AN IMPROVED SOLUTION FOR RESOURCE SCHEDULING OPTIMIZATION BY RESOURCE GROUPING METHOD IN CLOUD COMPUTING

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1 I J C T A, 9(28) 2016, pp International Science Press AN IMPROVED SOLUTION FOR RESOURCE SCHEDULING OPTIMIZATION BY RESOURCE GROUPING METHOD IN CLOUD COMPUTING S. Magesh Kumar* and S Siva Subramanian** Abstract: Cloud computing is a swiftly growing area. It has emerged as a new massive-scale distributed computing paradigm that provides a dynamically scalable carrier delivery and consumption platform make possible via virtualization of hardware and program and it deals with on demand dynamic resource allocation for delivering dependable and guaranteed services to the consumer. It s based on pay-as-you-use model to public. Methods: Resource scheduling in cloud computing is regarded to be a complicated task since more than one copies of the same tasks are assigned to different computer systems. An increasingly major requirement is the potential to maximize the service performance at minimal rate subject to diverse goals and constraints.. Findings: Results show proposed algorithm performs efficiently in computational cloud environment and curb the cost, and also strengthen the scalability, efficiency, elasticity and reliability. Keywords: Cloud Computing, Resource Scheduling Optimization, Virtual Machines 1. INTRODUCTION Cloud computing is crucial for business zone and research establishments, in most recent couple of years. It is mostly about how the registering resources are virtualized, and with scheduler the resources in the consistent integration, concentrate on the best way to manage data centre resources virtualization, and user submitted to the mission needs and resources to maximum utilization rate for the user to provide administration, and how to efficiently schedule user s tasks, plausible distribution system resources, to realize the resource load balance is withal the key factors of raising the cloud computing platform performance and administration quality. Cloud computing is a model for empowering universal, tremendous, on-curiosity procedure access to a mutual pool of configurable processing resources (eg., networks, servers, storage, applications, and services) that can be expeditiously provisioned and discharged with minimal management exertion or service provider organization. In cloud computing, the resource status and the system status are to be overseen successfully. The cloud computing models are Platform as An administration (PAAs), Software as a Service (SAAs) and Infrastructure as a Service (IAAs). Dynamic resource management can be effectually finished in cloud approach by using virtualization technology. As a outcome power efficiency can also be extended via assigning a couple of digital machines to a single physical server. Consequently vigor consumption will also be lowered by means of turning off probably the most servers or putting them in sleep mode. The cloud computing structure can be executed in an alternate assortment of designs and distinctive organization. The reason for resource scheduling enhancement in the cloud computing is that the different service and the resources gave by the resource traders can be pooled together which can be * Research Scholar, Bharath University mageshkumars@yahoo.com ** Professor Department of CSE, Dhanalakshmi College of Engineering.

