Fuzzy based Workflow Scheduling on Cloud Systems to Meet The Deadline of Real Time Tasks

IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 04, 2016 | ISSN (online): 2321-0613

Fuzzy Based Workflow Scheduling on Cloud Systems to Meet the Deadline of Real Time Tasks 2

Mrs Rekha A Kulkarni1 Dr. Suhas H Patil2 Dr. N Balaji3 Coe Bvcoe, Pune, Maharastra 3JNTU Kakinada,Vizianagaram

Abstract— As there is fast change in the design of computing systems and networking technologies, distributed computing is gaining prominence. Cloud computing being the form of distributed computing provides numbers of services to the user or to the large scale applications. Cloud computing is characterized by providing on demand services, resource pooling, rapid elasticity etc. Workflow scheduling (WFS) on cloud systems mainly focuses on task allocation to achieve the desired workload balancing by providing optimal utilization of available resources so as to meet quality of service (QOS) parameters. In a heterogeneous environment like cloud handling real time tasks poses challenges of VM scheduling, VM migration and load balancing to meet the deadlines. Implementation of real-time tasks poses a lot of problems due to the unpredictability of the tasks involved and due to the lack of complete task knowledge prior to the execution process.

Fuzzy logic can be used in dynamic scheduling of these tasks based on the load information at various VMs processing units. Fuzzy logic uses the system's deadline miss ratio (DMR) and system throughput to calculate a value which provides a system performance metric used to drive the system to the desired level of performance.

Key words: Workflow, VM migration, Fuzzy logic, QOS I. INTRODUCTION Cloud computing is a new technology in academic world.

On cloud computing platform, resources are provided as service and by needs, and it guarantees to the subscribers that it sticks to the Service Level Agreement (SLA).

However, due to the situation that the resources are shared and the needs of the sub scribers have big dynamic heterogeneity and platform irrelevance, it will definitely lead to resource waste if the resources cannot be distributed properly. Besides, the cloud computing platform also needs to dynamically balance the load among the serve rs in order to avoid hotspot and improve resource utility.

Therefore, how to dynamically and efficiently manage resources and to meet the needs of subscribers become the problems to be solved.

Cloud computing is emerging as new technology in the field of distributed computing. On cloud computing platform, resources are provided as service on demand.

While providing services to its uses or to the applications it tries to provide guarantees to meet the quality of service. For real-time applications and services, the timeliness is a major criterion in judging the quality of service. Due to the nature of real time applications over the Internet, the timeliness refers to more than the deadline guarantee as that for hard real-time systems. In this regard, an important performance metric for cloud computing can thus be the sum of certain value or utility that is accrued by processing all real-time service requests.

As one of the measures of the quality of service meeting of the deadline in real scenario possess the

challenge as workflow scheduling on cloud requires that the VM should be scheduled dynamically based on the load.

The other issue involved is the having the good load balance to improve the system performance.

Despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments.

The virtualization technology which is the base for cloud computing offers number of benefits to the users: It offers applications an abstract view through interfaces of the underlying hardware platform and resources. [6,7] virtualization has several benefits for enabling cloud computing: Functional execution isolation.

The hypervisor handles the protection among virtual machines (VMs) and, therefore, among the applications on different VMs. Users can be granted privileges within their virtual machine without compromising the isolation or host integrity. Virtualization enables the provisioning of highly specialized and customized environments that may contain specific purpose operating systems, libraries, and run-time execution environments. In fact, virtualization offers functional isolation therefore enabling multiple views over the same physical hardware.

Easier management.

Customized run-time environments can be started up, migrated, shut down, in a very flexible way, depending on the needs of who provides the underlying hardware.

Hypervisors and their live-migration capabilities allow for enhancing reliability of hosted virtualized applications, making them independent of the reliability of the underlying hardware, in a seamless and transparent manner for applications.

II. RELATED WORK A. Real-Time Challenges in Cloud Environment Virtualization brings a number of challenging issues for real time workloads[3][1]. First, the increased level of resource sharing among multiple operating systems makes it difficult to run software in predictable ways, as the performance of each virtual machine (VM) depends on the amount of resources (e.g., computing, storage or networking) other VMs are consuming. What is worse, in a cloud computing environment multiple VMs sharing the same physical hosts and networks are often hosted on behalf of different and independent tenants (customers). Furthermore, dynamic creation and migration of VMs introduces potentially high and bursts work-loads that can greatly interfere with the performance of VMs sharing the same physical resources.

