Article
Telecommunications
Yingchun Wang, Jingyi Wang, Weizhan Zhang, Yufeng Zhan, Song Guo, Qinghua Zheng, Xuanyu Wang
Summary: With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have emerged as a main research focus. Although deep learning has achieved tremendous success in various research fields, deploying such applications on resource-restricted mobile devices remains a challenge.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Article
Computer Science, Information Systems
Jaya Prakash Champati, Ben Liang
Summary: This study focuses on the task scheduling problem in computational offloading systems, proposing an optimization method that considers the weighted sum of completion time and offloading cost. By algorithm design, the tasks are optimized for minimum completion time while reducing competitive ratio. The results show that the proposed algorithms outperform traditional algorithms in terms of average performance.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2021)
Review
Computer Science, Hardware & Architecture
Ahmad Salah AlAhmad, Hasan Kahtan, Yehia Ibrahim Alzoubi, Omar Ali, Ashraf Jaradat
Summary: Mobile cloud computing (MCC) is a popular technology, but it currently faces critical security issues including authentication, privacy, and trust. Existing MCC models lack comprehensive protection for data, resources, and communication channels. Further research and design solutions are needed from model developers and practitioners.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Higinio Mora, Francisco A. Pujol, Tamai Ramirez, Antonio Jimeno-Morenilla, Julian Szymanski
Summary: Recent advances in the Internet of Things have shown that resource-constrained devices can enhance their performance by leveraging external computing resources in the network. This study explores computational platforms based on Mobile Cloud Computing (MCC) to improve device performance. The main contribution is the research on architectures with multiple offloading options, using a combination of computing layers to outsource processing load. A proof-of-concept application demonstrates the effectiveness of this approach across network layers, with simulations showing high flexibility and overcoming unfavorable scenarios.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Junyu Lu, Qiang Li, Bing Guo, Jie Li, Yan Shen, Gongliang Li, Hong Su
Summary: This study proposes and implements a lightweight computation offloading framework to support offloading of compute-intensive tasks and efficient server deployment. The effectiveness of the framework in reducing energy consumption, boosting performance, and handling intensive offloading requests is demonstrated through experiments and simulations.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Computer Science, Information Systems
Junwen Lu, Hao Yongsheng, Kesou Wua, Yuming Chen, Qin Wang
Summary: Mobile cloud computing provides rich computational resources for mobile users, network operators, and cloud computing providers. Offloading applications to remote cloud resources can save energy in a dynamic mobile cloud computing environment. Our proposed algorithm outperforms other methods in energy consumption reduction and number of finished jobs.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Hardware & Architecture
Yiqi Shi, Jianguo Sun, Duo Liu, Liang Kou, Boquan Li, Qing Yang, Liguo Zhang
Summary: This paper proposes an image fusion strategy and mobile data offloading method based on cloud computing platform to address the limitations of mobile phone cameras, achieving satisfactory image quality and execution performance.
MOBILE NETWORKS & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Yashwant Singh Patel, Manoj Reddy, Rajiv Misra
Summary: This article investigates the balance between energy consumption and monetary cost in mobile cloud computing, proposing the 'MinEMC' optimization problem and the 'Off-Mat' heuristic algorithm to address the task offloading decision problem effectively.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Rezvan Gholivand, Zeinab Movahedi
Summary: In this paper, an end-to-end communication and computation offloading architecture is proposed to solve the MEC offloading problem using C-RAN with an ILP model for optimization. The approach combines utility functions and modified min-cut algorithms for efficient problem-solving.
COMPUTER COMMUNICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
Mahsa Shadi, Saeid Abrishami, Amir Hossein Mohajerzadeh, Behrooz Zolfaghari
Summary: A cloudlet-assisted ready-time partitioning technique is proposed to partition each task of users' workflow exactly when it is ready to run, aiming at minimizing energy consumption of mobile devices and meeting user-defined deadlines.
JOURNAL OF SUPERCOMPUTING
(2021)
Review
Computer Science, Information Systems
Sanaz Taheri-abed, Amir Masoud Eftekhari Moghadam, Mohammad Hossein Rezvani
Summary: Mobile Cloud Computing (MCC) is no longer sufficient to handle the increasing data volume and real-time application delays. Challenges such as security, energy consumption, storage space, bandwidth, mobility support, and location awareness have made this problem more complex. Edge Computing (MEC) and Fog Computing (FC) are critical techniques for the IoT, and machine learning-based computation offloading mechanisms in these environments are reviewed in this paper.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Mahmood ul Hassan, Amin A. Al-Awady, Abid Ali, Muhammad Munawar Iqbal, Muhammad Akram, Jahangir Khan, Ali Ahmad AbuOdeh
Summary: The use of smartphones and mobile devices, as well as Mobile Cloud Applications based on cloud computing, has increased significantly. This paper proposes a Dynamic Decision-Based Task Scheduling Approach for Microservice-based Mobile Cloud Computing Applications (MSCMCC) to address the problems of overhead, lengthy boot time, and high costs in existing cloud-based frameworks. The proposed approach, including the Task Offloading and Microservices based Computational Offloading (TSMCO) framework, effectively improves mobile server utilization, reduces costs, and enhances boot time, resource utilization, and task arrival time for various applications.
