Energy-efficient Runtime Resource Management for Adaptable Multi-application Mapping

Abstract

Modern embedded computing platforms consist of a high amount of heterogeneous resources, which allows executing multiple applications on a single device. The number of running application on the system varies with time and so does the amount of available resources. This has considerably increased the complexity of analysis and optimization algorithms for runtime mapping of firm real-time applications. To reduce the runtime overhead, researchers have proposed to pre-compute partial mappings at compile time and have the runtime efficiently compute the final mapping. However, most existing solutions only compute a fixed mapping for a given set of running applications, and the mapping is defined for the entire duration of the workload execution. In this work we allow applications to adapt to the amount of available resources by using mapping segments. This way, applications may switch between different configurations with varied degree of parallelism. We present a runtime manager for firm real-time applications that generates such mapping segments based on partial solutions and aims at minimizing the overall energy consumption without deadline violations. The proposed algorithm outperforms the state-of-theart approaches on the overall energy consumption by up to 13% while incurring an order of magnitude less scheduling overhead.

EVEREST: A design environment for extreme-scale big data analytics on heterogeneous platforms

“…(2) the optimization goal set for execution (e.g., performance or energy consumption) [35], [36], (3) the additional dynamic requirements (e.g., security monitoring, data features [37]), and (4) the available techniques for data management (e.g., data representations and distributed allocation). The selection will generalize the concept of affinity between the code variants and the available system configurations and requirements.…”