Cost-driven scheduling of service processes in hybrid cloud with VM deployment and interval-based charging

Liang, Helan; Du, Yuyue; Gao, Enting; Sun, Jun

doi:10.1016/j.future.2020.01.035

Cited by 18 publications

(4 citation statements)

References 34 publications

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“…For a VM or physical node, CPU and memory are two important resources that are often used to measure its load or utilization in the construction of scheduling models, such as the literatures [14,22,[25][26][27][28][29][30][31][32][33][34][35][36][37]. Because…”

Section: Load Of a Vm Or Physical Nodementioning

confidence: 99%

“…[31] leveraged VMs' memory to sense the cache behaviour data of physical machines when designing memory-aware scheduling strategy for cyber-physical systems. [33] adopted CPU and memory to measure the processing capability of VMs or physical nodes. In [22], memory and network bandwidth were used to measure the load of a physical node.…”

Section: Load Of a Vm Or Physical Nodementioning

confidence: 99%

See 1 more Smart Citation

QoS-aware and Multi-Objective Virtual Machine Dynamic Scheduling for Big Data Centers in Clouds

Zhang

Yan

2021

Preprint

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Efficient resource scheduling is one of the most critical issues for big datacenters in clouds to provide continuous services for users. Many existing scheduling schemes based on tasks on virtual machine (VM), pursued either load balancing or migration cost under certain response time or energy efficiency, which cannot meet the true balance of the supply and demand between users and cloud providers. The paper focuses on the following multi-objective optimization problem: how to pay little migration cost as much as possible to keep system load balancing under meeting certain quality of service (QoS) via dynamic VM scheduling between limited physical nodes in a heterogeneous cloud cluster. To make these conflicting objectives coexist, a joint optimization function is designed for an overall evaluation on the basis of a load balancing estimation method, a migration cost estimation method and a QoS estimation method. To optimize the consolidation score, an array mapping and a tree crossover model are introduced, and an improved genetic algorithm (GA) based on them is proposed. Finally, empirical results based on Eucalyptus platform demonstrate the proposed scheme outperforms exiting VM scheduling models.

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Section: Load Of a Vm Or Physical Nodementioning

confidence: 99%

Section: Load Of a Vm Or Physical Nodementioning

confidence: 99%

QoS-aware and Multi-Objective Virtual Machine Dynamic Scheduling for Big Data Centers in Clouds

Zhang

Yan

2021

Preprint

View full text Add to dashboard Cite

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“…This structure necessitates frequent data transfers across compute cores, complicating job execution in hybrid environments with varying core performance and bandwidth constraints. Task scheduling in hybrid clouds aims to balance performance and cost, focusing on completion time and cost-effectiveness as primary QoS metrics [20][21][22][23][24][25][26], with some studies also exploring multi-objective optimization [27],…”

Section: Introductionmentioning

confidence: 99%

Adaptive Cross-platform Scheduling Framework for NWP in Hybrid Clouds

Ding

2024

Preprint

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Numerical Weather Prediction (NWP) requires real-time, high-accuracy processing, straining traditional high-performance computing clusters with limited resources, complex operations, and long queue times. Hybrid clouds merge the security of local clusters with the scalability of public clouds, providing a viable solution for high-performance computations. However, it also poses challenges: parallel programming for local clusters is not suitable for the various settings of hybrid clouds; complex parallelization policies increase communication overhead and complicate scheduling; and traditional static resource binding can lead to load imbalance in heterogeneous environments. This paper proposes an adaptive cross-platform scheduling strategy tailored to the characteristics of NWP models. This approach harmonizes the advantages of traditional and cloud-based parallel computing, integrating two distinct parallel programming methodologies and reconfiguring the parallel programming framework of the forecasting models. Experimental results show that the framework effectively improves adaptability and resource utilization, significantly improves computational efficiency and reduces operational overhead in hybrid cloud deployments.

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“…There are three service models: infrastructure as a service, platform as a service, and software as a service 1 . Currently, a great deal of research is being carried out on cloud services, covering aspects such as the syntax, semantics, and standards of the unified service description language, 2‐4 service/application architecture, 5 service discovery and selection, 6‐8 service deployment and configuration, 9,10 service composition, 11,12 service evaluation, 13,14 and so on.…”

Section: Introductionmentioning

confidence: 99%

An elastic recommender process for cloud service recommendation scalability

Zhou

Wang

et al. 2022

Concurrency and Computation

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Cloud computing services are ubiquitous in society and cloud recommender systems play a crucial role in intelligently selecting services for cloud users. Currently, recommendations are static with low scalability. Only one recommendation list is generated at a time and the recommender strategy in the recommendation cycle is not adjustable. This paper presents a new elastic recommender process (ERP) for cloud users. A Markov model is used to characterize the dynamic relationship between different user states. The ERP generates an elastic recommendation that can be used to dynamically adjust the recommender strategy to meet the user's needs based on their browsing records in the current service cycle without the recommender system's involvement. Experimental results show that the ERP improves the effectiveness of the recommender thus increasing the accuracy and diversity of its recommendations.

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Cost-driven scheduling of service processes in hybrid cloud with VM deployment and interval-based charging

Cited by 18 publications

References 34 publications

QoS-aware and Multi-Objective Virtual Machine Dynamic Scheduling for Big Data Centers in Clouds

QoS-aware and Multi-Objective Virtual Machine Dynamic Scheduling for Big Data Centers in Clouds

Adaptive Cross-platform Scheduling Framework for NWP in Hybrid Clouds

An elastic recommender process for cloud service recommendation scalability

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