Optimization Task Scheduling Techniques on Load Balancing in Cloud Using Intelligent Bee Colony Algorithm

Transcription

1 Volume 116 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu Optimization Task Scheduling Techniques on Load Balancing in Cloud Using Intelligent Bee Colony Algorithm 1 A.P. Shameer and 2 A.C. Subhajini 1 Department of Computer Applications, Noorul Islam University, Nagarcoil, Kanyakumari, Tamilnadu, India. shameerap@rediffmail.com 2 Department of Computer Applications, Noorul Islam University, Nagarcoil, Kanyakumari, Tamilnadu, India. acsubjajini@yahoo.co.in Abstract Cloud computing is one of the talented field in the advanced web based technology. It is a platform for providing collection of large number of resources and virtualization and is pay on demand based model. Cloud provides shared resources over internet in an innovative way for different purposes. The usage of cloud data accessing are increasing daily. Usually clouds data centers handle large number of users every moments and it is a major issues faced in the area of cloud. Cloud which uses more than one node and dynamically distribute workload to all nodes and ensure that no single node is overloaded. In load balance mainly focus is to boost the performance of the system and reduce less carbon emission by resource utilization and optimum usage of resources. Many researchers are done lot of algorithm to do load balancing and good scheduling task. Here we have observed and studied existing various load balancing algorithms and contrast them based on based on many arguments like throughput, reliability, power saving, performance utilization, scalability, stability etc. Key Words:Computing, load balancing, data center, virtual machine, virtualization. 341

2 1. Introduction Cloud computing is a transformative computing paradigm that includes distributing resources and services over internet which emphasizes commercial computing. It involves provisioning of computing, networking and storage resources on demand and providing dynamic pool of application as metered services to users [1]. Resources can be provisioned rapidly and elastically which also facilitate the IT infrastructure requirements. Cloud resources are pooled to serve multiple users using multi tenancy. The data can be moved from any system to large data centers can done easily. Application and resources are work in cloud on pay only operational expense which helps in adjustment of capacity in a high speed way [2].One of the peculiarities of cloud is scalability. Resources can be scaled up on demand to meet the performance requirement of applications. Load Balancing is a vital issue in cloud. Allocate workloads across multiple servers to meet the application workloads. The purpose is to achieve maximum utilization of resources, minimizing the response time, maximizing throughput. In the event of failure of any service, the load balancer can be automatically rerouting the user traffic to the healthy resources. Load balancer provides a mechanism for allocate the excess dynamic local workload evenly across all the nodes. The routing of user request is determined based on a load balancing algorithm. It gives high user satisfaction and making sure that no node is overwhelmed. Proper way of load balancing can achieve most favorable resource utilization. Some resources are more loaded by the task requested by users and some nodes are less loaded or may not use at all. The aim is to avoid such conditions. It allows to allocating dynamic local workload to all nodes equally among the resources including processor, network, hard disk etc. The primary purpose of load balancing is to distribute the work load of an application onto multiple computers and to avoid some resources are heavily loaded and some are lightly loaded by the task submitted by users, so the application can process in a good manner. Use of good load balancing procedure can utilize resource in right way, rejection rate and waiting time can also minimized, good performance, maximized throughput and make job execution cost is less. In this paper, we present task scheduling using enhanced ABC algorithm which provides minimum time and cost while balancing the load. Here, we develop an algorithm which is based on execution time, communication time, execution cost, resource utilization, and energy and to attain best resource consumption which make best use of the throughput and reduce the overall response time. 2. Load Balancing The increase in web traffic and different application in the web world is increasing day by day where millions of data are created every second. Load balancing has become a very common research field due to need of balancing the load on this heavy traffic. Cloud load balancing is the process of distributing 342

