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Fisher: An Efficient Container Load Prediction Model with Deep Neural Network in Clouds 16

Fisher: An Efficient Container Load Prediction Model with De...

引用

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

作者： Tang, Xuehai Liu, Qiuyang Dong, Yangchen Han, Jizhong Zhang, Zhiyuan Chinese Acad Sci Inst Informat Engn Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100190 Peoples R China

ISBN: (纸本)9781728111414

Recently, more and more applications have been deployed in container, and prediction of container load is essential in Clouds for improving resource utilization and achieving service-level agreements. However, accurate prediction of container load in Clouds remains an extremely challenge because the container load fluctuates drastically at small timescales. Furthermore, with many metrics of container in Clouds, it is hard to find which metrics are going to be useful. To address these challenges, we design an efficient container load prediction model named Fisher to improve the accuracy and efficiency of prediction. It mainly includes two modules: a metrics selection module and a neural network training module. We first selects relevant metrics by metrics selection module which is a novel algorithm for shape-based time-series clustering. Afterwards, we apply a powerful deep neural network model built with bidirectional long short-term memory to predict actual load one-step-ahead. We evaluate Fisher using a 30-day load trace from a data center with 500 containers. Experiments show that Fisher can reduce training metrics while maintaining prediction accuracy. More importantly, our model significantly improves prediction accuracy over 50% compared to other state-of-the-art methods based on autoregressive integrated moving-average and long short-term memory.

关键词： Cloud computing Container Load prediction Bidirectional Long short-term memory Time series analysis

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Unsupervised Anomaly Event Detection for Cloud Monitoring using Online Arima 11

Unsupervised Anomaly Event Detection for Cloud Monitoring us...

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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： Schmidt, Florian Suri-Payer, Florian Gulenko, Anton Wallschlager, Marcel Acker, Alexander Kao, Odej TU Berlin Complex & Distributed IT Syst Grp Berlin Germany

ISBN: (纸本)9781728103594

Virtualization offers cost efficient usage of digital resources. thus, dedicated hardware solutions are transferred into virtualized services running in the cloud. Such softwarization of hardware is for example the IP multimedia subsystems, which telecommunication system providers currently move to the cloud. the dedicated hardware solutions provided a reliability of 99.999% in the past, but the virtualized services come with higher complexity due to the fragile computation stack and cannot provide such high requirements. Zero touch administration of such fragile systems can help to detect automatically anomalies, find root causes and execute remediation actions. this work focusses on the detection of degraded state anomalies. We propose an unsupervised detection approach using the Online Arima forecasting algorithm using real-time monitoring data. this approach is evaluated on a testbed running an open source implementation of the IP multimedia subsystem (Clearwater) executed on a replicated Openstack cloud environment. Results show the applicability of the Online Arima based anomaly detection with high detection rates and low number of false alarms.

关键词： anomaly detection service reliability NFV

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Hybrid Approach Based on Combination of Backpropagation and Evolutionary Algorithms for Artificial Neural Networks Training by Using Mobile Devices in distributed computing Environment 11th

Hybrid Approach Based on Combination of Backpropagation and ...

引用

11th international conference on Large-Scale Scientific Computations (LSSC)

作者： Zankinski, Iliyan Barova, Maria Tomov, Petar Bulgarian Acad Sci Inst Informat & Commun Technol Acad G Bonchev StrBlock 2 Sofia 1113 Bulgaria

ISBN: (纸本)9783319734415;9783319734408

When Evolutionary Algorithms (EAs) are used for Artificial Neural Networks (ANNs) training, the most valuable advantage is the potential for this training to be done in parallel or even using distributed computing. With the capabilities of modern mobile devices, for example their use for distributed computations, they can be used much more extensively for scientific calculations. It is well known that distributed computing systems are limited by their communication bandwidth, because of network latency. In such environment some EAs are pretty suitable for distributed implementation. this is because of their high level of parallelism and relatively less intensive network communication needs. Subset of distributed computing is volunteer computing where users donate some of the computing power provided by devices under their control. this research proposes Android Live Wallpaper volunteer computing implementation of a system used for financial time series prediction. the forecasting module is organized as ANN, which is trained by hybrid combination of Backpropagation and EAs.

关键词： Artificial Neural Networks Evolutionary Algorithms distributed computing

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Dynamic Multi-Objective Workflow Scheduling for Cloud computing Based on Evolutionary Algorithms 11

Dynamic Multi-Objective Workflow Scheduling for Cloud Comput...

