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i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 11 Issue 2

Most Cited

Volume 11 Issue 2 July - December 2024

Research Paper

Leveraging Mobile Cloud Computing for Real-Time Data Processing: Novel Approach

Swarn Kaur* , Rama Chawla**

*-** Department of Computer Science and Engineering, State Institute of Engineering and Technology, NiloKheri, Haryana, India.

Kaur, S., & Chawla, R. (2024). Leveraging Mobile Cloud Computing for Real-Time Data Processing: Novel Approach. i-manager’s Journal on Cloud Computing, 11(2), 1-10. https://doi.org/10.26634/jcc.11.2.21661

Abstract

Cell phones have become an essential part of daily life. Studies indicate that most corporate and telecommunication sectors plan to integrate Mobile Cloud Computing (MCC) to meet their business needs efficiently. MCC combines cloud computing with mobile devices, enhancing computational power and storage capacity. This study provides a comprehensive analysis of real-time data processing in MCC environments, exploring challenges, opportunities, and future directions. It discusses computational offloading, network latency, security concerns, and resource management strategies to optimize performance. Since 2009, the term 'mobile cloud computing' (MCC), which blends cloud computing with mobile computing, has gained popularity and become a hot topic in the IT community. As MCC is still in its infancy, identifying future research directions requires a solid understanding of the technology. To achieve this goal, this study provides an overview of MCC's history, fundamental principles, features, ongoing studies, and potential future directions. After reviewing MCC's evolution from mobile computing to cloud computing, its key features and ongoing research are examined. The infrastructure and characteristics of mobile cloud computing are also analyzed. The remainder of the study explores the challenges associated with mobile cloud computing, provides an overview of various relevant research efforts, and identifies possible avenues for further investigation.

References

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Research Paper

Advanced Framework for Multi-Objective Optimization of Computation Offloading in Heterogeneous MEC Environments

M. Jyothirmai* , Kesavan Gopal, M. Sailaja*

, Jawaharlal Nehru Technological University Kakinada (JNTUK), Kakinada, Andhra Pradesh, India.

Lovely Professional University (LPU), Phagwara, Punjab, India.

Jyothirmai, M., Gopal, K., and Sailaja, M. (2024). Advanced Framework for Multi-Objective Optimization of Computation Offloading in Heterogeneous MEC Environments. i-manager’s Journal on Cloud Computing, 11(2), 11-18. https://doi.org/10.26634/jcc.11.2.21482

Abstract

The proliferation of data-intensive mobile applications has necessitated efficient computation offloading techniques to mitigate resource constraints in mobile devices (MDs). Existing approaches fail to address multi-objective optimization challenges effectively. This study proposes an Enhanced Adaptive Cat Hunt Optimization (EACHO) algorithm, designed to optimize energy consumption (EC), delay, and resource utilization in heterogeneous Mobile Edge Computing (MEC) environments. The model leverages Directed Acyclic Graphs (DAGs) for task representation and adaptive parameters for real-time decision-making. Experimental results demonstrate that EACHO achieves significant reductions in delay (0.0172 seconds), EC (0.251 × 10-3 J), and cost (0.387) compared to state-of-the-art methods. These findings highlight the robustness and scalability of EACHO for diverse MEC scenarios.

References

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Research Paper

Intelligent Adaptive Optimization for Workflow Scheduling in Cloud Environment

Felsy C* , Isaac Sajan R., Manju C Thayammal*, Manchu M.****

*-**** Ponjesly College of Engineering, Kanniyakumari, Tamil Nadu, India.

Felsy, C., Sajan, R. I., Thayammal, M. C., and Manchu, M. (2024). Intelligent Adaptive Optimization for Workflow Scheduling in Cloud Environment. i-manager’s Journal on Cloud Computing, 11(2), 19-27. https://doi.org/10.26634/jcc.11.2.21509

Abstract

The workflow scheduling in cloud computing systems has gained significance in both commercial and artificial operations. However, generating effective and affordable scheduling methods under deadline constraints remains extremely challenging, particularly for large-scale workflow operations. To address this issue, this study analyzes the workflow scheduling problem in the cloud with the goal of reducing the overall execution cost while keeping the processing time within a set deadline. An Intelligent Adaptive Optimization (IAO) algorithm is developed based on the problem-specific knowledge of workflow scheduling in the cloud. In the proposed IAO, an operator for discrete propagation is created using knowledge of idle time in an hourly cost model to dynamically explore the extensive search space. The adaptive refraction operator is designed to reduce stagnation and increase available resources. The dynamic grouping-based breaking operator is created to leverage the excellent block structure knowledge of the task allocation scheme and speed up convergence. The IAO approach has been shown to outperform several state-of-the- art algorithms in extensive simulation experiments conducted on a well-known scientific workflow.

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Review Paper

Cloud Computing: Virtualization, Service Models, and Deployment Options

Bisman Singh* , Bhumika Tuli, Rakesh Kumar*

- Department of Computer Science and Engineering, Chandigarh Group of Colleges, Mohali, Punjab, India.

Department of Quality Assurance and Regulatory Affairs, Auxein Medical Pvt. Ltd., Sonipat, Haryana, India.

Singh, B., Tuli, B., and Kumar, R. (2024). Cloud Computing: Virtualization, Service Models, and Deployment Options. i-manager’s Journal on Cloud Computing, 11(2), 28-43. https://doi.org/10.26634/jcc.11.2.21197

Abstract

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Review Paper

A Review Paper on Cloud Computing

Sai Karthikeya Bheemavaram* , Ravikumar Peddanuru, Siva Yeswanth Posa, Santhosh Pula, G. Sreenivasula Reddy

- Department of Electronics and Communication Engineering, Chaitanya Bharathi Institute of Technology, Proddatur, Andhra Pradesh, India.

Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Proddatur, Andhra Pradesh, India.

Bheemavaram, S. K., Peddanuru, R., Posa, S. Y., Pula, S., and Reddy, G. S. (2024). A Review Paper on Cloud Computing. i-manager’s Journal on Cloud Computing, 11(2), 44-49. https://doi.org/10.26634/jcc.11.2.21638

Abstract

Cloud computing has evolved significantly and it's importance is evident in the vast amount of data processing occurring daily across various sectors. Cloud computing remains a key topic in computer science studies, particularly in big data. Trends focus on AI-driven cloud scalability, edge computing, multi-cloud adoption, serverless computing, and DevOps integration, all contributing to improved operational efficiency. Advancing cloud technology to achieve full maturity remains a priority. This study provides a brief assessment of cloud computing by reviewing related articles, highlighting its evolution and ongoing developments.

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[10]. Tpointtech. (2025). Introduction to Cloud Computing.

i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 11 Issue 2

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Volume 11 Issue 2 July - December 2024

Leveraging Mobile Cloud Computing for Real-Time Data Processing: Novel Approach

Swarn Kaur* , Rama Chawla**

*-** Department of Computer Science and Engineering, State Institute of Engineering and Technology, NiloKheri, Haryana, India.

Kaur, S., & Chawla, R. (2024). Leveraging Mobile Cloud Computing for Real-Time Data Processing: Novel Approach. i-manager’s Journal on Cloud Computing, 11(2), 1-10. https://doi.org/10.26634/jcc.11.2.21661

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Advanced Framework for Multi-Objective Optimization of Computation Offloading in Heterogeneous MEC Environments

M. Jyothirmai* , Kesavan Gopal**, M. Sailaja***

*,*** Jawaharlal Nehru Technological University Kakinada (JNTUK), Kakinada, Andhra Pradesh, India. ** Lovely Professional University (LPU), Phagwara, Punjab, India.

Jyothirmai, M., Gopal, K., and Sailaja, M. (2024). Advanced Framework for Multi-Objective Optimization of Computation Offloading in Heterogeneous MEC Environments. i-manager’s Journal on Cloud Computing, 11(2), 11-18. https://doi.org/10.26634/jcc.11.2.21482

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Intelligent Adaptive Optimization for Workflow Scheduling in Cloud Environment

Felsy C* , Isaac Sajan R.**, Manju C Thayammal***, Manchu M.****

*-**** Ponjesly College of Engineering, Kanniyakumari, Tamil Nadu, India.

Felsy, C., Sajan, R. I., Thayammal, M. C., and Manchu, M. (2024). Intelligent Adaptive Optimization for Workflow Scheduling in Cloud Environment. i-manager’s Journal on Cloud Computing, 11(2), 19-27. https://doi.org/10.26634/jcc.11.2.21509

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Cloud Computing: Virtualization, Service Models, and Deployment Options

Bisman Singh* , Bhumika Tuli**, Rakesh Kumar***

*-** Department of Computer Science and Engineering, Chandigarh Group of Colleges, Mohali, Punjab, India. *** Department of Quality Assurance and Regulatory Affairs, Auxein Medical Pvt. Ltd., Sonipat, Haryana, India.

Singh, B., Tuli, B., and Kumar, R. (2024). Cloud Computing: Virtualization, Service Models, and Deployment Options. i-manager’s Journal on Cloud Computing, 11(2), 28-43. https://doi.org/10.26634/jcc.11.2.21197

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A Review Paper on Cloud Computing

Sai Karthikeya Bheemavaram* , Ravikumar Peddanuru**, Siva Yeswanth Posa***, Santhosh Pula****, G. Sreenivasula Reddy*****

*-**** Department of Electronics and Communication Engineering, Chaitanya Bharathi Institute of Technology, Proddatur, Andhra Pradesh, India. ***** Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Proddatur, Andhra Pradesh, India.

Bheemavaram, S. K., Peddanuru, R., Posa, S. Y., Pula, S., and Reddy, G. S. (2024). A Review Paper on Cloud Computing. i-manager’s Journal on Cloud Computing, 11(2), 44-49. https://doi.org/10.26634/jcc.11.2.21638

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M. Jyothirmai* , Kesavan Gopal, M. Sailaja*

, Jawaharlal Nehru Technological University Kakinada (JNTUK), Kakinada, Andhra Pradesh, India.

Lovely Professional University (LPU), Phagwara, Punjab, India.

Felsy C* , Isaac Sajan R., Manju C Thayammal*, Manchu M.****

Bisman Singh* , Bhumika Tuli, Rakesh Kumar*

- Department of Computer Science and Engineering, Chandigarh Group of Colleges, Mohali, Punjab, India.

Department of Quality Assurance and Regulatory Affairs, Auxein Medical Pvt. Ltd., Sonipat, Haryana, India.

Sai Karthikeya Bheemavaram* , Ravikumar Peddanuru, Siva Yeswanth Posa, Santhosh Pula, G. Sreenivasula Reddy

- Department of Electronics and Communication Engineering, Chaitanya Bharathi Institute of Technology, Proddatur, Andhra Pradesh, India.

Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Proddatur, Andhra Pradesh, India.