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检索条件"任意字段=17th International Conference on Collaborative Computing: Networking Applications and Worksharing"

共 953 条记录，以下是11-20 订阅

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Rethinking Application Container networking in a Multi-cluster world 17

Rethinking Application Container Networking in a Multi-clust...

引用

IEEE 17th international conference on Cloud computing (IEEE CLOUD)

作者： Govindarajan, Chander Naik, Priyanka Govindarajan, Kavya Goel, Seep IBM Res Bengaluru India

ISBN: (纸本)9798350368543;9798350368536

Multi-cloud deployments are set to change the very fabric of cloud computing, allowing applications to be deployed over multiple clusters and cloud providers, offering flexibility and geo-redundancy. Existing multi-cloud networking approaches either require tight coupling of clusters or separation of control and management for intra and inter cluster networking. Unlike compute and storage, networking for multi-cluster cannot be approached as an extension to single cluster operations. Multi-cloud necessitates that networking must be rethought in terms of the application and not the underlying networking solution, cluster type or cloud provider. We propose Application networking Interface (ANI), an application centric, provider agnostic interface to control and manage networking solutions for single and multi-cluster contexts. ANI presents exciting opportunities for application networking and we envision that it will revamp the outlook towards multi-cloud networking.

关键词： application networking CNI multi-cloud multi-cloud networking multi-cluster service mesh

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Protect applications and Data in Use in IoT Environment Using collaborative computing 19th

Protect Applications and Data in Use in IoT Environment U...

引用

19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Peng, Xincai Cang, Li Shan Zhang, Shuai Iqbal, Muddesar University of Electronic Science and Technology of China Chengdu China College of Engineering Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9783031545276

In IoT systems, traditional encryption can be used to protect IoT applications and data at rest or in transit that transforms data in to ciphertext making it unreadable. However, it is very challenging to protect IoT systems against attacks targeting data and applications in use. Using homomorphic encryption, this work proposed a lightweight collaborative computing scheme to protect both applications and data in IoT environment that includes IoT devices, mobile apps, and cloud server. A novel key management system scheme proposed as a trusted third party to collaboratively generate and distribute keys by cloud servers and IoT devices, in which data is only visible to the data owner but keep encrypted to other parties. A SEAL-CKKS scheme and a K-means clustering algorithms were validated, and the experimental results demonstrated the effectiveness of proposed schemes, in which the K-means clustering algorithm in the plaintext state, the proposed scheme still maintains an accuracy up to 84.1%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Data privacy

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iContinuum: An Emulation Toolkit for Intent-Based computing Across the Edge-to-Cloud Continuum 17

iContinuum: An Emulation Toolkit for Intent-Based Computing ...

引用

IEEE 17th international conference on Cloud computing (IEEE CLOUD)

作者： Akbari, Negin Toosi, Adel N. Grundy, John Khalajzadeh, Hourieh Aslanpour, Mohammad S. Ilager, Shashikant Monash Univ Dept Software Syst & Cybersecur Clayton Vic Australia Deakin Univ Sch Info Technol Burwood Australia TU Wien Inst Informat Syst Engn Vienna Austria

ISBN: (纸本)9798350368543;9798350368536

the Internet of things (IoT) has led to a surge in smart devices, generating vast volumes of data. Cloud computing offers scalability but does not suffice for many real-time and privacy-sensitive IoT applications. this limitation has prompted a blend of both edge and cloud resources, creating the need for seamless integration, known as the "compute continuum". Testing applications and resource management techniques within this continuum is vital but can be very complex. Simulation and emulation are preferred methods, with emulation providing more accurate representations of real-world environments. In this paper, we introduce iContinuum, a novel emulation toolkit facilitating an intent-based platform for edge-to-cloud testing and experimentation. Leveraging Software-Defined networking (SDN) and containerization, iContinuum enables experimentation and performance evaluation while aligning application requirements with actual performance. We present our detailed architecture, implementation, and evaluation of iContinuum, showcasing how our proposed toolkit bridges the gap between simulation and realworld deployment within compute continuum environments, and further demonstrate the effectiveness of Intent-Based Scheduling through a specific use case.