This makes the problem of temporal interferences among VMs even more critical and important, as the performance exhibited by one VM does not only depend on the workload

All rights reserved by www.ijsrd.com

1300

Fuzzy Based Workflow Scheduling on Cloud Systems to Meet the Deadline of Real Time Tasks (IJSRD/Vol. 4/Issue 04/2016/312)

imposed within the cloud infrastructure by the same tenant, but also on the one imposed by VMs of other tenants. A situation that, albeit acceptable in an infantry stage of cloud computing, is not destined to last for too long, as users will gain confidence in the technology and evolve their still primitive requirements. We report below some of the most important contributions in the area of real-time scheduling and resource management for QoS-sensitive virtualized workloads.

From the distributed cloud computing real-time perspective, the essential target is the improvement of the virtualization technology to guarantee contracted resources at the level of the execution platform in order to meet the applications timing properties. Other important directions are the design of scheduling and execution algorithms that enable, in the same VM, job co-existence with temporal and spatial isolation guarantees. Albeit the real-time research community addressed hierarchical scheduling from a theoretical standpoint, the concrete support of these concepts within virtualized environments is practically null, leaving developers and practitioners clueless about the way to possibly achieve temporal isolation and performance guarantees for critical tasks within guest operating systems.

Support for dynamic job scheduling would enable any new job to join a system in execution without altering the temporal properties of the whole system. This requires to implement multi-level resource managers integrating general purpose network protocols as they are the mainstream in cloud, e.g., Ethernet. One other challenge will be the real-time live migration where VMs containing realtime applications are transferred between different physical servers.

Virtualization technology provides an effective solution to the management of dynamic resources on cloud computing platform virtualization technology is able to carry out remapping between virtual Machine (VM) and physical resources according to the load change so as to achieve the load balance of the whole system in a dynamic manner how to schedule VM resources to realize load balancing in cloud computing and to improve resource utility becomes an important research point Currently in cloud computing, it mainly considers the current system condition in VM resources scheduling but seldom considers the pervious condition before scheduling and the influence on system load after scheduling which usually leads to load imbalance. Most of the load balancing exists in VM migration. Yet, when the entire VM resources are migrated, due to the large granularity of VM resources and the great amount of data transferred in migration and the suspension of VM service, the migration cost becomes a problem. This paper presents a scheduling strategy to realize load balancing. According to historical data and current state and through genetic algorithm this method computes in advance the influence it will have when the current VM service resources that need deploying are arranged to every physical node, then it choose s the deployment that will have the least influence on the system. In this way, the method realizes the best load balancing and reduces or avoids dynamic migration. The VM can improve its running efficiency while meeting the QoS needs of subscribers.

B. Real-Time Scheduling and Resource Management [3] What is worse, in a cloud computing environment multiple VMs sharing the same physical hosts and networks are often hosted on behalf of different and independent (customers).

Furthermore, dynamic creation and migration of VMs introduces potentially high and bursty work-loads that can greatly interfere with the performance of VMs sharing the same physical resources. This makes the problem of temporal interferences among VMs even more critical and important, as the performance exhibited by one VM does not only depend on the workload imposed within the cloud infrastructure by the same tenant, but also on the one imposed by VMs of other tenants. A situation that, albeit acceptable in an infantry stage of cloud computing, is not destined to last for too long, as users will gain confidence in the technology and evolve their still primitive requirements.

We report below some of the most important contributions in the area of real-time scheduling and resource management for QoS-sensitive virtualized workloads.

The main idea is to use fuzzy logic algorithm [4] to investigate the priority of a job to be executed first. Second fuzzy algorithm is used to adapt priorities of other jobs being waiting in case of new job arrives and considering deadlines for these jobs. This fuzzy logic load scheduling algorithm is used within multi-objective algorithm to minimize average delay, number of overdue jobs, and throughput times of jobs.

A scheduling system must react to the requests within a fixed amount of time which is called deadline.

