International Journal of Research & Development in Technology and Management Science Kailash Volume - 21 Issue 1 ISBN March 2014

Transcription

1 A GREEDY ALGORITHM FOR TASK SCHEDULING & RESOURCE ALLOCATION PROBLEMS IN CLOUD COMPUTING By Lal Shri Vratt Singh Research Scholar Dept. of Computer Science, Jamia Hamdard University And Jawed Ahmed Assistant Professor Dept. of Computer Science, Jamia Hamdard University ABSTRACT Cloud computing is service based, on demand and pay per usage computing where elastic and scalable IT functionalities are delivered to multiple user ends using internet as a medium Distributed computing, utility computing and virtualization play a major role to provide an efficient base for the building of cloud computing concept. It gives a virtual pool of computing resources which are used by the users over the internet. After looking the benefits and advantages of cloud computing, the market is shifting from traditional technologies to cloud based technologies. Resource allocation is a concern that is used in many computing fields, such as datacenter management and operating systems. In cloud systems, resource allocation scheme can be used as a mechanism that aims to ensure that the applications' requirements are properly and correctly fulfilled by the provider's infrastructure. The providers of cloud computing services keep this most important thing in their mind that the resources are to be used efficiently so that they may gain maximum profit. This leads resource allocation and task scheduling as a core and challenging issue in cloud systems.

2 This paper deals with some efficient task scheduling and resource allocation algorithms that give the optimum result in cloud computing environment. KEYWORDS: Cloud computing, task scheduling, resource allocation algorithm, activity based costing, optimized algorithm. I. INTRODUCTION The approach of cloud computing has become very much popular to support and operate on demand services. It is a computing style where elastic and scalable IT resource functionalities Figure 1: Cloud computing architecture are delivered to the users using internet as services. In the cloud environment, the scheduling strategy determines the best selection of computing resources for efficient and the effective execution of the tasks after considering the restrictions and Static / dynamic behavior of subsequent tasks. As the cloud computing is becoming popular, the scheduling mechanism of cloud computing is receiving a big growing attention. Basically, scheduling is the process in which the tasks are mapped to the available resources after looking the requirements and characteristics of the tasks. It is essential concern while dealing with cloud environment that different task parameters need to be kept into consideration in order to achieve efficient scheduling.

3 II. PROBLEMS WITH RESOURCE ALLOCATION AND TASK SCHEDULING IN CLOUDE COMPUTING A. RESOURCE ALLOCATION IN CLOUD COMPUTING The Computational resources may be required to the user for specific time duration of a task. For example, a large number of computer systems can be required by a scientist to simulate a project for few hours, but it might not need, at any specific period of time. A software trainer may want to set up a cluster of computer systems with specific software configuration that is to be available to the trainee software professionals during the training sessions at specific times during the week. A telecom organization may process its existing setup that allows the hosting of many websites, but may need to substitute that setup in addition with more resources during the period of increasing web traffic. In other words, those resources have to be put ready and available right away with very Figure 2: Inputs in the allocation of resources advance notice [1]. The resource allocation mechanism has been used in many areas of computing, such as grid computing, operating systems and datacenter management. In the cloud environment, the resource allocation system can be looked as any mechanism that ensures the correct mapping of applications requirements to the providers infrastructure. Apart from this assurance to the developer, the current status of the each resource should be considered by the resource allocation mechanism in the cloud environment so that the implementation of algorithm may allocate physical and/or virtual resources more efficiently to the cloud applications for minimizing the operational cost. [6]