关键词： Compute Continuum Emulation IBN Intents SDN Container Orchestration Kubernetes

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A Reliable Service Function Chain Orchestration Method Based on Federated Reinforcement Learning 18th

A Reliable Service Function Chain Orchestration Method Based...

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18th European-Alliance-for-Innovation (EAI) international conference on collaborative computing - networking, applications and worksharing (CollaborateCom)

作者： Xiao, Zhiwen Tao, Tao Chen, Zhuo Yang, Meng Shang, Jing Wu, Zhihui Guo, Zhiwei China Mobile Informat Technol Ctr Beijing 100033 Peoples R China

ISBN: (纸本)9783031243820;9783031243837

the novel cloud-edge collaborative computing architecture can provide more efficient and intelligent services close to users. Reliable service function chain orchestration among datacenters is critical to ensuring computing efficiency. In this study, a service orchestration model is proposed to improve the reliability while reducing cost. the solution is a federated reinforcement learning framework that shares decision-making experiences to obtain reliable and effective service orchestration results between different datacenter environments. the simulation results demonstrate that the proposed orchestration method reaches convergence faster and has a significant performance in terms of improving service reliability.

关键词： Service function chain Cloud-edge collaborative computing Federated reinforcement learning Reliability

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DeepFetch: A Node-Aware Greedy Fetch System for Distributed Cache of Deep Learning applications 17

DeepFetch: A Node-Aware Greedy Fetch System for Distributed ...

引用

17th international conference on networking, Architecture and Storage

作者： Kong, Lijuan Mei, Fei Zhu, Chunjie Cheng, Wen Zeng, Lingfang Zhejiang Lab Hangzhou Peoples R China

ISBN: (纸本)9798331520519;9798331520502

Data I/O poses a significant bottleneck for distributed deep learning applications. Utilizing computing-node attached storage as a cache has become a prevalent solution to this problem. Given the large size of training datasets and the limited capacity of a single-node local storage, training samples are loaded in shards across computing nodes, requiring a deep learning job on one node to access samples from neighboring nodes. Compared to local node access, these neighboring accesses are highly inefficient for small samples. To mitigate this issue, we propose a node-aware prefetch algorithm that greedily fetches samples from neighboring nodes. Evaluation results show that our approach improves neighboring access performance by 60x for small samples of 64B size. For large samples of 1MB size, our approach still exhibits a 26% improvement.

关键词： Deep Learning Prefetch Cache PFS

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collaborative Cloud-Edge computing with Mixed Wireless and Wired Backhaul Links: Joint Task Offloading and Resource Allocation 19th

Collaborative Cloud-Edge Computing with Mixed Wireless and...

引用

19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Zhang, Daqing Sun, Haifeng School of Computer Science and Technology Southwest University of Science and Technology Mianyang621010 China

ISBN: (纸本)9783031545207

Mobile Edge computing (MEC) is a promising technology that provides computing services at the edge of wireless networks to reduce the latency and the energy consumption for Smart Mobile Devices (SMDs). Additionally, the Ultra-Dense Network (UDN) will play a key role in providing high transmission capacity for SMDs in 5G networks. In order to improve the edge cloud efficiency within limited communication and computing resources, this paper proposes a joint task offloading and resource allocation scheme collaborated between cloud computing and edge computing in the UDN. Since wireless backhaul is more economical than expensive wired backhaul deployments, we consider the mixed deployment of either wired or wireless backhaul between each Small Base Station (SBS) and the Macro Base Station (MBS) in UDN scenarios, then formulate an optimization problem to minimize the system-wide computation overhead, and apply the Linear Decreasing Weight Particle Swarm Optimization (LDWPSO) algorithm to solve the problem. Numerical experiments validate the effectiveness of our proposed scheme compared to other baseline schemes. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Resource allocation

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ECCRG: A Emotion- and Content-Controllable Response Generation Model 19th

ECCRG: A Emotion- and Content-Controllable Response Generati...

