Energy-efficient soft real-time CPU scheduling for mobile multimedia systems
By Wanghong Yuan, Klara Nahrstedt. Symposium on Operating Systems Principles, 2003
441 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Hardware and Architecture
View Paper: https://www.semanticscholar.org/paper/28657806b32c2e018c350f8378e51d484798d014
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Khaled Ben Letaief
- Changsheng You
- Kaibin Huang
- Jun Zhang
- Yuyi Mao
- Rui Zhang
- Xuemin Shen
- Hyukjin Chae
In Hardware and Architecture
- David Wetherall
- John Wilkes
- Khaled Ben Letaief
- Amin Vahdat
- Yonggang Wen
- Haiyun Luo
- Xiaohui Gu
- Kaibin Huang
Citations in the corpus
- CloudScale: elastic resource scaling for multi-tenant cloud systems (2011, background): “Although initial work [19, 42] on power saving focused on mobile devices, it has become increasingly important to consider energy saving while managing large-scale hosting centers.”
- Enforcing Performance Isolation Across Virtual Machines in Xen (2006, background): “The problem of performance isolation has been actively addr essed by multimedia systems [8, 18].”
- A Survey on Mobile Edge Computing: The Communication Perspective (2017, background): “This model can also be generalized to handle the soft deadline requirement which allows a small portion of tasks to be completed after τd [58].”
- Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization (2020, methodology): “The computing energy model is adopted from [1], [30].”
- Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning (2016, methodology): “In the following, we summarize the energy consumption model for computation [32], [33], communication [3], [8], [16] and flying [22]–[25].”
- Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer (2015, background): “Adopting the model in [17], [18], the required number of CPU cycles for computing given data is randomly generated as follows.”
- Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel (2013, background): “, the complexity of the algorithm, has been treated in [27], [19], [28], and is thus beyond the scope of this paper.”
- Reducing Network Energy Consumption via Sleeping and Rate-Adaptation (2008, background): “and OS techniques to extend battery lifetimes in mobiles [10, 30 ].”