LEADER 04135nam  2200973z- 450 
001 9910557714203321
005 20231214133030.0
035   $a(CKB)5400000000046183
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76847
035   $a(EXLCZ)995400000000046183
100   $a20202201d2021      |y             0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aEdge/Fog Computing Technologies for IoT Infrastructure
210   $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021
215   $a1 electronic resource (231 p.)
311   $a3-0365-1456-2 
311   $a3-0365-1455-4 
330   $aThe prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies.
606   $aInformation technology industries$2bicssc
610   $acloud computing
610   $acontainer orchestration
610   $acustom metrics
610   $aDocker
610   $aedge computing
610   $aHorizontal Pod Autoscaling (HPA)
610   $aKubernetes
610   $aPrometheus
610   $aresource metrics
610   $afog computing
610   $atask allocation
610   $amulti-objective optimization
610   $aevolutionary genetics
610   $ahyper-angle
610   $acrowding distance
610   $acontainers
610   $aleader election
610   $aload balancing
610   $astateful
610   $amulti-access edge computing
610   $aorchestrator
610   $atask offloading
610   $afuzzy logic
610   $a5G
610   $afog/edge computing
610   $aservice provisioning
610   $aservice placement
610   $aservice offloading
610   $aInternet of Things (IoT)
610   $atask scheduling
610   $amarkov decision process (MDP)
610   $adeep reinforcement learning (DRL)
610   $aresource management
610   $aalgorithm classification
610   $aevaluation framework
610   $aweb
610   $aWeb Assembly
610   $aOpenCL
610   $aLWC
610   $afast implementation
610   $aInternet of things
610   $aIoT actor
610   $adata manager
610   $aGDPR
610   $acomputing
610   $acomputational offloading
610   $adynamic offloading threshold
610   $aminimizing delay
610   $aminimizing energy consumption
610   $amaximizing throughputs
615  7$aInformation technology industries
700   $aYoo$b Seong-eun$4edt$01328750
702   $aKim$b Taehong$4edt
702   $aKim$b Youngsoo$4edt
702   $aYoo$b Seong-eun$4oth
702   $aKim$b Taehong$4oth
702   $aKim$b Youngsoo$4oth
906   $aBOOK
912   $a9910557714203321
996   $aEdge$93039491
997   $aUNINA