Multi-objective workflow optimization strategy (MOWOS) for cloud computing

Konjaang, J. Kok; Xu, Lina

doi:10.1186/s13677-020-00219-1

Cited by 39 publications

(36 citation statements)

References 59 publications

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“…In [16], the multi-objective workflow optimization strategy (MOWOS) employs the tasks splitting mechanism to split large tasks into sub-tasks to reduce their scheduling length. The simulation results showed that, the MOWOS algorithm had less execution cost and, better execution MS, and also it utilized the resources better than the existing HSLJF [15] and SECURE [19] algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…This is due to the interlink that exists between these objectives. MS and cost optimization problem persist because the virtual machine (VM) selection (which is a key to managing resource utilization (RU) to improve system throughput) is usually ignored by researchers [16].…”

Section: Introductionmentioning

confidence: 99%

“…Note that, most real-world problems are of the multi-criteria type. The review of the literature shows that some research gaps such as VM selection and task mapping criteria still exist, which require further investigation [16]. The obtained results of the experiment indicate that the optimized smart MPI approach (O-SMPIA) outperform the SMPIA [18] in terms of total execution time (TET), MS and RU.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

Mokhtari¹,

Motameni²,

Bayat³

2022

Computer Systems Science and Engineering

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In this study, a new feature is added to the smart message passing interface (SMPI) approach (SMPIA) based on the prioritization method, which can completely eliminate the task starvation and lack of sufficient resources problems through prioritizing the tasks. The proposed approach is based on prioritizing the tasks and the urgency of implementation. Tasks are prioritized based on execution time, workload, the task with a more sensitive priority is executed earlier by the free source. The idea of demand-bound functions (DBFs) was extended to the SMPIA setting based on partitions and caps. For each task, two DBFs are constructed, DBFLO and DBFHI, for the LO and HI criticality modes, respectively. The simulation results returned by MATLAB showed that with the optimized SMPIA (O-SMPIA), the parameters of maximum service execution time, response time, delay time, and throughput improved in this work. In addition, the results confirmed that the reduction of execution time, completion time, and resource consumption time did not affect the response time and throughput of workflow tasks and did not cause inefficient use of resources in virtual machines (VMs) and data centers (DCs). The evaluation of performance metrics showed that the delay, response time of the Greedy algorithm was less than that of Max-Min and Min-Min. At the same time, the execution time of Max-Min was less than the others and the throughput of the Greedy was longer. The effect and throughput of O-SMPIA became more obvious as change to the job count and the number of cloud workloads increased. It is also worth mentioning that one of the main advantages of the O-SMPIA to other methods is the efficient use of time to execute all the defined tasks by CPU.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

Mokhtari¹,

Motameni²,

Bayat³

2022

Computer Systems Science and Engineering

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“…The genetic algorithm, artificial bee colony optimization, and decoding heuristic are integrated to schedule workflow tasks to cloud resources to simultaneously minimize the makespan and cost [23]. Konjaang et al [24] designed a MaxVM selection and a MinVM selection for task allocation to reduce the monetary cost and makespan of workflows while guaranteeing their deadlines. Adhikari et al [25] aggregated four competing objectives of workflow scheduling into a fitness function, and employed the Firefly algorithm to search the near-optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows

Zhou¹,

Jiao

2022

IEEE Access

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Cloud workflow scheduling often encounters two conflicting optimization objectives of makespan and monetary cost, and is a representative multi-objective optimization problem (MOP). Its challenges mainly come from three aspects: 1) a large number of tasks in a workflow cause large-scale decision variables; 2) the two optimization objectives are of quite different scales; 3) and cloud resources are heterogeneous and elastic. So far, many studies focus on adopting multi-objective evolutionary algorithms (MOEAs) to solve the cloud workflow scheduling problem without mining the domain knowledge. To make a good tradeoff between the makespan and monetary cost, this paper puts forward a knowledge-driven multi-objective evolutionary workflow scheduling algorithm, abbreviated as KMEWSA, including two novel features. On the one hand, the structural knowledge of workflow is mined to simplify the large-scale decision variables into a series of small-scale components, such accelerating the convergence speed of MOEAs. On the other hand, the knowledge on the Pareto front range is mined to estimate the ideal and nadir points for the objective space normalization during the search process, which helps maintain population diversity for MOEAs. At last, based on twenty real-world workflows and parameters of Amazon EC2, extensive experiments are performed to compare the KMEWSA with three baseline algorithms. The results demonstrate the effectiveness of the KMEWSA in balancing makespan and monetary cost for deploying workflows in cloud computing.

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“…In scientific applications, the workflow scheduling acknowledged as an NP-hard problem [5] is one of the core issues because workflow tasks are interdependent and cannot be executed in a simple way [6]. Single scientific workflow usually contains numerous interdependent tasks that are tricky to schedule and difficult to perform at less time [7]. Edge computing environments typically own a number of computational machines (servers) provided by different service providers with dissimilar configurations, transmission, and acquisition time overhead [8,9].…”

Section: Introductionmentioning

confidence: 99%

Scientific Workflow Makespan Minimization In Edge Multiple Service Providers Environment

Bukhari

Younus²,

Jaffari

et al. 2021

Preprint

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The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

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Multi-objective workflow optimization strategy (MOWOS) for cloud computing

Cited by 39 publications

References 59 publications

Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

Solving the Task Starvation and Resources Problem Using Optimized SMPIA in Cloud

Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows

Scientific Workflow Makespan Minimization In Edge Multiple Service Providers Environment

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