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

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Rethinking Application Container networking in a Multi-Cluster World

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

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IEEE international conference on Cloud computing, CLOUD

作者： Chander Govindarajan Priyanka Naik Kavya Govindarajan Seep Goel IBM Research India

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

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 ex-citing opportunities for application networking and we envision that it will revamp the outlook towards multi-cloud networking.

关键词： Couplings Cloud computing Containers Fabrics

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Augmenting collaborative Practices: the Hololiver Case Study

Augmenting Collaborative Practices: The Hololiver Case Study

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international IEEE conference on Signal-Image Technologies and Internet-Based System

作者： Agnese Augello Giuseppe Caggianese Luigi Casoria Luigi Gallo Roberto Montalti Pietro Neroni Gianluca Rompianesi Roberto Troisi Institute for high performance computing and networking (ICAR) National Research Council Palermo Italy Institute for high performance computing and networking (ICAR) National Research Council Naples Italy Department of Public Health Division of Hepato-Biliary-Pancreatic Minimally Invasive and Robotic Surgery and Kidney Transplantation Federico II University Hospital Naples Italy Department of Clinical Medicine and Surgery Division of Hepato-Biliary-Pancreatic Minimally Invasive and Robotic Surgery and Kidney Transplantation Federico II University Hospital Naples Italy

Advances in Mixed Reality (MR) technologies are reshaping collaborative practices. the seamless integration of physical and virtual elements enhances the perception of the working environment, providing a more enriched collaborative task experience. While revealing intriguing potential across various sectors, wearing head-mounted displays (HMDs) can pose challenges in communication and in understanding others’ behaviours. this paper analyses the main elements of collaborative augmented practices through the case study of Hololiver, a MR system developed to assist surgeons in planning laparoscopic liver surgeries. the work discusses guidelines for designing interfaces to preserve awareness in MR interactions.

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An Efficiency Evaluation Method for Cloud-Edge collaborative Network 17

An Efficiency Evaluation Method for Cloud-Edge Collaborative...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Jin, Shen Qu, Qinghai Feng, Yuqing Zhang, Ningchi Cong, Lin Wang, Ying Yu, Peng State Grid Jibei Informat & Telecommun Co Beijing Peoples R China Beijing Fibrlink Commun Co Ltd Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781728186160

With the rapid development of 5G commercialization on a large scale and edge computing, cloud-edge collaboration technology has been widely used in various industries, and how to achieve high efficiency cloud-edge network environment has become a research hotspot. In this paper, we propose a network efficiency evaluation model based on analytic hierarchy process (AHP) and logistic regression (LR) algorithm in cloud-edge collaborative environment. Using AHP to calculate the weight matrix of indicators and formulating multiple discrete parameter measures into the same dimensional area to obtain the final comprehensive efficiency value of the network. It describes the efficiency value in cloud-edge collaborative environment as a qualitative concept and realizes the qualitative evaluation in cloud-edge collaborative environment. On this basis, comparative experiments are carried out to evaluate the comprehensive efficiency of the network by using the traditional method and the method proposed in this paper, which verifies the practicability and effectiveness of the efficiency evaluation method.

关键词： AHP LR cloud-edge collaboration network efficiency valuation

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Analysis of ML Algorithms to Support Elastic Service Chaining in eHealth Vertical applications 17

Analysis of ML Algorithms to Support Elastic Service Chainin...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Jardim, Sandino Dantas Silva, Felipe S. Neto, Augusto Bustos, Harold Immich, Roger Fontes, Ramon Fed Univ Mato Grosso UFMT Barra Do Garcas MT Brazil Fed Univ Rio Grande do Norte UFRN Natal RN Brazil LaTARC Res Lab IFRN Natal RN Brazil State Univ Rio Grande do Norte UERN Mossoro RN Brazil

ISBN: (纸本)9781728186160

the efficient design of SFC-enabled eHealth applications requires an accurate provision of the underlying infrastructure. this provision requires both computing and networking resources to meet stringent QoS requirements under any conditions of service demand. Cloud providers often offer automatic elasticity strategies based on monitoring specific metrics that lead to a waste of resources, time/energy consumption, and the problem of starvation with competing services. Our findings provide evidence that proactive-based elasticity overcomes these issues, when assisted by Machine Learning (ML) methods for predicting Internet traffic load. An optimal autoscaling algorithm depends on high precision and fast predictions to provide accurate results. thus, this paper assesses ML algorithms to support SFC-enabled eHealth vertical applications. the experimental results suggest that the evaluated models achieved similar accuracy metrics, with an MLP architecture delivering the best performance in terms of time training and average prediction time.

