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An On-the-fly Scheduling Strategy for distributed Stream Processing Platform 16

An On-the-fly Scheduling Strategy for Distributed Stream Pro...

引用

16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Wang, Wen'an Zhang, Chuang Chen, Xiaojun Li, Zhao Ding, Hong Wen, Xin Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781728111414

distributed stream processing can accomplish real-time processing of continuous streaming big data to obtain valuable information with high velocity. To maintain continuously stable and efficient running of stream applications, however, continuous online scheduling operations are required in the context of highly dynamic data stream. For this reason, this paper proposes the on-the-fly scheduling strategy in a distributed stream processing environment, which dynamically predicts abnormal events through double exponential smoothing and adopts trafficaware active migration protocol to adjust the network routing structure on-the-fly to balance the inter-worker load. Moreover, an evaluation method is proposed to quantitatively analyze the various scheduling objectives. Finally, we commendably apply the scheduling strategy to a stream processing platform, which regards docker instance as basic scheduling units. Meanwhile, based on the platform and the evaluation method, we complete performance comparison experiments of the scheduling algorithm. the experimental results indicate that our algorithm has excellent performance in throughput of topology, average processing time and balance of task load, which is suitable for deployment in a distributed environment with large-scale nodes and tasks.

关键词： distributed stream processing prediction algorithm exponential smoothing traffic-aware migration evaluation method

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A Review of Adversarial Behaviour in distributed Multi-Agent Optimisation 11

A Review of Adversarial Behaviour in Distributed Multi-Agent...

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11th IEEE/ACM international conference on Utility and Cloud computing (UCC-Companion) / 5th IEEE/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Fanitabasi, Farzam Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781728103594

this paper addresses the challenges of distributed multi-agent optimisation, in environments with potential adversarial agents. In distributed multi-agent optimisation, each agent has a local cost function and the collective goal is to optimise the global cost function, which takes as input, the output of each agent's local cost function. Such optimisation algorithms are used in different fields such as distributed machine learning, distributed robotics, and recently, distributed energy planning. A prominent assumption in most of these algorithms is that all the agents are cooperative, non-faulty, and non-adversarial. Yet, in scenarios with multi-agent systems, such assumptions are not always valid. Recently, there has been some research in the area of resilience and fault-tolerant distributed multi-agent optimisation. However, these studies either assume a fully Byzantine environment, or have their own adversary model with different sets of assumptions about adversary capabilities. this makes the analysis and comparison between the results challenging. this paper presents a review of such algorithms which helps in defining and investigating restricted adversarial behaviour in distributed multi-agent optimisation. In comparison to the Byzantine environments which carry no assumptions about the agent's capability or purpose, in this paper adversaries have restrictions in regard to their knowledge, capabilities, purpose, and impact. this approach makes it easier to provide guarantees about the system performance depending on the specific adversary model and avoid the overly conservative, 'one size fits all' algorithms needed in fully Byzantine environments. Moreover, using the gained insights, previously unexplored aspects of adversarial behaviour in distributed multi-agent optimisation are discovered and indicated as possible future research directions.

关键词： Adversarial distributed Optimisation Multi-Agent System Byzantine Agent Adversary Modelling

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A Periodic Task-Oriented Scheduling Architecture in Cloud computing 16

A Periodic Task-Oriented Scheduling Architecture in Cloud Co...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Zhang, Peng Li, Yan Lin, Hailun Wang, Jianwu Zhang, Chuang Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Natl Comp Network Emergency Response & Coordinat Beijing Peoples R China Univ Maryland Baltimore Cty Dept Informat Syst Baltimore MD 21228 USA

ISBN: (纸本)9781728111414

With the rapid development of cloud computing, more and more applications are deployed in the cloud. this trend brings cloud infrastructure providers to build distributed data centers to provide physical resources for multi-domain-deployed jobs of these applications. However, the heterogeneity of resources and the different execution periodicity of tasks in jobs pose great challenges to the efficiency of large scale job executions. To solve this problem, we propose a periodic task-oriented scheduling architecture in cloud computing. this architecture provides resource domains for the scheduler to shield the details of the underlying heterogeneous resources. Moreover, it implements job-level scheduling and task-level scheduling by our proposed PTS algorithm and RMDU4T algorithm to improve the execution efficiency and resource utilization. Experimental results show the proposed architecture can reduce the scheduling times to less than 10% and reduce the average job completion time by 10%similar to 40% under different distribution of tasks and resources, and the CPU resource utilization is improved by about 15%.

