Albalawi, 2024
ViewPDF| Publication | Publication Date | Title |
|---|---|---|
| Shahidinejad et al. | Resource provisioning using workload clustering in cloud computing environment: a hybrid approach | |
| Saravanan et al. | Enhancing investigations in data migration and security using sequence cover cat and cover particle swarm optimization in the fog paradigm | |
| Kalra et al. | A review of metaheuristic scheduling techniques in cloud computing | |
| Siddesha et al. | A novel deep reinforcement learning scheme for task scheduling in cloud computing | |
| Indhumathi et al. | Design of task scheduling and fault tolerance mechanism based on GWO algorithm for attaining better QoS in cloud system | |
| Manikandan et al. | LGSA: Hybrid task scheduling in multi objective functionality in cloud computing environment | |
| Li et al. | Batch jobs load balancing scheduling in cloud computing using distributional reinforcement learning | |
| Srikanth et al. | Effectiveness review of the machine learning algorithms for scheduling in cloud environment | |
| Saif et al. | Hybrid meta-heuristic genetic algorithm: Differential evolution algorithms for scientific workflow scheduling in heterogeneous cloud environment | |
| Hussain et al. | RAPTS: resource aware prioritized task scheduling technique in heterogeneous fog computing environment | |
| Singh et al. | Energy aware resource allocation via MS-SLnO in cloud data center | |
| Kumar et al. | Deadline-aware cost and energy efficient offloading in mobile edge computing | |
| Zhou et al. | DPS: Dynamic pricing and scheduling for distributed machine learning jobs in edge-cloud networks | |
| Dogani et al. | A two-tier multi-objective service placement in container-based fog-cloud computing platforms | |
| Kumar et al. | Parameter investigation study on task scheduling in cloud computing | |
| Abraham et al. | Multi-objective optimization techniques in cloud task scheduling: A systematic literature review | |
| Ammavasai | RETRACTED: Dynamic task scheduling in edge cloud systems using deep recurrent neural networks and environment learning approaches | |
| Pachipala et al. | Workload prioritization and optimal task scheduling in cloud: introduction to hybrid optimization algorithm | |
| Deng et al. | NS-OWACC: nature-inspired strategies for optimizing workload allocation in cloud computing | |
| Choppara et al. | Resource Adaptive Automated Task Scheduling Using Deep Deterministic Policy Gradient in Fog Computing | |
| Albalawi | Dynamic Scheduling Strategies for Load Balancing in Parallel and Distributed Systems | |
| Albalawi | Dynamic scheduling strategies for cloud-based load balancing in parallel and distributed systems | |
| Belgacem et al. | A new task scheduling approach based on spacing multi-objective genetic algorithm in cloud | |
| Golmohammadi et al. | A review on workflow scheduling and resource allocation algorithms in distributed mobile clouds | |
| Zhang et al. | Load balancing in edge computing using integer linear programming based genetic algorithm and multilevel control approach |