Meeting Soft Deadlines in Scientific Workflows Using Resubmission Impact

Plankensteiner, Kassian; Prodan, Radu

doi:10.1109/tpds.2011.221

Cited by 42 publications

(33 citation statements)

References 11 publications

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“…And also resubmission may lead to much late finish time for tasks and fail to meet deadlines. On the other side, the task replication creates multiple copies of a task and assign each copy to different resources to ensure successful execution of the task before its deadline [8]. Xiaomin Zhu et al [9] developed a fault-tolerant model that extends the traditional Primary Backup model on cloud and further, proposed a dynamic fault-tolerant scheduling algorithm to improve the resource utilization and execution of SWf tasks in the presence of node failures in virtualized clouds.…”

Section: Related Workmentioning

confidence: 99%

Fault-Tolerant Scheduling for Scientific Workflows in Cloud Environments

Vinay

Kumar

2017

2017 IEEE 7th International Advance Computing Conference (IACC)

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Abstract-Executing clustered tasks has proven to be an efficient method to improve the computation of Scientific Workflows (SWf) on clouds. However, clustered tasks has a higher probability of suffering from failures than a single task. Therefore, fault tolerance in cloud computing is extremely essential while running large-scale scientific applications. In this paper, a new heuristic called Cluster based Heterogeneous Earliest Finish Time (C-HEFT) algorithm to enhance the scheduling and fault tolerance mechanism for SWf in highly distributed cloud environments is proposed. To mitigate the failure of clustered tasks, this algorithm uses idle-time of the provisioned resources to resubmit failed clustered tasks for successful execution of SWf. Experimental results show that the proposed algorithm have convincing impact on the SWf executions and also drastically reduce the resource waste compared to existing task replication techniques. A trace based simulation of five real SWf shows that this algorithm is able to sustain unexpected task failures with minimal cost and makespan.

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Section: Related Workmentioning

confidence: 99%

Fault-Tolerant Scheduling for Scientific Workflows in Cloud Environments

Vinay

Kumar

2017

2017 IEEE 7th International Advance Computing Conference (IACC)

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“…Plankensteiner and Prodan [21] proposed an algorithm for scheduling of workflow applications on Grids with tasks replication to meet deadline constraints. Our algorithm is inspired by such method by considering particularities of Cloud infrastructures, such as cost and capacity of dynamic provisioning, when making replication decisions in Cloud environments.…”

Section: Related Workmentioning

confidence: 99%

Meeting Deadlines of Scientific Workflows in Public Clouds with Tasks Replication

Calheiros

Buyya

2014

IEEE Trans. Parallel Distrib. Syst.

205

118

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Abstract-The elasticity of Cloud infrastructures makes them a suitable platform for execution of deadline-constrained workflow applications, because resources available to the application can be dynamically increased to enable application speedup. Existing research in execution of scientific workflows in Clouds either try to minimize the workflow execution time ignoring deadlines and budgets or focus on the minimization of cost while trying to meet the application deadline. However, they implement limited contingency strategies to correct delays caused by underestimation of tasks execution time or fluctuations in the delivered performance of leased public Cloud resources. To mitigate effects of performance variation of resources on soft deadlines of workflow applications, we propose an algorithm that uses idle time of provisioned resources and budget surplus to replicate tasks. Simulation experiments with four well-known scientific workflows show that the proposed algorithm increases the likelihood of deadlines being met and reduces the total execution time of applications as the budget available for replication increases.

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“…In MapReduce [11], to handle worker failures, the tasks on a failed worker are reset back to their initial states, and re-execute on other workers. Plankensteiner et al proposed a new heuristic named Resubmission Impact to achieve fault tolerance in distributed systems [12]. Nonetheless, the resubmission may significantly delay the finish time of tasks after failures, which is catastrophic for real-time tasks due to their strict timing requirements.…”

Section: Related Workmentioning

confidence: 99%

“…If there is no suitable active host, the system migrates some VMs among the active hosts to make room for the new VM (see lines 7-11). If it still fails, a host in sleep status will be turned on, and then the new VM is allocated on it (see lines [12][13][14][15][16][17]. When some VMs are idle for a relatively long time, it means that the system is in the light workload and some resources are wasted.…”

Section: Elastic Resource Provisioning Mechanismmentioning

confidence: 99%

FESTAL: Fault-Tolerant Elastic Scheduling Algorithm for Real-Time Tasks in Virtualized Clouds

Wang

Bao

Zhu

et al. 2015

IEEE Trans. Comput.

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As clouds have been deployed widely in various fields, the reliability and availability of clouds become the major concern of cloud service providers and users. Thereby, fault tolerance in clouds receives a great deal of attention in both industry and academia, especially for real-time applications due to their safety critical nature. Large amounts of researches have been conducted to realize fault tolerance in distributed systems, among which fault-tolerant scheduling plays a significant role. However, few researches on the faulttolerant scheduling study the virtualization and the elasticity, two key features of clouds, sufficiently. To address this issue, this paper presents a fault-tolerant mechanism which extends the Primary-Backup (PB) model to incorporate the features of clouds. Meanwhile, for the first time, we propose an elastic resource provisioning mechanism in the fault-tolerant context to improve the resource utilization. On the basis of the fault-tolerant mechanism and the elastic resource provisioning mechanism, we design novel fault-tolerant elastic scheduling algorithms for real-time tasks in clouds named FESTAL, aiming at achieving both fault tolerance and high resource utilization in clouds. Extensive experiments injecting with random synthetic workloads as well as the workload from the latest version of the Google cloud tracelogs are conducted by CloudSim to compare FESTAL with three baseline algorithms, i.e., Non-Migration-FESTAL (NMFESTAL), Non-Overlapping-FESTAL (NOFESTAL), and Elastic First Fit (EFF). The experimental results demonstrate that FESTAL is able to effectively enhance the performance of virtualized clouds.Index Terms-cloud, fault-tolerant scheduling, elasticity, primary-backup model. ! 0018-9340 (c)

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Meeting Soft Deadlines in Scientific Workflows Using Resubmission Impact

Cited by 42 publications

References 11 publications

Fault-Tolerant Scheduling for Scientific Workflows in Cloud Environments

Fault-Tolerant Scheduling for Scientific Workflows in Cloud Environments

Meeting Deadlines of Scientific Workflows in Public Clouds with Tasks Replication

FESTAL: Fault-Tolerant Elastic Scheduling Algorithm for Real-Time Tasks in Virtualized Clouds

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