An Efficient Data Processing Frameworkfor Cloud Services Using Nephele. Prof.S.Nagadevi,Sharmila.P

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1 An Efficient Data Processing Frameworkfor Cloud Services Using Nephele Prof.S.Nagadevi,Sharmila.P Department of Computer Science and Engineering,SRM University, Chennai, India Abstract Recently, there has been a dramatic increase in the popularity of cloud computing systems that provides an illusion of infinite computing resources to cloud users so that they can increase or decrease their resource consumption rate according to the demands.the computerresources offered in the cloud are extremely dynamic and probably heterogeneous in nature.with the emergence of parallel data processingmost of the majorcloud computing companies started to incorporate frameworks for parallel data processing in their portfolio, making it feasible for customers to access these services and to deploy their programs. However, these processing frameworks have been designed for static, homogeneous cluster setups disregarding the nature of cloud. Consequently, increasing processing time and cost.the cloud resource management,which is to allocate and schedule computing resources in a way that the providers achieve high resource utilization meeting theperformance requirements of users can be addressed using the data processing framework Nephelewhich offers efficient parallel data processing in clouds fully utilizing the dynamic resource allocation offered by IaaS clouds for both, task scheduling and execution.scheduling the resource and tasks separately involves more waiting and response time. In the proposed system, a scheduling algorithm known as Linear Scheduling for Tasks and Resources (LSTR) is used in Nephele, which performs both tasks and resources scheduling respectively and allocate resources maximizing the system throughput and resource utilization. Keywords Cloud Computing, Parallel Data Processing,task scheduling,resourceutilization, dynamic resource allocation. 1. Introduction Cloud computing aims to give users virtually unlimited pay-per-use computing resources without the burden of managing the underlying infrastructure.it is defined as a model for enabling ubiquitous, convenient, on-demand access to a shared pool of configurable computing resources (e.g., networks, server storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. 62

2 There are three types of cloud computing: Infrastructure as a Service (IaaS) Platform as a Service (PaaS) Software as a Service (SaaS). Fig 1.a Cloud Computing Using Software as a Service, users also rent application software and databases. The cloud providers manage the infrastructure and platforms on which the applications run. End users access cloud based applications through a web browser or a light weight desktop or mobile app while the business software and user's data are stored on servers at a remote location. Proponents claim that cloud computing allows enterprises to get their applications up and running faster, with improved manageability and less maintenance, and enables IT to more rapidly adjust resources to meet fluctuating and unpredictable business demand. Fig 1.b Cloud Computing Layers 63

3 For companies that only have to process large amounts of data occasionally running their own data center is obviously not an option. Instead, Cloud computing has emerged as a promising approach to rent a large IT infrastructure on a short-term pay-per-usage basis.operators of socalled IaaS clouds, like Amazon EC2, let customers allocate,access and control a set of Virtual Machines (VMs)which run inside their data centers and only charge them for the period of time the machines are allocated.the VMs are typically offered in different types, eachtype with its own characteristics. Since cloud computing is sold on demand on the basis of time constrains basically specified in minutes or hours. Thus scheduling should be made in such a way that the resource should be utilized efficiently. 1.1Related Work An important notifying advantage of infrastructure-as-a-service (IaaS) clouds is that it provides users on-demand access to resources. To provide on-demand access, cloud providers must eithersignificantly overprovision their infrastructure such as paying a high price for operating resources with low utilization or to reject a large proportion of user requests in which case the access is no longer on-demand. At the same time, the important concept is that not all users require truly on demand access to resources of IaaS. Many applications and workflows are designed for recoverable systems where interruptions in service are expected. Resource consumption and resource allocation have to be integrated so as to improve the resource utilization. The percentage of resource used and the type of resource allocated protrudes directly to the resource utilization, iethe number of request submitted and the availability of the VM s for allocation should be integrated and taken into consideration. Ad hoc parallel data processing is one of the major obstacles for the Infrastructure as a Service (IaaS) clouds. Thus many cloud computing organizations have started to integrate the framework for parallel data processing, which brings easy access to the customers for using theservices and for deploying their application codes. The processing of parallel frameworks focuses only on the static infrastructure rather than dynamic. Daniel Warneke et al evaluated the opportunities and the challenges in establishing dynamic resource allocation offered in the IaaScloud environment. AnirbanKundu et al evaluated the memory utilization in cloud computing based on transparency. Transparency is used as a virtualization in which the user is unaware of the origin of the resource unit, rather the request is satisfied as a single point resource satisfaction. The resource allocation is made based on the selection criteria which will improve the efficiency of the cloud environment.moreover, the use of virtualization and resource time sharing introduces significant performance penalties for the demanding scientific computing workloads. 1.2 Existing System Existing solutions to task scheduling problems in Nephele are unsuitable for cloud computing because they only focus on a specific purpose like the minimization of execution time or workload and do not use characteristics of Cloud computing for task scheduling.in addition to that, the scheduling algorithmsused in Nephele, performs task and resource scheduling separatelywhich results in more waiting and response time. 64