关键词： Service Chaining eHealth Machine Learning proactive autoscaling

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A Novel Probabilistic-Performance-Aware Approach to Multi-workflow Scheduling in the Edge computing Environment 16th

A Novel Probabilistic-Performance-Aware Approach to Multi-wo...

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16th international conference on collaborative computing: networking applications and worksharing

作者： Ma, Yuyin Yang, Ruilong Peng, Yiqiao Long, Mei Sun, Xiaoning Zheng, Wanbo Li, Xiaobo Ma, Yong Chongqing Univ Sch Comp Chongqing Peoples R China Bashu Secondary Sch Chongqing Peoples R China Kunming Univ Sci & Technol Kunming Peoples R China ZBJ NETWORK Co Ltd Chongqing Peoples R China Chongqing Anim Husb Tech Extens Ctr Chongqing Peoples R China Jiangxi Normal Univ Nanchang Sch Comp & Informat Engn Nanchang Peoples R China

ISBN: (纸本)9783030675363;9783030675370

Edge computing is a decentralized computing infrastructure in which data, calculation, storage and applications are located somewhere between the data source and the computing facilities. While the edge servers enjoy the close proximity to the end-users to provide services at reduced latency and lower energy costs, we use from limitations in computational and radio resources, which calls for smart, quality-of-service (QoS) guaranteed and efficient task scheduling methods and strategies. For addressing the edge-environment-oriented multi-workflow scheduling problem, in this paper, we propose a probabilistic QoS-aware approach to multi-workflow scheduling over edge servers with time-varying QoS. Our proposed method leveraged a probability-mass function-based QoS aggregation model and a discrete firefly algorithm for generating the multi-workflow scheduling plans. In order to prove the effectiveness of our proposed method, we conducted an experimental case study based on varying types of workflows and a real-world dataset for edge server positions. It can be seen that our method clearly outperforms its competitors in terms of completion time, cost, and deadline validation rate.

关键词： Edge computing Workflow scheduling Probabilistic model Quality-of-service (QoS)

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A Computation Offloading Algorithm for Cloud Edge collaborative Network Based on Sleep Mechanism 17

A Computation Offloading Algorithm for Cloud Edge Collaborat...

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17th IEEE international Wireless Communications and Mobile computing conference (IEEE IWCMC)

作者： Wu, Qian Zhou, Jinyu Zhou, Jing Weng, Junhong Liu, Qingchuan Xing, Yifei Xu, Siya China Southern Power Grid Co Ltd Shenzhen Power Supply Co Ltd Shenzhen Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781728186160

the mobile edge computing (MEC) technology is introduced into the traditional mobile cloud computing (MCC) mode to form a cloud-edge-terminal collaborative network architecture (CETCN) to jointly process the massive data and improve the operating efficiency. However, there are still some problems such as high energy consumption of some edge nodes and limited terminal energy in this network architecture. In order to solve the above problems, this paper proposes a computation offloading algorithm based on sleep mechanism. Firstly, time delay and energy consumption models of cloud layer, edge layer and terminal layer are built according to the requirements of different scenes of smart city. then, the communication and computing resources of each network segment are allocated jointly with the optimization goals of time delay and energy consumption, and the edge server sleep mechanism is used to further improve the resource utilization of the edge nodes. the simulation results show that the proposed algorithm can reduce the network delay and energy consumption, and has practicability.

关键词： Cloud-edge-terminal collaborative Computation offloading Mobile edge computing Sleep mechanism

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Bridging NextG Network Connectivity Induced Digital Divide with Sustainable ICT

Bridging NextG Network Connectivity Induced Digital Divide w...

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international conference on collaborative computing: networking, applications and worksharing (CollaborateCom)

作者： Kuheli Sai David Tipper Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798350386707

ISBN: (纸本)9798350386714

Next-Generation (NextG) wireless networks could widen the digital divide if not deployed equitably. Without intentional planning and provisioning of services from the network service providers and government, underserved and marginalized communities could be left behind, exacerbating pre-existing societal inequalities further in the digital domain. To address this societal problem, we propose a data-driven solution that prioritizes the installation of micro edge data centers, a key component of NextG networks, in socio-economically distressed neighborhoods within an urban area. Leveraging publicly available open 311 data as an indirect measurement approach, we demonstrate that public from these geographic localities possess the digital literacy skills needed to benefit from faster internet connectivity. Further, to improve the sustainability of micro edge data centers we propose powering them with solar powered nano-electrical grids. this approach is cost-effective for providers, as cost of buildings and housings in geographic regions having lower socio-economic conditions are smaller compared with other localities. In addition, we discuss the cost of deployment for these sustainable information and communication technology (ICTs) too. We demonstrate the feasibility of our proposed solution approach through a case study based on city of Pittsburgh.