3 workloads and computing resources in a cloud computing environment. Cloud computing use virtual machine, store and link the different nodes for their specific purpose. The load balancing is required on CPU load, memory capacity and network. Load Balancing is done in such a way that the entire load is distributed among various nodes in a distributive system. If there is a failure of host, it will lead to isolation of web resource in the web world [8][16]. Load balancing can be accomplishing best resource consumption which best use of the throughput and reduce overall response time and it reduces the overall waiting time of the resources. The Process of allocating bigger processing load to smaller processing nodes is a crucial issues and all workload are divide dynamically. Workload is the amount of total processing time it takes to execute every tasks allocate to the machine. Every virtual machine in cloud does the equal quantity of work that makes best use of the throughput and reducing the response time. Hence load balancing perform a major roll to improve the performance of the cloud service. 3. Load Balancing Cloud Environment Load balancing (LB) in cloud computing gives well-organized results to a variety of issues exist in cloud computing environment set-up and usage. This consist two things, first one is the resource sharing and second one is task scheduling in distributed environment. During the scheduling of the task, it will be ensure the resource availability on demand, resource utilization, energy saving and cost minimization. For finding the efficiency of LB algorithms simulation environment are necessary. CloudSim [8] and [14] simulation tool is used for modeling of Cloud. During Cloud life cycle, CloudSim permit VMs to be managed by hosts and datacenters. CloudSim architecture gives four things. These allow configuring the environment and evaluating the effectiveness of LB algorithms. The architecture of CloudSim consists Datacenters, Hosts, VM and Application. Data Center utilization and energy consumption of all IT-related equipment such as servers, storage and network switches and providing IaaS to the cloud users. Hosts are Physical Servers that offer SaaS to the users and it possess storage space and memory. VM permit deployment of custom application service models. Sample figure of Cloud architecture with four basic entities is shown in Fig Related Work Fig. 1: Cloud Architecture with Four Entities Load balancing is a significant feature of cloud computing and it distributes the workload to all the nodes and to avoid overload and under load situations. User 343

4 always demand heterogeneous resources and therefore it is very crucial in the cloud scheduling and load balancing for increasing the efficiency. Recently many researches are addressed task scheduling problems. The following section we have reviewed comprehensive survey reports to related researches paper work. Quang-Hung [6] suggest a genetic algorithm to discover an optimal solution for Virtual Machine allocation. This algorithm deal with those issues by introduced the third-tier, the broker which plays the role of the intermediate to find the tradeoff between client s requirements and the profits of the providers. Sharifi [18] have suggested a scheduling algorithm to map VMs onto PMs to boost the energy fall while keeping the performance isolation between applications. It focused on managing ECs and efficient use of processor and disk resources using live migration of VMs. Choudhary and Peddoju [29] have proposed an algorithm, all the requests are grouped on the basis of the task requirement like minimum execution time or minimum cost and prioritized. Using greedy approach, resource selection is done. Finally, Jafari Navimipour and Sharifi Milani [13] have proposed cuckoo algorithm to schedule the tasks in Cloud computing which is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies. Sandeep Sharma [11] has considered two typical load balancing approaches static and dynamic. The study shows that the static load balancing algorithms are more stable than the dynamic. Hu et al. [15] this algorithm use the scheduling strategy on load balancing of VM resource by using Genetic algorithm for scheduling. This method considered the previous data and the present status of work in advance to the performance of the system which can solve the problem of load imbalance in Cloud. Lot of existing load balancing algorithms were studied like Genetic algorithm for scheduling to increase the system performance, a LBACO Algorithm to reduce makespan, Bee Life algorithm and Greedy algorithm to achieve an affirmative response from the end users. We will get good reliability depends on how we handles load and cloud is power full to handle many users. To increase the capacity and capability of the cloud, we need good load balancing techniques. Existing work done are studied and compared with some parameter. The pictorial representation of the load balancing algorithm execution is given in fig 2. Fig. 2: Load Balancing Algorithm Execution 344

5 5. Proposed Methodology In our work, we introducing a new algorithm for task scheduling by means of Artificial Bee Colony, aim is to complete all scheduling task is get down to the minimum feasible time. The suggested algorithm is shortening the execution time of task scheduling and maintains the load balance of VMs. Load balancing can affect the overall execution of a framework. In ABC, the position of a food gives a possible solution to the scheduling problem and the nectar amount of a food gives the fitness of the associated solution for given scheduling problem. The number of the employed bees is equal to the number of solutions in the population. Some parameters used here are the number of food source, number of onlookers, number of search circles, number of range and number of iteration. We used 100 food sources, number of onlookers is 200, search circle is 1000, number of range is 30 and iteration is 10. The method is defined as: n job and m machines are declared and each job is processed on m machine without any disturbance and each work on m is fixed duration. Only one operation can be process at a time, not possible in two. So priority is given to each job. Find out minimum time complete all jobs. Some mathematical formula and equation are given below for the proposed work. Consider that PM = { PM1, PM 2,..., PM n } is a set of cloud physical machine. VM i = { VM1, VM 2,..., VM I } Is a set of virtual machines (VM) types and T = { T1, T2,..., T m } is a set of task. Each task contains a task set T = { t t,..., }. Some i 1, 2 t n equations are used for the proposed work which is given below Fit α ET + α CT + α EC + α E + RU (1) = α5 The total execution time (ET) is calculated based on equation. task 1 Number ( ) of ET = ( ET of corresponding VM Size of the task) (2) max ET number of task Communication time (CT) is calculated based on equation; 1 task CT = Nmuber of CT Size of the task (3) Max ( CT ) Number of task i= 1 ( ) i= 1 Execution cost (EC) is calculated using the equation EC = Nmber of task i= 1 Executiontime Communicationtime Number of task Energy calculation is verifying using equation; Number of task E = [ size of Task Energy] i= 1 (4) (5) Resource utilization is found using equation; = Number of task sizeof VM Sizeof taski RU Number of task i= 1 Sizeof VM 1 (6) From the above formula, we can observe that the task scheduling is difficult one. Using a mathematical programming approach, working out such problems 345

