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Improved Bat Algorithms for Solving Flowtime Minimization Task Scheduling Problems in D2D-Enabled MEC

Improved Bat Algorithms for Solving Flowtime Minimization Ta...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Linhua Ma Qingran Yan Yi Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Device-to-device (D2D)-enabled mobile edge computing (MEC) is a promising communication network in which computation tasks on mobile devices (MDs) can not only be processed locally but also can be scheduled to the MEC server or nearby MDs via D2D links in order to improve computational and energy efficiency. Due to the explosive growth of information and consequent large-scale data, the conflict between reducing task response time and the limited resources of MEC server and MDs has become an essential bottleneck. In this paper, we consider a D2D-enabled MEC system and study a task scheduling problem for minimizing the flowtime of all computation tasks under the energy constraints of MDs. To solve this problem, we develop a family of metaheuristic algorithms based upon our proposed framework of improved bat algorithm (IBA), which incorporates multiple best bat individuals. Also, we introduce a new linear updating scheme to update the bat positions and facilitate the evolution process of the metaheuristics. More importantly, we provide a rigorous theoretical analysis to prove the convergence of IBA by using this new updating scheme. Furthermore, we employ an improved initial solution generation strategy for enhancing the effectiveness of IBA. Numerical experiments demonstrate that the proposed algorithms can achieve high-quality scheduling solutions, in terms of shorter flowtimes, more effectively and more efficiently over baseline algorithms.

关键词： Multi-access edge computing Processor scheduling NP-hard problem Metaheuristics Scheduling Mobile handsets Computational efficiency Servers Time factors Convergence

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Duo: Improving Data Sharing of Stateful Serverless applications by Efficiently Caching Multi-Read Data

Duo: Improving Data Sharing of Stateful Serverless Applicati...

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international symposium on Parallel and distributed Processing (IPDPS)

作者： Zhuo Huang Hao Fan Chaoyi Cheng Song Wu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

A growing number of applications are moving to serverless architectures for high elasticity and fine-grained billing. For stateful applications, however, the use of serverless architectures is likely to lead to significant performance degradation, as frequent data sharing between different execution stages involves time-consuming remote storage access. Current platforms leverage memory cache to speed up remote access. However, conventional caching strategies show limited performance improvement. We experimentally find that the reason is that current strategies overlook the stage-dependent access patterns of stateful serverless applications, i.e., data that are read multiple times across stages (denoted as multi-read data) are wrongly evicted by data that are read only once (denoted as read-once data), causing a high cache miss ***, we propose a new caching strategy, Duo, whose design principle is to cache multi-read data as long as possible. Specifically, Duo contains a large cache list and a small cache list, which act as Leader list and Wingman list, respectively. Leader list ignores the data that is read for the first time to prevent itself from being polluted by massive read-once data at each stage. Wingman list inspects the data that are ignored or evicted by Leader list, and pre-fetches the data that will probably be read again based on the observation that multi-read data usually appear periodically in groups. Compared to the state-of-the-art works, Duo improves hit ratio by 1.1×-2.1× and reduces the data sharing overhead by 25%-62%.

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Geo-distribute Cloud applications at the Edge 27th

Geo-distribute Cloud Applications at the Edge

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27th international European Conference on Parallel and distributed computing (Euro-Par)

作者： Cherrueau, Ronan-Alexandre Delavergne, Marie Lebre, Adrien INRIA LS2N Lab Nantes France

ISBN: (纸本)9783030856656;9783030856649

With the arrival of the edge computing a new challenge arises for cloud applications: How to benefit from geo-distribution (locality) while dealing with inherent constraints of wide-area network links? The admitted approach consists in modifying cloud applications by entangling geo-distribution aspects in the business logic using distributed data stores. However, this makes the code intricate and contradicts the software engineering principle of externalizing concerns. We propose a different approach that relies on the modularity property of microservices applications: (i) one instance of an application is deployed at each edge location, making the system more robust to network partitions (local requests can still be satisfied), and (ii) collaboration between instances can be programmed outside of the application in a generic manner thanks to a service mesh. We validate the relevance of our proposal on a real use-case: geo-distributing OpenStack, a modular application composed of 13 million of lines of code and more than 150 services.

