HPS-HDS
| Autor: | Florin Pop, Radu Prodan, Alexandru Iosup |
|---|---|
| Přispěvatelé: | Computer Systems, Network Institute, Massivizing Computer Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Ubiquitous computing
Computer Networks and Communications Computer science Distributed computing Big data Cloud computing 02 engineering and technology computer.software_genre Scheduling algorithms Scheduling (computing) Utility computing Computer cluster 0202 electrical engineering electronic engineering information engineering Resource management SDG 7 - Affordable and Clean Energy business.industry Quality of service Fault tolerance 020206 networking & telecommunications Grid computing Hardware and Architecture Scalability Heterogeneous distributed systems 020201 artificial intelligence & image processing business computer Software |
| Zdroj: | Future Generation Computer Systems, 78(Part 1), 242-244. Elsevier Pop, F, Iosup, A & Prodan, R 2018, ' HPS-HDS : High Performance Scheduling for Heterogeneous Distributed Systems ', Future Generation Computer Systems, vol. 78, no. Part 1, pp. 242-244 . https://doi.org/10.1016/j.future.2017.09.012 |
| ISSN: | 1872-7115 0167-739X |
| Popis: | Heterogeneous Distributed Systems (HDS) are often characterized by a variety of resources that may or may not be coupled with specific platforms or environments. Such type of systems are Cluster Computing, Grid Computing, Peer-to-Peer Computing, Cloud Computing and Ubiquitous Computing all involving elements of heterogeneity, having a large variety of tools and software to manage them. As computing and data storage needs grow exponentially in HDS, increasing the size of data centers brings important diseconomies of scale. In this context, major solutions for scalability, mobility, reliability, fault tolerance and security are required to achieve high performance. More, HDS are highly dynamic in its structure, because the user requests must be respected as an agreement rule (SLA) and ensure QoS, so new algorithm for events and tasks scheduling and new methods for resource management should be designed to increase the performance of such systems. In this special issues, the accepted papers address the advance on scheduling algorithms, energy-aware models, self-organizing resource management, data-aware service allocation, Big Data management and processing, performance analysis and optimization. |
| Databáze: | OpenAIRE |
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