2 42 S. Magesh Kumar and S Siva Subramanian progressively access by different clients. Some swarm based enhancement strategies are taking care of this advancement issue furthermore attempt to accomplish all inclusive ideal arrangements. In this approach, a newly introduces and implements Resource Grouping Algorithm to take care of resource scheduling improvement issue. These kinds of process execute strict standards on the cloud aspect, comparable to specific software management application or digital machine or, vast applicability in public clouds. The proposed strategy does now not drive any desires of the cloud platform instead of giving isolated execution in cloud computing and it progresses the management burden of resource optimization. The rest of the paper as follows: Section 2 narrates related work for Resource Scheduling Optimization in Cloud Computing. Section 3 summarizes the Problem Statement. Proposed System, Proposed Algorithm for Optimized Resource Scheduling and Architecture of Scheduler are tailored in section 4. Performance Evaluation has discussed and Results are depicted in section 5. Conclusion is presented in Section RELATED WORK The resource scheduling in cloud computing has become a serious issue that has to be conquered. This is most often because of the assorted and vibrant nature of the cloud and as a result of that it becomes a combinatorial optimization key issue. Few heuristic methods corresponding to genetic algorithm, simulated annealing, tabu search, particle swarm optimization, ant colony optimization can be used for scheduling of resources in cloud computing. Scheduling of the resources has also done by Bacterial foraging optimization algorithm but within the cloud environment. The efficiency of the algorithm is measured by the use of Cloud Sim.[1]. Resource scheduling optimization algorithm of vigor consumption for cloud computing based on the task tolerance (ECCT), In ECCT algorithm, the utilization of vigor resources within the unit time was improved with the aid of growing the parallelism measure of tasks, which was once improved by using improving the mission tolerance, and the energy consumption was optimized in the end. The complexity of ECCT algorithm is determined by the gap of the resource utilization between the initial useful resource usage and full load operation. [2] Top Challenges in the Project management are allocating the Resources, Leveling and Scheduling. Due to the complexity of initiatives, resource allocation and leveling had been handled as two precise sub issues solved mainly making use of heuristic programs that could not have assure optimal results. The Genetic Algorithms (fuel) process is used to seek for near-optimum solution, when you consider that both elements simultaneously. Within the extended heuristics, random priorities are introduced into chosen tasks and there have an effect on the agenda is monitored. The GA approach then searches for an highest quality set of tasks priorities that produces shorter mission period and higher-leveled resource profiles.[3] Simulated Annealing is probabilistic meta-heuristic approach. This is built for the worldwide optimization issue of locating a good approximation to the worldwide most desirable in a given function in a enormous and distinct search space. This technique is encouraged by way of the physical approach annealing where an outstanding is gradually cooled in order that eventually when the formation is frozen, it has occurred at the minimal power configuration. [4] Load balancing is crucial for optimization of resources in allotted environments. The principal goal of the cloud computing carrier vendors is to make use of cloud computing resources successfully to enhance the overall performance. Load balancing in cloud computing atmosphere is a technique to distribute workload throughout a couple of desktops to obtain best possible resource utilization with minimum response time. Novel VM assign algorithm which allocates incoming consumer request to available digital

3 An Improved Solution for Resource Scheduling Optimization by Resource Grouping Method machines relying on the burden i.e., VM with least work load is determined after which new request is allocated. [5] The important focus of Job Scheduling is assigning jobs to the cloud knowledge centers and allocates the resources to be had in the cloud in order that the complete time of tasks execution (Makespan) is minimized. The scheduling method starts by querying the couple of users requests and assigning the desired resource traits in GIS. According to the tasks houses (equivalent to CPU execution time, memory measurement) the tasks will probably be grouped situated on priority then map the bought duties to the cloud resources [6] Analysis is to combine VMs with special useful resource traits effectively in order that the capacities of servers are good utilized. The process multiplexes digital to bodily resources adaptively situated on the altering demand. This approach achieves overload avoidance at the same time the quantity of resources are extended, for that reason the resources utilization are balanced for programs with multi useful resource constraints using hybrid AB colony algorithm. [7] The objective of allocating the resources to tasks is for all services to satisfy their competence goal. Only some jobs stipulate extraordinary resources at the same time running concurrently. It is crucial for efficient working of cloud to strength these jobs on proper resources for top of the line efficiency, and various venture parameters have to be considered for correct scheduling. The accessible resources must be utilized without affecting the service parameters. Because the number of customers increases, the resources must be scheduled in a proper way. Consequently, there is mandatory for enhanced algorithms to schedule the resources. [8] Virtualization is most essential in Cloud Computing when considering with aid of offering a platform for optimizing complicated information technology resources. The main intention of the virtualization is a potential to run the couple of VMs on a single computing device with the aid of sharing all of the resources that belong to the hardware. The foremost issue of load balancing services occurs when the services from the users make a request to access to the identical server whilst other servers have no request from the services. This approach is called distributed load imbalance process. On this predicament, it can be solved through scheduling the tasks or the offerings earlier than making use of the approach. Therefore, an excellent assignment scheduler can develop the performance of resource utilization and might cut down the make span of assigned tasks which is called allotted load steadiness system. Virtual Machine Scheduling can be optimized by Synthetic Bee Colony (ABC) on Cloud Computing [9]. Scheduling the resources in cloud Environment is a decisive problem. Scheduling the model, rate, nice of service, time, and stipulations of the request for access to services are reasons to be beleaguered. Scheduling strategy may also be altered by way of good task scheduler for changing atmosphere and load balancing scheduling policy. An Optimization algorithm known as Synthetic Bee Colony (ABC) reproduces the intellectual foraging competency of honey bees. Hence, Virtual Machine is scheduled and optimized by ABC on Cloud Environment preemptively and in assorted tasks. [10] 3. PROBLEM STATEMENT The process of scheduling the various available resources to the corresponding Processing task is a combinatorial problem. The core objective of the Cloud Scheduler is to schedule the resources competently. Dynamic allocation of the resources to the processing task is done in cloud and is a very complicated process due to the assignment of several copies of the identical tasks to different resources. Cloud resources are assorted and dynamic in nature therefore the scheduling in cloud is a NP-hard problem.

