several users may request same application at a same time. If requests came at same time then who get first. So right scheduling method need to be adopted in cloud computing. In this paper different scheduling methods are discussed and comparison is made on the basis of output of all these scheduling methods. Section 2, presents related work. In section 3, cloud computing model for scheduling is presented. Section 4, application used in our experiment. Section 5, contains different scheduling method with simulation. In section 6, conclusion and future scope is given. II. RELATED WORK Dharamendra Chouhan, S. M. Dilip Kumar and Jerry Antony Ajay describes Multilevel Feedback Queue scheduling for grid computing for avoiding starvation in jobs occurring First Come First Serve scheduling. In this lower priority job will complete quickly without moving to lower level hierarchy. High throughput and good response time is achievable. Priya R. Lodha and Avinash P. Wadhe gives a study of workflow scheduling algorithm in cloud computing in which working is with respect to resource sharing. This paper explores task scheduling according to the environment where they can be used efficiently.maha Jabalia, Asma Ben Letaifa and Sami Tabbane described comparative study on game theoretic Approach for resource allocation in cloud computing architectures. Overview is given on all adopted resource allocation policy. In this paper security is neglected and considered as a risk in adoption of a cloud. R. Raju, R.G. Babukarthik and T. Vengattaraman describes minimize the make span using hybrid algorithm for cloud computing cuckoo search method is used and completion time is reduced using hybrid approach. Quyet Thang NGUYEN, Nguyen QUANG-HUNG gives a virtual machine allocation in cloud computing for minimizing Total execution time on each machine. According to this paper each machine on cloud has several virtual machines. In this three mixed integer linear mathematical model are constructed to represent and solve allocation of jobs to virtual machines. Young Choon Lee and Albert Y. Zomaya describe workflow scheduling and resource abundance using stretch-out and compact. Scheduling is compacted by 5

2 rearranging task making use of free slots. In this scheduling compaction algorithm explains resource efficiently can be improved by using inefficient slot present both explicitly and implicitly. Wenhong Tian, Yong Zhao, Minxian Xu, Yuanliang Zhong, and Xiashuang Sun present a toolkit for modeling and simulation of real time virtual machine allocation in data cloud center. In this two existing simulation system at application level for cloud computing are studied. Amir Nahir, Ariel Orda and Danny Raz describes schedule First, Manage Later: Network- Aware Load Balancing technique in which they created several replica of a job and sending each replica to each server. There is degradation in signal propagation delay. Amit Kumar Das, Tamal Adhikari and Choong Seon Hong describe an intelligent approach for virtual machine and QoS provisioning in cloud computing. This paper use adaptive QoS aware virtual machine provisioning mechanism developed that ensures best utilization of all system resources. In this an algorithm is proposed to minimize rejection rate during making connection with data center. Mohamad Abu Sharkh, Abdelkadir Ouda and Abdallah Shami describes a resource scheduling model for cloud computing. In this paper four scheduling algorithms are combined to schedule virtual machine and then schedule connection request on network. Machine- Machines are named as client. On machine jobs are generated and execute. Applications provided in cloud are used by client and can be called user.switch- Switch in cloud computing connect clients and group of client that act as a grid with help of switches. Router- Scheduling is done on router to transmit packets on its right route. Model for cloud computing III. CLOUD COMPUTING MODEL Cloud computing use is increasing day by day. Data centers are accessible through cloud for example application like what s app, Line and several other. As millions of users are using these applications there is need to schedule that all users can access application stored in data center. So different scheduling techniques need to be used. Cloud computing is divided into three types Public cloud- It is an infrastructure in which anyone can join cloud at any time at any place. Large type of cloud and used as a paid service.private cloud- It is an infrastructure which works inside an organization and only employee can control and manage it.hybrid cloud- It is a combination of public and private cloud services. It uses both qualities to achieve organization goal. Cloud computing components model are Figure 2. Cloud Computing Model This model is used for scheduling in cloud computing. It contains different types of resource for cloud. In above model CPU, router, switch and a cloud for making communication between machines. They are connected through a wire. Scheduling is applied between routers to transmit packets. Application are generated and assigned to different machines to execute them. IV. APLICATION USED Scheduling depends on requirement of user. To select best scheduling mechanism for cloud computing need to first understand user requirement. For example what kind of application running on cloud? Scheduling in cloud is not an easy task. Different scheduling methods using video and voice applications for cloud are developed. Video Conferencing Figure1. Cloud Computing Component Model Video conferencing is a real time application. Proper scheduling method is required to handle real time applications. If any packet dropped during transmission, value of information in form of video can be changed and it can be a cause of bad decision for any organization or user. So here is a need to treat 6

