1. Progress Report
2017/02/08

2. Agenda The difficulties in conducting experiments.
Survey of recent related works.
Plan on how to improve the paper.

3. Difficulties in Conducting Experiments
Lack of hardware support.
Asymmetric multi-core that supports per-core DVFS.
The latest develop boards are still per-cluster DVFS.
ARM Juno r2, ODROID XU4 … etc.
Cannot find suitable workload / benchmark
Most of the CPU benchmarks focus on core performance.
Hard to synthesis “Throughput guaranteed jobs” using these benchmarks.

4. Survey of Recent Related Works
SPARTA: Runtime Task Allocation for Energy Efficient Heterogeneous Many-cores.
CODES/ISSS’16 (UC, Irvine)
Maximize energy-efficiency(instruction-per-Joule) without sacrifice performance.
Energy-efficient scheduling for moldable real- time tasks on heterogeneous computing platforms
Journal of Systems Architecture 2017
Minimizing the total energy consumption while guaranteeing that all deadlines are respected.

5. Comparison Our work SPARTA Moldable Real-time Linaro GTS DVFS Per-core
Per-cluster
(CPUFreq Ondemand)
Target Tasks
Throughput guaranteed tasks
General Tasks
Soft real-time data processing applications
Task Mapping
Task-to-core
Task-to-cluster
Short-term scheduler
Energy-credit based scheduler
Complete Fair Scheduler x
Evaluation
Experiment + simulation (ODROID XU3)
Simulation (ODROID XU3)
Experiment
Workload ?
Made up of MiBench and PARSEC
Synthetic micro-benchmarks
Randomly generated synthetic task sets

6. Plan on How to Improve The Paper
Select a better application domain.
Edge computing (?)
Avoid real-time applications.
Emphasize the differences.
Per-core DVFS can save more energy compares to current per-Cluster DVFS.
Most of the existing works rely on CFS, which is not energy-aware.

7. Plan on How to Improve The Paper
Improve the offline simulator to emulate different big.LITTLE architectures and collect more information.
ODROID (Exynos5422: A15+A7), Juno r1(A57+A53), Juno r2(A72+A53) … etc.
# of task migration, … etc.

8. A Possible Scenario Public computing node.
Like wifi hotspot, but for computing purpose.
Computing nodes are stand-alone and distributed, not aggregate in a data center.
Reduce the transmission overhead.
Users connect to the node and offload workloads from their devices.
Specific the time they are willing to wait.
Estimate the amount of workloads.
Charge by the throughput.
Apply energy-credit based scheduler on such nodes to reduce their energy consumption.

9. Discussion