4 44 S. Magesh Kumar and S Siva Subramanian 4. PROPOSED SYSTEM Cloud Providers and Cloud Users plays a significant role in Cloud Environment System, This is because, providers maintain tremendous computing resources in their big data centers and hire resources out to users on a per-usage basis. On the other side, there are users who have functions with unpredictable consignments and hire resources from provider to run their requests. First, a consumer sends a request for resources to a supplier. When the provider accepts the request, it looks for resources to meet the request and allocates the resources to the requesting consumer, usually as a form of virtual machines (VMs). Then the person uses the allocated resources to run requests and can pay for the resources which can be used. When the consumer is completed with the resources, they would be returned to the supplier. Appropriate scheduling is mandatory to meet user s standards and fulfills the exceptional of offerings. Hence, grouping mechanism on scheduling the resources are desirable in cloud computing. Grouping means collection of components on the basis of certain performance or features characteristic. Resource grouping in cloud implies that resources of identical type can be clustered together and then scheduled communally. Grouping approach might be very useful practice to resolve the resource scheduling issues and also allocates the processing task to the resources. Grouping strategy on the resources can be applied to solve the scheduling hindrance. Grouping method is sited on processing ability, bandwidth, and size of memory those attributes of resources and also applies grouping approaches: Grouping by deadline, location, participant and role : Here depicts grouping based resource Scheduling model, Scheduler and Resource Grouping mechanism and its illustration. 4.1 Proposed Resource Scheduling Model The four common constructing blocks of Cloud model are consumer, scheduler, Cloud Information system (CIS) and resources. Users tasks are sent to the scheduler in order to schedule to the resources with an intention of reduce the processing time and use the resources efficiently. Figure 1 illustrates scheduling structure, design of the scheduler and its interactions with other entities. The scheduler is a provider that resides in a person desktop. When the user creates a record of duties within the user desktop, these tasks are sent to the scheduler for scheduling. The scheduler acquires potential of accessible resources from the Cloud Information service (CIS). Based on these, the resource scheduling mechanism is used for grouping the resources and then processing task selection for grouped resources. When all the resources are placed into groups with chosen tasks, the grouped resources are allocated to their corresponding processing task for computation by the dispatcher. The Cloud Information service (CIS) supplies information about all the registered tasks in a cloud. This service continues track of the entire tasks traits within the cloud. CIS collects tasks characteristic information like running system, method structure, processing capability, network bandwidth and processing fee. It also provides customers the availability perception of the processing taskss. The information collector collects expertise from the Cloud Information Service (CIS). It assembles the processing task and its running capacity to the resource information desk. It also gathers information of the network bandwidth and processing price of each and every listed resource offered with the aid of the CIS. The expertise collector is utilized by the grouping and resource choice carrier to accumulate integral understanding to perform project grouping.