6 will obtain a huge amount of computational time and cost for a big size problem. To overcome this problem, we utilize multi-objective task scheduling while balancing the load with minimum time and cost function in our paper work. 6. Problem Solution Artificial Bee Colony Techniques Artificial bee colony algorithm is an optimization method to calculate the optimal solution based on the gifted character of bees which was developed in It is a novel meta-heuristic approach motivated by the foraging behavior of bees. In ABC, honey swarm consist three categories called employed bees, onlookers and scouts. Half of the colony consists of the employed bees and remaining half is onlookers. The numbers of employee bees same as the total number of food source. The employee bees became scout when food source is abandoned. Search is defined as follows Employed bees have to look for the food sources within the neighborhood of the food source in their memory and bring back the food sources information to inform the Onlooker bees which are waiting at the hives. Onlooker bees calculate the Fitness value and choose the optimal food source for sending the Employed bees to collect. If any food source was chosen and all the food was collected. Scout bees randomly search for the new food sources Steps incorporated in this practice is; Initialize population Iteration Move the employee bees on their food Place the onlooker to food and determine nectar amount Move scout for searching new food source Memorize the best food source Until some condition The proposed work is a new model by means of ABC algorithm, which gives a well performance in task scheduling and load balancing to decrease the makespan. Firstly, ABC create initial population randomly S (i=0) of FS (food source) solution. FS is size of employed bees equal to onlooker bees. Each solution P i is a 1-D vector, D is optimization parameter. The population is repeated iteration (i=1, 2, 3 up to some condition). Consider m task and p process on cloud nodes. There are some assumptions and constraints as given below and various phases are also described with mathematical equations used for calculation. Initialize Phase The new solutions are produce for employed bees. The initial (S pq ) is generated randomly. 346

7 S pq ={Qt 11,Qt 12,... Qt ij } Where, S is the initial solution, Qt is the make span time and p, q is the job and machine Fitness Calculation After the food source is selected, the onlooker bees select a new food source position in the neighboring area of selected food source. The employed bees evaluate the fitness of their solution and inform with onlooker bees. Each bee produces a candidate solution by perturbing the old solution in memory using the expression below. Fit i = Mn( ST (t)) ; ST(t) =Mn( Tm i=1, j,tm i, j=1 ) = Tm i, j ST (t) is the makespan time, the Tm is the processing time, i is the task order and j is the system order. Probability Finding The Probability value of the food source pi is calculated by the following expression is shown below. F i P i = FS Fi i =1 Where F i is the fitness, i which is proportional to the nectar amount in the position i and FS. We can find out probability rate after greedy method of selection. Fitness has higher priority rate is achieved. Onlooker Task The main job of an onlooker bee is to select a food source, based on the probability value associated with that food source. The probability P i is calculated as follows. Fi Pi = SN F n n= 1 Where F i is the fitness value of the solution i. SN is the number of food sources. Scout Bees PHASE The abandoned counters of all employed bees are neglected. The scout bees become the employed bees. The employed bee which cannot be improved self solution until the count reaches to the limit. The scout generates food source randomly is shown below. Yij = lb, j + rand[0,1]( ub, j lb, j) Where, ub,j and lb,j are the both limits of food position in j. 347

8 ABC Pseudo Code Table 1: Pseudo Code for ABC Algorithm 7. Result Evaluation The result obtained from the proposed ABC algorithm based multi-objective task scheduling technique. We have implemented our proposed task scheduling using Java with CloudSim tools and performed on Windows 2007 OS at 2 GHz dual core with 2 GB main memory. The scheduling process is related to Execution Time, Communication Cost, Communication Time, and Energy and Resource Utilization. When scheduling the task, load also balanced. Figure 3-7 shows the performance of proposed multi-objective task scheduling. Fig. 3: Execution Time by Varying Number of Task Fig. 4: Communication Time by Varying Number of Task 348

9 Fig. 5: Execution Cost by Varying Number of Task Fig. 6: Energy by Varying Number of Task Fig. 7: Resource Utilization by Varying Number of Task The above Figure 3 to 7 shows the Execution time of proposed methodology by varying number of task. A number of the task is increases means the execution time and cost also increase. The good scheduler, schedule the task within the deadline and minimum cost while balancing the load. Here, the X-axis implies the iterations and Y-axis implies the execution time. The proposed work runs with totally forty iterations. In this experimentation, we utilized 15 physical machines, 30 virtual machine, and 25 tasks. Our proposed work utilizes a large number of resources compare to other approaches. In Figure 3, our proposed approach takes a minimum time of sec for ten task, sec for twenty tasks, sec for thirty task and sec for forty tasks. Figure 4 shows the performance of proposed method using communication time by 349

