EHEFT-R: multi-objective task scheduling scheme in cloud computing

Zhang, Honglin; Wu, Yaohua; Sun, Zaixing

doi:10.1007/s40747-021-00479-7

Cited by 15 publications

(10 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We are already utilizing the nodelevel cache to reduce the communication tasks and have already implemented a separate scheduler and simulation mentioned in the paper. Conversely, EHEFT-R utilizes remapping resource allocation rules to optimize optimal machines for the ranked tasks [17].…”

Section: Related Workmentioning

confidence: 99%

Julia Cloud Matrix Machine: Dynamic Matrix Language Acceleration on Multicore Clusters in the Cloud

Hwan¹,

Kim²,

Ryu³

et al. 2022

Preprint

View full text Add to dashboard Cite

Matrix computations are widely used in increasing sizes and complexity in the fields of scientific computing and engineering. But with current matrix language implementations it is a challenging task to fully utilize Cloud compute capacities. We present a new framework called cloud matrix machine, which extends the Julia high-performance compute language to automatically parallelize matrix computations for the cloud. With this framework, users are shielded from the complexity of explicitly parallel computations. Instead, users employ a novel matrix data type with lazy evaluation semantics to facilitate implicit parallelization of matrix operations. A combination of offline profiling, dynamic simulation, and scheduling are utilized to select optimal tile sizes, schedule, and execute matrix operations. All computations occur in the Cloud, with minimal user intervention. We conducted an extensive experimental evaluation on a set of eight benchmarks using up to eight nodes (288 vCPUs) in the AWS public cloud. Our framework achieved speedups of up to a factor of 3.49 ×, within 20.5 % of the theoretically possible maximum speedup. CCS CONCEPTS• Computing methodologies → Shared memory algorithms; Parallel computing methodologies.

show abstract

Section: Related Workmentioning

confidence: 99%

Julia Cloud Matrix Machine: Dynamic Matrix Language Acceleration on Multicore Clusters in the Cloud

Hwan¹,

Kim²,

Ryu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In the second paper, "Portfolio optimization model with uncertain returns based on prospect theory" [13], the authors developed an uncertain revenue portfolio optimization model from the perspective of expected utility maximization based on the prospect theory. Considering the complex non-smooth and nonconcave characteristics of the model, they devised an improved grey Wolf optimization (GWO) algorithm.…”

Section: Other Data-driven Operations Management Problemsmentioning

confidence: 99%

“…

Data-driven operations managementWe can roughly divide the accepted 15 papers into four groups according to their topics: data-driven supply chain management [1-4], data-driven process scheduling [5-8], data-driven healthcare operations management [9][10][11], and other data-driven operations management problems [12][13][14][15]. In the following, we formally introduce related works in detail.

…”

mentioning

confidence: 99%

Guest editorial on “data-driven operations management”

Wang

Cheng

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…In [32], an enhanced heterogeneous earlier finish time based on rule (EHEFT-R) task scheduling algorithm was proposed. The goal of this algorithm is to optimize task execution efficiency, quality of service (QoS) and energy consumption.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Workflow Scheduling Algorithm for Cloud Computing Environment

2022

IJIES

View full text Add to dashboard Cite

Cloud computing has gained many attentions worldwide. Workflow systems become a significant method for develop scientific applications. Therefore, workflow scheduling is considered one of the most important issues in cloud computing. It concerns about mapping tasks on cloud resources (i.e., Virtual machines (VMs)), to improve scheduling performance. Because the existing heterogeneous earliest finish time (HEFT) algorithm is considered one of the best and simplest algorithms, many algorithms have been proposed to improve the performance of the HEFT algorithm. According to our previous work, a modification has been done to HEFT algorithm to enhance the performance, called modified heterogeneous earliest finish time (M-HEFT). The goal of M-HEFT algorithm is to reduce make span, maximize resource utilization and increase load balance. According to the work in this paper, an enhancement has been added to our previous M-EFT algorithm to reduce the tradeoff among make span, resource utilization, and load balance, called enhanced modified heterogeneous earliest finish time (EM-HEFT). The enhanced EM-HEFT algorithm consists of two phases; task prioritization and task-VM mapping. In task prioritization phase, a priority will be provided to each task in directed acyclic graph (DAG) by introducing new factors in priority value to be more aware about task requirements. According to task-VM phase, tasks are allocated to resources as in our previous M-HEFT algorithm. To evaluate the performance of the proposed EM-HEFT algorithm, a comparative study has been done among the proposed algorithm and four existed algorithms (HEFT, Efficient scheduling algorithm use critical path and static level attribute, Optimized Min-Min (OMin-Min) and our previous M-HEFT). The experimental results show that the proposed algorithm outperforms other algorithms by minimizing make span by 25%, improving resource utilization by 43% and load balance by 14% in average.

show abstract

EHEFT-R: multi-objective task scheduling scheme in cloud computing

Cited by 15 publications

References 16 publications

Julia Cloud Matrix Machine: Dynamic Matrix Language Acceleration on Multicore Clusters in the Cloud

Julia Cloud Matrix Machine: Dynamic Matrix Language Acceleration on Multicore Clusters in the Cloud

Guest editorial on “data-driven operations management”

An Enhanced Workflow Scheduling Algorithm for Cloud Computing Environment

Contact Info

Product

Resources

About