5 An Improved Solution for Resource Scheduling Optimization by Resource Grouping Method Figure 1. Scheduling model for Resource and Task Computing 4.2 Architecture of Scheduler The structure of the scheduler approach is described in Figure 2.The process accepts tasks from the customers particularly by their assignment identification, project size (in Million guidelines (MI)), mission input FILE size (in Mb) and whole number of tasks submitted by means of the user. After gathering details of user tasks, scheduler collects all the on hand computational resources information distinct via their useful resource identity, resource MIPS (computational vigor of the useful resource in Million guidelines per 2d), useful resource BANDWIDTH (in Mb/sec.), and useful resource price (in rate/sec). After gathering the details of user tasks and the on hand resources, the scheduler will pick a useful resource and multiplies the resource MIPS with the given granularity time, which is the time within which a task is processed on the useful resource. The worth of this calculation produces the whole Million guidelines (MI) for that detailed useful resource to approach within a detailed granularity time. The method selects duties in first-come first-serve (FCFS) order, after which tasks are grouped established on the resulting complete MI of resource and bandwidth. New IDs are assigned to grouped duties and scheduler submits the assignment organizations to their respective assets for computation. After executing the staff assignment, outcome goes to again to the corresponding users and the useful resource is again available to scheduler approach. Figure 2. Architecture of Scheduler

6 46 S. Magesh Kumar and S Siva Subramanian 4.3 Proposed Algorithm The scheduler accepts number of resources, tasks, average Million Instruction of tasks, deviation percentage of Million Instruction granularity size and processing of all tasks overhead. Resources are chosen. Then resource grouping algorithm is applied to the First Come First Serve order to allocate the resource groups to different processing tasks. n: Total number of processing task. r: Total number of Resources accessible. MIPS: Million instructions per second or processing ability of a resource. MI: Million instructions or processing necessities of user task. Tot-length: Total processing necessities (MI) of Task group (in MI) Tot-GMI: Total length of all tasks. Algorithm: Resource Grouping and Scheduling Algorithm Step 1: The scheduler accepts r Number of resources to be scheduled for n number of processing task Step 2: Scheduler accepts the Task-list T[] and Resource-list R[] Step 3: initialize all tasks length to 0 Step 4: Assign Task ID j to 1 and the index i to 1 Step 5: Find the MIPS of resource j Step 6: Multiply granularity size précised by the user with the MIPS of j th resource Step 7: Find the task length (MI) from the list Step 8: If task length is greater than the resource MIPS 8.1: The task cannot be assigned to the resource 8.2: Find the MIPS of the next resource 8.3: go to step 7 Step 9: If task length is less than resource MIPS Step 10: Do steps 10.1 to 11 while resource MIPS is greater than or equal to total length of all task and list has ungrouped resources 10.1: Add previous total length and current task length and set to current total length 10.2: Find the next task length Step 11: If the resource MIPS is less than total length 11.1: deduct the task length from Tot-length Step 12: If Tot-length is not equal to zero repeat steps 12.1 to : construct a new resource-group of length equal to Tot-length 12.2: Newly constructed resource-group is assigned with unique ID 12.3: Include the resource-group into a newly constructed resource group list 12.4: Allocated resource ID can be inserted into the Destination resource list of all grouped job

