Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds

Wu, Quanwang; Ishikawa, Fuyuki; Zhu, Qingsheng; Xia, Yunni; Wen, Junhao

doi:10.1109/tpds.2017.2735400

Cited by 167 publications

(57 citation statements)

References 34 publications

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“…The algorithm focuses on the utilization and bandwidth of resources in the cloud environment without considering the cost and deadline, which are more important factors for users. Wu et al [27] propose two algorithms named ProLis and L-ACO to minimize the execution cost under the deadline constraint of workflow scheduling on the cloud. The ProLis algorithm distributes a deadline to each task, ranks the tasks, and sequentially allocates each task to satisfy the QoS.…”

Section: Related Workmentioning

confidence: 99%

An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

Gao

et al. 2019

J Wireless Com Network

122

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Large-scale applications of Internet of things (IoT), which require considerable computing tasks and storage resources, are increasingly deployed in cloud environments. Compared with the traditional computing model, characteristics of the cloud such as pay-as-you-go, unlimited expansion, and dynamic acquisition represent different conveniences for these applications using the IoT architecture. One of the major challenges is to satisfy the quality of service requirements while assigning resources to tasks. In this paper, we propose a deadline and cost-aware scheduling algorithm that minimizes the execution cost of a workflow under deadline constraints in the infrastructure as a service (IaaS) model. Considering the virtual machine (VM) performance variation and acquisition delay, we first divide tasks into different levels according to the topological structure so that no dependency exists between tasks at the same level. Three strings are used to code the genes in the proposed algorithm to better reflect the heterogeneous and resilient characteristics of cloud environments. Then, HEFT is used to generate individuals with the minimum completion time and cost. Novel schemes are developed for crossover and mutation to increase the diversity of the solutions. Based on this process, a task scheduling method that considers cost and deadlines is proposed. Experiments on workflows that simulate the structured tasks of the IoT demonstrate that our algorithm achieves a high success rate and performs well compared to state-of-the-art algorithms.

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Section: Related Workmentioning

confidence: 99%

An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

Gao

et al. 2019

J Wireless Com Network

122

View full text Add to dashboard Cite

show abstract

“…A Deadline-constrained Cost Optimization Approaches was designed in [19] with the application of ant colony optimization for workflow scheduling in clouds. A fuzzy dominance sort based heterogeneous earliest-finish-time (FDHEFT) algorithm was presented in [20] to diminish cost and makespan for workflow scheduling in the cloud.…”

Section: Related Workmentioning

confidence: 99%

Gene Optimized Deep Neural Round Robin Workflow Scheduling in Cloud

Shanmugasundaram¹,

Kumar²,

Kittur³

2019

IJACSA

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Workflow scheduling is a key problem to be solved in the cloud to increases the quality of services. Few research works have been designed for performing workflow scheduling using different techniques. But, scheduling performance of existing techniques was not effective when considering a larger number of user tasks. Besides, the makespan of workflow scheduling was higher. In order to solve such limitations, Gene Optimized Deep Neural Round Robin Scheduling (GODNRRS) Technique is proposed. The designed GODNRRS Technique contains three layers namely input, hidden and output layer to efficiently perform workflow scheduling in the cloud. The GODNRRS Technique initially gets the number of user tasks as input in the input layer and forwards it to the hidden layer. After taking input, GODNRRS Technique initializes gene population with the assist of virtual machines in Amazon cloud server at the first hidden layer. Next, GODNRRS Technique determines fitness function for each virtual machine using their energy, memory, CPU time, bandwidth capacity at the second hidden layer. Afterward, GODNRRS Technique defines a weight for each virtual machine at the third hidden layer depends on their fitness function estimation. Consequently, GODNRRS Technique distributes the user tasks to optimal virtual machines according to their weight value at the fourth hidden layer in a cyclic manner. At last, the output layer renders the scheduled tasks result. Thus, GODNRRS Technique handles workflows in the cloud with improved scheduling efficiency and lower energy and makespan. The GODNRRS Technique conduct the experimental evaluation using metrics such as scheduling efficiency, makespan, and energy consumption with respect to a different number of user tasks from LIGO , Montage and cybershake real-time applications. The experimental result show that the GODNRRS Technique is able to increases the efficiency and also reduces the makespan of workflows scheduling in the cloud as compared to state-of-the-art works. 1000 88 89 92 99 85 89 90 95 87 90 91 98

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“…In [8,24], authors have proposed a meta-heuristic algorithm to minimize cost with deadline constraints. The investigation of server power consumption with different number of VMs and various utilizations was discussed in [9].…”

Section: Related Workmentioning

confidence: 99%

“…The final constraint ensures that the control parameter (i.e., the channel communication rate) should be positive and less than the maximum network capacity. The powercost at the end-to-end link comes from equation (8) and the outcome can be given as…”

Section: Proposed Rtaes Technique For Optimization Problemmentioning

confidence: 99%

“…A cost-constrained Heterogeneous Earliest Finish-Time (HEFT) model was studied in [6,7], and these algorithms provides the optimum budget in different scenarios. The execution cost minimization for scientific workflows with a deadline constraint is considered and the meta-heuristic algorithm is proposed in [8]. The investigation of servers' power consumption with different numbers of VMs and various utilization has discussed in [9].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Adaptive Energy-Scheduling Algorithm for Virtualized Cloud Computing

B¹,

Krishnappa²,

Rajan³

et al. 2019

IJDPS

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Cloud computing becomes an ideal computing paradigm for scientific and commercial applications. The increased availability of the cloud models and allied developing models creates easier computing cloud environment. Energy consumption and effective energy management are the two important challenges in virtualized computing platforms. Energy consumption can be minimized by allocating computationally intensive tasks to a resource at a suitable frequency. An optimal Dynamic Voltage and Frequency Scaling (DVFS) based strategy of task allocation can minimize the overall consumption of energy and meet the required QoS. However, they do not control the internal and external switching to server frequencies, which causes the degradation of performance. In this paper, we propose the Real Time Adaptive Energy-Scheduling (RTAES) algorithm by manipulating the reconfiguring proficiency of Cloud Computing-Virtualized Data Centers (CCVDCs) for computationally intensive applications. The RTAES algorithm minimizes consumption of energy and time during computation, reconfiguration and communication. Our proposed model confirms the effectiveness of its implementation, scalability, power consumption and execution time with respect to other existing approaches.

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Deadline-Constrained Cost Optimization Approaches for Workflow Scheduling in Clouds

Cited by 167 publications

References 34 publications

An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

Gene Optimized Deep Neural Round Robin Workflow Scheduling in Cloud

Real-Time Adaptive Energy-Scheduling Algorithm for Virtualized Cloud Computing

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