Real-time scheduling systems are vital to industrialized infrastructure such as command and control, process control, flight control, and air traffic control systems [4]. In all cases, processing time has an essential function and having the right response too late is as bad as not having it at all. There are two common inference processes [4]. First is called Mamdani’s fuzzy inference method proposed in 1975 by Ebrahim Mamdani and the other is Takagi-Sugeno-Kang, or simply Sugeno, method of fuzzy inference introduced in 1985 . These two methods are the same in many respects, such as the procedure of fuzzifying the inputs and fuzzy operators. The main difference between Mamdani and Sugeno is that the Sugeno output membership functions are either linear or constant but Mamdani’s inference expects the output membership functions to be fuzzy sets.

In our proposed method fuzzy logic is been utilized to assign the jobs priority based on their deadline and other system information and at the second stage fuzzy logic is been employed to decide on VM scheduling and migration decisions which can help us to achieve the desired level of quality of service.

III. PROPOSED METHODOLOGY “Fuzzy logic “, A form of knowledge representation suitable for notions that cannot be defined precisely but which depend upon their context. It enables computerized devices to reason more like human fuzzy set. Fuzzy logic is multivalued logic that allows intermediate values to be defined between conventional evaluations. Notions like rather tall or very fast can be formulated mathematically and processed by computers in order to apply more human like way of thinking in the programming of computers.

All rights reserved by www.ijsrd.com

1301

Fuzzy Based Workflow Scheduling on Cloud Systems to Meet the Deadline of Real Time Tasks (IJSRD/Vol. 4/Issue 04/2016/312)

The performance of a scheduling algorithm is measured in terms of additional processor required to be added at a schedule without deadline violations as compared to optimal algorithm it has been proved that finding a minimal schedule for a set of real-time tasks in multiprocessor system is NP-hard. With the above analysis, the ability to satisfy timing constraints of such real-time applications plays a significant role in distributed environment. However, the existing schedulers are not perfectly suitable for real-time tasks, because they lack strict requirement of deadlines. A real-time scheduler must ensure that processes meet deadlines, regardless of system load or inherent delays which are caused by various components of distributed systems.

Our Proposed system S will be represented as S={ I,O,F,Fa,Su} Where I=Set of inputs {t1……..tn}{p1…..pn} O={ Set of output } finding optimal mapping of tasks to processors.

F= {Set of functions f1,f2,f3,f4…} Where F1 is gathering of information about various resources in system.

F2 generation of scheduling policy based on the parameters collected from f2.

F3 is to decide which isthe best policy for given application.

F4 fuzzy function to decide on VM migration.

Su: Success case when scheduling policy improves the performance by meeting the dead line of real time task.

Fa: failure case when deadline of time critical applications are not being met.

Fuzzy [4] inference is the process of formulating the mapping from a given input set to an output using fuzzy logic. Laxity is the maximum time that a task can wait before being executed (i.e., laxity = deadline - computation time). Much of the power of fuzzy logic is derived from its ability to draw conclusion and generate responses based on vague, incomplete, and imprecise qualitative data.

To build a fuzzy system, inputs and output(s) to it must be first selected. After that, they must be partitioned into appropriate conceptual categories which actually represents a fuzzy set on a given input or output domain.

The conceptual partitions developed for the input and output dimensions are used to create a fuzzy rule set which determines the behavior of the fuzzy system being constructed. Changing the input variables causes the corresponding membership functions to be changed accordingly.

Fuzzy inference systems (FIS) are conceptually very simple. They consist of an input, a processing, and an output stage. The input stage maps the inputs to the appropriate membership functions and truth values. The processing stage invokes each appropriate rule and generates a corresponding result. It then combines the results. Finally, the output stage converts the combined result back into a specific output value an infrastructure for meeting the deadlines in real time For the first proposed fuzzy algorithm, the input stage consists of three linguistic variables. The first one is job exterior priority which is the priority assigned to the job from the outside world. The second input variable is the job waiting time. Third input parameter is job processing time. The output is the determined job processing priority.

This output is fed together with job deadline time as inputs

to second fuzzy algorithm and its output is final job processing priority. Fuzzy logic is used to schedule jobs according to processing priorities. Job priority only affects jobs in a machines queue, not how jobs get to a job queue.

Fig. 1: Fuzzy logic for workflow scheduling After fixing the job priority the next task is to schedule them on VMs in case if the VM has to be migrated how to fulfill the deadline of real time task in the challenge.

