Abstract-Recent advances in chip design and integration technologies have led to the development of Single-Chip Cloud computers which are a microcosm of cloud datacenters. Those computers are based on Network-on-Chip (NoC) architectures with deep memory hierarchies. Developing scheduling algorithms to reduce data access latency as well as energy consumption is a major challenge for such architectures. In this paper, we propose a set of algorithms to jointly address the problem of task scheduling and data allocation in a unified approach. Moreover, we present a feasible system model for NoC based multicores considering a three-level memory hierarchy that effectively captures the energy consumed by various elements of system including: processing cores, caches, and NoC subsystem. Simulation results show the superiority of proposed algorithms compared to two state-of-the-art algorithms found in the literature. The experimental results clearly indicate that algorithms performing data and task scheduling in a joint fashion are superior against techniques implementing task and data scheduling separately.
In REF , the problem of interdependent task scheduling was addressed within an edge system of deep memory hierarchies to obtain energy efficiency.
Leveraging on Deep Memory Hierarchies to Minimize Energy Consumption and Data Access Latency on Single-Chip Cloud Computers
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing
Computer Science