关键词： Edge computing Resource sharing DSL Service composition Service mesh Modularity

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Multi-Agent Systems for Autonomous IoT Network Management Using distributed Reinforcement Learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

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international symposium on Advanced computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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Breast Cancer Classification Using DDPM and Holographic Guidance Diffusion Framework

Breast Cancer Classification Using DDPM and Holographic Guid...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Yuxin You Hong-Seng Gan Akinobu Shimizu Muhammad Hanif Ramlee Hafiz Basarudin Viet-Vu Vu School of AI and Advanced Computing Xi’an Jiaotong – Liverpool University Suzhou China Institute of Engineering Tokyo University of Agriculture and Technology Tokyo Japan Department of Biomedical Engineering and Health Sciences Universiti Teknologi Malaysia Johor Bahru Malaysia Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia Faculty of Information Technology and Communications CMC University Hanoi Vietnam

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Breast cancer is a serious health threat to women. The early detection of breast cancer is crucial to improving survival rates and reducing the difficulty of treatment. In recent years, denoising diffusion probability models (DDPM) have performed well in image generation, but their application to breast cancer classification remains unknown. Additionally, conditional priors are often the most important source of information for diffusion models to solve problems, and fuzzy lesions or high-noise images pose a significant challenge for the model to extract accurate prior information. To address these issues, we propose a new classification model based on DDPM and combine it with the holographic guided diffusion framework (HGDF) to remove noise from images through specific guidance and make full use of HPC resources to improve the classification performance of breast cancer images. Results from the experiment indicate that the model has excellent accuracy in ultrasound images (BUS dataset) and mammography images (MAMMO dataset), with accuracy of 86.17 % ± 1.74% and 94.32% ± 2.31%, respectively.

关键词： Training Accuracy Ultrasonic imaging Computational modeling Noise reduction Noise Diffusion processes Breast cancer Lesions Medical diagnostic imaging

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A Novel Maize Futures Price Prediction Model based on EEMD-CNN-IGRU

A Novel Maize Futures Price Prediction Model based on EEMD-C...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Jingyang Wang Jingyu Han Wei Liu Ying Li Qiuhong Sun Xiaokang Zhou School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China Faculty of Business Data Science Kansai University Osaka Japan

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Maize, a significant global food crop, is essential in agriculture and the economy. The price of maize futures is affected by many factors, and its data is a nonlinear, unstable, and long-term correlation, so it is difficult to predict it accurately. Accordingly, this paper presents a combined model based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN), and Improved Gated Recurrent Unit (IGRU). EEMD decomposes the maize price data to produce multiple Intrinsic Mode Function (IMF) components and residual sequences. Subsequently, the noisy IMF components are removed, and the remaining IMF components are reconstructed into low, medium, and high frequencies. The CNN is tasked with the extraction of eigenvalues for these components. The IGRU improves in two key respects on the original Gated Recurrent Unit (GRU) model. First, it enhances the update and reset gates. Second, it incorporates the Self-Attention (SA) module. Thereby, the model's predictive capabilities are improved. This study uses the primary maize futures trading data from the Dalian Commodity Exchange as the experimental data. A comparative analysis of the EEMD-CNN-IGRU model with seven baseline models shows that it outperforms other models in all evaluation indexes.

关键词： Analytical models Climate change Trade agreements Disasters Predictive models Logic gates Eigenvalues and eigenfunctions Convolutional neural networks Indexes High frequency

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Research on Incentive Mechanisms and Resource Allocation of Edge Intelligence Networks for Multiple Federated Learning Services

Research on Incentive Mechanisms and Resource Allocation of ...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Xin Jin Fang Fu Zhicai Zhang School of Physics and Electronic Engineering Shanxi University Taiyuan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The integration of artificial intelligence and edge computing has given rise to edge intelligence. Federated Learning (FL), as a key technology of edge intelligence, utilizes data and computation power on edge devices to achieve decentralized edge intelligence. The problem of incentive mechanism design and resource allocation in FL has been two key issues that hinder the smooth progress of the learning process. We study an edge intelligence network that supports multiple simultaneous FL services, and there are multiple task publishers seeking FL services and mobile device alliances providing services for them. We address these two key issues. First, how task publishers can incentivize device alliances to contribute resources to facilitate the execution of FL services. Second, how the device alliances allocate resources across multiple FL services that are in progress to enable bounded rational decision-making. In order to solve the former, we transform the incentive design under information asymmetry into a contract game problem. For the latter, we propose an evolutionary game model to adjust their resource allocation strategies, while considering the influence of bounded rationality and external factors on the decision-making of device alliances. The simulation results show that our incentive mechanism shows excellent performance in the case of information asymmetry. Additionally, We experimentally simulated the dynamic evolution of device alliance strategies.