4 1.3 Proposed System The scheduling algorithms mainly focus on the distribution of the resources among the requestors that will maximize the selected QoS parameters.the scheduling algorithm is designed in Nephele data processing framework considering the tasks and the availablevirtual machines together and named LSTR scheduling strategy. This is designed to maximize the resource utilization.the scheduling algorithm is carried out based on the prediction that the initial response to the request is made only after collecting the resource for a finite amount of time but not allocating the resource as they arrive. The dynamic allocation could be carried out by the scheduler dynamically on request for additional resources. This is made by the continuous evaluation of the threshold value. It sorts the collected request excluding the arrival time. The shortest request in both A[RQi] and B[RQi] is processed first which results in the allocating resource to more number of requests than in FIFO technique Advantages of Proposed System Performs both tasks and resources scheduling respectively and allocate resources maximizing the system throughput and resource utilization. Reduces waiting time and response time spent on scheduling task and resources It mainly focuses on the distribution of the resources among the requestors maximizing the selected QoS parameters. Designed to run data analysis job on a large amount of data ensuring a high throughput computing. Cost effective processing of data exploiting all the available resources to the full extent. 2. Background Nephele is a new data processing framework for cloud environment that takes up many ideas of previous processing frameworks but refines them to better match the dynamic and opaque nature of a cloud.nephele s architecture follows a classic master-worker pattern. Before submitting a Nephele compute job, a user must start a VM in the cloud which runs the so called Job Manager (JM). The Job Manager which receivesthe client s jobs is responsible for scheduling them and coordinates their execution. It communicates with the interface,cloud Controller thatthe cloud operator provides to control the instantiation of VMs. By means of the Cloud Controller the Job Manager can allocate or deallocate VMs according to the current job execution phase. Fig. 2.a.Structural overview of Nephele 65

5 The actual execution of tasks which a Nephele job consists of is carried out by a set of instances. Each instance runs a so-called Task Manager (TM). A Task Manager receives one or more tasks from the Job Manager at a time, executes them and after that informs the Job Manager about their completion or possible errors. Unless a job is submitted to the Job Manager, the set of instances are expected (and hence the set of Task Managers) to be empty. Upon job reception the Job Manager then decides, depending on the job s particular tasks, how many and what type of instances the job should be executed on and when the respective instances must be allocated/deallocated to ensure a continuous but cost-efficient processing. The newly allocated instances boot up with a previously compiled VM image. The image is configured to automatically start a Task Manager and register it with the Job Manager. Once all the necessary Task Managers have successfully contacted the Job Manager, it triggers the execution of the scheduled job.the persistent storage is supposed to store the job s input data and eventually receive its output data. It must be accessible for both the Job Manager as well as for the set of Task Managers, even if they are connected by a private or virtual network. Fig. 2.b.An Illustration of Workflow innephele 2.1 Job Graph Jobs in Nephele are expressed as directed acyclic graph (DAG).Each vertex in the graph represents a task of overall processing job, the graph s edge define the communication flow between these tasks. Defining a Nephele job comprises following mandatory steps: The user must connect to virtual machine and start his task The task program must be assigned to a vertex Finally, the vertices must be connected by edges to define the communication paths of the job. Tasks are expected to contain sequential code and process so-called records, the primary data unit in Nephele. Programmers can define arbitrary types of records. From a programmer s perspective records enter and leave the task program through input or output gates. Those input and output gates can be considered endpoints of the DAG s edges which are defined in the following step. Regular tasks (i.e., tasks which are later assigned to inner vertices of the DAG) must have at least one or more input and output gates. 66