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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： Ismayilov, Goshgar Topcuoglu, Haluk Rahmi Marmara Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781728103594

Cloud computing is a dominant heterogeneous and distributed system, that offers on-demand resource capacity for different requirements of customers. Cloud workflow scheduling is a largely studied research area that targets efficient utilization of cloud resources. In this paper, we model a dynamic workflow scheduling problem, which is among the first attempts that incorporate dynamism on resource failures and changing number of objectives. A set of dynamic multi-objective evolutionary algorithms from the literature are utilized for dynamic workflow scheduling problem, where four of them are variants of the commonly used NSGA-II algorithm (DNSGA-II-HM, DNSGA-II-A, DNSGA-II-B, DNSGA-II-RI) and the remaining one is dynamic extension of the multi-objective particle swarm optimization algorithm (DMOPSO). In our experimental study, five different objectives are considered, which are minimization of the makespan, the cost and the energy, and maximization of the reliability and the resource utilization. the empirical study of the given five algorithms is conducted with real-world applications from Pegasus workflow management systems, where the DNSGA-II-B procedure outperforms the other alternatives for most of the test instances, based on the number of non-dominated solutions, the Schott's spacing and the hyper-volume metrics.

关键词： Workflow scheduling resource failures dynamic multi-objective evolutionary algorithms

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Heuristic-based IoT Application Modules Placement in the Fog-Cloud computing Environment 11

Heuristic-based IoT Application Modules Placement in the Fog...

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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： Natesha, B., V Guddeti, Ram Mohana Reddy Natl Inst Technol Karnataka Dept Informat Technol Mangalore India

ISBN: (纸本)9781728103594

Nowadays many Smart City applications make use of Internet of things (IoT) devices for monitoring the environment. the increase in use of IoT for smart city applications causes exponential increase in the volume of data. Using centralised cloud for time sensitive IoT applications is not feasible due to more delay because of the network congestion. Hence, fog computing is used for processing the data near to the edge of the network, where processing is done by distributed network nodes. But, there is a challenge to select the fog nodes which can host and process the application modules. the placement of application module on these fog devices is known as NP-hard problem. Hence, we need better placement strategies to decide placement of application modules in fog infrastructure to minimize the application latency. In this paper, we design a First-Fit Decreasing (FFD) heuristic based approach for placing IoT application modules on Fog-Cloud and carried out the experiment using iFogsim simulator. the simulation results demonstrate that the proposed method shows significant decrease in both the application latency and energy consumption of Fog-Cloud as compared to the benchmark method.

关键词： Internet of things Cloud computing Fog computing Latency Energy Consumption and Network usage

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DROPLET: distributed Operator Placement for IoT Applications Spanning Edge and Cloud Resources 11

DROPLET: Distributed Operator Placement for IoT Applications...

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

作者： Elgamal, Tarek Sandur, Atul Phuong Nguyen Nahrstedt, Klara Agha, Gul Univ Illinois Dept Comp Sci 1304 W Springfield Ave Urbana IL 61801 USA

ISBN: (纸本)9781538672358

Internet of things (IoT) applications generate massive amounts of real-time streaming data. IoT data owners strive to make predictions/inferences from these large streams of data often through applying machine learning, and image processing operations. A typical deployment of such applications includes edge devices to provide processing/storage operations closer to the location where the streaming data is captured. An important challenge for IoT applications is deciding which operations to execute on an edge device and which operations should be carried out in the cloud. In this paper, we propose a scalable dynamic programming algorithm called DROPLET, to partition operations in IoT applications across shared edge and cloud resources, while minimizing completion time of the end-to-end operations. We evaluate DROPLET using three real-world applications. Our results show that DROPLET finds a partitioning of operations having overall completion time within 4% of the optimum for these applications. It also scales to thousands of operations and outperforms closest heuristics in the literature, by being 10 times faster in running time while finding partitioning of operations with total completion time that is 20% better for the large applications that we simulated. We analyze DROPLET to show that it scales with total number of operations in log-linear time.

关键词： Cloud computing Computational modeling Containers Delays Data models Heuristic algorithms Streaming media"

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Performance Interference-Aware Vertical Elasticity for Cloud-hosted Latency-Sensitive Applications 11

Performance Interference-Aware Vertical Elasticity for Cloud...

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

作者： Shekhar, Shashank Abdel-Aziz, Hamzah Bhattacharjee, Anirban Gokhale, Aniruddha Koutsoukos, Xenofon Vanderbilt Univ Dept EECS 221 Kirkland Hall Nashville TN 37235 USA

ISBN: (纸本)9781538672358

Elastic auto-scaling in cloud platforms has primarily used horizontal scaling by assigning application instances to distributed resources. Owing to rapid advances in hardware, cloud providers are now seeking vertical elasticity before attempting horizontal scaling to provide elastic auto-scaling for applications. Vertical elasticity solutions must, however, be cognizant of performance interference that stems from multi-tenant collocated applications since interference significantly impacts application quality-of-service (QoS) properties, such as latency. the problem becomes more pronounced for latency-sensitive applications that demand strict QoS properties. Further exacerbating the problem are variations in workloads, which make it hard to determine the right kinds of timely resource adaptations for latency-sensitive applications. To address these challenges and overcome limitations in existing offline approaches, we present an online, data-driven approach which utilizes Gaussian Processes-based machine learning techniques to build runtime predictive models of the performance of the system under different levels of interference. the predictive online models are then used in dynamically adapting to the workload variability by vertically auto-scaling co-located applications such that performance interference is minimized and QoS properties of latency-sensitive applications are met.

