Cloud-aware data intensive workflow scheduling on volunteer computing systems

Ghafarian, Toktam; Javadi, Bahman

doi:10.1016/j.future.2014.11.007

Cited by 50 publications

(28 citation statements)

References 35 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Evolving distributed computing environments have resulted in the development of numerous techniques to facilitate partitioning of tasks that can be executed at multiple geographic locations [32], [33]. For example, workflows are partitioned for execution in different locations [34], [35].…”

Section: Challenge 3 -Partitioning and Offloading Tasksmentioning

confidence: 99%

Challenges and Opportunities in Edge Computing

Varghese

Wang

Barbhuiya

et al. 2016

2016 IEEE International Conference on Smart Cloud (SmartCloud)

433

217

View full text Add to dashboard Cite

Many cloud-based applications employ a data centre as a central server to process data that is generated by edge devices, such as smartphones, tablets and wearables. This model places ever increasing demands on communication and computational infrastructure with inevitable adverse effect on Qualityof-Service and Experience. The concept of Edge Computing is predicated on moving some of this computational load towards the edge of the network to harness computational capabilities that are currently untapped in edge nodes, such as base stations, routers and switches. This position paper considers the challenges and opportunities that arise out of this new direction in the computing landscape.

show abstract

Section: Challenge 3 -Partitioning and Offloading Tasksmentioning

confidence: 99%

Challenges and Opportunities in Edge Computing

Varghese

Wang

Barbhuiya

et al. 2016

2016 IEEE International Conference on Smart Cloud (SmartCloud)

433

217

View full text Add to dashboard Cite

show abstract

“…They also verify the effectiveness of their algorithm under the price of the electricity consumed by their peers. To make full use of computing resources and increase the percentage of workflows that meet the deadline, Ghafarian et al [27,28] proposed a workflow scheduling algorithm. The proposed workflow scheduling algorithm partitions a workflow into sub-workflows to minimize data dependencies among the sub-workflows.…”

Section: Task Scheduling Algorithms In Volunteer Computing Platformsmentioning

confidence: 99%

Dynamic Task Scheduling Algorithm with Deadline Constraint in Heterogeneous Volunteer Computing Platforms

Qiao

Lin

et al. 2019

Future Internet

View full text Add to dashboard Cite

Volunteer computing (VC) is a distributed computing paradigm, which provides unlimited computing resources in the form of donated idle resources for many large-scale scientific computing applications. Task scheduling is one of the most challenging problems in VC. Although, dynamic scheduling problem with deadline constraint has been extensively studied in prior studies in the heterogeneous system, such as cloud computing and clusters, these algorithms can’t be fully applied to VC. This is because volunteer nodes can get offline whenever they want without taking any responsibility, which is different from other distributed computing. For this situation, this paper proposes a dynamic task scheduling algorithm for heterogeneous VC with deadline constraint, called deadline preference dispatch scheduling (DPDS). The DPDS algorithm selects tasks with the nearest deadline each time and assigns them to volunteer nodes (VN), which solves the dynamic task scheduling problem with deadline constraint. To make full use of resources and maximize the number of completed tasks before the deadline constraint, on the basis of the DPDS algorithm, improved dispatch constraint scheduling (IDCS) is further proposed. To verify our algorithms, we conducted experiments, and the results show that the proposed algorithms can effectively solve the dynamic task assignment problem with deadline constraint in VC.

show abstract

“…and schedule the data intensive workflow on cloud resources as well as volunteer computing if the task is taking more time as compare to expect than the task has be schedule to the cloud [9]. Two strategies have been proposed, first technique reduce the cost by considering the deadline and second strategy improve the cost by considering the deadline.…”

Section: Ghafarian Et Al (2015) Devised the Technique To Placementioning

confidence: 99%

Extended Balanced Scheduler with Clustering and Rep- lication for Data Intensive Scientific Workflow Applications in Cloud Computing

Kaur¹,

Aggarwal²

2018

JERA

View full text Add to dashboard Cite

Cloud computing is an advance computing model using which several applications, data and countless IT services are provided over the Internet. Task scheduling plays a crucial role in cloud computing systems. The issue of task scheduling can be viewed as the finding or searching an optimal mapping/assignment of set of subtasks of different tasks over the available set of resources so that we can achieve the desired goals for tasks. With the enlargement of users of cloud the tasks need to be scheduled. Cloud’s performance depends on the task scheduling algorithms used. Numerous algorithms have been submitted in the past to solve the task scheduling problem for heterogeneous network of computers. The existing research work proposes different methods for data intensive applications which are energy and deadline aware task scheduling method. As scientific workflow is combination of fine grain and coarse grain task. Every task scheduled to VM has system overhead. If multiple fine grain task are executing in scientific workflow, it increase the scheduling overhead. To overcome the scheduling overhead, multiple small tasks has been combined to large task, which decrease the scheduling overhead and improve the execution time of the workflow. Horizontal clustering has been used to cluster the fine grained task further replication technique has been combined. The proposed scheduling algorithm improves the performance metrics such as execution time and cost. Further this research can be extended with improved clustering technique and replication methods.

show abstract

Cloud-aware data intensive workflow scheduling on volunteer computing systems

Cited by 50 publications

References 35 publications

Challenges and Opportunities in Edge Computing

Challenges and Opportunities in Edge Computing

Dynamic Task Scheduling Algorithm with Deadline Constraint in Heterogeneous Volunteer Computing Platforms

Extended Balanced Scheduler with Clustering and Rep- lication for Data Intensive Scientific Workflow Applications in Cloud Computing

Contact Info

Product

Resources

About