A survey of machine learning for computer architecture and systems
It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
A survey of resource management in multi-tier web applications
Web applications are mostly designed with multiple tiers for flexibility and software
reusability. It is difficult to model the behavior of multi-tier Web applications due to the fact …
reusability. It is difficult to model the behavior of multi-tier Web applications due to the fact …
Saving time and cost on the scheduling of fog-based IoT applications using deep reinforcement learning approach
Due to the rapid growth of intelligent devices and the Internet of Things (IoT) applications in
recent years, the volume of data that is generated by these devices is increasing …
recent years, the volume of data that is generated by these devices is increasing …
Cloud resource scheduling with deep reinforcement learning and imitation learning
The cloud resource management belongs to the category of combinatorial optimization
problems, most of which have been proven to be NP-hard. In recent years, reinforcement …
problems, most of which have been proven to be NP-hard. In recent years, reinforcement …
Sibyl: Adaptive and extensible data placement in hybrid storage systems using online reinforcement learning
Hybrid storage systems (HSS) use multiple different storage devices to provide high and
scalable storage capacity at high performance. Data placement across different devices is …
scalable storage capacity at high performance. Data placement across different devices is …
Understanding and auto-adjusting performance-sensitive configurations
Modern software systems are often equipped with hundreds to thousands of configurations,
many of which greatly affect performance. Unfortunately, properly setting these …
many of which greatly affect performance. Unfortunately, properly setting these …
Achieving autonomous power management using reinforcement learning
System level power management must consider the uncertainty and variability that come
from the environment, the application and the hardware. A robust power management …
from the environment, the application and the hardware. A robust power management …
Dynamic resource management of heterogeneous mobile platforms via imitation learning
The complexity of heterogeneous mobile platforms is growing at a rate faster than our ability
to manage them optimally at runtime. For example, state-of-the-art systems-on-chip (SoCs) …
to manage them optimally at runtime. For example, state-of-the-art systems-on-chip (SoCs) …
An energy-aware online learning framework for resource management in heterogeneous platforms
Mobile platforms must satisfy the contradictory requirements of fast response time and
minimum energy consumption as a function of dynamically changing applications. To …
minimum energy consumption as a function of dynamically changing applications. To …
Generative and multi-phase learning for computer systems optimization
Machine learning and artificial intelligence are invaluable for computer systems
optimization: as computer systems expose more resources for management, ML/AI is …
optimization: as computer systems expose more resources for management, ML/AI is …