7 An Improved Solution for Resource Scheduling Optimization by Resource Grouping Method Step 13: Initialize Tot-GMI to zero Step 14: Find the MIPS of the successive resource Step 15: Multiply granularity size précised by the user with the MIPS of resource Step 16: Find the length (MI) of the task from the list Step 17: Go to step 8 Step 18: Repeat the above until each resource in the list are grouped into resource-groups Step 19: When all the resources are grouped and allocated to a tasks, send all the resource groups to their related tasks list of Grouped resources Step 20: After the execution of the resource-groups by the allocated tasks throw them back to the Destination task list. 5. PERFORMANCE EVALUATION AND RESULTS For evaluating the performance of the proposed resource grouping in cloud computing three matrices are considered particularly make span, rate and reliability. The reliability is the probability of the failure based on genuine measurements, the make span is the whole execution time and indicates the price per unit resources. The make span is measured in seconds and the price is measured in cloud dollars(c$). The reliability is measured founded on the rate, bandwidth and complexity. Table 1 suggests the characteristics of the resources and cloudlets that are being used. Fig.3 shows cost comparison of different algorithms that includes Resource Grouping Methods(RGM) Table 1 Scheduling Parameters and their values Parameters Values Number of Computer Systems 100 Number of tasks 1000 Bandwidth B/S Cost per Job 2.456$ Number of machine per resource RGM GA ACO Priority Based 0 Algorithms Figure 3. Cost Comparisons of Different Algorithms

8 48 S. Magesh Kumar and S Siva Subramanian 6. CONCLUSION Scheduling resources are imperative activity in Cloud computing, because a cloud provider has to provide many users in Cloud Computing System. In the cloud environment several copies of the identical tasks are assigning to the different resources. Henceforth, the dynamic allocation of resources is a difficult process. Accordingly there is mandatory for the scheduling procedure that has to allocate the resources to the processing tasks routinely. Thus, scheduling the resources is the most significant issue in establishing Cloud Computing Systems. In this paper we have discussed the difficulties of resource scheduling in computational Cloud, where user presents resources and tasks. Proposed approach illustrates efficient Resource-Grouping Based Scheduling Algorithm. It is obvious that proposed approaches decreases the total processing time and processing cost and also furthermore minimize the communication overhead. References 1. Liji Jacob Resource Scheduling in Cloud using Bacterial Foraging Optimization Algorithm, International Journal of Computer Applications ( ) Volume 92 No.1, April 2014,pp : Liang Hao, Gang Cui, Mingcheng Qu, Wende Ke, Resource Scheduling Optimization Algorithm of Energy Consumption for Cloud Computing Based on Task Tolerance, JOURNAL OF SOFTWARE, VOL. 9, NO. 4, APRIL 2014, pp : Tarek Hegazy, Optimization of Resource Allocation and Leveling using Genetic Algorithms, Journal Of Construction Engineering And Management, May/June 1999, pp: Akshat Dhingra, Sanchita Paul, Green Cloud: Smart Resource Allocation and Optimization using Simulated Annealing Technique, Indian Journal of Computer Science and Engineering (IJCSE), ISSN : , Vol. 5 No.2 Apr-May 2014, pp: B Preethi Optimization Of Resources in Cloud Computing Using Effective Load Balancing Algorithms, International Advanced Research Journal in Science, Engineering and Technology Vol. 1, Issue 1, September 2014, pp: Tasquia Mizan, Shah Murtaza Rashid Al Masud, Rohaya Latip, Modified Bees Life Algorithm for Job Scheduling in Hybrid Cloud, International Journal of Engineering and Technology Volume 2 No. 6, June, 2012 ISSN: , pp: N. Sasikala Effective Load Balancing for Cloud Computing using Hybrid AB Algorithm, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Issue 4, April 2014, pp: Saurabh Bilgaiyan, Santwana Sagnika, Madhabananda Das, An Analysis of Task Scheduling in Cloud Computing using Evolutionary and Swarm-based Algorithms, International Journal of Computer Applications ( ) Volume 89 No.2, March 2014, pp: B. Kruekaew and W. Kimpan, Virtual Machine Scheduling Management on Cloud Computing Using Artificial Bee Colony, Proceedings of the International MultiConference of Engineers and Computer Scientists 2014 Vol I, IMECS 2014, March 12-14, 2014, pp: S.Swathi Preemptive Virtual Machine Scheduling Using CLOUDSIM Tool, International Journal of Advances in Engineering, 2015, 1(3), ISSN: , pp: Jignesh Lakhani and Hitesh A. Bheda, An Approach to Optimized Resource Scheduling using Task Grouping in Cloud, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 9, September 2013, ISSN: X pp: