2019 IEEE International Conference on Autonomic Computing (ICAC)最新文献_第2页

Autonomic Forecasting Method Selection: Examination and Ways Ahead 自主预测方法的选择:检验与展望

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00028

Marwin Züfle, A. Bauer, Veronika Lesch, Christian Krupitzer, N. Herbst, Samuel Kounev, V. Curtef

{"title":"Autonomic Forecasting Method Selection: Examination and Ways Ahead","authors":"Marwin Züfle, A. Bauer, Veronika Lesch, Christian Krupitzer, N. Herbst, Samuel Kounev, V. Curtef","doi":"10.1109/ICAC.2019.00028","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00028","url":null,"abstract":"Proactive adaptation improves the system performance of Autonomic Computing systems as it recognizes adaptation concerns in advance and adapts or prepares adaptation accordingly. To support this, forecasting methods use historical data to predict future system states. According to the \"No-Free-Lunch-Theorem\", there is no general forecasting method that performs best in all scenarios. Usually at design time, expert knowledge is required to decide on the forecasting method based on the anticipated characteristics of the resulting time series data. The uncertainty that results from the gap between design time and runtime for adaptive systems, as well as the environmental uncertainty at runtime, decreases the possibility that a forecasting method chosen at design time can cope with runtime demands. A common approach to tackle this problem is to use recommendation systems that automatically choose the forecasting methods. In this paper, we introduce a novel approach for forecasting method selection and a recommendation-based ensemble forecasting approach. We compare our approaches with one of the most widely used recommendation approaches for time series forecasting. Whereas the reference system uses static recommendation rules, we contrast a modified version which supports dynamic rule learning. The results of the evaluation show that our approaches outperform the original approach with static rule learning.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128766019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Model Checking a Self-Adaptive Camera Network with Physical Disturbances 具有物理干扰的自适应摄像机网络的模型检验

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00021

Gautham Nayak Seetanadi, Karl-Erik Årzén, M. Maggio

{"title":"Model Checking a Self-Adaptive Camera Network with Physical Disturbances","authors":"Gautham Nayak Seetanadi, Karl-Erik Årzén, M. Maggio","doi":"10.1109/ICAC.2019.00021","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00021","url":null,"abstract":"The paper describes the design and verification of a self-adaptive system, composed of multiple smart cameras connected to a monitoring station, that determines the allocation of network bandwidth to the cameras. The design of such a system poses significant challenges, since multiple control strategies are active in the system simultaneously. In fact, the cameras adjust the quality of their streams to the available bandwidth, that is at the same time allocated by the monitoring station. Model checking has proven successful to verify properties of this complex system, when the effect of actions happening in the physical environment was neglected. Extending the verification models to include disturbances from the physical environment is however nontrival due to the state explosion problem. In this paper we show a comparison between the previously developed deterministic model and two alternatives for disturbance handling: a probabilistic and a nondeterministic model. We verify properties for the three models, discovering that the nondeterministic model scales better when the number of cameras increase and is more representative of the dynamic physical environment. We then focus on the nondeterministic model and study, using stochastic games, the behavior of the system when the players (cameras and network manager) collaborate or compete to reach their own objectives.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124506589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Online Power Consumption Estimation for Functions in Cloud Applications 云应用中函数的在线功耗估算

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00018

Norbert Schmitt, Lukas Iffländer, A. Bauer, Samuel Kounev

{"title":"Online Power Consumption Estimation for Functions in Cloud Applications","authors":"Norbert Schmitt, Lukas Iffländer, A. Bauer, Samuel Kounev","doi":"10.1109/ICAC.2019.00018","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00018","url":null,"abstract":"The growth of cloud services leads to more and more data centers that are increasingly larger and consume considerable amounts of power. To increase energy efficiency, informed decisions on workload placement and provisioning are essential. Micro-services and the upcoming serverless platforms with more granular deployment options exacerbate this problem. For this reason, knowing the power consumption of the deployed application becomes crucial, providing the necessary information for autonomous decision making. However, the actual power draw of a server running a specific application under load is not available without specialized measurement equipment or power consumption models. Yet, granularity is often only down to machine level and not application level. In this paper, we propose a monitoring and modeling approach to estimate power consumption on an application function level. The model uses performance counters that are allocated to specific functions to assess their impact on the total power consumption. Hence our model applies to a large variety of servers and for micro-service and serverless workloads. Our model uses an additional correction to minimize falsely allocated performance counters and increase accuracy. We validate the proposed approach on real hardware with a dedicated benchmarking application. The evaluation shows that our approach can be used to monitor application power consumption down to the function level with high accuracy for reliable workload provisioning and placement decisions.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128489705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Autonomic Cloud Placement of Mixed Workload: An Adaptive Bin Packing Algorithm 混合工作负载的自主云布局:自适应装箱算法

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00030

A. Tantawi, M. Steinder

{"title":"Autonomic Cloud Placement of Mixed Workload: An Adaptive Bin Packing Algorithm","authors":"A. Tantawi, M. Steinder","doi":"10.1109/ICAC.2019.00030","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00030","url":null,"abstract":"Cloud computing offers a platform where virtual entities, such as virtual machines, containers, and pods, are hosted in a physical infrastructure. Such virtual entities request resources, such as CPU, memory, and GPU, among other constraints. The cloud placement engine, also referred to as the scheduler, needs to place, in real time, such virtual entities in the cloud. Typically, resource demand is heterogeneous and the mix varies over time. Therefore, the scheduler needs to change its placement policy dynamically in order to accommodate the change in the mixed demand, resulting in lower rejection probability. A novel, autonomic, Adaptive Bin Packing (ABP) algorithm which attempts to equalize measures of variability in the demand and the allocated resources in the cloud, without the need to set any configuration, is introduced. ABP is compared to simplistic, extreme packing policies (spread and pack) as well an optimized packing policy. Experimental results based on simulations are presented, and the behavior of ABP and its adaptability to the demand mix is demonstrated. Further, ABP performs close to the optimized policy, yet evolves to an extreme policy as the mix becomes homogeneous.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"598 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132763851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Autonomic Computing Challenges in Fully Autonomous Precision Agriculture 自主计算在完全自主精准农业中的挑战

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00012

Jayson G. Boubin, J. Chumley, Christopher Stewart, S. Khanal

{"title":"Autonomic Computing Challenges in Fully Autonomous Precision Agriculture","authors":"Jayson G. Boubin, J. Chumley, Christopher Stewart, S. Khanal","doi":"10.1109/ICAC.2019.00012","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00012","url":null,"abstract":"Precision agriculture examines crop fields, gathers data, analyzes crop health and informs field management. This data driven approach can reduce fertilizer runoff, prevent crop disease and increase yield. Frequent data collection improves outcomes, but also increases operating costs. Fully autonomous aerial systems (FAAS) can capture detailed images of crop fields without human intervention. They can reduce operating costs significantly. However, FAAS software must embed agricultural expertise to decide where to fly, which images to capture and when to land. This paper explores fully autonomous precision agriculture where FAAS map crop fields frequently. We have designed hardware and software architecture. We use unmanned aerial systems, edge computing components and software driven by reinforcement learning and ensemble models. In early results, we have collected data from an Ohio cornfield. We use this data to simulate a FAAS modeling crop yield. Our results (1) show that our approach predicts yield well and (2) can quantify computational demand. Computational costs can be prohibitive. We discuss how research on adaptive systems can reduce costs and enable fully autonomous precision agriculture. We also provide our simulation tools and dataset as part of our open source FAAS middleware, SoftewarePilot.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133305845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 31

CoPPer: Soft Real-Time Application Performance Using Hardware Power Capping 铜:软实时应用性能使用硬件功率封顶