Fuzzy logic can be applied for use of VM migration in deciding where to move the VM or select those set of resources which satisfy the requirements of the job.

IV. EXPERIMENTAL RESULTS Implementation of above said approach can be done with the help of cloud simulator which can be used in the laboratories for experimentation. We have used CLOUDSIM in as simulation tool to study the effect of fuzzy logic on scheduling policy. The fuzzy logic helps to meet the deadlines of the tasks.

V. CONCLUSION Meeting the deadline for Resource and Timing Constraints is a Challenge in a cloud system. While working with shared environment users will compete for resources to meet their timing deadlines. In such a scenario, resource scheduling plays a critical part in meeting an application’s expectations.

In large clusters, tasks complete at such a high rate that resources can be reassigned to new jobs on a timescale much smaller than job duration. The job to be scheduled next according to fairness might not have data on the nodes that are currently free. In a heterogeneous environment like cloud where processing speeds are likely to vary among nodes, high processing nodes are expected to complete more tasks. In such a scenario, the concept of equally distributing data in order to provide data locality is likely to create network bottleneck. For heterogeneous systems, effective data placement strategies are required in order to ensure efficient task scheduling. So the proposed system tries to select a scheduler at run time which can meet the timing requirements based on the information available locally and globally. With the help of fuzzy logic it is possible to address the uncertainties involved while dealing with large

All rights reserved by www.ijsrd.com

1302

Fuzzy Based Workflow Scheduling on Cloud Systems to Meet the Deadline of Real Time Tasks (IJSRD/Vol. 4/Issue 04/2016/312)

amount of processors. With the help of Cloud simulators it is possible to create tasks.

REFERENCES [1] Tommaso Cucinotta Chenyang Lu Marisol GarcíaValls ,“Challenges in real-time virtualization and predictable cloud computing” Journal of Systems Architecture 60 (2014) 726–740 [2] Jianhua Gu Jinhua Hu Tianhai Zhao Guofei Sun A New Resource Scheduling Strategy Based on Genetic Algorithm in Cloud Computing Environment journal of computers, vol. 7, no. 1, january 2012, [3] Ehab Nabiel Alkhanak, Sai Peck Lee, Saif Ur Rehman Khan, “Cost-aware challenges for workflow scheduling approaches in cloud computing environments: Taxonomy and opportunities Future Generation Computer Systems 50 (2015) 3–21 [4] M.M.M. Fahmy ,“A fuzzy algorithm for scheduling non-periodic jobs on soft real-time single processor system”, Ain Shams Engineering Journal (2010) 1, 31– 38 [5] Shuo Liu, Gang Quan,“On line scheduling of real time services for cloud computing “ , ,,2010 IEEE 6th world Congress on services.

[6] W. Huang, J. Liu, B. Abali, D. Panda, A case for high performance computing with virtual machines, in: Proc. of 20th Annual ACM Int’l Conference on Supercomputing, 2006, pp. 125–134.

[7] M. Mergen, V. Uhlig, O. Krieger, J. Xenidis, Virtualization for high performance computing, in: Proc.

[8] K. Liu, H. Jin, J. Chen, X. Liu, D. Yuan, and Y. Yang, “A Compromised-Time-Cost Scheduling Algorithm in SwinDeW-C for Instance-Intensive Cost- Constrained Workflows on Cloud Computing Platform.” J. of High Performance Computing Applications, 24:445–456, Nov 2010.

[9] J. Gu, J. Hu, T. Zhao, and G. Sun, “A new resource scheduling strategy based on genetic algorithm in cloud computing environment,” J. Comput., vol. 7, no. 1, pp.

42–52, 2012.

[10] A. Oprescu and T. Kielmann, “Bag-of-Tasks Scheduling under Budget Constraints.” in 2nd International Conference on Cloud Computing, Los Angeles, USA, 2009.

[11] A. Oprescu and T. Kielmann, “Bag-of-Tasks Scheduling under Budget Constraints.” in 2nd International Conference on Cloud Computing, Los Angeles, USA, 2009 computing based on hybrid particle swarm algorithm," [12] S. H. Jang, T. Y. Kim, J. K. Kim and J. S. Lee, “The Study of Genetic Algorithm-based Task Scheduling for Cloud Computing,” IJCA, vol. 5, no. 4, pp. 157-162, December 2012.

