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Enhancing Performance of Cloud: Fog computing Architecture, Challenges and Open Issues 9

Enhancing Performance of Cloud: Fog Computing Architecture, ...

引用

9th IEEE international conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2021

作者： Rani, Meena Guleria, Kalpna Panda, Surya Narayan Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Fog computing also referred as 9;fogging9; or 9;fog networking9; through which a decentralized computing structure is explained that is situated among the data producing devices as well as cloud. In logica... 详细信息

ISBN: (纸本)9781665417037

Fog computing also referred as 'fogging' or 'fog networking' through which a decentralized computing structure is explained that is situated among the data producing devices as well as cloud. In logical locations, users are enabled for placing resources that include data as well as applications produced by them, with such flexible structure for enhancing the performance. the fog computing involves edge computing, cloud's power as well as various services which are brought closer to location where data is produced as well as processed. the terms edge computing and fog computing is utilized by various peoples interchangeably, as the primary aim of both technologies involves bringing intelligence as well as processing closer wherein data is developed. Generally, it is performed for enhancing efficiency, however, it can also be performed for compliance and security reasons. this paper explores the different concepts involved in fog computing, importance of fog computing and highlights various opportunities in the fog computing area. Paper also presents the key points that why this technology needs to be successful. Finally, it addresses various issues, challenges in fog computing that it should deal with. © 2021 IEEE.

关键词： Fog computing

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Rank-Based Prefetching and Multi-level Caching Algorithms to Improve the Efficiency of Read Operations in distributed File Systems 9th

Rank-Based Prefetching and Multi-level Caching Algorithms to...

引用

9th international conference on Big Data Analytics (BDA)

作者： Nalajala, Anusha Ragunathan, T. Gopisetty, Rathnamma Garrapally, Vignesh SRM Univ Hyderabad Andhra Pradesh India Gitam Univ Hyderabad India Kakatiya Inst Technol & Sci Warangal Andhra Pradesh India

ISBN: (纸本)9783030936198;9783030936204

In the era of big data, web-based applications deployed in cloud computing systems have to store and process large data generated by the users of such applications. distributed file systems are used as the back end storage component in the cloud computing systems and they are used for storing large data efficiently. Improving the read performance of the distributed file system is the important research problem as most of the web-based applications deployed in the cloud computing systems carry out read operations more frequently. Prefetching and caching are the two important techniques used for improving the performance of the read operations in the distributed file system. In this paper, we have proposed novel rank-based prefetching, multi-level caching and rank-based replacement algorithms for the effective caching process. Our simulation results reveal that the proposed algorithms improve the performance of the read operations carried out in the distributed file systems better than the algorithms proposed in the literature.

关键词： distributed file system Rank Prefetching Multi-level caching Cache replacement policies

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Adaptive Resource Dimensioning with Joint Workload Placement for Cloud Stack Layers 9

Adaptive Resource Dimensioning with Joint Workload Placement...

引用

9th international conference on Future Internet of things and Cloud, FiCloud 2022

作者： Mouradian, Carla Li, Wubin Recse, Akos Szabo, Robert Ericsson Research Montreal Canada Eötvös Lorand University Budapest Hungary

ISBN: (数字)9781665493505

ISBN: (纸本)9781665493505

distributed cloud combines the best of telco and cloud technology and can run any application across multiple sites. Each site might be equipped with multiple virtualization layers (multi-layer cloud stack), e.g., OpenStack and/or Kubernetes. Workloads can be hosted in different virtualization layers. While this brings flexibility and elasticity for service provisioning, it also increases the complexity of life cycle management (LCM) during service instance design and assign. Especially, the compute and networking resources are typically overprovisioned due to a lack of an automated mechanism to scale them up/down or in/out according to their use. Adaptive resource dimensioning of cloud layers is needed to significantly smooth the process of service provisioning and to better utilize the virtualization layers' resources. To address these challenges, in this paper, we propose a joint resource dimensioning and workload placement solution with multiple virtualization layers during service instance design and assign time. We demonstrate the feasibility of our solution with illustrative examples. © 2022 IEEE.

关键词： distributed cloud

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Mathematical Modeling of Transaction Latency on Ethereum 12

Mathematical Modeling of Transaction Latency on Ethereum

引用

12th IEEE international conference on Joint Cloud computing (IEEE JCC) / 9th IEEE international conference on Mobile Cloud computing, Services, and Engineering (IEEE MobileCloud)

作者： Guo, Jinyan Jiang, Zigui Li, Li Bian, Jing Sun Yat Sen Univ Sch Math Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou 510006 Peoples R China Guangdong Inst Sci & Technol Informat Guangzhou 510030 Peoples R China Parallel & Distributed Proc Lab PDL Sci & Technol Changsha 410073 Peoples R China

ISBN: (纸本)9781665434799

Applications on blockchain are currently limited by the relatively poor performance of the blockchain network such as low TPS, high latency and the resulting high transaction fees. Hence, performance optimization is one crucial problem of blockchain. Focusing on the most prosperous public blockchain Ethereum, we model the on-chain transaction confirmation process with the knowledge of Poisson process and queueing theory, derives the mean transaction-confirmation time, and explores the effect of different transaction fees on latency. We also conduct a numeric simulation of the model, which indicates that the model fits in well with the real world blockchain.

