Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing
Regular price
€38.99
Age Group_Uncategorized
Age Group_Uncategorized
automatic-update
B01=Amit Kumar Singh
B01=Leando Soares Indrusiak
B01=Piotr Dziurzanski
Category1=Non-Fiction
Category=UTC
Category=UTR
Category=UYF
COP=Denmark
Delivery_Pre-order
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Language_English
manycore
multicore
network-on-chip
PA=Not yet available
performance predictability
Price_€20 to €50
PS=Forthcoming
real-time systems
Resource allocation
scheduling
softlaunch
Product details
- ISBN 9788770044523
- Weight: 320g
- Dimensions: 156 x 234mm
- Publication Date: 21 Oct 2024
- Publisher: River Publishers
- Publication City/Country: DK
- Product Form: Paperback
- Language: English
Secure
checkout
Fast Shipping
Easy returns
The availability of many-core computing platforms enables a wide variety of technical solutions for systems across the embedded, high-performance and cloud computing domains. However, large scale manycore systems are notoriously hard to optimise. Choices regarding resource allocation alone can account for wide variability in timeliness and energy dissipation (up to several orders of magnitude). Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing covers dynamic resource allocation heuristics for manycore systems, aiming to provide appropriate guarantees on performance and energy efficiency. It addresses different types of systems, aiming to harmonise the approaches to dynamic allocation across the complete spectrum between systems with little flexibility and strict real-time guarantees all the way to highly dynamic systems with soft performance requirements. Technical topics presented in the book include: • Load and Resource Models• Admission Control• Feedback-based Allocation and Optimisation• Search-based Allocation Heuristics• Distributed Allocation based on Swarm Intelligence• Value-Based AllocationEach of the topics is illustrated with examples based on realistic computational platforms such as Network-on-Chip manycore processors, grids and private cloud environments.
Leando Soares Indrusiak, Piotr Dziurzanski, Amit Kumar Singh
