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An input-centric performance model for computational offloading of mobile applications

JOURNAL OF SYSTEMS AND SOFTWARE 2018年 138卷 1-18页

作者： Rehn, Adam Holdsworth, Jason Hamilton, John Tee, Singwhat James Cook Univ 14-88 McGregor Rd Smithfield Cairns Qld Australia

computational offloading frameworks are a widely-researched technology for optimising mobile applications through the use of cloud resources. Existing frameworks fail to fully account for the effect of input data characteristics on application behaviour. Comprehensive timing models exist in the literature, but feature information requirements and performance overheads that preclude use on mobile devices. In this paper, we propose a conceptual model for an input-centric view of application performance. Our proposed model simplifies the existing count- and-weights and pipeline timing models to significantly reduce their information and processing requirements, facilitating use on resource-constrained mobile devices. Our proposed model also utilises symbolic execution techniques to account for the effects of application input data characteristics. Validation with both synthetic and real device datasets demonstrates that our model provides an extremely accurate approximation of the count-and-weights model. Results demonstrate the predictive power of our model for linear execution paths with no loops or recursion. Further work with improved symbolic execution techniques may look to expand application of our proposed model to real-world use cases. The proposed input-centric approach provides a promising foundation for incorporating a deeper level of application-specific knowledge into computational offloading framework cost models, with the potential to contribute to higher-quality offloading decisions. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Timing model Symbolic execution computational offloading

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An Improved and Efficient computational offloading Method Based on ADMM Strategy in Cloud-Edge Collaborative Computing Environment for Resilient Industry 5.0

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1392-1402页

作者： Shu, Wanneng Nie, Shaoliang Jian, Wencheng Ge, Xin South Cent Minzu Univ Coll Comp Sci Wuhan 430074 Peoples R China

With the rapid growth of data demand and the rise of the Industry 5.0, cloud-edge collaborative computing achieves intelligent connectivity and data sharing between devices for intelligent management and optimization of the production process. computational offloading, as a key technology in the cloud-edge collaborative computing environment, provides higher-quality user services and realizes higher data requirements for intelligent manufacturing and production. In this paper, we proposes an Improved Efficient Alternating Direction Method of Multipliers (IEADMM) for computational offloading in cloud-edge collaborative computing environment, aiming to reduce the total system cost with the optimization goal of minimizing the total time delay. The proposed IEADMM fully utilizes the Alternating Direction Method of Multipliers ADMM) to minimize the total delay by increasing the convergence speed. The experimental result shows that our proposed IEADMM has good performance and practicality for computational offloading in cloud-edge collaborative computing environment, and can be widely applied in practical application scenarios such as intelligent monitoring, Internet of Things (IoT), mobile games, etc.

关键词： Task analysis Servers Costs computational modeling Symbols Optical fiber networks Improved efficient alternating direction method of multipliers cloud-edge collaborative computing MEC server computational offloading resilient industry 50

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Joint computational offloading and Data-Content Caching in NOMA-MEC Networks

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IEEE ACCESS 2021年 9卷 12943-12954页

作者： Huynh, Luan N. T. Pham, Quoc-Viet Nguyen, Tri D. T. Hossain, Md. Delowar Shin, Young-Rok Huh, Eui-Nam Kyung Hee Univ Dept Comp Sci & Engn Yongin 17104 South Korea Pusan Natl Univ Res Inst Comp Informat & Commun Busan 46241 South Korea

Multi-access edge computing (MEC) can improve the users' computational capacity and battery life by moving computing services to the network edge. In addition, data-content caching on a MEC server improves the user quality of experience and decreases the backhaul network congestion. Moreover, non-orthogonal multiple access (NOMA) has recently been implemented to increase network throughput and capacity. Combining these techniques can improve the user performance and benefit the network. This paper investigates a combined computational offloading and data-content caching problem for NOMA-MEC systems. The aim was to achieve the minimum total completion latency of all users by jointly optimizing the offloading decision, caching strategy, computational resource, and power allocation. This satisfies the constraints within the scope of the potential violation for energy consumption, offloading decision, and the computation and storage capacity of the MEC server. The formulated problem is a mixed-integer non-linear programming and a non-convex problem. To solve this challenging problem, a block successive upper-bound minimization method was implemented to obtain efficient solutions. Numerous simulation results were presented to demonstrate the convergence and efficacy of the proposed algorithm. Compared with other schemes of all-offloading, local-only, and equal resources, our proposed algorithm can approximately reduce the total completion latency by 17.68%, 26.02%, and 70.98%.