关键词： Blockchain Ethereum Mathematical modeling Stochastic process Queueing theory

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Developing Verified Multidomain Connectivity using distributed Blockchain for Mobile Edge of the Network in 5g and Even Beyond

Developing Verified Multidomain Connectivity using Distribut...

引用

2023 international conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kundra, Danish Chitkara University Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350322309

MECs enable the connectivity of mobile networks components with Web accessibility in 5G and later by reducing the workload on the core processor units (CPU) just at information centers base. the protection of individual data in a heterogeneous technology model for multi-server interaction is essential considering the ever-growing demand of applications. Most contemporary methods don't focus on the privacy of customers or products;rather, they focus on the anonymous of topologies. In this research, different MEC platforms are designed using blockchain, and adaptable method is used as either a transportation & a topologies privacy-preserving router. Typically, routed agreement is achieved by participation methods that keep topological confidentiality secret. Blockchain technology is used to create multiplexed commitment & confidence connection as well as cooperative flow monitoring through membership. through the membership services & agreement method, blockchain is used to establish multiplexed loyalty & trusted connection and collaboration flow validation. Experimenting to determine the viability and effectiveness of our idea. the statistics show that the suggested strategy can significantly increase the legitimacy & effectiveness of MEC cooperation. © 2023 IEEE.

关键词： Mobile edge computing

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Proteus: distributed machine learning task scheduling based on Lyapunov optimization 12

Proteus: Distributed machine learning task scheduling based ...

引用

12th IEEE international conference on Joint Cloud computing (IEEE JCC) / 9th IEEE international conference on Mobile Cloud computing, Services, and Engineering (IEEE MobileCloud)

作者： Xiang, Yongan Shi, Bin Zhang, Chongle Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian Peoples R China Baidu Inc Beijing Peoples R China

ISBN: (纸本)9781665434799

With the prevalence of machine learning applications, an increasing number of machine learning tasks is transplanted into cloud computing platform. the cloud for machine learning consists of heterogeneous resources such as GPUs, CPUs, memory, etc, which brings challenges for resource management. So, how to schedule machine learning tasks and allocate appropriate GPU resources for computing, so that the cluster can maximize the use of resources and reduce task computing time has become a concern in the industry and academia. this paper proposes a scheduling strategy named Proteus, which is based on Lyapunov optimization. through the Proteus, we can make our tasks have a minimum turnaround time in a long time sequence, which is the time from the submission of the task to the completion of the task. By performing a comprehensive analysis, we implement the scheduling algorithm and conducts several simulation experiments. the experimental result shows that our scheduling strategy can achieve significant results in most task scheduling environments, reducing the turnaround time of tasks to 40%-50% of the original. this shows that the Proteus can provide higher resource utilization and performance of cloud clusters and reduce task turnaround time.

关键词： distributed machine learning Lyapunov optimization task scheduling

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Running Scientific computing Workloads in JointCloud computing Environment 12

Running Scientific Computing Workloads in JointCloud Computi...

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12th IEEE international conference on Joint Cloud computing (IEEE JCC) / 9th IEEE international conference on Mobile Cloud computing, Services, and Engineering (IEEE MobileCloud)

作者： Jin, Dian Li, Yan Cao, Donggang Peking Univ Minist Educ Key Lab High Confidence Software Technol Beijing Peoples R China

ISBN: (纸本)9781665434799

Cloud computing has been widely adopted by personal developers and enterprises because of its on-demand and elastic resource usage paradigm. Currently most cloud applications are running on one single cloud. Cloud vendors provide users a bewildering choice of VMs and the choice can have a big impact on performance and cost. therefore, it is difficult to choose the appropriate VMs to process jobs efficiently and economically. Besides, more and more applications are demanding to run across several clouds with heterogeneous resources to satisfy the requirements like best cost efficiency, avoidance of vender lock-in, and geolocation sensitive service. Existing systems lack a mechanism to unify the management of heterogeneous computing resources across clouds. In this paper, we present Docklet to build a virtual private cloud in JointCloud environments, managing heterogeneous and distributed cloud resources and selecting suitable resources according to the needs of users. At the same time, it provides computing framework support for scientific computing workloads, having the customized software stacks, configurations readily available. With Docklet, users only need one browser to do their jobs online. In the end, we evaluate the performance of running scientific workloads in Docklet.

