Forthcoming and Online First Articles

International Journal of Cloud Computing

International Journal of Cloud Computing (IJCC)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Cloud Computing (2 papers in press)

Regular Issues

  • Designing a Hybrid Heuristic-aided Approach for Replica Placement and Migration Strategy for SaaS Applications in Edge Cloud   Order a copy of this article
    by Puneet Pahuja  
    Abstract: The replica placement and migration mechanism for software-as-a-service (SaaS) developments in the edge cloud is developed. The placement of replica problem is rectified by utilising the hybrid position of wild geese and golden tortoise beetle (HPWGTB). For the similar data module, the different replicas should be placed on different data nodes. The multi-objective constraints such as network transmission cost, node load, and file unavailability are considered for an effective replica placement and migration. The developed hybrid HPWGTB is utilised to improve the load balancing of data nodes, decrease the response time, and reduce the resource utilisation of networks. The migration relationship between the target node and source node is considered for developing a migration of replica approach for accessing hotspots and minimising the migration time. The experimental outcomes are validated by comparing them with other optimisation approaches.
    Keywords: SaaS Applications in Edge Cloud; Replica Placement; Replica Migration; Load Balancing; Multi-Objective Constraints; Hybrid Position Of Wildgeese And Golden Tortoise Beetle.
    DOI: 10.1504/IJCC.2025.10067418
     
  • An Effective Algorithm for Predicting Load and Dynamic Task Scheduling in Cloud Fog Architecture for Smart Homes   Order a copy of this article
    by Krishna Kant Agarwal, Sujeet Kumar, Jitendra Kumar Seth, Abhishek Kumar Gupta, Sonia Lamba 
    Abstract: The need for smart homes with many devices and services continues to rise quickly. With this surge, smart homes need task scheduling and load-prediction algorithms to provide the proper services for the residents. A deep learning-based dynamic job scheduling and load prediction technique for cloud-fog smart homes is proposed in this paper. This algorithm forecasts task arrival rates at each fog node and assigns them to available fog nodes. It dynamically schedules tasks based on fog node workload. Another option is to send non-real-time jobs to the cloud and real-time tasks to the fog layer. This optimises load distribution for performance. Using these task assignee models and features, the program optimises prioritised tasks, scores, network latency, and device resource characteristics. We simulate the algorithm's performance in various workloads in this part. The Proposed Algorithms achieved in higher percentile for 93.79% Latency, 95.00% Throughput, 95.34% Response time, 96.28% Scalability, 94.20% Fault-tolerance, 97.41% scheduling capacity, 91.41% load balancing capacity, 95.22% priority management. The results indicate that such an algorithm significantly surpasses the conventional task scheduling methods in load balancing and shortens the average task response time.
    Keywords: Cloud Computing; Fog Computing; Smart Homes; Task Scheduling; Metaheuristic Algorithms; Deep Learning.
    DOI: 10.1504/IJCC.2025.10069250