International Journal of Cloud Computing (5 papers in press)

Regular Issues

• Designing a Hybrid Heuristic-aided Approach for Replica Placement and Migration Strategy for SaaS Applications in Edge Cloud  
  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  
  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
   
  
• An optimized AI-driven swarm-based enhanced task scheduling model for cloud computing environment  
  by Surinder Kaur, Jaspreet Singh, Vishal Bharti 
  Abstract: In cloud computing environment, to address issues such as limited total completion time, resources utilization, an Enhanced Task Scheduling (ETS) model with optimized artificial intelligence driven by swarm is proposed in this paper In proposed method, supervised machine learning algorithm named an Artificial Neural Networks (ANN) with swarm-based optimization methods is used to balance scheduling In research work, Moth Flame Optimization (MFO) is used as optimization to separate out the Virtual Machines (VMs) based on their basic properties like CPU utilization, Memory and Bandwidth Initially, the tasks are scheduled using the usual method, then optimize the ETS model based on resource allocation and balancing issues with the help of Back-Propagation Algorithm (BPA) with ANN (ANN-BPA) to analyse the scheduling and problem identification mechanism The ANN-BPA-based task scheduling model is outperformed by the present technique and basic ANN-based model, which enhances resource utilization by 7 54% and decreases time by 0.6s
  Keywords: Cloud Computin; Resource Allocation; Task Scheduling; ANN-BPA; PSO; ABC; CSA; MFO.
  DOI: 10.1504/IJCC.2025.10069555
   
  
• An Enhanced Two-Level Data Sanitisation and Elliptic Curve Cryptography Encryption Model for Securing Electronic Healthcare Data in a Hybrid Cloud Platform  
  by Ambica V, Viji Amutha Mary A 
  Abstract: A secure framework for storing EHR in the hybrid cloud platform is implemented. At first, the required medical data is gathered from the database of the hospitals and split into sensitive and insensitive parts. The sensitive part is first encrypted with the data sanitisation method. The keys obtained are optimally chosen by the modified uniform number-based red fox optimisation (MUN-RFO). Then, the insensitive part is encrypted by optimal key-based data encryption scheme (OKDES), in which the same MUN-RFO algorithm is utilised to choose the optimal keys. The sensitive and insensitive data is combined and stored in the Hybrid Cloud. From the result analysis, the cost function of the MUN-RFO-OKDES is lower by 0.022% of HBA-OKDES, 0.011% of DHOA-OKDES, 0.0025% of EFO-OKDES and 0.001% of RFO-OKDES at 5th iteration for dataset 2. Numerous simulations are carried out to prove the security provided by the given data storage model in a hybrid cloud.
  Keywords: Hybrid Cloud Platform; Encryption Model For Securing Electronic Healthcare Data; Optimal Key-based Data Encryption Scheme; Modified Uniform Number-based Red Fox Optimization; Data Sanitization.
  DOI: 10.1504/IJCC.2025.10070159
   
  
• Federated Architecture for Serverless Platforms Aimed at Transparent Execution in the Edge-Cloud Continuum  
  by Vojdan Kjorveziroski, Sonja Filiposka 
  Abstract: The stateless nature of serverless computing makes it a viable choice for establishing the long-desired edge-cloud continuum. Current efforts to provide a unified view over both the cloud and the edge are vendor-centric, with proprietary interfaces. This makes interoperability between different infrastructures difficult, while also raising questions about future-proofing. To overcome this problem, we introduce a federation layer which provides a unified view over distinct edge-cloud solutions. We summarise the current open questions and define a set of functional requirements for a unifying federation layer. We identify the main pillars required for fulfilling the requirements from a technical perspective and discuss concrete implementation approaches. Finally, we also showcase a practical verification of the architecture, leveraging a federation of geographically distributed Kubernetes clusters. The verification is done using multiple serverless runtime options, with computing environments scattered both in the cloud and in the edge, overcoming various connectivity restrictions in place.
  Keywords: serverless computing; edge-cloud continuum; orchestration; webassembly; federated infrastructures; Kubernetes; compute clusters; distributed systems.