关键词： Performance evaluation Federated learning Simulation Mechanism design Games Transforms Mobile handsets Resource management Contracts Edge computing

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An Analysis of Edge computing with Multi Access in 5-G Technology

An Analysis of Edge Computing with Multi Access in 5-G Techn...

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Advance computing and Innovative Technologies in engineering (ICACITE), international Conference on

作者： Sitaram Gupta Sonia Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri

The demands of the rapidly expanding internet. However, since most end users have confined storage space and limiting computing power, it lately evolved into a natural issue how to execute computationally complex apps on resource-bound consumers. The emerging fifth generation (5G) corporation can benefit from Mobile Edge computing (MEC) innovation upgrade portable assets by facilitating figure concentrated applications, handling a lot of information prior to sending them to the cloud, finding distributed computing assets inside the radio access organization (RAN) close to versatile clients, and giving setting mindful administrations utilizing RAN information. In reality, the paradigm shift from 4G to 5G is accompanied by the introduction of new technical concepts. We first give a comprehensive introduction of MEC technology and some of its possible applications. The most recent research on MEC integration with upcoming technologies that will be employed in 5G and beyond is then is summarized.

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Modeling Multi-Threaded Aggregated I/O for Asynchronous Checkpointing on HPC Systems

Modeling Multi-Threaded Aggregated I/O for Asynchronous Chec...

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international symposium on Parallel and distributed computing

作者： Mikaila J. Gossman Bogdan Nicolae Jon C. Calhoun Holcombe Department of Electrical and Computing Engineering Clemson University Clemson SC Mathematics and Computer Science Division Argonne National Laboratory Lemont IL

HPC systems encompass more components with each new generation. As a result, the process of interacting with stable storage systems like parallel file systems (PFS) becomes increasingly difficult. Larger systems often result in more frequent failures, increasing the need and frequency to incorporate fault-tolerant mechanisms. One example is checkpoint-restart (C/R), where applications or systems save their data to non-volatile storage devices, such as a PFS. On failure, the system or application is restored to a saved state and computation continues. Today, asynchronous C/R is gaining traction for its ability to checkpoint data to permanent storage concurrently with the application. However, asynchronous C/R brings about many new challenges. For starters, asynchronous C/R introduces complex resource contention between the application and the C/R implementation. Additionally, some implementations adopt file-per-process writing strategies, which overwhelm PFS’ at high core counts. In this work, we explore how multi-threaded POSIX I/O impacts aggregated throughput. To this extent we characterize the influence of different I/O parameters, such as the number of writer threads and how they access storage devices, has on aggregated I/O. We use the information gathered in this study to identify best practices when performing aggregated I/O as a first step in designing an efficient I/O aggregation scheme for asynchronous C/R.

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Enabling Complete Atomicity for Cross-Chain applications Through Layered State Commitments

Enabling Complete Atomicity for Cross-Chain Applications Thr...

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Reliable distributed Systems

作者： Yuandi Cai Ru Cheng Yifan Zhou Shijie Zhang Jiang Xiao Hai Jin Services Computing Technology and System Lab Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9798331530037

ISBN: (纸本)9798331530044

Cross-chain Decentralized applications (dApps) are increasingly popular for their ability to handle complex tasks across various blockchains, extending beyond simple asset transfers or swaps. However, ensuring all dependent transactions execute correctly together, known as complete atomicity, remains a challenge. Existing works provide financial atomicity, protecting against monetary loss, but lack the ability to ensure correctness for complex tasks. In this paper, we introduce Avalon, a transaction execution framework for cross-chain dApps that guarantees complete atomicity for the first time. Avalon achieves this by introducing multiple state layers above the native one to cache state transitions, allowing for efficient management of these state transitions. Most notably, for concurrent cross-chain transactions, Avalon resolves not only intra-chain conflicts but also addresses potential inconsistencies between blockchains via a novel state synchronization protocol, enabling serializable cross-chain execution. We implement Avalon using smart contracts in Cosmos ecosystem and evaluate its commitment performance, demonstrating acceptable latency and gas consumption even under conflict cases.

关键词： Atomic layer deposition Smart contracts Ecosystems Decentralized applications Consensus protocol Synchronization Reliability Interoperability

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