Resource Management

In this paper, we propose MPR (Market-based Power Reduction), a scalable market-based approach where users actively participate in reducing the HPC system’s power consumption to mitigate overloads. In MPR, HPC users bid to supply, in exchange for incentives, the resource reduction required for handling the overloads.

In this paper, we propose PEMA (Practical Efficient Microservice Autoscaling), a lightweight microservice resource manager that finds efficient resource allocation through opportunistic resource reduction. PEMA’s lightweight design enables novel workload-aware and adaptive resource management.

In this paper, we propose PEMA (Practical Efficient Microservice Autoscaling), a lightweight microservice resource manager that finds efficient resource allocation through opportunistic resource reduction. PEMA’s lightweight design enables novel workload-aware and adaptive resource management.

In our work, we are developing a lightweight learning-based resource manager for microservices that does not require extensive data and avoid causing service level objective violation during learning. We start with ample resource allocation for microservices and identify resource reduction opportunities to gradually decrease the resource to efficient allocation.

A mobile edge offloading engine that offloads task from mobile devices to edge and cloud servers based on cloud computation availability, mobile energy, and task execution time. It utilizes reinforcement learning to learn the environment without any previous knowledge.