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Identifying anti-tumor heat shock proteins based on evolutionary information using deep learning method

Identifying anti-tumor heat shock proteins based on evolutio...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Yi Fu Ji Zhao Juan Mei Yi Ding School of Internet of Things Engineering Wuxi City College of Vocational Technology Wuxi China Wuxi Research Center for Environmental Science & Engineering Wuxi China

ISBN: (纸本)9781665454636

Heat shock proteins (HSPs) belong to stress proteins. The functions of HSPs are mainly reflected in three aspects: molecular chaperones, regulation of apoptosis and immune responses. Recent studies have shown that there is a certain correlation between HSPs and tumor cell. HSPs are participated in the invasion, proliferation and metastasis of tumor cells. Therefore, developing an accurate model for identification anti-tumor HSPs is a key step to understand molecular functions of HSPs and human tumor diseases. In this study, we propose using deep learning methods to identify anti-tumor HSPs. To seek out the optimal model for the dataset, several hyper-parameters are optimized according to the results of 10-fold cross-validation. Finally, the performance of the proposed model is further determined through an independent dataset. The experimental results indicated that the proposed model could classify anti-tumor HSPs with accuracy (ACC) of 93.76%, sensitivity (SN) of 92.80%, specificity (SP) of 93.33%, and Matthew's correlation coefficient (MCC) of 86.39% on the 10-fold cross-validation. Compared with other deep learing methods, using convolutional neural network (CNN) can achieve a significant improvement for identifying of anti-tumor HSPs.

关键词： Deep learning Heating systems Sensitivity Electric shock Neural networks Feature extraction Convolutional neural networks

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A Composite Method for Joint Optimization of Computation Offloading, Power Assignment, and Resource Allocation in Mobile Edge computing

A Composite Method for Joint Optimization of Computation Off...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Huayu Qin Yi Zhang Jin Sun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

By deploying computing resources and services on network edge, mobile edge computing (MEC) can effectively address the energy limitations of mobile devices (MDs) through the computation offloading mechanism. This paper studies the problem of joint optimization of computation offloading, power assignment, and resource allocation, with the objective of reducing the total energy consumption in an MEC environment. We decompose the joint optimization problem into two sub-problems and suggest a composite method that integrates an analytical approach into the decision-making procedure of a deep reinforcement learning (DRL)-based algorithm. We first use the actor network of the twin delayed deep deterministic gradient (TD3), which is an advanced DRL algorithm, to determine the computation offloading rate for each MD as well as resource allocation on the MEC server. To facilitate the training process of DRL, we provide a rigorous analysis to derive the closed-form solution to the optimal assignment of the MD’s operating frequency for local computation as well as transmission power for computation offloading. Distinct from other DRL-based offloading methods that solely rely on DRL techniques to determine the values of all decision variables, our composite method effectively enhances the DRL algorithm’s decision-making capability in producing high-quality solutions. The evaluation results on a multi-user MEC system justify the superiority of the proposed method over baseline algorithms in producing more energy-efficient offloading solutions.

关键词： Training Energy consumption distributed processing Multi-access edge computing Decision making Energy efficiency Mobile handsets Resource management Servers Optimization

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Collaborative and Integrated Platform to Support distributed Manufacturing System Using a Service-Oriented Approach Based On Cloud computing Paradigm

Collaborative and Integrated Platform to Support Distributed...

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Computational, Communication and Information Technology (ICCCIT), international Conference on

作者： Ankita Nainwal Vishal Sharma Neeraj Varshney Nandini Shirish Boob NV Uma Reddy K. Jyothsna Reddy Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Lovely Professional University Phagwara India Department of Computer Engineering and Applications GLA University Mathura Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bangalore Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

Increasing interest in distributed manufacturing systems is a result of the dynamic nature of today's business environment and with that comes the popularity of their use. This has given rise in the interest in distributed manufacturing systems. The indirect consequence of this has led to much higher use of distributed manufacturing systems. A large number of production processes, dispersed across a multitude of locations geographically separate, must be controlled and integrated in order to achieve production agility, flexibility and efficiency of costs. In our most recent project, we developed an integrated, collaborative manufacturing platform, the centerpiece of which was LAYMOD. To address the issues posed by such systems, this research presents a collateral and integrated platform based on a service oriented approach that can operate within the cloud computing paradigm. These systems present problems which the study is conducted in order to solve. Utilising the features of cloud computing, it aims to serve as a platform in solving process coordination and hence improving efficiency and scalability of distant manufacturing processes and potential real time communications among various stake holders. Furthermore, statistical research on the rates of platform module adoption in different settings provides specifics of (a) use patterns of some platform modules and (b) what capabilities are provided by some platform modules. When extrapolated to a wider size, our work is applied towards the creation of collaborative and integrated platforms for a number of the various manufacturing processes which are currently in use.

关键词： Cloud computing Manufacturing processes Scalability Collaboration Production Companies Real-time systems Service-oriented architecture Standards business

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Evolution of Issues in distributed Systems: A Systematic Review 19

Evolution of Issues in Distributed Systems: A Systematic Rev...

