FCM

Song, Jiachen; Li, Xi; Sun, Beilei; Cheng, Zhinan; Wang, Chao; Zhou, Xuehai

doi:10.1145/2902961.2902989

Cited by 8 publications

(1 citation statement)

References 6 publications

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“…But there are much more contributing factors to rendering time of frames than only the number of API calls and the size of texture data like the size of 3D data models. Song et al [11] proposed a fine-grained GPU power management for closed source mobile games which works based on the number of vertices, the number of commands and the size of textures. Such an approach doesn't take into account the effects of other contributing factors like the structure and performance of the graphics API that processes all the aforementioned parameters.…”

Section: Related Workmentioning

confidence: 99%

An API-Level Frame Workload Model for Real-Time Rendering Applications

Mohammadi¹,

Ghanbari²,

Hashemi³

2020

Preprint

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Section: Related Workmentioning

confidence: 99%

An API-Level Frame Workload Model for Real-Time Rendering Applications

Mohammadi¹,

Ghanbari²,

Hashemi³

2020

Preprint

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Economic Impact of Resource Optimisation in Cloud Environment Using Different Virtual Machine Allocation Policies

Ahmad

Maroof

McClean

et al. 2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Economic impact of energy saving techniques in cloud server

et al. 2019

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In recent years, lot of research has been carried in the field of cloud computing and distributed systems to investigate and understand their performance. Economic impact of energy consumption is of major concern for major companies. Cloud Computing companies (Google, Yahoo, Gaikai, ONLIVE, Amazon and eBay) use large data centers which are comprised of virtual computers that are placed globally and require a lot of power cost to maintain. Demand for energy consumption is increasing day by day in IT firms. Therefore, Cloud Computing companies face challenges towards the economic impact in terms of power costs. Energy consumption is dependent upon several factors, e.g., service level agreement, virtual machine selection techniques, optimization policies, workload types etc. We address a solution for the energy saving problem by enabling dynamic voltage and frequency scaling technique for gaming data centers. The dynamic voltage and frequency scaling technique is compared against non-power aware and static threshold detection techniques. This helps service providers to meet the quality of service and quality of experience constraints by meeting service level agreements. The CloudSim platform is used for implementation of the scenario in which game traces are used as a workload for testing the technique. Selection of better techniques can help gaming servers to save energy cost and maintain a better quality of service for users placed globally. The novelty of the work provides an opportunity to investigate which technique behaves better, i.e., dynamic, static or non-power aware. The results demonstrate that less energy is consumed by implementing a dynamic voltage and frequency approach in comparison with static threshold consolidation or non-power aware technique. Therefore, more economical quality of services could be provided to the end users.

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FCM

Cited by 8 publications

References 6 publications

An API-Level Frame Workload Model for Real-Time Rendering Applications

An API-Level Frame Workload Model for Real-Time Rendering Applications

Economic Impact of Resource Optimisation in Cloud Environment Using Different Virtual Machine Allocation Policies

Economic impact of energy saving techniques in cloud server

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