6 Channel types for each edge connecting two vertices the user can determine a channel type. Before executing a job, Nephele requires all edges of the original Job Graph to be replaced by at least one channel of a specific type. The channel type dictates how records are transported from one subtask to another at runtime. Currently, Nephele supports network, file, and inmemory channels. The choice of the channel type can have several implications on the entire job schedule.once the Job Graph is specified, the user submits it to the Job Manager, together with the credentials obtained from the cloud operator. 2.2 Execution Graph Once the valid Job Graph is received from the user, Nephele s Job Manager transforms it into Execution Graph. An Execution Graph is Nephele s primary data structure for scheduling and monitoring the execution of a Nephele job. The Execution Graph contains all the information required to schedule and executes the received job on the cloud. It explicitly models task parallelization and the mapping of tasks to instances. Depending on the level of annotations the user has provided with his Job Graph, Nephele may have different degrees of freedom in constructing the Execution Graph. Task 1 can besplitted into two parallel subtasks which are both connected to the task Output 1 via file channels and are all scheduled to run on the same instance. The exact structure of the Execution Graph is explained in the following Fig 2.2 Execution Graph created from the original Job Graph 2.3 Algorithm Step1: The requests are collected between every pre-determined interval of time. Step 2: Resources Ri -> {R1,R2, R3,,Rn} Step 3: Requests RQi -> {RQ1, RQ2, RQ3,,RQn} Step 4: Threshold (static at initial) Step 5: Th= summation of Ri Step 6: for every unsorted array A & B Step 7: sort A & B Step 8: for every RQi 67

7 Step 9: if RQi<Th then Step 10: add RQi in low array, A[RQi] Step 11: else if RQi>Th then Step 12: add RQi in high array B[RQi] Step 13: for every B[RQi] Step 14: ** allocate resource for RQi of B Step 15: Ri= Ri-RQi; Step 16: satisfy the resource of A[RQi] Step 17: for every A[RQi] 18 ** allocate resource for RQi of A Step 19: Ri= Ri-RQi; Th= summation of Ri Step 20: satisfy the resource of B[RQi] ** Best fit strategy is used to satisfy the request alternatively in A[RQi] and B[RQi] based on the available VM. Ri=(R1,R2,R3..Rn). 4. Conclusion The challenges and opportunities for efficient parallel data processing in cloud environments are analyzedusing Nephele, data processing framework to exploit the dynamic resource provisioning offered by today s IaaS clouds job,as well as the possibility to automatically allocate/deallocate virtual machines in the course of a job execution, can help to improve the overall resource utilization andconsequentlyreduce the processing cost. 5. References [1] Daniel Warneke and OdejKao, Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud,IEEE Transactions on Parallel and Distributed System, vol 22, june 2011 [2] Abirami S.P.1 and ShaliniRamanathan, Linear Scheduling Strategy for Resource Allocation in Cloud Environment, International Journal on Cloud Computing: Services and Architecture(IJCCSA),Vol.2, No.1,February 2012 [3]HaiZhong, Kun Tao, Xuejie Zhang, An Approach to Optimized Resource Scheduling Algorithm for Open-source Cloud Systems, IEEE Computer Society, , [4] D.Battre, S.Ewen, F.Hueske, O. Kao, V. Markl, and D.Warneke Nephele/PACTs: A Programming Model and Execution Framework for Web-Scale Analytical Processing, Proc.ACMSymp.Cloud Computing (SoCC 10), pp , [5] Chenhong Zhao, Shanshan Zhang, Qingfeng Liu, Independent Tasks Scheduling Based on GeneticAlgorithm in Cloud Computing, IEEE Computer society, ,2009. [6]AnirbanKundu, Chandan Banerjee, Sutirtha Kr. Guha, ArnabMitra, 4Souvik Chakraborty, Chiranjit Pal, Rahul Roy, Memory Utilization in Cloud Computing using Transparency. 68

8 [7] Amazon Web Services LLC, Amazon Elastic Compute Cloud (Amazon EC2), [8] Amazon Web Services LLC, Amazon Elastic MapReduce, [9]AmazonWebServicesLLC.AmazonSimpleStorage Service [10] R.Chaiken, B. Jenkins, P.-A.Larson, B. Ramsey, D. Shakib, S. Weaver, and J. Zhou. SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets. Proc. VLDB Endow 1(2): ,

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