4 ALLOCATION OF VIRTUAL MACHINES (VMs): Allocation of virtual machines can be divided into parts. The first part considers the new requests for virtual machine provisioning and the transferring of virtual machines on hosts. The second part concentrates on optimizing the current allocation of virtual machines. If we talk about the complexity of the allocation part of the algorithm, it would be nxm which can be analyzed by calculating the number of virtual machines n which have to be allocated and the number of hosts m. Further the optimization of virtual machines current allocation is brought out in two steps- (i) selection of the virtual machines that need to be transferred and (ii) placement of the selected virtual machines on the hosts through allocation algorithm. B. PROBLEMS IN TASK SCHEDULING CLOUD SERVIVE SCHEDULING The scheduling of cloud service is carried out at user level and system level [11]. If the problems are raised between the providers and customers due to service provisioning, it is dealt at user level scheduling while the resource management within datacenter is handled at system level scheduling. A datacenter is integrated with many physical machines and the millions of tasks are received and assigned to the physical machine at datacenter. The performance of the datacenter is closely related with these assignments. In addition to system utilization other factors such as the quality of services (QoS), sharing of resources, real time satisfaction (RTS), reliability, service level agreement (SLA) etc. should be kept into consideration. USER LEVEL SCHEDULING To regulate the demand and supply of the cloud resources, the auction-based and marketbased schedulers are suitable schedulers. In the cloud computing environment, the effective allocation is the market-based resource allocation where the user gets the virtualized resources as a service.

5 A set of market-oriented task scheduling algorithms to an AuctionNet for heterogeneous distributed environment is proposed in [12]. Pricing model development using processorsharing for the clouds and its applications to composite services with dependency consideration the development of two sets of profit-driven scheduling algorithms are proposed in [13]. The provisioning of the services is completely based on the service level agreement (SLA) in cloud systems. The service level agreement provides a signed contract between the service provider and the customer containing the terms and conditions of the agreement including with non-functional requirements such as quality of services, penalties and obligations in case of breach of trust. Thus, the scheduling strategies along with efficient resource allocation and multiple SLA parameters are needed. A novel scheduling heuristic considers multiple SLA parameters for deploying. STATIC AND DYNAMIC SCHEDULING In the static scheduling the data are pre-fetched and pipelined to the different stages of the task execution. The static scheduling has relatively less runtime overhead. In the dynamic scheduling the job tasks/components are dynamic in nature and are not known before their execution so the execution time of the task cannot be judged in advance and the tasks allocation is carried out on fly as the application executes. An environment of task execution that avoids scalable static scheduling techniques provides a flexible pricing model to the user which reduces scheduling overheads for the cloud environment provider, has been discussed in [11]. In cloud domain which is having three-tier structure, the mechanism of service request scheduling consisting with service and resource providers along with customers should fulfill the requirements of consumers and service providers both. An algorithm to the dynamic feedback is proposed in [13] for the scheduling mechanism of preemptable jobs. In the cloud environment the fully utilization of resources and the procedure of feedback are efficiently improved by the preemptable scheduling. This also works well in such conditions where the contentions of resources are fierce. Because every task on the cloud environment is quite different from each other so accurate cost of cloud resources cannot be measured by the traditional way of task scheduling. For the task scheduling, an optimized algorithm and its

6 implementation are introduced in [16] which is based on activity based costing. In the hybrid cloud environment, an experiment is performed in [17] on different strategies of optimization for cost-optimal dynamic scheduling. In the cloud systems, an improved efficient backfill algorithm is proposed in [18] for achieving the quality of services. This algorithm uses the balanced spiral method (BSM). This paper has analyzed so many scheduling algorithms which support parallel jobs such as CBA and EASY. A scheduling algorithm which measures both parameters- computation performance and cost of the resources is proposed in [19]. This algorithm groups the user tasks according to the processing capability of a particular cloud resource and pushes the grouped jobs to the cloud resources and also enhances the computation/communication ratio. Due to the grouping of the jobs, the computation/communication ratio is optimized by the communication of resources and coarse-grained jobs. The tremendous emission of heat, carbon dioxide and energy from the cloud computing servers results pollution in the environment. Green task scheduling mechanisms which significantly reduce the pollution by minimizing the above concerned issues are proposed in [10] and [11]. The scheduling strategies which consider the issues and parameters other than explained above are discussed in [16] and [17]. Throughout the network, the information about the execution nodes which are available, is aggregated through the fully decentralized scheduler and is used to allocate jobs to those execution nodes which are able to execute them in time. The study of communication model and realistic network topology proposes Deadline Reliability Resource-aware (DDR) scheduling algorithm in [13]. An algorithm which is based on reinforcement learning is proposed in [14] that considers recovery and failure scenarios of the entities in cloud systems. It makes the scheduling fault tolerable while the utilities are attained for a long term. A latest framework of job scheduling strategy in tree network has been proposed in [10].