3 video type of applications carefully. As video packets transmitted or stored in cloud are of large size. Bulk transmission is required from both sides. Different format of videos are available today. So each video format has its own priority in sense of its use. Each format also has different size for same data. As high speed requires in each field, so different scheduling methods are required to handle different formats. Used different types of video traffic for scheduling are:- Heavy video traffic: For heavy traffic in network VCR quality video is used. VCR quality video is a high resolution video. Using this type of traffic in cloud maximum load can be produced. Medium video traffic: For medium load in cloud video traffic having excellent service of type is used. It produces a medium load in cloud. Medium-low level video traffic: Third type of traffic used in cloud is medium-low level video traffic. It produces medium low load on cloud. Low video traffic: To produce small load low video traffic in cloud is used. Voice Voice is also a real time application runs on cloud. Its size is small as compared to video. But impact of voice type of application for on any user is greater than video. It is because, watching video with few packets dropped at receiving time user can estimate what video want to say. As video is a combination of both frames and voice, so it is easy to estimate about video. But in case of only voice transmission, that is not easy as in case of video. It is because if voice packets losses during transmission value of information change that resides in voice. It can be a reason of loss for any organization and user using uncompleted data. For our experiment we used different type of voice application categorized on the basis of type of services. It produce different type of load for different users. Voice applications running on cloud are:- PCM quality voice: PCM (Pulse Code Modulation) quality voice use digital audio and clarity is high. Low quality voice: To produce low load in cloud we used low quality voice. IP telephony voice: IP telephony voices are like VoIP. GSM quality speech: GSM quality speech used because it is of higher quality with lower bit rate. PCM quality and silence suppressed voice: Its quality is same as in PCM quality voice but with 50% small size. environment on the basis of metrics throughput, delay and CPU utilization. Simulation runs for 200 seconds in every case. Results are shown in form of packets. VI. SIMULATED RESULTS FOR VIDEO AND VOICE On the basis of different parameter like throughput, delay and CPU utilization results are evaluated for video application as well as voice application using different scheduling algorithms. Results Using First Come First Serve Scheduling This scheduling technique is the simplest technique among all scheduling techniques. Jobs are transmitted to router and router transmits jobs to their destination on the same way as received by router. At router jobs are stored into a queue. Jobs are entering from one end of a queue and came out from other end of the queue. CPU utilization is less and throughput depends on queue size. Advantage of using First Come First Serve (FCFS) is no burden on scheduler. On other side, if data is of large size then small size data starves for execution. Long jobs do not release resources for short jobs. Results of FCFS running video and voice application are shown below Figure 3. Throughput for video and voice in packets Figure 3 shows throughput for video and voice application. Throughput achieved maximum for voice and in case of video slightly less than voice. V. EXPERIMENTAL RESULTS Results for video and voice application are evaluated using OPNET IT Guru simulator in cloud computing Figure 4. End to end delay in case of video and voice Above figure 4 shows end-to-end delay in packets for application video and voice. It shows end-to-end 7

4 delay for video application is high as compared to voice.. In above figure end-to-end delay is shown using PQ scheduling. It shows delay is below 0.8 for video and above for voice. Figure 8. CPU utilization Figure 5. CPU utilization Above figure shows use of CPU (Central Processing Unit). Use of central processing unit is same for both applications. Result Using Priority Queue Scheduling This scheduling technique works on the basis of importance given to each job according to the user. Highest priority assigned to that job which one user want to execute first. Lower priority job will execute at last. This scheduling method is best utilized where user is clear to assign priority to each job. Starvation occurs only for lower priority jobs. They can wait for a period of time to get resources available and can be executed in last. Results of Priority Queue PQ scheduling running video and voice application are:- Priority Queue scheduling uses same amount of CPU utilization for both application. Result Using Round Robin Scheduling Round Robin (RR) scheduling technique is used to remove resource starvation. In this technique equal time slice is given to each job to complete their execution. Jobs are executed only for a fixed time of quantum in a round circle and after completing first circle each job which is not terminated in first time quantum will get same time quantum again to complete their execution until not completely executed. Jobs which complete their execution in given time is removed from the queue. Results using Round Robin scheduling for video and voice application on Cloud computing environment are Figure 6. Throughput for video and voice in packets Above Figure 6 shows throughput for video and voice applications using PQ scheduling method. It shows throughput obtained in case of voice is high as compared to video. Figure 9. Throughput using Round Robin scheduling Above figure shows results using RR scheduling method running video and voice application in cloud. It shows throughput obtained for video 8s high as compared to voice application. Figure 7. End-To-End delay Figure 10. End-to-End delay 8