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00015

Connor Imes, Huazhe Zhang, Kevin Zhao, H. Hoffmann

{"title":"CoPPer: Soft Real-Time Application Performance Using Hardware Power Capping","authors":"Connor Imes, Huazhe Zhang, Kevin Zhao, H. Hoffmann","doi":"10.1109/ICAC.2019.00015","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00015","url":null,"abstract":"Dynamic voltage and frequency scaling (DVFS) has been the cornerstone of innumerable software approaches to meeting application timing requirements with minimal energy. However, recent trends in technology—e.g., moving voltage converters on chip—favor hardware control of DVFS, as hardware can both react faster to external events and perform fine-grained power management across a device. We respond to these trends with CoPPer, which instead uses hardware power capping to meet application performance requirements with high energy efficiency. We find that meeting performance requirements with power capping is more challenging than using DVFS because the relationship between power and performance is non-linear and has diminishing returns at high power values. CoPPer overcomes these difficulties by using adaptive control to approximate non-linearities and a novel gain limit to avoid over-allocating power when it is no longer beneficial. We evaluate CoPPer with 20 parallel applications and compare it to both a classic linear DVFS controller and to a sophisticated control-theoretic, model-driven software DVFS manager. CoPPer provides all the functionality of the sophisticated DVFS-based approach, without requiring a user-specified model or time-consuming, exhaustive application/system pre-characterization. Compared to DVFS, CoPPer's gain limit reduces energy by 6% on average and by 12% for memory-bound applications. For high performance requirements, the energy savings are even greater: 8% on average and 18% for memory-bound applications.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116074059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 23

Workflow Variability for Autonomic IoT Systems 自主物联网系统的工作流程可变性

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00014

Damian Arellanes, K. Lau

引用次数: 10

Forecasting a Storm: Divining Optimal Configurations using Genetic Algorithms and Supervised Learning 预测风暴:利用遗传算法和监督学习预测最优配置

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00025

Michael Trotter, Timothy Wood, Jinho Hwang

{"title":"Forecasting a Storm: Divining Optimal Configurations using Genetic Algorithms and Supervised Learning","authors":"Michael Trotter, Timothy Wood, Jinho Hwang","doi":"10.1109/ICAC.2019.00025","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00025","url":null,"abstract":"With the advent of Big Data platforms like Apache Storm, computations once deemed infeasible locally become possible at scale. However, doing so entails orchestrating powerful yet expensive clusters. With its focus on stream processing, Storm optimizes for low-latency and high throughput. However, to realize this goal and thereby maximize the utility of these clusters' resources, operators must execute these tasks under their optimal configurations. Yet, the search space for finding such configurations is so vast and time-consuming to explore so as to be effectively intractable due to issues like the temporal overhead of testing new candidate configurations, the sheer number of permutations of parameters within each configuration and their interdependence among each other. In order to efficiently cover the search space, we automate the process with genetic algorithms. Moreover, we fuse this technique not only with additional cluster information gleaned from JMX profiling and Storm performance data but also with classifiers constructed from training data from past executions of a plethora of Storm topologies. Utilizing a diverse set of Storm benchmark topologies as evaluation data, we show that the fully enhanced genetic algorithms can efficiently find configurations that perform on average 4.67x better than \"rules of thumb\"-derived manual baselines. Moreover, we demonstrate that our fully refined classifiers enhance the GA throughput on average across the topologies by 22% while reducing search time by a factor of 6.47x.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133132182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Self-Improving Autonomic Systems for Antifragile Cyber Defence: Challenges and Opportunities 用于反脆弱网络防御的自我改进自主系统:挑战与机遇

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00013

Mohan Baruwal Chhetri, Anton V. Uzunov, Quoc Bao Vo, S. Nepal, R. Kowalczyk

引用次数: 7

Enhancing Learning-Enabled Software Systems to Address Environmental Uncertainty 增强学习型软件系统以应对环境不确定性

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00023

Michael Austin Langford, B. Cheng

{"title":"Enhancing Learning-Enabled Software Systems to Address Environmental Uncertainty","authors":"Michael Austin Langford, B. Cheng","doi":"10.1109/ICAC.2019.00023","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00023","url":null,"abstract":"An overarching problem with Learning-Enabled Systems (LES) is determining whether training data is sufficient to ensure the LES is resilient to environmental uncertainty and how to obtain better training data to improve the system's performance when it is not. Automated methods can ease the burden for developers by augmenting real-world data with synthetically generated data. We propose an evolution-based method to assist developers with the assessment of learning-enabled systems in environments not covered by available datasets. We have developed Enki, a tool that can generate various conditions of the environment in order to discover properties that lead to diverse and unique system behaviors. These environmental properties are then used to construct synthetic data for two purposes: (1) to assess a system's performance in an uncertain environment and (2) to improve system resilience in the presence of uncertainty. We show that our technique outperforms a random generation method when assessing the effect of multiple adverse environmental conditions on a Deep Neural Network (DNN) trained for the commonly-used CIFAR-10 benchmark.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114191255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7