2019 IEEE International Conference on Autonomic Computing (ICAC)最新文献

Chisel: Reshaping Queries to Trim Latency in Key-Value Stores 凿:重塑查询以减少键值存储中的延迟

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-09-13 DOI: 10.1109/ICAC.2019.00016

R. Birke, Juan F. Pérez, Sonia Ben Mokhtar, N. Rameshan, L. Chen

引用次数: 1

Characterizing Disk Health Degradation and Proactively Protecting Against Disk Failures for Reliable Storage Systems 分析硬盘健康退化特征，为可靠的存储系统提供硬盘故障预防措施

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-16 DOI: 10.1109/ICAC.2019.00027

Song Huang, Shuwen Liang, Song Fu, Weisong Shi, Devesh Tiwari, Hsing-bung Chen

{"title":"Characterizing Disk Health Degradation and Proactively Protecting Against Disk Failures for Reliable Storage Systems","authors":"Song Huang, Shuwen Liang, Song Fu, Weisong Shi, Devesh Tiwari, Hsing-bung Chen","doi":"10.1109/ICAC.2019.00027","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00027","url":null,"abstract":"The booming of cloud computing, online services and big data applications have resulted in dramatic expansion of storage systems. Meanwhile, disk drives are reported to be the most commonly replaced hardware component. Disk failures cause service downtime and even data loss, costing enterprises multi-trillion dollars per year. Existing disk failure management approaches are mostly reactive and incur high overheads. To overcome these problems, in this paper, we present a proactive, cost-effective solution to managing large-scale production storage systems. We aim to uncover the entire process in which disk's health deteriorates and forecast when disk drives will fail in the future. Due to a common lack of diagnostic information of disk failures, we rely on the Self-Monitoring, Analysis and Reporting Technology (SMART) data and explore statistical analysis techniques to identify the start of disk degradation. We then model the disk degradation processes as functions of SMART attributes, which eliminates the dependency on time and thus I/O workload. Experimental results from over 23,000 enterprise-class disk drives in a production data center show that our derived models can accurately quantify the degradation of disk health, which enables us to proactively protect data against disk failures. We also investigate several types of disk failures and propose remediation mechanisms to prolong disk lifetime.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127399755","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: 10

Adaptively Accelerating Map-Reduce/Spark with GPUs: A Case Study 使用gpu自适应加速Map-Reduce/Spark:一个案例研究

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-16 DOI: 10.1109/ICAC.2019.00022

K. R. Jayaram, Anshul Gandhi, Hongyi Xin, S. Tao

{"title":"Adaptively Accelerating Map-Reduce/Spark with GPUs: A Case Study","authors":"K. R. Jayaram, Anshul Gandhi, Hongyi Xin, S. Tao","doi":"10.1109/ICAC.2019.00022","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00022","url":null,"abstract":"In this paper, we propose and evaluate a simple mechanism to accelerate iterative machine learning algorithms implemented in Hadoop map-reduce (stock), and Apache Spark. In particular, we describe a technique that enables data parallel tasks in map-reduce and Spark to be dynamically and adaptively scheduled on CPU or GPU, based on availability and load. We examine the extent of performance improvements, and correlate them to various parameters of the algorithms studied. We focus on end-to-end performance impact, including overheads associated with transferring data into and out of the GPU, and conversion between data representations in the JVM and on GPU. We also present three optimizations that, in our analysis, can be generalized across many iterative machine learning applications. We present a case study where we accelerate four iterative machine learning applications – multinomial logistic regression, multiple linear regression, K-Means clustering and principal components analysis using singular value decomposition, implemented in three data analytics frameworks – Hadoop Map-Reduce (HMR), IBM Main-Memory Map-Reduce (M3R) and Spark. We observe that the use of GPGPUs decreases the execution time of these applications on HMR by up to 8X, M3R by up to 18X, and Spark by up to 25X. Through our empirical analysis, we offer several insights that can be helpful in designing middleware and cluster managers to accelerate map-reduce and Spark applications using GPUs.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130040137","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: 4