关键词： cloud computing periodic task job scheduling cross-domain scheduling

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S2R: Service trading based response time optimization in mobile edge computing 16

S2R: Service trading based response time optimization in mob...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Mishra, Pritish Tiwary, Mayank Yang, Laurence T. Puthal, Deepak SAP Labs Prod & Innovat Bangalore Karnataka India St Francis Xavier Univ Antigonish NS Canada Univ Technol Sydney Sydney NSW Australia

ISBN: (纸本)9781728111414

New caching forms coupled with Mobile Edge computing (MEC) supports new computing architecture at the physical proximity of the end-users. One of the similar architectures is mobile cloudlet, where a smartphone offers computations to another smartphone. However, the mobile edge computing architecture still has lots of issues related to response time optimization and resource provisioning. this paper focuses on a novel auction strategy to optimize resource utilization and as a result, proposed scheme minimizes the response time. the auction scheme is developed using multiple-leader and multiple-follower Stackelberg game model, which optimizes the utility of both users and mobile edges using non-cooperative approach for users and co-operative approach for mobile edges. Finally, the proposed scheme is simulated in NS3 environment and compared with existing schemes to validate the performance of proposed scheme. We observe that with the proposed scheme, there is a steep increase in overall utility in terms of response time and resource utilization.

关键词： Mobile Cloudlets Price Trading Multiple-Leader-Multiple-Follower Stackelberg Game Response Time Smart-Phones

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Anatomy of a fog-to-cloud distributed recommendation system in airports 11

Anatomy of a fog-to-cloud distributed recommendation system ...

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11th IEEE/ACM international conference on Utility and Cloud computing (UCC-Companion) / 5th IEEE/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Salis, Antonio Bulla, Roberto Mancini, Glauco Cocco, Paolo Jensen, Jens Engn Sardegna Srl Telco & Media Div Cagliari Italy UKRI STFC Rutherford Appleton Lab Dept Comp Sci Chilton England

ISBN: (纸本)9781728103594

Airports are confusing environments even at the best of times. Recent advances in IoT have made it possible to develop real-time improved traveler assistance tools for mobile phones, which we describe in this paper. these tools will be particularly valuable for the elderly or disabled travelers, or people with other constraints that require assistance, but will be highly valuable also to the seasoned traveler. the tool covers the essential path through the airport onto the flight, but also allows the traveler to fill their waiting time by discovering the facilities of the airport, aided by a recommender system using machine-learning. At the same time the system provides the airport with anonymized user "heat maps" which can highlight congestion and other aggregated data to highlight situations that require intervention, such as emergencies. Initially targeting Android, the app makes use of the fog-to-cloud platform developed by the Horizon2020-funded mF2C project.

关键词： Cloud computing fog computing fog-to-cloud distributed systems IoT collaborative filtering machine learning proximity marketing

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Proceedings - 11th IEEE/ACM international conference on Utility and Cloud computing Companion, UCC Companion 2018

Proceedings - 11th IEEE/ACM International Conference on Util...

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11th IEEE/ACM international conference on Utility and Cloud computing Companion, UCC Companion 2018

ISBN: (纸本)9781728103594

the proceedings contain 67 papers. the topics discussed include: hierarchical document classification using overlapped features;human activity detection via WiFi signals using deep neural networks;data coloring for securing open-data in cloud computing environment;transparent deployment of scientific workflows across clouds - kubernetes approach;towards a multi-tier fog/cloud architecture for video streaming;dataflow adapter: a tool for integrating legacy applications into distributed stream processing;developing an e-health system based on IoT, fog and cloud computing;detecting epileptic seizures using deep learning with cloud and fog computing;robust and resilient migration of data processing systems to public hadoop grid;and cloud challenge: secure end-to-end processing of smart metering data.

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Fault-Tolerant Query Execution over distributed Bitmap Indices 5

Fault-Tolerant Query Execution over Distributed Bitmap Indic...

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11th IEEE/ACM international conference on Utility and Cloud computing (UCC-Companion) / 5th IEEE/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Burdick, Sam Risner, Jahrme Chiu, David Sawin, Jason Amazon Com Seattle WA 98109 USA Univ Puget Sound Math & Comp Sci Tacoma WA 98416 USA Univ St Thomas Comp & Informat Sci St Paul MN USA Univ Puget Sound Tacoma WA 98416 USA