PERVASIVE AND MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Shuiguang Deng, Zhengzhe Xiang, Javid Taheri, Mohammad Ali Khoshkholghi, Jianwei Yin, Albert Y. Zomaya, Schahram Dustdar
Summary: This paper discusses the deployment of microservice-based applications in the MEC environment and proposes an approach to optimize deployment costs while considering resource constraints and performance requirements. Through a series of experiments, it is shown that the approach can improve the average response time of mobile services.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Ivan Rodriguez-Conde, Celso Campos, Florentino Fdez-Riverola
Summary: This paper analyzes relevant research conducted under the mobile cloud computing paradigm, and introduces the methods and technologies of collaborative intelligence to bring vision tasks supported by state-of-the-art deep convolutional neural networks closer to the end-user. It provides a detailed explanation of the main methods used to partition and deploy these models on the UE-edge-cloud continuum, as well as the measures taken to optimize the overall performance. The paper also discusses the current challenges and future research directions in this field.
Article
Computer Science, Information Systems
Abid Ali, Muhammad Munwar Iqbal
Summary: This study proposes a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC), which can reduce the cost and improve mobile server utilization.
Article
Computer Science, Theory & Methods
Mohammad Goudarzi, Marimuthu Palaniswami, Rajkumar Buyya
Summary: This article presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. The current works in the literature are analyzed based on the new classification schemes, research gaps of each category are identified, and respective future directions are described.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Hardware & Architecture
Balawal Shabir, Anis U. Rahman, Asad Waqar Malik, Rajkumar Buyya, Muazzam A. Khan
Summary: This paper proposes a deep reinforcement learning approach for task offloading decisions in vehicular fog computing, where local and global models are trained collaboratively to achieve efficient resource utilization.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Information Systems
Satish Kumar, Tao Chen, Rami Bahsoon, Rajkumar Buyya
Summary: In this article, we propose DebtCom, a framework that determines whether to trigger recomposition based on the technical debt metaphor and time-series prediction of workload. Our core idea is that recomposition can be unnecessary if the under-/over-utilization only cause temporarily negative effects, and the current composition plan, although carries debt, can generate greater benefit in the long-term. The results confirm that, in contrast to the state-of-the-art, DebtCom achieves better utility while having lower cost and number of recompositions, rendering each composition plan more sustainable.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Multidisciplinary Sciences
Deepika Saxena, Ashutosh Kumar Singh, Chung-Nan Lee, Rajkumar Buyya
Summary: The unsustainable demand and ineffective load management in Cloud Data Centres (CDCs) lead to high energy consumption, resource contention, excessive carbon emission, and security threats. A novel Sustainable and Secure Load Management (SaS-LM) Model is proposed to address these issues by dynamically adjusting the load to maximize security and sustainability. An evolutionary optimization algorithm called Dual-Phase Black Hole Optimization (DPBHO) is used to estimate resource usage and detect congestion. SaS-LM is evaluated using real-world Google Cluster VM traces and shows significant reductions in carbon emission and energy consumption, as well as improved resource utilization.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Theory & Methods
Yogesh Sharma, Deval Bhamare, Nishanth Sastry, Bahman Javadi, Rajkumar Buyya
Summary: This article reviews how intent-driven service management systems manage and fulfill SLA requirements and proposes four intent management activities performed in a closed-loop manner. The article categorizes and compares existing SLA management techniques in IDSM systems and suggests future research directions.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Theory & Methods
Samodha Pallewatta, Vassilis Kostakos, Rajkumar Buyya
Summary: This article introduces the utilization of fog computing paradigm and microservice architecture in IoT applications, and their relationship. Efficient placement algorithms are required for microservices-based IoT applications to achieve diverse performance requirements and overcome the challenges introduced by the architecture.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Theory & Methods
Anupama Mampage, Shanika Karunasekera, Rajkumar Buyya
Summary: Serverless computing has gained much attention in recent years as it shifts the burden of resource management to cloud service providers. However, efficiently managing resources while maintaining function performance is challenging due to the dynamic and multi-tenant nature of serverless systems.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Theory & Methods
Guangyao Zhou, Wenhong Tian, Rajkumar Buyya
Summary: Cloud computing is a large-scale distributed computing system that dynamically provides elastic services to users. Resource scheduling in Cloud computing, which aims to minimize makespan, is usually NP-Hard. This paper proposes multi-search-routes-based algorithms, integrating LPT and BFD as basic search routes with the OneStep neighborhood search algorithm, to optimize scheduling schemes for homogeneous and heterogeneous resources. Theoretical derivations and extensive experiments demonstrate the superiority of the proposed algorithms in minimizing makespan problems.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Hardware & Architecture
Xiaogang Wang, Jian Cao, Rajkumar Buyya
Summary: This paper proposes an adaptive cloud bundle provisioning and multi-workflow scheduling model that dynamically scales resources on multi-type VM instances for the execution of complex workflows. The performance of the proposed algorithms is demonstrated to be superior to that of existing policies.
