Cluster Computing, Journal Year: 2024, Volume and Issue: 28(2)
Published: Dec. 13, 2024
Language: Английский
Telematics and Informatics Reports, Journal Year: 2024, Volume and Issue: 13, P. 100116 - 100116
Published: Jan. 8, 2024
Language: Английский
Citations
68
Computer Science Review, Journal Year: 2024, Volume and Issue: 53, P. 100651 - 100651
Published: July 5, 2024
Language: Английский
Citations
30
Biomass and Bioenergy, Journal Year: 2025, Volume and Issue: 194, P. 107620 - 107620
Published: Jan. 18, 2025
Language: Английский
Citations
2
Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 119, P. 109514 - 109514
Published: Aug. 8, 2024
Language: Английский
Citations
12
Journal of Network and Computer Applications, Journal Year: 2024, Volume and Issue: 227, P. 103891 - 103891
Published: April 28, 2024
Language: Английский
Citations
10
Future Generation Computer Systems, Journal Year: 2024, Volume and Issue: 160, P. 457 - 471
Published: June 13, 2024
The development of cloud, fog, and edge computing has led to great advances in reducing latency saving bandwidth, these methods have therefore been broadly applied various domains, including healthcare, transportation, the Internet Things (IoT). Traditional solutions proven be insufficient fulfilling demanding prerequisites low high data rates. Additionally, publicly available cloud fail meet required standards for ensuring privacy protection. Consequently, Private Edge Cloud Systems (PECSs) garnered attention as a prospective solution owing their capacity mitigate risks significant capacity. PECS research seen growth, but there is lack detailed review its issues, approaches, applications literature. To explore potential application value PECS, this paper provides systematic intelligent platforms architecture PECSs. Specifically, an overview fundamental characteristics PECSs provided. Second, we classify architectures analyze implementation techniques realization methods. Third, discuss four specific scenarios. Finally, promising future directions are discussed. findings show that can effectively requirements protection fertile domain further research.
Language: Английский
Citations
6
Sensors, Journal Year: 2024, Volume and Issue: 24(8), P. 2381 - 2381
Published: April 9, 2024
IoT-based smart transportation monitors vehicles, cargo, and driver statuses for safe movement. Due to the limited computational capabilities of sensors, IoT devices require powerful remote servers execute their tasks, this phenomenon is called task offloading. Researchers have developed efficient offloading scheduling mechanisms reduce energy consumption response time. However, most research has not considered fault-tolerance-based job allocation logistics trucks, data-aware scheduling, priority-based offloading, or multiple-parameter-based fog node selection. To overcome limitations, we proposed a Multi-Objective Task-Aware Offloading Scheduling Framework Logistics (MT-OSF). The model prioritizes tasks into delay-sensitive computation-intensive using offloader forwards two lists Scheduler (TAS) further processing on cloud nodes. uses multi-criterion decision-making process, i.e., analytical hierarchy process (AHP), calculate nodes’ priority scheduling. AHP decides based energy, bandwidth, RAM, MIPS power. Similarly, TAS also calculates shortest distance between IoT-enabled vehicle which are assigned execution. A task-aware scheduler schedules nearby nodes while allocating data centers FCFS algorithm. Fault-tolerant manager used check failure; if any fails, system re-executes allocates another failure ratio. simulated in iFogSim2 demonstrates 7% reduction time, 16% consumption, 22% ratio comparison Ant Colony Optimization Round Robin.
Language: Английский
Citations
5
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 7, 2025
The fog computing paradigm is better for creating delay-sensitive applications in Internet of Things (IoT). As the devices are resource constrained, deployment diversified IoT requires effective ways determining available resources. Therefore, implementing an efficient management strategy optimal choice satisfying application Quality Service (QoS) requirements to preserve system performance. Developing with many QoS criteria a non-deterministic polynomial time (NP) complete problem. study applies Hybrid Prairie Dog and Dwarf Mongoose Optimisation Algorithm-based Resource Scheduling (HPDDMOARS) Technique effectively position meet criteria. This HPDDMOARS technique formulated as weighted multi-objective placement mechanism which targets optimizing three main parameters that considered energy, cost makespan into account. It employed Optimization Algorithm (PDOA) exploring possibility helps mapping services scenario. also derived significance (DMOA) exploiting local factors helped at least one objective index. hybridized benefits PDOA DMOA mutually balancing phases exploration exploitation such potential between tasks computational resources can be achieved environment. experimental validation proposed different number confirmed minimized energy consumptions 22.18%, reduced 24.98%, lowered 18.64% than baseline metaheuristic approaches.
Language: Английский
Citations
0
Computing, Journal Year: 2025, Volume and Issue: 107(1)
Published: Jan. 1, 2025
Language: Английский
Citations
0
Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 1739 - 1763
Published: Jan. 22, 2025
Language: Английский
Citations
0