ISBN: (纸本)9781538655023

Advances in storage software and filesystems have proliferated a vast array of easy-to-use distributed storage services, removing the barrier for a growing number of organizations to geo-distribute large data sets. While leaving data in their distributed environments is convenient for data collection, various types of processing (that might use multiple data sources) are precluded due to the prohibitive costs of data movement. Users are therefore burdened with finding creative ways of performing data analysis, often requiring expert knowledge in multiple domains. this paper reports on the design and implementation of a query engine that enables high-level queries over distributed data sets. Our system generates bitmap indices at multiple geo-distributed data sources in order to approximate large amounts of raw data values. the bitmaps are replicated for fault-tolerance and performance. Upon accepting a high-level (SQL-like) query, our system generates a query plan, resolves dependencies, and schedules for its execution over the distributed system. the system has been tested rigorously, and experimental results show that most overheads (i.e., query planning, node spawning, etc.) are negligible. Our testing also shows that our system is capable of delivering query results in the face of node failures, with no observable impact on query execution for up to 20% of the system failing. the system also provides a framework that is easily extendible for future research on the interplay between distributed systems and bitmap indices.

关键词： Indexes distributed databases Fault tolerance Fault tolerant systems Engines Organizations Remuneration

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A Performance Study of Geo-distributed IoT Data Aggregation for Fog computing 11

A Performance Study of Geo-Distributed IoT Data Aggregation ...

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11th IEEE/ACM international conference on Utility and Cloud computing (UCC-Companion) / 5th IEEE/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Imai, Shigeru Varela, Carlos A. Patterson, Stacy Rensselaer Polytech Inst Dept Comp Sci Troy NY 12181 USA

ISBN: (纸本)9781728103594

We investigate MapReduce-based data aggregation for Internet-of-things data in a multi-tier, geo-distributed data-center architecture. Specifically, we consider 1) end-to-end hierarchical data aggregation and 2) query response for aggregated data requests made by geo-distributed clients. We first develop a realistic performance model based on previous empirical studies. We then study application performance for various deployment architectures, ranging from a purely cloud-based approach to a geo-distributed architecture that combines cloud, fog, and edge resources. From simulations created based on U.S. Census data, we characterize the trade-off between end-to-end data aggregation time and query response time. Our experiments show that for data aggregation, a purely-cloud based deployment is 53% faster than a deployment with edge resources;however, for query response, the edge approach is 46% faster due to the edge resource proximity to query clients.

关键词： geo-distributed map-reduce edge fog

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Proceedings - 11th IEEE/ACM international conference on Utility and Cloud computing, UCC 2018

Proceedings - 11th IEEE/ACM International Conference on Util...

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11th IEEE/ACM international conference on Utility and Cloud computing, UCC 2018

ISBN: (纸本)9781538655047

the proceedings contain 26 papers. the topics discussed include: hierarchical and frequency-aware model predictive control for bare-metal cloud applications;profiling and predicting application performance on the cloud;a model driven engineering approach for flexible and distributed monitoring of cross-cloud applications;pattern-based deployment models and their automatic execution;combining VM preemption schemes to improve vertical memory elasticity scheduling in clouds;pacer: automated feedback-based vertical elasticity for heterogeneous soft real-time workloads;scheduling scientific workflows on clouds using a task duplication approach;and task runtime prediction in scientific workflows using an online incremental learning approach.

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A data placement strategy for scientific workflow in hybrid cloud 11

A data placement strategy for scientific workflow in hybrid ...

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11th IEEE international conference on Cloud computing (CLOUD) Part of the IEEE World Congress on Services

作者： Liu, Zhanghui Xiang, Tao Lin, Bing Ye, Xinshu Wang, Haijiang Zhang, Ying Chen, Xing Fuzhou Univ Coll Math & Comp Sci Fuzhou Fujian Peoples R China Fujian Key Lab Network Comp & Intelligent Informa Fuzhou Fujian Peoples R China Fujian Normal Univ Coll Phys & Energy Fuzhou Fujian Peoples R China Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China

ISBN: (纸本)9781538672358

In cloud computing environments, data centers can provide high-performance computing resources and distributed storage space. Scientific workflows often need to be implemented across multiple data centers, where copious amounts of application data are stored. Moving data across geographically distributed data centers leads to intolerable delays and hinders the efficient execution of scientific workflows, which are large-scale data-intensive. Reasonable data placement can reduce data scheduling between the data centers effectively. In this paper, an adaptive discrete particle swarm optimization (PSO) algorithm based on genetic algorithm has been proposed to decrease the number of data transmissions across data centers. the algorithm overcame the premature convergence defect of PSO by introducing the mutation and crossover of genetic algorithm. Moreover, it effectively improved the diversity in the process of population evolution. Compared with the previous work, the simulation results showed that the proposed strategy greatly reduced the volume of data transfer while reducing the number of data movement across data centers.

关键词： Cloud computing Data Placement Scientific Workflow

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