GreenRoute: A Generalizable Fuel-Saving Vehicular Navigation Service 绿色路线:一个通用的节油车辆导航服务

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-16 DOI: 10.1109/ICAC.2019.00011

Yiran Zhao, Shuochao Yao, Dongxin Liu, Huajie Shao, Shengzhong Liu

{"title":"GreenRoute: A Generalizable Fuel-Saving Vehicular Navigation Service","authors":"Yiran Zhao, Shuochao Yao, Dongxin Liu, Huajie Shao, Shengzhong Liu","doi":"10.1109/ICAC.2019.00011","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00011","url":null,"abstract":"This paper presents GreenRoute, a fuel-saving vehicular navigation system whose contribution is motivated by one of the key challenges in the design of autonomic services: namely, designing the service in a manner that reduces operating cost. GreenRoute achieves this end, in the specific context of fuel-saving vehicular navigation, by significantly improving the generalizability of fuel consumption models it learns (in order to recommend fuel-saving routes to drivers). By learning fuel consumption models that apply seamlessly across vehicles and routes, GreenRoute eliminates one of the key incremental costs unique to fuel-saving navigation: namely, the cost of upkeep with ever-changing fuel-consumption-specific route and vehicle parameters globally. Unlike shortest or fastest routes (that depend only on map topology and traffic), minimum-fuel routes depend additionally on the vehicle engine. This makes fuel-efficient routes harder to compute in a generic fashion, compared to shortest and fastest routes. The difficulty results in two additional costs. First, more route features need to be collected (and updated) for predicting fuel consumption, such as the nature of traffic regulators. Second, fuel prediction remains specific to the individual vehicle type, which requires continual upkeep with new car types and parameters. The contribution of this paper lies in deriving and implementing a fuel consumption model that avoids both of the above two sources of cost. To measure route recommendation quality, we test the system (using 21 vehicles and over 2400 miles driven in seven US cities) by comparing fuel consumption on our routes against both Google Maps' routes and the shortest routes. Results show that, on average, our routes save 10.8% fuel compared to Google Maps' routes and save 8.4% compared to the shortest routes. This is roughly comparable to services that maintain individualized vehicle models, suggesting that our low-cost models do not come at the expense of quality reduction.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116448298","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: 5

Capacity-Driven Scaling Schedules Derivation for Coordinated Elasticity of Containers and Virtual Machines 容器和虚拟机协调弹性的容量驱动伸缩计划推导

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00029

Yesika M. Ramirez, Vladimir Podolskiy, M. Gerndt

{"title":"Capacity-Driven Scaling Schedules Derivation for Coordinated Elasticity of Containers and Virtual Machines","authors":"Yesika M. Ramirez, Vladimir Podolskiy, M. Gerndt","doi":"10.1109/ICAC.2019.00029","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00029","url":null,"abstract":"With the growing complexity of microservice applications and proliferation of containers, scaling of cloud applications became challenging. Containers enabled the adaptation of the application capacity to the changing workload on the finer level of granularity than it was possible only with virtual machines. The common way to automate the adaptation of a cloud application is via autoscaling. Autoscaling is provided both on the level of virtual machines and containers. Its accuracy on dynamic workloads suffers significantly from the reactive nature of the available autoscaling solutions. The aim of the paper is to explore potential improvements of autoscaling by designing and evaluating several predictive-based autoscaling policies. These policies are naive (used as a baseline), best resource pair, only-Delta-load, always-resize, resize when beneficial. The scaling policies were implemented in Scaling Policy Derivation Tool (SPDT). SPDT takes the long-term forecast of the workload and the capacity model of microservices as input to produce the sequence of scaling actions scheduled for the execution in future with the aims to meet the service level objectives and minimize the costs. Policies implemented in SPDT were evaluated for three microservice applications and several workload patterns. The tests demonstrate that the combination of horizontal and vertical scaling enables more flexibility and reduces costs. Schedule derivation according to some policies might be compute-intensive, therefore careful consideration of the optimization objective (e.g. cost minimization or timeliness of the scaling policy) is required from the user of SPDT.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"97 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":"130596495","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: 8

