International Journal of Cloud Computing (5 papers in press)

Regular Issues

• Virtual Machine Workload Prediction using Deep Learning  
  by Abhilash C. S, Chaithra Usha, Veena Garag, Priyanka H 
  Abstract: This paper presents a novel approach to optimise resource allocation in virtualised systems, aiming to maximise performance and minimise operational expenses. Leveraging deep learning models, specifically long-short-term memory (LSTM) and bidirectional gated recurrent unit (bi-GRU), the method focuses on forecasting CPU load patterns in virtual machines (VMs). Accurate predictions are crucial for proactive resource management in dynamic cloud-based infrastructures. LSTM and bi-GRU excel in handling time series forecasting due to their ability to detect temporal connections in sequential data. Using pre-processed historical CPU load data, the models undergo training with hyperparameter adjustments to enhance performance. Experimental results demonstrate that the proposed models outperform others, achieving lower average root mean square error (RMSE) values (0.05636) and mean absolute error (MAE) values (0.03721). Comparative analysis with LSTM, GRU, bi-LSTM, bi-GRU, LSTM-GRU, and bi-LSTM-GRU confirms the high predictive capabilities of LSTM and bi-GRU, with the bidirectional architecture of bi-GRU enhancing accuracy by capturing connections between previous and upcoming time steps.
  Keywords: virtual machines; VMs; long-short-term memory; LSTM; bi-GRU; CPU load prediction; cloud computing.
  DOI: 10.1504/IJCC.2024.10062593
   
  
• AltWOA: Enhancing Query Performance with Clustering-Based Optimisation  
  by Mursubai Sandhya Rani, N.Raghavendra Sai 
  Abstract: Big data (BD) is gaining a lot of attention in the information field due to the data growth in the preceding ten years. A fundamental purpose of philosophical "query optimization (QO)" approaches in a BD environment is data retrieving. To offer beneficial and practical choices for BD query optimisation, numerous technologies that focus on the cloud have been developed. Existing significant data query optimisation approaches often struggle to efficiently process complex queries on massive datasets, leading to performance bottlenecks and resource wastage. Despite significant advancements in big data query optimisation, there remains a need for innovative techniques that can seamlessly handle diverse workloads and data distributions while optimizing resource utilisation and query performance. To solve query optimization issues, this paper suggests an Altruistic Whale Optimization Algorithm. In the following stage, the AltWOA optimizer increases the total query processing effectiveness while ignoring the energy-efficient query techniques. The metrics classification and computation time are tested for various data sizes, instances, and dataset records.
  Keywords: Big data (BD); query optimization (QO); Altruistic Whale Optimization Algorithm (AltWOA); fast Markov clustering algorithm.
  DOI: 10.1504/IJCC.2024.10064274
   
  
• Enriched Cloud Computing Data Security through a Triple Level Encryption Model for Healthcare Systems  
  by Hari Priya, Brintha N. C 
  Abstract: Cloud computing (CC) stores and accesses data over the internet, posing security risks from within the cloud service provider (CSP) or outsiders, especially in medical systems. Cryptography is crucial for security, but performance must not be compromised. This research proposes enhancing security with minimal performance impact by refining Blowfish cipher and using an elliptic curve-based algorithm for key coding. Serpent encryption divides data into blocks and combines them for triple-level encryption. Hash functions reduce processing time, even with multiple users accessing the same document. The approach aims to bolster confidentiality in medical cloud data while ensuring data integrity with digital signatures. Software evaluation shows improvements in throughput, execution time, and memory consumption.
  Keywords: cloud computing; CC; medical data; triple-level encryption; hash function; patient privacy; and security.
  DOI: 10.1504/IJCC.2024.10066187
   
  
• 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
   
  
• Hierarchical Blockchain Based Secure Data Storage in Cloud Using Merkle Tree Approach  
  by Judy Flavia B, Balika J. Chelliah 
  Abstract: An authenticated manager must reinforce huge applications and operating systems, keeping information in the cloud while resisting potentially unreliable service providers. This article explores the presence of multiple service providers based on the cloud to store and manage customer information. It has investigated the creation of an optimal system for shared cloud storage to facilitate flexible operations and information migration. This paper uses the Merkle tree approach to describe the experimental outcomes and designs of security systems provided by blockchain in cloud computing. We proposed that good authenticity and security be implemented with minimal significant degradation. The method was based on the position of the conscious Merkle tree that employs a three-tuple technique that has proven reliable in terms of authentication and encryption of security functions. The proposed system performance was measured by parameters such as retrieval time of information in blockchain technology, cost of storage, and average time with conventional information storage methods. The results indicate that the proposed blockchain technology solution has a response time almost 50% faster than conventional methods. Also, it claims that the storage cost would be around 20% lower.
  Keywords: blockchain technology; cloud computing security; Merkle tree; cost of storage and authentication; proposed system performance; information storage methods; flexible operations; information migration.
  DOI: 10.1504/IJCC.2024.10067783