关键词： Resource management Task analysis Servers NOMA Delays Optimization Energy consumption Multi-access edge computing non-orthogonal multiple access block successive upper-bound minimization computational offloading data-content caching

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A Study on the Critical Analysis of computational offloading Frameworks for Mobile Cloud Computing

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2015年 47卷 47-60页

作者： Shiraz, Muhammad Sookhak, Mehdi Gani, Abdullah Shah, Syed Adeel Ali Univ Malaya Fac Comp Sci & Informat Technol Mobile Cloud Comp Res Lab Kuala Lumpur Malaysia

Despite substantial technological advancements in recent years, Smart Mobile Devices (SMDs) are still low-potential computing devices. Therefore, Mobile Cloud Computing (MCC) can deploy computational offloading for augmenting SMDs. The contemporary computational offloading Frameworks (COFs) implement resource-intensive procedures for computational offloading, which involve the overhead of transmitting application binary code and deploying distributed platforms at runtime. As a result, the energy consumption costs and turnaround time for the mobile applications and the overhead of data transmission can be increased. Nevertheless, the resource -limited nature of SMDs requires lightweight techniques for leveraging the application processing services of computational clouds. This paper critically analyzes the resource-intensive nature of the latest existing computational offloading techniques for MCC and highlights technical issues in the establishment of distributed application processing platforms at runtime. A prototype application is evaluated with different computation intensities in a real MCC environment Analysis of the results shows that additional computing resources are utilized in the deployment of distributed application processing platforms at runtimes. For example, 31.6% additional energy is consumed, 39% additional time is required and 13241.2 KB of data are transmitted for offloading different computational intensive components of the prototype mobile application. Finally, we highlight technical issues in the existing computational offloading techniques for MCC, which draw attention to future research possibilities for computational offloading for MCC and which may assist in developing lightweight procedures for computational offloading in MCC (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Mobile Cloud Computing Distributed Systems computational offloading Resource-Intensive Lightweight

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Secure Authentication Protocol for Efficient computational offloading Service in the Mobile Cloud Computing

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JOURNAL OF INTERNET TECHNOLOGY 2020年第2期21卷 457-467页

作者： Munivel, E. Kannammal, A. PSG Coll Technol Dept Elect & Commun Engn Coimbatore Tamil Nadu India

Battery-powered mobile devices are convenient to use anywhere and anytime. Nowadays maximum people using the mobile device, such as Smartphone, Tablet computers to use mobile services anytime and anywhere. Moreover, most of the Smartphone having wireless communication like Wi-Fi and 4G. For some applications, the requirement of computing power may be very high, but the actual configuration of mobile devices are very limited, such as CPU, memory, storage, and battery. Among these computational resources, bandwidth and battery are the most significant problems for Smartphone. Efficient computational offloading is the best solution for extending the usage of Smartphone by executing the resource-intensive task to offload from mobile to the remote cloud servers can extend processing capability and support for multiple categories of application. However, this technique is having difficulty in offloading the process to a remote cloud server without the proven security of entity verification. To deal with these challenges, here we are proposing new security protocol to authenticate mobile and cloud server with zero knowledge proof of authentication to verify the communication entities and recommends to offload the service. The proposed protocol will get verify by the University of Oxford developed verification tool Scyther.

关键词： Authentication Mobile cloud computing Smartphone Cloud server computational offloading

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A Collaborative computational offloading Strategy for Latency-Sensitive Applications in Fog Networks

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IEEE INTERNET OF THINGS JOURNAL 2022年第6期9卷 4565-4572页

作者： Sarkar, Indranil Adhikari, Mainak Kumar, Neeraj Kumar, Sanjay Natl Inst Technol Raipur Dept Informat Technol Raipur 492010 Madhya Pradesh India Indian Inst Informat Technol Lucknow Dept Comp Sci Lucknow 226002 Uttar Pradesh India Thapar Inst Engn & Technol Comp Sci & Engn Patiala 147004 Punjab India Univ Petr & Energy Studies Sch Comp Sci Dehra Dun 248007 Uttarakhand India Asia Univ Dept Comp Sci & Informat Engn Taichung 413 Taiwan