[13] H. Ye, J. Wong, G. Iszlai and M. Litoiu, “Resource Provisioning for Cloud Computing,” Proceedings of CASCON 2009, November 2009 [14] T. Henzinge, A. V. Singh, V. Singh, T. Wies and D. Zufferey, “Flex-PRICE: Flexible Provisioning of Resources in a Cloud Environment,” In CLOUD, 2010.

[15] M. Paul and G. Sanyal, "Task-scheduling in cloud computing using credit based assignment problem,"

International Journal on Computer Science and Engineering (IJCSE) 3.10, pp. 3426-3430, 2011.

[16] S. Xue and W. Wu, "Scheduling workflow in cloud computing based on hybrid particle swarm algorithm," TELKOMNIKA Indonesian Journal of Electrical Engineering 10.7, pp.1560-1566, 2012.

[17] X. Wu, M. Deng, R. Zhang, B. Zeng and S. Zhou, “A task scheduling algorithm based on QoS driven in cloud computing,” Advances in Engineering Software, Procedia Computer Science, 17, pp. 1162-1169, 2013.

[18] B. M. Xu, C. Y. Zhao, E. Z. Hu and B. Hu, “Job scheduling algorithm based on Berger model in cloud environment,” Advances in Engineering Software, 42(7), 419-425, 2011.

[19] I. A. Moschakis and H. D. Karatza, “Performance and cost evaluation of Gang Scheduling in a Cloud Computing system with job migrations and starvation handling,” In: Proceedings of IEEE Symposium on Computers and Communications (ISCC), pp. 418–423, 2011.

[20] S. Ghanbari and M. Othman, “A priority based job scheduling algorithm in cloud computing,” Procedia Engineering, 50, pp. 778-785, 2012.

[21] L. F. Bittencourt and E. R. M. Madeira, “HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds,” Journal of Internet Services and Applications, vol. 2, pp. 207–227, 201112] [22] S. H. Jang, T. Y. Kim, J. K. Kim and J. S. Lee, “The Study of Genetic Algorithm-based Task Scheduling for Cloud Computing,” IJCA, vol. 5, no. 4, pp. 157-162, December 2012.

[23] H. Ye, J. Wong, G. Iszlai and M. Litoiu, “Resource Provisioning for Cloud Computing,” Proceedings of CASCON 2009, November 2009 [24] T. Henzinge, A. V. Singh, V. Singh, T. Wies and D. Zufferey, “Flex-PRICE: Flexible Provisioning of Resources in a Cloud Environment,” In CLOUD, 2010.

[25] M. Paul and G. Sanyal, "Task-scheduling in cloud computing using credit based assignment problem," International Journal on Computer Science and Engineering (IJCSE) 3.10, pp. 3426-3430, 2011.

[26] S. Xue and W. Wu, "Scheduling workflow in cloud TELKOMNIKA Indonesian Journal of Electrical Engineering 10.7, pp.1560-1566, 2012.

[27] X. Wu, M. Deng, R. Zhang, B. Zeng and S. Zhou, “A task scheduling algorithm based on QoS driven in cloud computing,” Advances in Engineering Software, Procedia Computer Science, 17, pp. 1162-1169, 2013.

[28] B. M. Xu, C. Y. Zhao, E. Z. Hu and B. Hu, “Job scheduling algorithm based on Berger model in cloud environment,” Advances in Engineering Software, 42(7), 419-425, 2011.

[29] I. A. Moschakis and H. D. Karatza, “Performance and cost evaluation of Gang Scheduling in a Cloud Computing system with job migrations and starvation handling,” In: Proceedings of IEEE Symposium on Computers and Communications (ISCC), pp. 418–423, 2011.

[30] S. Ghanbari and M. Othman, “A priority based job scheduling algorithm in cloud computing,” Procedia Engineering, 50, pp. 778-785, 2012.

All rights reserved by www.ijsrd.com

1303

Fuzzy Based Workflow Scheduling on Cloud Systems to Meet the Deadline of Real Time Tasks (IJSRD/Vol. 4/Issue 04/2016/312)

[31] L. F. Bittencourt and E. R. M. Madeira, “HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds,” Journal of Internet Services and Applications, vol. 2, pp. 207–227, 2011.

All rights reserved by www.ijsrd.com

1304