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IEEE international conference on Cloud computing, CLOUD

作者： Swagata Biswas Swarnava Dey Arijit Mukherjee TCS Research and Innovation Tata Consultancy Services Kolkata India

the current trend in industry to augment lightweight and inexpensive robots with cloud-based distributed intelligence for executing complex, collaborative tasks has led to the emergence of fields like Internet of Robotic things (IoRT) and Cloud Robotics (CR). However, reliable execution of orchestrated jobs in a networked setup with chances of infrastructural failures remains a concern. the only perceivable solution is resource redundancy, where the traditional approaches are not cost effective for tightly budgeted IoRT/CR deployments. In this work, an agile redundancy model, At most M, is proposed, which provides a handle to tune the trade-off between resource cost and reliability. the model is evaluated in a simulated warehouse environment with robots, drones, AGVs and private cloud servers deployed to accomplish multiple pickup and delivery tasks. the benefits w.r.t. resource usage cost by using our optimized redundancy model is illustrated and the trade-off between cost and reliability is demonstrated.

关键词： Task analysis Redundancy Radio frequency Robots Cloud computing Maintenance engineering

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SAM: A Semantic-Aware Middleware for Mobile Cloud computing

SAM: A Semantic-Aware Middleware for Mobile Cloud Computing

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IEEE international conference on Cloud computing, CLOUD

作者： Harun Baraki Corvin Schwarzbach Stefan Jakob Alexander Jahl Kurt Geihs Distributed Systems Group University of Kassel Kassel Germany

Mobile Cloud computing (MCC) requires an infrastructure that is combining the capabilities of resource-constrained but mobile and context-aware devices with that of immovable but powerful resources in the cloud. Application execution shall be boosted and battery consumption reduced. Our goal is to relieve mobile devices by enabling applications to cooperate transparently with components provided by third parties remotely, locally, or both. For this purpose, we developed SAM - the Semantic-aware Middleware - and the SAM Store. the latter resembles a registry. It allows component providers to publish semantically enriched interfaces and to offer corresponding local and remote implementations. SAM makes use of the SAM Store to match local interprocess communication (IPC) messages transparently against local and remote components. this architecture facilitates the integration of powerful, but resource-intensive remote components, which may be prioritized over local ones, and fosters various patterns of cooperation that demonstrate useful solutions in the area of MCC. By means of a realistic scenario, our evaluation proves the efficiency with respect to latency and practicality.

关键词： Cloud computing Ontologies Semantics Mobile handsets Middleware Synchronization Task analysis

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DSES: A Blockchain-Powered Decentralized Service Eco-System

DSES: A Blockchain-Powered Decentralized Service Eco-System

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IEEE international conference on Cloud computing, CLOUD

作者： Zhenfeng Gao Yushun Fan Cheng Wu Jia Zhang Chang Chen Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering Carnegie Mellon University CA USA Ziggurat Technology Beijing China

Existing service ecosystems typically rely on some centralized service registries (e.g., ***) as "middle people" to record service behaviors thus to provide service ranking and recommendation. Excessive centralization increasingly becomes the bottleneck and hinders the further growth of the service ecosystems. As the first attempt to apply the fundamental technique underneath the emerging Bitcoin network into the field of service oriented computing, this paper proposes to build a service ecosystem as a decentralized blockchain-oriented service network, called Decentralized Service Eco-System (DSES). Whenever any activity occurs in the system (e.g., APIs are used together in a published mashup), all involved parties will individually store and maintain a copy of the detailed record (provenance) at their local databases. Such a distributed database-oriented solution will enable services who do not fully trust each other to maintain a set of global states. In this way, service discovery and recommendation can be realized in a distributed manner that promises higher scalability and maintainability. As a proof of concept, a prototyping system of DSES is constructed using the real-world data from ***, based on the INKchain, a newly open-source consortium blockchain mechanism extending the Hyperledger Fabric.