7 WORKFLOW SCHEDULING Within a directed acyclic graph the structure of the involved applications is enabled through its workflow [11], where nodes and edges represent the constituent tasks and their interdependencies to the applications [12]. In the work flow each task may be communicated with another task and a set of related tasks forms a single workflow. In workflow execution management, the workflow scheduling is regarded as the key issue. In the cloud systems, a survey on different workflow scheduling algorithms is presented in [13]. A study of different scheduling algorithms, issues and problems related to the cloud workflow is discussed in [14] while [15] covers the study of instance-intensive workflows in cloud systems. III. RELATED WORK AND SOLUTIONS A. RESOURCE ALLOCATION ALGORITHM The optimal use of the cloud resources is driven out very cleverly using the platformplacement algorithms. Eucalyptus which is available publically has an algorithm that pays a little attention on the placement of virtual machines within the clout systems. Open Nebula comes out with an interesting approach for the placement of virtual machines using its rank assessment by which so many useful policies are implemented. Nimbus presents Workspace Pilot and Workspace Resource Manager that are liable to take the responsibility for deploying the jobs and nodes respectively in the cloud environment. As such, those developers who treat the optimization of resource usage as a very critical requirement, it can be more attractive to discuss [6]. For IaaS clouds, the scheduling and resource allocation are key process. These clouds consider the virtual machines as scheduling units, which are allocated to physical resources of heterogeneous support. To confirm the resource allocation, Eucalyptus executes Round Robin and First-fit algorithms while Nebula uses Preemption Scheduling, Advance Reservation and Queuing System algorithms. Before task scheduling on cloud resources, some characteristics of the cloud systems should be kept into mind which include:-

8 Pay per use[1] Rapid elasticity Location independent resource pooling Ubiquitous network access On-demand self service The cloud resources are shared by millions of user via submitting their tasks to the offered cloud domain. It is a big challenge to schedule these lots of tasks efficiently within the cloud systems. To do this efficiently, various scheduling strategies are documented in [2] to [10]. These scheduling strategies focus on various factors such as cost matrices which are given by credit of tasks for a particular resource [2], light weighted VM scheduler based on Backfill strategy to dispatch jobs, Meta-scheduler based on QoS [3], requirements of QoS [4] and [5], cloud environment heterogeneity and its workload [8]. In the cloud environment, the allocation of resources and task scheduling decide the number of systems required for minimizing the total cost. The problems, related to the resource allocation are continuously occurred due to the highly dynamic nature of cloud computing, as the availability of servers is addressed while the customers demand for it at the same time. Thus this analysis concentrates on scheduling algorithms within the cloud systems after focusing above discussed issues, strategies, characteristics and challenges. For the IaaS clouds, it s a key process to schedule resources. Virtual machines are generally used as scheduling units in these IaaS clouds which have to be allocated to physical resources of heterogeneous type. In the cloud systems the selection of target resources takes place in various ways. It can be selected either randomly or by any other means (round robin or greedy i.e. waiting time and resource processing power based). The scheduling of the job selection can be done on the basis of SJF, coarse grained task grouping, FCFS or priority based scheduling etc. Scheduling algorithms are responsible for both, selection of the executable jobs and the corresponding resources which are to be assigned for the execution of jobs. As each successive selection strategy comes up with this approach that it will give a better solution and remove or minimize the prior algorithms drawbacks.

9 GREEDY ALLOCATION Greedy resource allocation algorithms are suitable for such heterogeneous resource environments which are dynamic in nature and are connected to a scheduler via homogeneous environment of communication [4]. Greedy approach is one of the best approaches which are used to solve the problems of job scheduling. GREEDY RESOURCE ALLOCATION ALGORITHM B. SOLUTION OF TASK SECHIDULING PROBLEM Now this paper focuses on developing an optimized algorithm which can help to optimize the parameters related to the performance of the system. After considering all above mentioned parameters, this paper will try to optimize the system performance and the users would see the ultimate improvement in the system performance.

