Optimization of resource-aware parallel and distributed computing: a review

This paper presents a review of state-of-the-art solutions concerning the optimization of computing in the field of parallel and distributed systems. Firstly, we contribute by identifying resources and quality metrics in this context including servers, network interconnects, storage systems, computa...

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Published in:The Journal of supercomputing Vol. 81; no. 7; p. 848
Main Authors: Czarnul, Paweł, Antal, Marcel, Baniata, Hamza, Griebler, Dalvan, Kertesz, Attila, Kessler, Christoph W., Kouloumpris, Andreas, Kovačić, Salko, Markus, Andras, Michael, Maria K., Nikolaou, Panagiota, Öz, Isil, Prodan, Radu, Rakić, Gordana
Format: Journal Article
Language:English
Published: New York Springer Nature B.V 09.05.2025
Subjects:
Algorithms
Ant colony optimization
Blockchain
Cloud computing
Computer centers
Computer networks
Distributed processing
Dynamic programming
Energy consumption
Game theory
Genetic algorithms
Greedy algorithms
High performance computing
Integer programming
Internet of Things
Linear programming
Optimization
Particle swarm optimization
Power
Software
State-of-the-art reviews
Storage systems
Workloads
Parallel and distributed architectures
Parallel and distributed computing
Optimization
Review of resource-aware parallel and distributed computing
ISSN:1573-0484, 0920-8542, 1573-0484
Online Access:Get full text
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Summary:This paper presents a review of state-of-the-art solutions concerning the optimization of computing in the field of parallel and distributed systems. Firstly, we contribute by identifying resources and quality metrics in this context including servers, network interconnects, storage systems, computational devices as well as execution time/performance, energy, security, and error vulnerability, respectively. We subsequently identify commonly used problem formulations and algorithms for integer linear programming, greedy algorithms, dynamic programming, genetic algorithms, particle swarm optimization, ant colony optimization, game theory, and reinforcement learning. Afterward, we characterize frequently considered optimization problems by stating these terms in domains such as data centers, cloud, fog, blockchain, high performance, and volunteer computing. Based on the extensive analysis, we identify how particular resources and corresponding quality metrics are considered in these domains and which problem formulations are used for which system types, either parallel or distributed environments. This allows us to formulate open research problems and challenges in this field and analyze research interest in problem formulations/domains in recent years.
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ISSN:1573-0484
0920-8542
1573-0484
DOI:10.1007/s11227-025-07295-7