关键词： Ecosystems Mashups Fabrics Contracts Peer-to-peer computing Bitcoin

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Large-scale multi-label ensemble learning on Spark 16

Large-scale multi-label ensemble learning on Spark

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16th IEEE international conference on Trust, Security and Privacy in computing and Communications / the 11th IEEE international conference on Big Data Science and Engineering / the 14th IEEE international conference on Embedded Software and Systems

作者： Gonzalez-Lopez, Jorge Cano, Alberto Ventura, Sebastian Virginia Commonwealth Univ Dept Comp Sci Richmond VA 23284 USA Univ Cordoba Dept Comp Sci Cordoba Spain

ISBN: (纸本)9781509049066

Multi-label learning is a challenging problem which has received growing attention in the research community over the last years. Hence, there is a growing demand of effective and scalable multi-label learning methods for larger datasets both in terms of number of instances and numbers of output labels. the use of ensemble classifiers is a popular approach for improving multi-label model accuracy, especially for datasets with high-dimensional label spaces. However, the increasing computational complexity of the algorithms in such ever-growing high dimensional label spaces, requires new approaches to manage data effectively and efficiently in distributed computing environments. Spark is a framework based on MapReduce, a distributed programming model that offers a robust paradigm to handle large-scale datasets in a cluster of nodes. this paper focuses on multi-label ensembles and proposes a number of implementations through the use of parallel and distributed computing using Spark. Additionally, five different implementations are proposed and the impact on the performance of the ensemble is analyzed. the experimental study shows the benefits of using distributed implementations over the traditional single-node single-thread execution, in terms of performance over multiple metrics as well as significant speedup tested on 29 benchmark datasets.

关键词： Multi-label learning Ensemble learning distributed computing Apache Spark Big data

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Performance Analysis of Large-Scale distributed Stream Processing Systems on the Cloud

Performance Analysis of Large-Scale Distributed Stream Proce...

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IEEE international conference on Cloud computing, CLOUD

作者： Tri Minh Truong Aaron Harwood Richard O. Sinnott Shiping Chen Department of Computer Science University of Melbourne Australia Data61 CSIRO Australia

Real-time data processing is often a necessity as it can provide insights that have less value if discovered off-line or after the fact. However, large-scale stream processing systems are non-trivial to build and deploy. While there are many frameworks that allow users to create large-scale distributed systems, there remains many challenges in understanding the performance, cost of deployment and considerations and impact of potential (partial) outages on real-time systems performance. Our work considers the performance of Cloud-based stream processing systems in terms of back-pressure and expected utilization. the performance of an exemplar stream application is explored using different Cloud-based virtual machine resources and where the scale of deployment and cost benefits are taken into consideration in relation to the overall performance. To achieve this, we develop an algorithm based on queueing theory to predict the throughput and latency of stream data processing while supporting system stability. Our methodology for making fundamental measurements is applicable to mainstream stream processing frameworks such as Apache Storm and Heron. the method is especially suitable for large-scale distributed stream processing where jobs can run for extended time periods. We benchmark the performance of the system on the national research cloud of Australia (Nectar), and present a performance analysis based on estimating the overall effective utilization.

关键词： Queueing analysis Real-time systems Topology throughput Storms Cloud computing Steady-state

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Oases: An Online Scalable Spam Detection System for Social Networks

Oases: An Online Scalable Spam Detection System for Social N...

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IEEE international conference on Cloud computing, CLOUD

作者： Hailu Xu Liting Hu Pinchao Liu Yao Xiao Wentao Wang Jai Dayal Qingyang Wang Yuzhe Tang Florida International University Intel Corporation Louisiana State University Syracuse University

Web-based social networks enable new community-based opportunities for participants to engage, share their thoughts, and interact with each other. theses related activities such as searching and advertising are threatened by spammers, content polluters, and malware disseminators. We propose a scalable spam detection system, termed Oases, for uncovering social spam in social networks using an online and scalable approach. the novelty of our design lies in two key components: (1) a decentralized DHT-based tree overlay deployment for harvesting and uncovering deceptive spam from social communities; and (2) a progressive aggregation tree for aggregating the properties of these spam posts for creating new spam classifiers to actively filter out new spam. We design and implement the prototype of Oases and discuss the design considerations of the proposed approach. Our large-scale experiments using real-world Twitter data demonstrate scalability, attractive load-balancing, and graceful efficiency in online spam detection for social networks.

关键词： Peer-to-peer computing Training Vegetation Routing Twitter distributed databases

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[WiP] A Workflow and Cloud Based Service-Oriented Architecture for distributed Manufacturing in Industry 4.0 Context

[WiP] A Workflow and Cloud Based Service-Oriented Architectu...