关键词： Bridges Data centers Costs Wireless networks Urban areas Buildings Digital divide Socioeconomics Next generation networking Telecommunication services

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Enabling Synergistic Knowledge Sharing and Reasoning in Large Language Models with collaborative Multi-Agents

Enabling Synergistic Knowledge Sharing and Reasoning in Larg...

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international conference on collaborative computing: networking, applications and worksharing (CollaborateCom)

作者： Ayushman Das Shu-Ching Chen Mei-Ling Shyu Saad Sadiq Data Science and Analytics Innovation Center University of Missouri-Kansas City Kansas City MO USA School of Science and Engineering University of Missouri-Kansas City Kansas City MO USA Microsoft Seattle WA USA

Despite the significant advancements in the field of Natural Language Processing (NLP), Large Language Models (LLMs) have shown limitations in performing complex tasks that require arithmetic, commonsense, and symbolic reasoning. Reasoning frameworks like ReAct, Chain-of-thought (CoT), Tree-of-thoughts (ToT), etc. have shown success but with limitations in solving long-form complex tasks. To address this, we pro-pose a knowledge-sharing and collaborative multi-agent assisted framework on LLMs that leverages the capabilities of existing reasoning frameworks and the collaborative skills of multi-agent systems (MASs). the objectives of the proposed framework are to overcome the limitations of LLMs, enhance their reasoning capabilities, and improve their performance in complex tasks. It involves generating natural language rationales and in-context few-shot learning via prompting, and integrates the reasoning techniques with efficient knowledge-sharing and communication-driven agent networks. the potential benefits of the proposed framework include saving time and money, improved efficiency for computationally intensive reasoning, and the ability to incor-porate multiple collaboration strategies for dynamically changing environments.

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MIRES: Recovering Mobile applications based on Backend-as-a-Service from Cyber Attacks 20

MIRES: Recovering Mobile Applications based on Backend-as-a-...

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17th EAI international conference on Mobile and Ubiquitous Systems - computing, networking and Services (MobiQuitous)

作者： Vaz, Diogo Matos, David Pardal, Miguel Correia, Miguel Univ Lisbon INESC ID Inst Super Tecn Lisbon Portugal

ISBN: (纸本)9781450388405

Many popular mobile applications rely on the Backend-as-a-Service (BaaS) cloud computing model to simplify the development and management of services like data storage, user authentication and notifications. However, vulnerabilities and other issues may lead to malicious operations on the mobile application client-side and malicious requests being sent to the backend, corrupting the state of the application in the cloud. To deal with these attacks after they happen and are successful, it is necessary to remove the immediate effects created by the malicious requests and subsequent effects derived from later requests. In this paper, we present MIRES, an intrusion recovery service for mobile applications based on BaaS. MIRES uses a two-phase recovery process that restores the integrity of the mobile application and minimizes its unavailability. We implemented MIRES in Android and with the Firebase platform and made experiments with 3 mobile applications that showed results of 1000 operations reverted in less than 1 minute and with the mobile application inaccessible only for less than 15 seconds.

关键词： Intrusion Recovery Mobile computing Backend-as-a-Service Cloud

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Edge collaborative Task Scheduling and Resource Allocation Based on Deep Reinforcement Learning 17th

Edge Collaborative Task Scheduling and Resource Allocation B...

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17th international conference on Wireless Algorithms, Systems, and applications (WASA)

作者： Chen, Tianjian Lyu, Zengwei Yuan, Xiaohui Wei, Zhenchun Shi, Lei Fan, Yuqi Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China Univ North Texas Denton Dept Comp Sci & Engn Denton TX 76203 USA Minist Educ Engn Res Ctr Safety Crit Ind Measurement & Contro Hefei 230009 Peoples R China

ISBN: (纸本)9783031192104;9783031192111

With the development of the sixth generation mobile network (6G), the arrival of the Internet of Everything (IoE) is accelerating. An edge computing network is an important network architecture to realize the IoE. Yet, allocating limited computing resources on the edge nodes is a significant challenge. this paper proposes a collaborative task scheduling framework for the computational resource allocation and task scheduling problems in edge computing. the framework focuses on bandwidth allocation to tasks and the designation of target servers. the problem is described as a Markov decision process (MDP). To minimize the task execution delay and user cost and improve the task success rate, we propose a Deep Reinforcement Learning (DRL) based method. In addition, we explore the problem of the hierarchical hash rate of servers in the network. the simulation results show that our proposed DRL-based task scheduling algorithm outperforms the baseline algorithms in terms of task success rate and system energy consumption. the hierarchical settings of the server's hash rate also show significant benefits in terms of improved task success rate and energy savings.

关键词： Edge collaborative Task scheduling Deep reinforcement learning Hierarchical server

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