10 IMPLEMENTATION OF ACTIVITY BASED COSTING (ABC) ALGORITHM IN CLOUD SYSTEMS Implementation of ABC algorithm can be explained by showing it in a parent-child relationship structure i.e. tree structure which is represented in figure 3. Using FIFO approach i.e. according to the tasks arrival time, the tasks will be stored in one parent queue. Then the requested resources and data for their concerned tasks will be checked and sorted into two disjoint queues- available and partially available. Further in the available queue, the dependency and/or independency of the tasks will be checked, according to which again they will be stored in respective queues. Now, in the queue which is partially available, there are some tasks which may need other data centers to provide them data resources. Then these tasks can be sorted in various queue categories like CT1, CT2, CT 3.and so on. These queue categories are decided on the basis of tasks resources, which they need. For example, if there are three tasks- task1, task2 and task4. These three tasks need resources from two locations of data centers- DCL1 and DCL2 then these all three tasks can be stored in one category. Let this category is CT1. Similarly, the categories CT2, CT3. and so on can be made for other similar types of tasks till the partially available queue gets empty. Now, there are major queues based on the dependency and independency of tasks and CT1, CT2.CTN. After it, again three priority-queues are made on the basis of High, Mid & Low priorities for each major queue which are mentioned above. Now this question arises that how the priority will be decided? The priority is decided by considering the four major factors- completing time, resources, space needed and profit. The following formula has been derived to decide the task-priority:-

11 Figure 3: The tree structure of task scheduling While deciding the priorities of the various tasks, the newly measured priority is checked with previous ones and then the tasks are placed into the priority-queues. When the placing of tasks into the queues gets finished, then the tasks from the High priority queues are selected for their execution. After the execution of the tasks from high priority queues, the tasks of mid priority queues are shifted to the queues with high priority, and by this way the execution of the tasks from all the queues gets started. Again one question arises here that how the selection of the task is sequenced, as there are many high priority queues? For giving the answer of this question, a very simple logic is used which is as below:-

12 Compare the priorities of those all tasks which are at top priority level within their respective high priority queues Then select the task having highest priority and assign space and resources. If there are remaining resources, then select next task using same strategy and assign space and resources. Repeat above steps till the end of remaining resources. When the allocation of all the resources gets full, then wait till the finishing of any task and as any task gets finished, choose next task and assign it the resources and space. ALGORITHM FOR ACTIVITY BASED COSTING IN CLOUD COMPUTING

13 IV. ANALYSIS OF THE PROPOSED ALGORITHM A. PERFORMANCE WITH RESPECT TO EXECUTION COST The tasks select the appropriate resources using the greedy approach and minimize their execution cost individually. By the proposed algorithm, the execution of tasks results better than sequential approach as shown in figure 4. As the cloudlets increase, this improvement in cost increases. Table 1: Comparison of execution cost

14 The following graph is showing the comparison between the execution cost of sequential and proposed algorithms. The tasks and execution cost are taken on x-axis and y-axis respectively:- Figure 4: Analysis of Execution Cost B. PERFORMANCE WITH RESPECT TO DEADLINE The following results show the performance of the cloud system in each trial as the cloudlets increase:- Table 2: Comparison of Completion Time The following graph is showing the comparison between the completion time of sequential and proposed algorithms. The tasks and completion time are taken on x-axis and y-axis respectively:-

15 Figure 5: Analysis of completion time V. CONCLUSION This paper comes out with new methods of task scheduling and resource allocation scheduling in cloud domain. It helps in the good understanding of resource allocation and task scheduling options. With the help of task scheduling, the resources of the cloud systems are efficiently allocated and managed and optimized. VI. FUTURE SCOPE A scheduling policy is always needed for the benefits of the customers and the service providers both. Each new approach, which may provide the better solution than previous strategies, is always welcome in research field. As far as the future scope of this work is concerned, the cost based scheduling policy may be studied more efficiently using this approach.

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