A Data-Driven Frequency Scaling Approach for Deadline-aware Energy Efficient Scheduling on Graphics Processing Units (GPUs)

Ilager, Shashikant; Muralidhar, Rajeev; Rammohanrao, Kotagiri; Buyya, Rajkumar

doi:10.48550/arxiv.2004.08177

Cited by 1 publication

(1 citation statement)

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“…GPU power consumption models are used to develop and optimize GPU architectures for power consumption [15]. GPU energy models are utilized for energy-aware GPU resource management [25]. Unlike power consumption, energy consumption is affected by other factors, such as execution time and temperature in the long execution time.…”

Section: Power and Energy Modelsmentioning

confidence: 99%

Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing

Alnori,

Djemame,

Alsenani

2024

Applied Sciences

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The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction.

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Section: Power and Energy Modelsmentioning

confidence: 99%