IEEE TRANSACTIONS ON COMPUTERS
(2023)
Article
Computer Science, Information Systems
Mohammad Goudarzi, Marimuthu Palaniswami, Rajkumar Buyya
Summary: Fog/Edge computing is a new computing paradigm that supports resource-constrained IoT devices by placing their tasks on edge and/or cloud servers. However, existing centralized Deep Reinforcement Learning (DRL)-based placement techniques lack generalizability and quick adaptability. To address this, we propose a distributed application placement technique based on IMPALA, which significantly improves the execution cost of IoT applications. Our technique utilizes recurrent layers to capture temporal behaviors and a replay buffer to enhance sample efficiency.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Muhammad Zakarya, Lee Gillam, Khaled Salah, Omer Rana, Santosh Tirunagari, Rajkumar Buyya
Summary: In many production clouds, except for Google, aggregation-based VM placement policies are used to efficiently provision datacenter resources in terms of energy and performance. However, placing VMs with similar workloads on the same machines can lead to contention for resources and degrade performance. Segregation-based methods also result in stranded resources and less economic efficiency. This article explores the impact of aggregation and segregation-based VM placement policies on energy efficiency, workload performance, and costs.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Environmental Sciences
K. C. Ujjwal, Jagannath Aryal, K. Shuvo Bakar, James Hilton, Rajkumar Buyya
Summary: In this paper, a Bayesian model is proposed to estimate the impacts of wildfires by utilizing observations and expert knowledge. The approach allows for investigating the influence of different priors and assessing the sensitivity of each input factor, enabling timely fire scenario analysis.
Article
Computer Science, Artificial Intelligence
Pradeep Raj Krishnappa Babu, J. Matias Di Martino, Zhuoqing Chang, Sam Perochon, Kimberly L. H. Carpenter, Scott Compton, Steven Espinosa, Geraldine Dawson, Guillermo Sapiro
Summary: Atypical facial expression is an early symptom of autism spectrum disorder (ASD), and automatic quantification of these behaviors can provide new biomarkers for screening and diagnosing ASD. In this study, facial landmarks were automatically computed using computer vision algorithms to analyze the complexity of facial dynamics in toddlers with ASD and typically developing toddlers. The results showed that toddlers with ASD had higher complexity in facial dynamics compared to typically developing toddlers, indicating that computer vision analysis of facial landmark movements is a promising approach for detecting and quantifying early behavioral symptoms associated with ASD.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Information Systems
Prabhakar Krishnan, Kurunandan Jain, Amjad Aldweesh, P. Prabu, Rajkumar Buyya
Summary: This paper introduces a network data plane-based architecture that combines SDN, NFV and ML/AI to improve network management in OpenStack Clouds, ensuring predictability, reliability and security. The framework consists of lightweight monitoring, anomaly-detecting intelligent sensors, ML/AI-based threat analytics engine, and defensive actions deployed as VNFs, enabling high-speed threat detection and rapid response. The simulations and analysis show that this framework substantially secures and outperforms prior OpenStack solutions for Cloud architectures.
JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Tharindu B. Hewage, Shashikant Ilager, Maria A. Rodriguez, Rajkumar Buyya
Summary: Cloud computing environment simulators allow for cost-effective experimentation of infrastructure designs. Existing simulators compromise on usability and extensibility. We propose an architectural framework called CloudSim Express that enables human-readable script-based simulations while minimizing the impact on simulator extensibility. The framework achieves significant reductions in code complexity and lines of code.
SOFTWARE-PRACTICE & EXPERIENCE
(2023)