5 Above figure shows end-to-end delay results using RR scheduling for video and voice application. In this delay is above 1 for both application but voice having less delay in comparison of voice. and down. So throughput is approximately same for both applications. Figure 11. CPU utilization Above figure shows same results for video and voice application using Round Robin scheduling method. Figure 13. Delay in MLFQ scheduling Above figure 13 shows end to end delay in MLFQ scheduling. Delay is same for both cases. Result Using Multilevel Feedback Queue Scheduling This scheduling technique is a combination of two scheduling Round Robin scheduling and First come First Serve scheduling. Multilevel Feedback Queue (MLFQ) scheduling uses multiple queues in which first queue is scheduled by Round Robin. Second queue is also scheduled by Round Robin scheduling but with a small time quantum as compared to first. In last queue First Come First Serve scheduling is adopted. Working of MLFQ scheduling is as Jobs enter into first queue and execute according to RR scheduling. Jobs which are of small size and completes in given time quantum, removed from the queue and jobs still require time to complete their execution are moved into second queue. They again get time to complete execution in RR scheduling manner. If they not completed then they moved into other queue and treated according to FCFS scheduling. Results using Multilevel Feedback Queue scheduling for video and voice applications in cloud are Figure 14. CPU utilization in MLFQ scheduling In MLFQ scheduling CPU utilization is near 0.5 for both cases. Result Using Multilevel Queue Scheduling Multilevel Queue (MLQ) scheduling technique is a combination of several scheduling. It uses Priority Queue scheduling, Round Robin scheduling and First Come First Serve scheduling. It uses several queues and each queue having its own scheduling mechanism to handle jobs. Priority is assigned to each queue and jobs are allotted to the queue on the basis of priority of the queue. In this technique categorization of job is made. This will maximize Central processing unit (CPU) utilization. Low starvation occurs due to job categorization and different types of scheduling. Results using MLQ scheduling for video and voice in a cloud computing environment are:- Figure 12. Throughput using MLFQ scheduling In above figure throughput obtained using MLFQ scheduling for both video and voice is changing up Figure 15. Throughput using MLQ scheduling 9

6 In above figure throughput for video is near about 0.75 and for voice is near about 0.9. Figure 16. End to End delay in MLQ scheduling In above figure delay for video is near about 0.8 and for voice is 0.25 maximum point. Figure 17. CPU utilization running MLQ scheduling. Above figure shows results for CPU usage. CPU utilization is above 0.5 for both cases. CONCLUSION Today cloud is widely used to run many applications and to solve complex problem occurring in daily life. Number of user uses cloud. To handle all user, sometime using same application, requires a special care. All users can access cloud service at a time need special scheduling approach. This paper describes FCFS, RR, PQ, MLFQ and MLQ scheduling technique for cloud. Maximum throughput is achieved using MLQ scheduling technique for voice and video application. End-to-End delay and CPU utilization are also better in case of MLQ scheduling method compared with other applied scheduling method. So MLQ scheduling method is best for video and voice application in cloud computing environment. As selection of appropriate scheduling method for cloud depends on user requirement. New methods can be generated by combining these methods if required. Future work is to apply new methods generated by combining two or three basic method according to the user requirement. REFERENCES [1] Dharmendra Chouhan, S. M. Dilip Kumar A MLFQ Scheduling Technique using M/M/c Queues for Grid Computing IJCSI Vol 10, issue 2, No 1, March [2] Priya R. Lodha and Avinash P. Wadhe, Study of Different Types of Workflow Scheduling Algorithm in Cloud Computing IJARCSEE Volume 2, Issue 4, April [3] Maha Jabelia, Asma Ben Letaifa, Mohamed Hamdi A Comparative Study on Game Theoretic Approaches for Resource Allocation in Cloud Computing Architectures IEEE [4] R.Raju and R. G. Babukarthik, Minimizing the Make span Using Hybrid Algorithm for Cloud Computing 3 rd IEEE International Advance Computing Conference (IACC) [5] Quyet Thang NGUYEN and Nyugen QUANG-HUNG, Virtual Machine Allocation In Cloud Computing For Minimizing Total Execution Time On Each Machine /13 IEEE, [6] Young Choon Lee and Albert Y. Zomaya Stretch out and Compact: Workflow scheduling with Resource Abundance IEEE [7] Wenhong Tian, Young Zhao, Minxian Xu A Toolkit for Modeling and Simulation of Real Time Virtual Machine Allocation in a Cloud Data Center IEEE [8] Amir Nahir, Ariel Orda, Danny Raz Schedule first, Manage Later: Network-Aware Load Balancing IEEE [9] Amit Kumar Das, Tamal Adhikari and Choong Seon Hong An Intelligent Approach for Virtual Machine and QoS Provisioning in Cloud Computing IEEE [10] Mohamad Abu Sharkh, Abdelkadir Ouda and Abdallah Shami A Resource Scheduling Model for Cloud Computing Data Center IEEE