关键词： cloud computing virtual private cloud JointCloud heterogeneous resources resource recommendation

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DDS-Based Information Interaction System for Ship Cloud Storage Data Center

DDS-Based Information Interaction System for Ship Cloud Stor...

引用

IEEE international conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Shuang Huang Fengyun Li Zihao Zhan Kaiyu Li Wenhao Zhang Jianguo Zhou Wuhan Second Ship Research Institute Wuhan China School of Electronic Information Wuhan University Wuhan China

As ship information network systems continue to increase in payload scale, problems such as difficult information storage and low interaction rate have become increasingly prominent. To address this, this paper proposes a distributed system for a ship cloud storage data center based on DDS, with a focus on system architecture and information interaction flow. A simulation platform for the warship system is built, and different types of information are tested to assess the information interaction and storage capabilities of the system. the experimental results demonstrate that DDS has good applicability in the distributed system of a ship cloud storage data center and has significant advantages in reliability.

关键词： Cloud computing Data centers Analytical models distributed databases Systems architecture Data models Security

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Research on Coverage and networking Capability of 5G 40M FDD Bandwidth Enhancement Compared with 100M TDD 9

Research on Coverage and Networking Capability of 5G 40M FDD...

引用

9th international conference on Intelligent computing and Wireless Optical Communications (ICWOC)

作者： Zhou, Yuetian Wei, Mingshuo Hu, Chunlei Xu, Xiaohang Xie, Weiliang China Telecom Res Inst Mobile Commun Res Dept Beijing Peoples R China

ISBN: (纸本)9781665435949

3GPP Release 16 will freeze the 2.1GHz FDD NR enhanced 40M large bandwidth standard in July 2020. At present, a small number of terminals have experimental capability. In the 3.5GHz TDD NR network with 100M bandwidth, how to use 2.1GHz FDD NR has become a hot issue in the industry. this paper compares the coverage and capacity of 2.1GHz FDD NR and 3.5GHz TDD NR through link budget and theoretical calculation;compares the uplink and downlink coverage of the two systems through single station pull-out experiment;selects the existing network with 2.1GHz FDD NR and 3.5GHz TDD NR deployed at the same site as the experimental site, and analyzes the uplink and downlink networking capacity of the two systems through Driving Test (DT). Considering the influence of the number of transmitting antennas of existing terminals and the transmitting power of network equipment on the network planning, the network capacity of this experiment includes 2.1GHz 4T4R and 3.5G 64TR, the terminal capacity includes 2.1GHz 1T4R, 2.1GHz 2T4R and 3.5GHz 2T4R, and the power consumption of network equipment involves 2.1GHz: 4x4OW and 4x6OW;3.5GHz: 160W and 200W. At present, there is no literature about the existing network experiment of multi-power consumption and multiterminal type, so this paper has important reference significance for operators to deploy 2.1GHz+3.5GHz network.

关键词： FDD large bandwidth high and low frequency cooperative networking 5G network evolution networking performance coverage capability

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A Proposal of Dynamic Job Scheduling Algorithm Considering CPU Core Utilization for User-PC computing System 9

A Proposal of Dynamic Job Scheduling Algorithm Considering C...

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9th international Symposium on computing and networking Workshops, CANDARW 2021

作者： Kamoyedji, Ariel Funabiki, Nobuo Htet, Hein Kuribayashi, Minoru Okayama University Dept. of Electrical And Communication Engineering Okayama Japan

the User-PC computing system (UPC) has been studied as a very low cost distributed computing platform that leverages idling resources of users9; personal computers (PCs). Designed according to the master-worker mod... 详细信息

ISBN: (纸本)9781665428354

the User-PC computing system (UPC) has been studied as a very low cost distributed computing platform that leverages idling resources of users' personal computers (PCs). Designed according to the master-worker model, the master PC accepts jobs from users and assigns them to available worker PCs. An efficient static job assignment algorithm considering CPU core utilization has already been devised for the UPC system. It finds an optimal assignment that minimizes the makespan in a static scenario, i.e. jobs are all available in the beginning and all worker PCs are idle. However, in reality, jobs dynamically join and leave the system and as such, some workers may be busy when new jobs arrive. In this paper, we propose a dynamic job scheduling algorithm for the UPC system by extending the static job assignment algorithm. It finds assigned workers to all queuing jobs either on new job arrival, or on job completion by a worker PC. For evaluation, we conducted experiments using six worker PCs and up to 42 jobs. Our algorithm could significantly reduce the makespan compared to other scheduling algorithms. © 2021 IEEE.

关键词： Personal computers

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