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IEEE international conference on Service-Oriented computing and Applications (SOCA)

作者： Kerem Kayabay Mert Onuralp Gökalp P. Erhan Eren Altan Koçyiğit Informatics Institute Middle East Technical University Ankara Turkey Inf. Inst. Middle East Tech. Univ. Ankara Ankara Turkey

ISBN: (纸本)9781538691342;9781538691335

In Industry 4.0, manufacturing organizations have to become more flexible and agile to stay competitive in the marketplace. Industry and academia focus on sharing globally distributed manufacturing resources and capabilities. this shift to collaborative environment calls for a service dominant logic in which reusable services model entire production processes. Accordingly, service-oriented architectures have been under spotlight by researchers to integrate distributed capabilities and resources of manufacturing. In this article, we identify rapidly developing relevant IT fields, state-of-the-art service-oriented manufacturing approaches, and discuss related challenges with those approaches. then we introduce a higher-level workflow and cloud based service-oriented architecture for (1) individuals to design and prototype products, (2) manufacturing organizations to focus on their core competencies and find complementary services for part of their production processes, and (3) manufacturing organizations to publish their idle capabilities and resources as services. We investigate these potential impacts and more in the next manufacturing era.

关键词： Manufacturing Cloud computing Industries Task analysis Service-oriented architecture Unified modeling language

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NEW MEthODS OF UNCHEATABLE GRID computing

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computing AND INFORMATICS 2018年第6期37卷 1293-1312页

作者： Yu, Jianhua Li, Yuan South China Normal Univ Sch Math Sci Guangzhou Guangdong Peoples R China Winston Salem State Univ Dept Math Winston Salem NC 27110 USA

Grid computing is the collection of computer resources from multiple locations to reach a common goal. According to the task publisher's computing power, we will classify the deceptive detection schemes into two categories, and then analyze the security of deceptive detection schemes based on the characteristics of computational task function. On the basis of double check, we proposed an improved scheme at the cost of time sacrifice called the secondary allocation scheme of double check. In our scheme, the security of double check has been greatly strengthened. Finally, we analyzed the common problem of High-Value Rare Events, improved the deceptive detection scheme due to [1], and then put forward a new deceptive detection scheme with better security and efficiency. this paper is revised and expanded version of a paper entitled "Deceptive Detection and Security Reinforcement in Grid computing" [2] presented at 2013 5th international conference on Intelligent networking and Collaborative Systems, Xi'an city, Shanxi province, China, September 9-11, 2013.

关键词： distributed deceptive detection ringers grid computing

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Temporal Task Scheduling for Delay-Constrained Applications in Geo-distributed Cloud Data Centers

Temporal Task Scheduling for Delay-Constrained Applications ...

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IEEE international conference on Cloud computing, CLOUD

作者： Jing Bi Haitao Yuan Jia Zhang MengChu Zhou School of Software Engineering Beijing University of Technology Beijing China School of Software Engineering Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering Carnegie Mellon University Moffett Field USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

A growing number of global companies select Green Cloud Data Centers (GCDCs) to manage their delay-constrained applications. the fast growth of users' tasks dramatically increases the energy consumed by GCDC, e.g., Google. the random nature of tasks brings a big challenge of scheduling tasks of each application with limited infrastructure resources of GCDCs. this work accurately computes a mathematical relation between task service rates and the number of tasks refusal in GCDC. Besides, it proposes a Temporal Task Scheduling (TTS) algorithm investigating the temporal variation in geo-distributed cloud data centers to schedule all tasks within their delay constraints. Furthermore, a novel dynamic hybrid meta-heuristic algorithm is developed for the formulated profit maximization problem, based on genetic simulated annealing and particle swarm optimization. the proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. Trace-driven simulations demonstrate that larger throughput and profit is achieved than several existing scheduling algorithms.

关键词： Task analysis Cloud computing Servers Delays Data centers Genetic algorithms Processor scheduling

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Algorithms of Authentication and Authorization by Proxy in distributed Information-computing Environment 11

Algorithms of Authentication and Authorization by Proxy in D...

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11th IEEE international conference on Application of Information and Communication Technologies (AICT)

作者： Demidov, Alexander Polovinkin, Daniil Potlova, Tatyana Shateev, Roman Sopina, Eugenia Orel State Univ Oryol Russia

ISBN: (纸本)9781538605011

this paper suggests algorithms to be used in a distributed information computing environment for authentication and authorization, which both are delegated to a proxy server. the server receives all the requests to the websites and forwards only those which match the set of rules set by administrators while validating session information if it is present. Introduction of this new party allows to implement single-sign on and proper access control in an existing network of portals with low development overhead.

关键词： authentication proxy authorization proxy single sign on network of portals distributed information computing environment information access control management

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