Affine Scalarization of Two-Dimensional Utility Using the Pareto Front 二维效用的仿射标量化

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00026

G. Horn, M. Rózanska

引用次数: 5

EMU-IoT - A Virtual Internet of Things Lab EMU-IoT——虚拟物联网实验室

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00019

B. Ramprasad, Marios Fokaefs, Joydeep Mukherjee, Marin Litoiu

引用次数: 12

Express-Lane Scheduling and Multithreading to Minimize the Tail Latency of Microservices 快速通道调度和多线程最小化微服务尾部延迟

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00031

Amirhossein Mirhosseini, Brendan L. West, G. Blake, T. Wenisch

引用次数: 9

Characterizing Service Level Objectives for Cloud Services: Realities and Myths 描述云服务的服务水平目标:现实与神话

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00032

Jianru Ding, Ruiqi Cao, Indrajeet Saravanan, Nathaniel Morris, Christopher Stewart

{"title":"Characterizing Service Level Objectives for Cloud Services: Realities and Myths","authors":"Jianru Ding, Ruiqi Cao, Indrajeet Saravanan, Nathaniel Morris, Christopher Stewart","doi":"10.1109/ICAC.2019.00032","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00032","url":null,"abstract":"Service level objectives (SLOs) stipulate performance goals for cloud applications, microservices, and infrastructure. SLOs are widely used, in part, because system managers can tailor goals to their products, companies, and workloads. Systems research intended to support strong SLOs should target realistic performance goals used by system managers in the field. Evaluations conducted with uncommon SLO goals may not translate to real systems. Some textbooks discuss the structure of SLOs but (1) they only sketch SLO goals and (2) they use outdated examples. We mined real SLOs published on the web, extracted their goals and characterized them. Many web documents discuss SLOs loosely but few provide details and reflect real settings. Systematic literature review (SLR) prunes results and reduces bias by (1) modeling expected SLO structure and (2) detecting and removing outliers. We collected 75 SLOs where response time, query percentile and reporting period were specified. We used these SLOs to confirm and refute common perceptions. For example, we found few SLOs with response time guarantees below 10 ms for 90% or more queries. This reality bolsters perceptions that single digit SLOs face fundamental research challenges.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"1 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":"130713575","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: 14

Quality-Elasticity: Improved Resource Utilization, Throughput, and Response Times Via Adjusting Output Quality to Current Operating Conditions 质量弹性:通过调整输出质量以适应当前操作条件，提高资源利用率、吞吐量和响应时间

2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00017

L. Larsson, William Tarneberg, C. Klein, E. Elmroth

{"title":"Quality-Elasticity: Improved Resource Utilization, Throughput, and Response Times Via Adjusting Output Quality to Current Operating Conditions","authors":"L. Larsson, William Tarneberg, C. Klein, E. Elmroth","doi":"10.1109/ICAC.2019.00017","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00017","url":null,"abstract":"This work addresses two related problems for on-line services, namely poor resource utilization during regular operating conditions, and low throughput, long response times, or poor performance under periods of high system load. To address these problems, we introduce our notion of quality-elasticity as a manner of dynamically adapting response qualities from software services along a fine-grained spectrum. When resources are abundant, response quality can be increased, and when resources are scarce, responses are delivered at a lower quality to prioritize throughput and response times. We present an example of how a complex online shopping site can be made quality-elastic. Experiments show that, compared to state of the art, improvements in throughput (57% more served queries), lowered response times (8 time reduction for 95th percentile responses), and an estimated 40% profitability increase can be made using our quality-elastic approach. When resources are abundant, our approach may achieve upwards of twice as high resource utilization as prior work in this field.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"32 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":"122275743","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