引用

19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Chen, Hui Wang, Bo Yang, Ke Song, Yi Hunan Seefore Information Technology Co. Ltd. Yueyang China College of Intelligence and Computing Tianjin University Tianjin China

ISBN: (纸本)9783031545276

Most methods of emotional dialogue generation focus on how to make the generated replies express the set emotion categories, while ignoring the control over the semantic content of the replies. To this end, in this paper, we propose a emotion- and content-controllable response generation model, ECCRG. ECCRG allows for text-controlled conditions and integration into the decoding process of the language model through a self-attention layer, enabling more precise control over the content of the generated responses. We use a variety of optimization objectives including self-reconfiguration loss and adversarial learning loss to jointly train the model. Experimental results show that ECCRG can embody the set target content in the generated responses, allowing us to achieve controllability on both emotion and textual content. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Semantics

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Secure and Private Approximated Coded Distributed computing Using Elliptic Curve Cryptography 19th

Secure and Private Approximated Coded Distributed Computing...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Qiu, Houming Zhu, Kun College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (纸本)9783031545276

In large-scale distributed computing systems, coded computing has attracted considerable attention since it can effectively mitigate the impact of stragglers. Nonetheless, several emerging issues seriously restrict the performance of coded distributed systems. First, the presence of colluding workers collude results in serious privacy leakage issues. Second, few existing works consider security issues in data transmission. third, the number of required results to wait for increases with the degree of polynomial functions. In this paper, we propose a secure and private approximated coded distributed computing (SPACDC) scheme that addresses the aforementioned issues simultaneously. the SPACDC scheme ensures data security during the transmission process by leveraging a proposed matrix encryption algorithm based on elliptic curve cryptography. Unlike existing coding schemes, our SPACDC scheme does not impose strict constraints on the minimum number of results required to wait for. Furthermore, the SPACDC scheme provides information-theoretic privacy protection for raw data. Finally, extensive performance analysis is provided to demonstrate the effectiveness of the proposed SPACDC scheme. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Distributed computer systems

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Resource Cooperative Scheduling Optimization Considering Security in Edge Mobile Networks 19th

Resource Cooperative Scheduling Optimization Considering Sec...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Fang, Cheng Yang, Peng Yi, Meng Du, Miao Li, Bing Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9783031545207

With the rapid development of technologies such as the Internet of things and artificial intelligence, the contradiction between limited user computing resources and real-time, fast, and safe processing of large amounts of data has become an urgent issue. the emergence of edge computing provides IoT applications with a low-latency, high-bandwidth, and high-performance computing service. Due to the complexity and dynamics of the edge computing environment itself, and the limited resources of the terminal, the security issue of resource collaborative scheduling in the edge mobile network has become an important research topic. Different from existing work, this paper proposes an efficient and secure multi-user resource cooperative scheduling model, which comprehensively considers resource allocation, task offloading, QoE requirements, and data security. In the model, ChaCha20 encryption technology is introduced as a security mechanism to prevent data from being maliciously stolen by attackers during the offloading process, and computing speed is used as an indicator to quantify QoE requirements. A resource collaborative scheduling algorithm that integrates security mechanisms and computing acceleration is also proposed to minimize the total cost of optimizing the edge computing system. Finally, the effectiveness and superiority of the model and algorithm are verified by simulation experiments. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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Effective Blockchain-Based Asynchronous Federated Learning for Edge-computing 18th

Effective Blockchain-Based Asynchronous Federated Learning f...

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18th European-Alliance-for-Innovation (EAI) international conference on collaborative computing - networking, applications and worksharing (CollaborateCom)

作者： Gao, Zhipeng Li, Huangqi Lin, Yijing Chai, Ze Yang, Yang Rui, Lanlan Beijing Univ Posts & Telecommunicat State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9783031243820;9783031243837

Since massive data are generated at the network's edge, the Internet of things devices can exploit edge computing and federated learning to train artificial intelligence (AI) models while protecting data privacy. However, heterogeneous devices lead to low efficiency and single-point-of-failure. Moreover, malicious nodes may affect training accuracy. therefore, we propose FedLyra, an effective blockchain-based asynchronous federated learning architecture, to improve the efficiency of aggregation and resist malicious nodes in a trusted and decentralized manner. We then propose a reputation mechanism that combines historical behaviors and the quality of local updates to resist disagreements and adversaries. With the help of the reputation mechanism, we propose a council-based decentralized aggregation mechanism to exclude malicious nodes. Experiments show that FedLyra can resist malicious nodes and ensure the accuracy of training results.

关键词： Federated learning Blockchain Edge-computing Asynchronous architecture Decentralization

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