10 varying number of task. For scheduling the ten tasks our proposed approach takes the minimum time of sec, for scheduling twenty tasks our proposed approach takes the sec, for scheduling thirty task our proposed approach takes sec and for scheduling forty task our proposed approach takes a minimum time of sec. when analyzing figure 4, our proposed approach takes minimum time compare to other works. Similarly, figure 5 shows the performance of proposed method using execution cost by varying number of task. If the number of task increases means the cost of the scheduling also increases. Here our proposed approach utilizes the minimum of for scheduling ten tasks. Moreover, figure 6 and 7 shows the energy and resource utilization performance. From the above discussion, we clearly understand our proposed approach achieved the better performance compare to other approaches. 8. Conclusion In the proposed research paper, enhanced Bee Colony algorithm based multiobjective task scheduling method is presented and its performance is verified and tested by the CloudSim stimulator. Our algorithm is to attain well-balanced load across virtual machines and convey to the minimum cost, time energy and utilization. Moreover, we explain the advantage of the ABC to optimize the task scheduling and resource allocation on cloud computing environment. The experiment output reveal that our suggested algorithm obtained high performance and maximize the throughput and minimize the response time compare to other approaches. References [1] Muhammad Roman, Asad Habib, Jawad Ashraf, Load Balancing in Partner-Based Scheduling Algorithm for Grid Workflow, International Journal of Advanced Computer Science and Applications 7(5) (2016). [2] Hitesh Bheda, Hiren Bhatt, An Overview of Load balancing Techniques in Cloud Computing Environments, International Journal of Engineering and Computer Science 4(2015). [3] Nitin Kumar Mishra, Nishchol Mishra, Load Balancing Techniques: Need, Objectives and Major Challenges in Cloud Computing-A Systematic Review, International Journal of Computer Applications 131(18) (2015). [4] Tinghuai Ma, Ya Chu, Licheng Zhao, Otgonbayar Ankhbayar, Resource Allocation and Scheduling in Cloud Computing: Policy and Algorithm, IETE Technical Review 31(1) (2014), [5] Sukhvir Kaur, Supriya Kinger, Review on Load Balancing Techniques in Cloud Computing Environment, International Journal of Science and Research 3(6) (2014). 350

11 [6] Arabi E. keshk, Ashraf B. El-Sisi, Medhat A. Tawfeek, Cloud Task Scheduling for Load Balancing Based on Intelligent Strategy, International Journal of Intelligent Systems and Applications (2014), [7] Tinghuai Ma, Ya Chu, Licheng Zhao, Otgonbayar Ankhbayar, Resource Allocation and Scheduling in Cloud Computing: Policy and Algorithm, IETE Technical Review 31(1) (2014), [8] Long Chen, Kehe Wu, Yi Li, A Load Balancing Algorithm Based on Maximum Entropy Methods in Homogeneous Clusters, Entropy (2014). [9] Zhang Q., Cheng L., Boutaba R., Cloud Computing: State-Of- Theart and research Challenges, Journal of Internet Service Applications 20 (2014). [10] Adhikari J., Patil S., Load Balancing the Essential Factor in Cloud Computing, IJERT 1(10) (2014). [11] Sharma S., Singh S., Sharma M., Performance Analysis of Load Balancing Algorithms, International Science Index 2(2) (2013). [12] Prasad Padhy R., Goutam Prasad Rao P., Load Balancing in Cloud Computing System, BTech thesis (2013). [13] Zhao Y., Huang W., Adaptive Distributed Load Balancing based on Live Migration of Virtual Machines in Cloud, IEEE 5 th International Joint Conference on INC, IMS and IDC (2009). [14] Mayanka Katyal, Atul Mishra, A Comparative Study of Load Balancing Algorithms in Cloud Computing Environment, International Journal of Distributed and Cloud Computing 1(2) (2013). [15] Nusrat Pasha, Amit Agarwal, Ravi Rastogi,Round Robin Approach for VM Load Balancing Algorithm in Cloud Computing Environment, International Journal of Advanced Research in Computer Science and Software Engineering 4(5) (2014). [16] Edson Flórez, Wilfredo Gómez, Lola Bautista, An Ant Colony Optimization Algorithm for Job Shop Scheduling Problem, Journal of Artificial Intelligence & Applications 4(4) (2013),

12 352