Optimization of a heterogeneous computing system architecture based on its coordination with a sensors group of a mobile unmanned platform

This article analyses the existing variety of sensors used in robotics and related fields and also proposes the architecture of a heterogeneous computing system designed to analyze data obtained from sensors of a mobile unmanned platform (MUP). A feature of unmanned platforms is the presence of tasks that require a significantly different level of performance of the on-board computing system for processing data from sensors of the corresponding type. Therefore, the adaptation of existing universal computing systems seems to be impractical, compared to the development of a specialized computing system with a heterogeneous architecture. The computing system is designed to solve problems of local navigation, stabilize the position of the MUP and control its movement, as well as control special equipment installed on the MUP. Often, if the goal is to ensure maximum efficiency, expressed in speed, accuracy and reliability, it is necessary to develop specialized devices. The article provides information on sensors of the main types used in robotics and indicates the requirements for the performance of a computing system necessary for processing data from sensors of this type. This, in turn, made it possible to propose a heterogeneous architecture containing processor subsystems focused on processing data from sensors requiring low, medium and high performance according to the considered classification.

Energy Optimization Heuristics for Budget-Constrained Workflow in Heterogeneous Computing System

With the rapid development of commercialized computation, the heterogeneous computing system (HCS) has evolved into a new method of service provisioning based on utility computing models, in which the users consume services and resources based on their quality of service requirements. In certain models using the pay-as-you-go concept, the users are charged for accessed services based on their usage. In addition, the commercialized HCS provider also assumes the responsibility to reduce the energy consumption to protect the environment. This paper considers a basic model known as directed acyclic graphs (DAG), which is designed for workflow applications, and investigates heuristics that allows the scheduling of various tasks of a workflow into the dynamic voltage and frequency scaling enabled HCS. The proposed approaches, which are Minimum-Cost-Up-to-Budget (MCUB) and Maximum-Cost-Down-to-Budget (MCDB), could not only satisfy budget constrains but could also optimize overall energy consumption. The approaches along with their variants are implemented and evaluated using four types of basic DAGs. From the experimental results, we conclude that MCDB outperforms MCUB in energy optimization and makespan criterion while meeting budget constraints faced by users.