Network data traffic has expanded exponentially over the past decade, resulting in massive congestion in heterogeneous networks. Nevertheless, it is nearly impossible to run latency-intensive applications to the end-users local processing unit due to limited system resources. Recently, fog computing has come up with a solution to reduce such data congestion by offloading some or the whole part of the task to the nearby fog nodes (FNs) or the clouds. But this offloading policy becomes more complex when the FN is unable to process the task and further offload it to another neighboring FN or the cloud. In this context, in this study, we have analyzed the offloading strategy in a hierarchical fog-cloud network consisting of several heterogeneous fog devices along with a helping fog and a centralized cloud server. We have considered the most possible practical situation where the FNs are equipped with different CPU frequencies and hence, the power consumption is also different. The total system cost is formulated as a mixed-integer nonlinear problem that aims to reduce the overall delay in the proposed network. To solve the NP-hard problem, we transform it into quadratically constrained quadratic programming (QCQP) formation and further solve it by the separable semidefinite relaxation (SDR) method. Finally, by adopting several benchmark data, we conduct comprehensive simulations to test the efficiency of the proposed offloading profile. The simulation results depict that the proposed strategy outperforms in many aspects when compared to various baseline algorithms.

关键词： Task analysis Delays Edge computing Cloud computing Power demand computational modeling Servers computational offloading delay fog computing latency-sensitive applications semidefinite relaxation (SDR)

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On Applicability of Imagery-Based CNN to computational offloading Location Selection

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IEEE ACCESS 2023年 11卷 2433-2444页

作者： Ometov, Aleksandr Mezina, Anzhelika Nurmi, Jari Tampere Univ Fac Informat Technol & Commun Sci Elect Engn Unit Tampere Finland Brno Univ Technol Fac Elect Engn & Commun Dept Telecommun Brno Czech Republic

The progress in computational offloading is heavily pushing the development of the modern Information and Communications Technology domain. The growth in resource-constrained Internet of Things devices demands the development of new computational offloading strategies to be sustainably integrated in beyond 5G networks. One of the solutions to said demand is enabling Mobile Edge Computing (MEC) powered by advanced methods of Machine Learning (ML). This paper proposes the application of ML-powered computational offloading strategy in a wireless cellular network by applying the traditional fundamental Travelling Salesman Problem (TSP) on computational offloading location selection. The main specificity of the proposed approach is the use of imagery data. Thus, the paper executes a literature review to identify existing strategies. It further proposes a novel method utilizing the location-like imagery data to identify the most suitable computational location by executing the search for an identified route between locations using the proposed Deep Learning (DL) model. The model was evaluated and achieved MAE - 1,575, MSA - 10,119,205, R-2 - 0.98 on the testing dataset, which outperforms or is comparable with other well-known architectures. Moreover, the training time is proven to be 2-10 times faster. Interestingly, the MAE values are relatively low compared to the target values that should be predicted (despite rather high MSE results), which is confirmed by the almost perfect R-2 value. It is concluded that the proposed neural network can predict the target values, and this solution can be applied to real-world tasks.

关键词： computational offloading machine learning mobile edge computing optimization

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Trust Enforced computational offloading for Health Care Applications in Fog Computing

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WIRELESS PERSONAL COMMUNICATIONS 2021年第2期119卷 1369-1386页

作者： Meena, V. Gorripatti, Meghana Suriya Praba, T. SASTRA Sch Comp Thirumalaisamudram 613401 Thanjavur India

Internet of Things (IoT) is a network of internet connected devices that generates huge amount of data every day. The usage of IoT devices such as smart wearables, smart phones, smart cities are increasing in the linear scale. Health care is one of the primary applications today that uses IoT devices. Data generated in this application may need computation, storage and data analytics operations which requires resourceful environment for remote patient health monitoring. The data related with health care applications are primarily private and should be readily available to the users. Enforcing these two constraints in cloud environment is a hard task. Fog computing is an emergent architecture for providing computation, storage, control and network services within user's proximity. To handle private data, the processing elements should be trustable entities in Fog environment. In this paper we propose novel Trust Enforced computation offloading technique for trust worthy applications using fOg computiNg (TEFLON). The proposed system comprises of two algorithms namely optimal service offloader and trust assessment for addressing security and trust issues with reduced response time. And the simulation results show that proposed TEFLON framework improves success rate of fog collaboration with reduced average latency for delay sensitive applications and ensures trust for trustworthy applications.

