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

2021 ◽  
Vol 2142 (1) ◽  
pp. 012020
Author(s):  
V S Stepanyuk ◽  
A M Emelyanov ◽  
D I Mirzoyan
Keyword(s):  
High Performance ◽  
Heterogeneous Computing ◽  
Computing System ◽  
Maximum Efficiency ◽  
Heterogeneous Architecture ◽  
Special Equipment ◽  
Processing Data ◽  
And Control ◽  
Speed Accuracy ◽  
Heterogeneous Computing System

Abstract 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

2019 ◽  
Vol 28 (09) ◽  
pp. 1950159 ◽  
Author(s):  
Junqiang Jiang ◽  
Wenbin Li ◽  
Li Pan ◽  
Bo Yang ◽  
Xin Peng
Keyword(s):  
Energy Consumption ◽  
Heterogeneous Computing ◽  
Rapid Development ◽  
Energy Optimization ◽  
Minimum Cost ◽  
Computing System ◽  
Directed Acyclic Graphs ◽  
Optimization Heuristics ◽  
Dynamic Voltage ◽  
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.

Grid Scheduling

2011 ◽  
pp. 86-111
Author(s):  
Florin Pop
Keyword(s):  
Distributed System ◽  
Critical Analysis ◽  
Heterogeneous Computing ◽  
Computing System ◽  
Optimization Techniques ◽  
Grid Scheduling ◽  
Grid Systems ◽  
Grid Environments ◽  
Scheduling Models ◽  
Heterogeneous Computing System

This chapter will present the scheduling mechanism in distributed systems with direct application in grids. The resource heterogeneity, the size and number of tasks, the variety of policies, and the high number of constraints are some of the main characteristics that contribute to this complexity. The necessity of scheduling in grid is sustained by the increasing of number of users and applications. The design of scheduling algorithms for a heterogeneous computing system interconnected with an arbitrary communication network is one of the actual concerns in distributed system research. The main concerns presented in the chapter refers to general presentation of scheduling for grid systems, specific requirements of scheduling in grids, critical analysis of existing methods and algorithms for grid schedulers, scheduling policies, fault tolerance in scheduling process in grid environments, scheduling models and algorithms and optimization techniques for grid scheduling.

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