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19th international symposium on distributed computing and applications for business engineering and science (DCABES)

作者： AlShammari, Maha M. Imam Abdulrahman Bin Faisal Univ Inst Res & Med Consultat Dept Environm Hlth Computat Unit Dammam Saudi Arabia

ISBN: (纸本)9781728197241

The globalization has increased the demand for complex distributed systems that can be accessed at multiple locations by multiple users. However, the issues pertaining to the distributed systems need specific attention in order to minimize the performance failures and increase the Quality of Service. This study can be a useful source of information to understand the evolution of the issues associated with distributed systems, and the current state of issues, which can help the developers in managing the issues during the development and maintenance. This study reviewed various issues related to distributed systems under 12 themes. We found that Security and Privacy, quality of service, resource management, and synchronization issues are a few major subjects that are being focused on current research studies. This study has concluded that the issues relating to distributed systems and issues related to the changing requirements and technologies are evolving from time to time according to each stage of development of distributed systems such as the design phase or the maintenance phase, in addition to its changing according to the new developments in the technology.

关键词： Scalability Heterogeneity Resource Management Transparency Openness Quality of Service Failure Management Synchronization Issues

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A Task Admission Scheduling Strategy Based on Adaptive Time Slicing in Mobile Edge computing

A Task Admission Scheduling Strategy Based on Adaptive Time ...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Yanpu Tang Zhuofan Liao Xiaoyong Tang School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The rise of Multi-access Edge computing (MEC) speeds up mobile user services and resolves service delays caused by long-distance transmission to cloud servers. However, in task-intensive scenarios, edge server processing limitations lead to buffer congestion, increasing latency and reducing Quality of Service (QoS). Furthermore, the challenges of edge server task processing are increased by the varying deadline requirements of different tasks, the time variability of task arrivals, and the real-time fluctuations of the network. In this paper, we propose an Adaptive Time Slice Admission Scheduling (ATSAS) strategy to solve these problems. Specifically, ATSAS proposes an Adaptive Time Slice Algorithm (ATSA) and a task Admission Scheduling Algorithm (ASA). ATSA dynamically adjusts the time slice based on real-time network conditions and task flow characteristics. ASA, assisted by ATSA, dynamically adjusts task priorities based on the remaining data amount, deadline requirements, real-time network conditions, and the location of the task buffer. After that, ASA makes different scheduling strategies for tasks. Through the coordination of ATSA and ASA, the proposed strategy ensures the high efficiency and fairness of processing tasks in different realistic scenarios. The simulation results show that, compared with the existing O2A and OTDS in different scenarios, the proposed ATSAS reduces the average number of waiting services in the edge server buffer by 38.63% and 35.34%, and accelerates the average response speed of tasks by about 39.76% and 32.41%.

关键词： distributed processing Multi-access edge computing Fluctuations Scheduling algorithms Simulation Heuristic algorithms Quality of service Dynamic scheduling Real-time systems Servers

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UniFaaS: Programming across distributed Cyberinfrastructure with Federated Function Serving

UniFaaS: Programming across Distributed Cyberinfrastructure ...

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international symposium on Parallel and distributed Processing (IPDPS)

作者： Yifei Li Ryan Chard Yadu Babuji Kyle Chard Ian Foster Zhuozhao Li Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Data Science and Learning Division Argonne National Laboratory Lemont IL USA Dept. of Computer Science University of Chicago Chicago IL USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Modern scientific applications are increasingly decomposable into individual functions that may be deployed across distributed and diverse cyberinfrastructure such as supercomputers, clouds, and accelerators. Such applications call for new approaches to programming, distributed execution, and function-level management. We present UniFaaS, a parallel programming framework that relies on a federated function-as-a-service (FaaS) model to enable composition of distributed, scalable, and high-performance scientific workflows, and to support fine-grained function-level management. UniFaaS provides a unified programming interface to compose dynamic task graphs with transparent wide-area data management. UniFaaS exploits an observe-predict-decide approach to efficiently map workflow tasks to target heterogeneous and dynamic resources. We propose a dynamic heterogeneity-aware scheduling algorithm that employs a delay mechanism and a re-scheduling mechanism to accommodate dynamic resource capacity. Our experiments show that UniFaaS can efficiently execute workflows across computing resources with minimal scheduling overhead. We show that UniFaaS can improve the performance of a real-world drug screening workflow by as much as 22.99% when employing an additional 19.48% of resources and a montage workflow by 54.41% when employing an additional 47.83% of resources across multiple distributed clusters, in contrast to using a single cluster.

关键词： Scheduling algorithms Parallel programming Heuristic algorithms Parallel processing Elasticity Dynamic scheduling Supercomputers

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business Scenario Driven Reinforcement Learning Testing Method

Business Scenario Driven Reinforcement Learning Testing Meth...