关键词： Cloud computing Data center Multi-tenancy Workload variability Latency sensitive Performance interference Vertical elasticity Virtualization Linux containers Docker Online predictive models Gaussian processes

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Motif search using Gibbs sampling: Notes on effectiveness in a distributed environment

Motif search using Gibbs sampling: Notes on effectiveness in...

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international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Joseph Marvin Imperial Czeritonnie Gail Ya-On Gregory Cu National University Manila Philippines De La Salle University Manila Philippines

ISBN: (数字)9781728130446

ISBN: (纸本)9781728130453

Motif search is a common problem in bioinformatics where unique DNA sequences (motifs) of a specific length inscribed in long strands signify binding sites for transcription factors. In this paper, we present some important notes on the implementation of motif search using Gibbs sampling algorithm in a distributed computing environment by analyzing visualization on speed and motif scoring of various distributed implementations. For the DNA sequences data, we used an open-source mouse genome fragments with lengths 250, 500, and 1000. We built upon our previous studies (Perera and Ragel, 2013; Chen and Jiang, 2006) by integrating a distributed environment of the motif search workloads (jobs) across 16 CPU cores contained on 2 computer nodes instead of the traditional way of parallelizing on a single computing device with multicore CPUs. Results show that using saving the DNA sequences in list and adding as a parameter argument obtained the fastest execution time compared to implementations by sending file dependencies and in-memory processing.

关键词： DNA Search problems Approximation algorithms Hardware Servers Bioinformatics Random access memory

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A Resource Usage Prediction System Using Functional-link and Genetic Algorithm Neural Network for Multivariate Cloud Metrics 11

A Resource Usage Prediction System Using Functional-link and...

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11th IEEE international conference on Service-Oriented computing and Applications (SOCA)

作者： thieu Nguyen Nhuan Tran Binh Minh Nguyen Giang Nguyen Hanoi Univ Sci & Technol Sch Informat & Commun Technol Hanoi Vietnam Slovak Acad Sci Inst Informat Bratislava Slovakia

ISBN: (纸本)9781538691335

Designing prediction-based auto-scaling systems for cloud computing is an attractive topic for scientists today. However, there are many barriers, which must be solved before applying these systems to practice. Some challenges include: improving accuracy for prediction models, finding a simple and effective forecast method instead of complex techniques, and processing multivariate resource metrics at the same time. So far, there are no existing proactive auto-scaling solutions for clouds that have addressed all those challenges. In this paper, we present a novel cloud resource usage prediction system using functional-link neural network (FLNN). We propose an improvement for the FLNN by exploiting genetic algorithm (GA) to train learning model in order to increase forecast effectiveness. To deal with multivariate input data, several mechanisms also are combined together to enable the ability of processing simultaneously different resource types in our system. this enables to discover implicit relationship among diverse metrics and based on that realistic scaling decisions can be made closer to reality. We use Google trace dataset to evaluate the proposed prediction system and data preprocessing mechanisms introduced in this work. the gained outcomes demonstrated that our system can work effectively under practical situations with good performance as compared with traditional techniques.

关键词： Cloud computing Functional-link neural network Functional-link and Genetic algorithm neural network FL-GANN Proactive auto-scaling Multivariate time series data Genetic algorithm Google trace dataset

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Adaptive Congestion Control for Handover in Heterogeneous Mobile Content-Centric networking 11

Adaptive Congestion Control for Handover in Heterogeneous Mo...

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11th international conference on Mobile computing and Ubiquitous Network (ICMU)

作者： Iwamoto, Daiki Sugahara, Daisuke Bandai, Masaki Yamamoto, Miki Kansai Univ Grad Sch Sci & Engn Osaka Japan Sophia Univ Fac Sci & Technol Tokyo Japan Kansai Univ Fac Engn Sci Osaka Japan

ISBN: (纸本)9784907626341

CCN (Content-Centric networking) is receiver-driven architecture whose communication is triggered by consumer requests. When a consumer changes its access point (handover), it cannot receive requested Data packets. However, the consumer can retransmit in-flight Interest packets to a new route and reconstruct its route to obtain corresponding Data packets. Each consumer has its own parameters for congestion control, e.g. congestion window size, congestion threshold and timeout value. In the case of a handover to a heterogeneous network, e.g. from LTE to Wi-Fi, when congestion parameters set just before the handover are carried over, they probably are inadequate for new network environments. In this paper, we propose an effective receiver-driven control, Congestion Parameter Initialization. Our policy is quite simple and a consumer initializes its congestion parameters just after the handover. Our simulation results show that our proposal can adapt its congestion parameters to new network environment quickly.

关键词： Content-Centric networking wireless mobile networks consumer mobility

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