关键词： Fog computing computational offloading Trusted node identification Health care Response time

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Cooperative computational offloading in Mobile Edge Computing for Vehicles: A Model-Based DNN Approach

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第3期72卷 3376-3391页

作者： Munawar, Suleman Ali, Zaiwar Waqas, Muhammad Tu, Shanshan Hassan, Syed Ali Abbas, Ghulam Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Comp Sci & Engn Topi 23460 Pakistan Univ Bahrain Coll Informat Technol Dept Comp Engn Sakhir 32038 Bahrain Edith Cowan Univ Sch Engn Perth WA 6027 Australia Beijing Univ Technol Fac Informat Technol Engn Res Ctr Intelligent Percept & Autonomous Cont Beijing 100021 Peoples R China Natl Univ Sci & Technol Sch Elect Engn & Comp Sci Islamabad 44000 Pakistan

Many advancements are being made in vehicular networks, such as self-driving, dynamic route scheduling, real-time traffic condition monitoring, and on-board infotainment services. However, these services require high computation power and precision and can be met using mobile edge computing (MEC) mechanisms for vehicular networks. MEC operates through the edge servers available at the roadside, also known as roadside units (RSU). MEC is very useful for vehicular networks because it has extremely low latency and supports operations that require near-real-time access to rapidly changing data. This paper proposes an efficient computational offloading, smart division of tasks, and cooperation among RSUs to increase service performance and decrease the delay in a vehicular network via MEC. The computational delay is further reduced by parallel processing. In the division of tasks, each task is divided into two sub-components which are fed to a deep neural network (DNN) for training. Consequently, this reduces the overall time delay and overhead. We also adopt an efficient routing policy to deliver the results through the shortest path to improve service reliability. The offloading, computing, division, and routing policies are formulated, and a model-based DNN approach is used to obtain an optimal solution. Simulation results prove that our proposed approach is suitable in a dynamic environment. We also compare our results with the existing state-of-the-art, showing that our proposed approach outperforms the existing schemes.

关键词： Task analysis Servers Delay effects computational modeling Routing Delays Cloud computing Deep neural network (DNN) computational offloading vehicular networks smart task division routing policy mobile edge computing

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Attention-Augmented MADDPG in NOMA-Based Vehicular Mobile Edge computational offloading

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IEEE INTERNET OF THINGS JOURNAL 2024年第16期11卷 27000-27014页

作者： Wu, Liangshun Qu, Junsuo Li, Shilin Zhang, Cong Du, Jianbo Sun, Xiang Zhou, Jiehan Xian Univ Posts & Telecommun Sch Automat Xian Key Lab Adv Control & Intelligent Proc Xian 710061 Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710061 Peoples R China Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA Univ Oulu Fac Informat Technol & Elect Engn Oulu 90570 Finland

Vehicular mobile edge computing (vMEC) and nonorthogonal multiple access (NOMA) have emerged as promising technologies for enabling low-latency and high-throughput applications in vehicular networks. In this article, we propose a novel multiagent deep deterministic policy gradient (MADDPG) approach for resource allocation in NOMA-based vMEC systems. Our approach leverages deep reinforcement learning (DRL) to enable vehicles to offload computation-intensive tasks to nearby edge servers, optimizing resource allocation decisions while ensuring low-latency communication. We introduce an attention mechanism within the MADDPG model to dynamically focus on relevant information from the input state and joint actions, enhancing the model's predictive accuracy. Additionally, we propose an attention-based experience replay method to expedite network convergence. The simulation results highlight the effectiveness of multiagent reinforcement learning (MARL) algorithms, such as MADDPG with attention, in achieving better convergence and performance in various scenarios. The influence of different model parameters, such as input data volumes, task load levels, and resource configurations, on optimization results is also evident. The decision making processes of agents are dynamic and depend on factors specific to the task and environment.

关键词： Attention mechanism computational offloading deep reinforce learning multiagent deep deterministic policy gradient (MADDPG) nonorthogonal multiple access (NOMA) vehicular Mobile Edge Computing (vMEC) Attention mechanism computational offloading deep reinforce learning multiagent deep deterministic policy gradient (MADDPG) nonorthogonal multiple access (NOMA) vehicular Mobile Edge Computing (vMEC)

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