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Software engineering, Artificial Intelligence, Networking and Parallel/distributed computing (SNPD-Winter), ACIS international Winter Conference on

作者： Lizhi Cai Jin Wang Shanghai Key Laboratory of Computer Software Testing and Evaluating School of Information Science and Engineering East China University of Science and Technology Shanghai China

Reinforcement learning has been successfully applied in software testing, but the existing testing methods cannot perform effective testing according to the characteristics of applications, and using outdated interactive experience during training, resulting in inefficient testing. In this paper, we propose BSDRTesting. Firstly, the demonstration experience of human users is collected according to the functional scenarios and business logic of each application, and combining reinforcement learning and imitation learning to maximize rewards while imitating user behavior, experience replay aims to sample experiences from the agent's self-exploration and expert demonstrations to improve sampling efficiency. At the same time, according to the input rules, the black-box testing method is used to fully test the input events, and finally an experience filtering mechanism is proposed, and the reward value and TD-Error are used as the basis for priority sampling. The experimental results on 10 open source applications show BSDRTesting has achieved significant improvements in code coverage and branch coverage compared with existing methods.

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Secure Channel Creation for Task Delivery in Vehicular Cloud computing to Avoid Denial of Service (DoS)

Secure Channel Creation for Task Delivery in Vehicular Cloud...

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Automation, computing and Renewable Systems (ICACRS), international Conference on

作者： A. Meenambika S. Pavalarajan N. Anu Lavanya B. Ajith Jerom S. Divya Department of Computer Science and Business Systems PSNA College of Engineering and Technology Dindigul India Department of CSE (Cyber Security) PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Vehicular Cloud computing (VCC) offers a promising platform for supporting various automotive applications and services. However, its distributed and dynamic nature makes it susceptible to Denial-of-Service (DoS) attacks, particularly during channel establishment. This research proposes a comprehensive approach to mitigate these risks by integrating a suite of security mechanisms. These include rate-limiting, resource management, traffic analysis, access control, authentication, encryption, intrusion detection, behavioral analysis, and blacklisting/whitelisting techniques. By incorporating these measures, the proposed approach enhances the security and reliability of VCC, ensuring the availability of critical automotive services and protecting vehicles from the impact of DoS attacks.

关键词： Cloud computing Renewable energy sources Intrusion detection Transportation Computer architecture Organizations Denial-of-service attack Safety Resource management Vehicle dynamics

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Dependency-aware Task Offloading and Resource Pricing in Vehicular Edge computing: A Stackelberg Game Approach

Dependency-aware Task Offloading and Resource Pricing in Veh...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Liang Zhao Shuai Huang Yuxiang Cao Huan Zhou Victor C. M. Leung Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Vehicular Edge computing (VEC) allows vehicles to offload their delay-sensitive tasks to nearby Road Side Units (RSUs) for processing, which improves network quality of service (QoS). However, the self-interested SDN controller is unwilling to ask RSUs to provide free computing resources for vehicles. At the same time, complicated dependencies between vehicular subtasks may cause non-ideal task delay and energy consumption. In order to solve these problems, this paper proposes a Stackelberg game-based Dependency-aware task Offloading and resource Pricing framework (SDOP). Specifically, we first model a vehicular edge network that partially offloads dependency-aware tasks. Then, we depict the interaction between the SDN controller and vehicles as a Stackelberg game, with the goal of maximizing the utility of both parties. Next, we present a Gradient Ascent Plus Genetic algorithm (GAPG) to solve the problem. Finally, numerous simulations are performed, and the results show that compared with other baseline schemes, the proposed GAPG can significantly improve the utility of both the SDN controller and vehicles under various scenarios.

关键词： Energy consumption Computational modeling Road side unit Process control Games Pricing Quality of service Delays Edge computing Genetic algorithms

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Pricing-Based Task Offloading Considering User Energy Consumption in MEC System

Pricing-Based Task Offloading Considering User Energy Consum...

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international symposium on Parallel and distributed Processing with applications, ISPA

作者： Xiyu Han Zhuofan Liao Xiaoyong Tang School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Multi-access Edge computing (MEC) can find its wide applications in various resource-constrained scenario where the user needs to pay a price to the server for meeting the latency requirements of their own tasks. However, to the best of our knowledge, there is no effective pricing model in MEC taking into account the impact of the user’s own energy consumption on the server’s price scheme. In fact, the transmission energy consumption affects the users’ offloading task decisions, which in turn affects the revenue of the edge servers. To fill in this gap, a novel pricing mechanism for energy-sensitive users is proposed in this paper, specifically: 1) We propose a joint optimization problem of computation offloading decisions and resource pricing that considers user energy consumption. 2) By constructing a two-stage Stackelberg game model, the first stage simplifies the problem by constraint relaxation theory and finds an approximate optimal offloading strategy through the Fast Genetic Algorithm (FGA). The second stage uses the gradient descent method to find the potential optimal prices, thereby achieving a balance between the lowest user cost and the highest server profit. Experimental results show that, compared to traditional algorithms, our approach optimizes the average user cost and user task completion time by 24.65% and 12.37%, respectively.

关键词： Energy consumption Costs Multi-access edge computing Computational modeling Pricing Games Servers Resource management Optimization Genetic algorithms

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