2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)最新文献_第4页

FILT: Optimizing KV-Embedded File Systems through Flat Indexing FILT:通过平面索引优化kv嵌入式文件系统

2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2020-11-01 DOI: 10.1109/ICDCS47774.2020.00150

Chen Chen, Tongliang Deng, Jian Zhang, Yanliang Zou, Xiaomin Zhu, Shu Yin

引用次数: 0

WOLT: Auto-Configuration of Integrated Enterprise PLC-WiFi Networks 自动配置集成企业PLC-WiFi网络

2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2020-11-01 DOI: 10.1109/ICDCS47774.2020.00066

Hisham Alhulayyil, Kittipat Apicharttrisorn, Jiasi Chen, K. Sundaresan, Samet Oymak, S. Krishnamurthy

{"title":"WOLT: Auto-Configuration of Integrated Enterprise PLC-WiFi Networks","authors":"Hisham Alhulayyil, Kittipat Apicharttrisorn, Jiasi Chen, K. Sundaresan, Samet Oymak, S. Krishnamurthy","doi":"10.1109/ICDCS47774.2020.00066","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00066","url":null,"abstract":"Power Line Communication (PLC) based WiFi extenders can improve WiFi coverage in homes and enterprises. Unlike in traditional WiFi networks which use an underlying high data rate Ethernet backhaul, a PLC backhaul may not support high data rates. Specifically, our measurements show that arbitrarily affiliating users to PLC-WiFi extenders or based on their WiFi channel qualities alone may lead to poor network performance due to the differences in PLC link capacities. Thus, in this paper we build a framework, WOLT, to solve the problem of assigning users to the appropriate PLC-WiFi extenders to increase the aggregate network throughput in an enterprise setting, where one may expect a relatively large number of power outlets. WOLT accounts for both the qualities of the two concatenated links viz., the PLC and WiFi links. It hinges on estimating the best capacity offered by the PLC links, and accounting for these while assigning users. It incorporates a polynomial-time algorithm that assigns only a subset of the users to maximize the aggregate throughput on the PLC links, and then assigns the remaining users such that the degradation in the aggregate throughput is minimized. WOLT is evaluated through simulations and real testbed experiments with commodity PLCWiFi extenders, and improves aggregate throughput by more than 2.5x compared to a greedy user association baseline.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114375670","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: 1

Understanding WiFi Cross-Technology Interference Detection in the Real World 了解现实世界中的WiFi交叉技术干扰检测

2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2020-11-01 DOI: 10.1109/ICDCS47774.2020.00061

T. Pulkkinen, J. Nurminen, P. Nurmi

{"title":"Understanding WiFi Cross-Technology Interference Detection in the Real World","authors":"T. Pulkkinen, J. Nurminen, P. Nurmi","doi":"10.1109/ICDCS47774.2020.00061","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00061","url":null,"abstract":"WiFi networks are increasingly subjected to cross-technology interference with emerging IoT and even mobile communication solutions all crowding the 2.4 GHz ISM band where WiFi networks conventionally operate. Due to the diversity of interference sources, maintaining high level of network performance is becoming increasing difficult. Recently, deep learning based interference detection has been proposed as a potentially powerful way to identify sources of interference and to provide feedback on how to mitigate their effects. The performance of such approaches has been shown to be impressive in controlled evaluations. However, little information exists on how they generalize to the complexity of everyday environments. In this paper, we contribute by conducting a comprehensive performance evaluation of deep learning based interference detection. In our evaluation, we consider five orthogonal but complementary metrics: correctness, overfitting, robustness, efficiency, and interpretability. Our results show that, while deep learning indeed has excellent correctness (i.e., detection accuracy), it can be prone to noise in measurements (e.g., struggle when transmission power is dynamically adjusted) and suffers from poor interpretability. Deep learning is also highly sensitive to the quality and quantity of training data, with performance decreasing rapidly when the training and testing measurements come from environments with different characteristics. To compensate for weaknesses of deep learning, as our second contribution we propose a novel signal modeling approach for interference detection and compare it against deep learning. Our results demonstrate that, in terms of errors, there are some differences across the two approaches, with signal modeling being better at identifying technologies that rely on frequency hopping or that have dynamic spectrum signatures but suffering in other cases. Based on our results, we draw guidelines for improving interference detection performance.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121802388","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

An Energy-Efficient Edge Offloading Scheme for UAV-Assisted Internet of Things 一种无人机辅助物联网的高能效边缘卸载方案

2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2020-11-01 DOI: 10.1109/ICDCS47774.2020.00167

Minghui Dai, Zhou Su, Jiliang Li, Jian Zhou

引用次数: 7

Characterizing Bottlenecks in Scheduling Microservices on Serverless Platforms 无服务器平台上调度微服务的瓶颈特征

2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2020-11-01 DOI: 10.1109/ICDCS47774.2020.00195

J. Gunasekaran, P. Thinakaran, N. Nachiappan, R. Kannan, M. Kandemir, C. Das

{"title":"Characterizing Bottlenecks in Scheduling Microservices on Serverless Platforms","authors":"J. Gunasekaran, P. Thinakaran, N. Nachiappan, R. Kannan, M. Kandemir, C. Das","doi":"10.1109/ICDCS47774.2020.00195","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00195","url":null,"abstract":"Datacenters are witnessing an increasing trend in adopting microservice-based architecture for application design, which consists of a combination of different microservices. Typically these applications are short-lived and are administered with strict Service Level Objective (SLO) requirements. Traditional virtual machine (VM) based provisioning for such applications not only suffers from long latency when provisioning resources (as VMs tend to take a few minutes to start up), but also places an additional overhead of server management and provisioning on the users. This led to the adoption of serverless functions, where applications are composed as functions and hosted in containers. However, state-of-the-art schedulers employed in serverless platforms tend to look at microservice-based applications similar to conventional monolithic black-box applications. To detect all the inefficiencies, we characterize the end-to-end life cycle of these microservice-based applications in this work. Our findings show that the applications suffer from poor scheduling of microservices due to reactive container provisioning during workload fluctuations, thereby resulting in either in SLO violations or colossal container over-provisioning, in turn leading to poor resource utilization. We also find that there is an ample amount of slack available at each stage of application execution, which can potentially be leveraged to improve the overall application performance.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127673246","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: 3

Multi-Objective Online Task Allocation in Spatial Crowdsourcing Systems 空间众包系统中的多目标在线任务分配

2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2020-11-01 DOI: 10.1109/ICDCS47774.2020.00104

Ellen Mitsopoulou, Juliana Litou, V. Kalogeraki

引用次数: 2

Context-Aware Deep Model Compression for Edge Cloud Computing 面向边缘云计算的上下文感知深度模型压缩

2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2020-11-01 DOI: 10.1109/ICDCS47774.2020.00101

Lingdong Wang, Liyao Xiang, Jiayu Xu, Jiaju Chen, Xing Zhao, Dixi Yao, Xinbing Wang, Baochun Li

{"title":"Context-Aware Deep Model Compression for Edge Cloud Computing","authors":"Lingdong Wang, Liyao Xiang, Jiayu Xu, Jiaju Chen, Xing Zhao, Dixi Yao, Xinbing Wang, Baochun Li","doi":"10.1109/ICDCS47774.2020.00101","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00101","url":null,"abstract":"While deep neural networks (DNNs) have led to a paradigm shift, its exorbitant computational requirement has always been a roadblock in its deployment to the edge, such as wearable devices and smartphones. Hence a hybrid edge-cloud computational framework is proposed to transfer part of the computation to the cloud, by naively partitioning the DNN operations under the constant network condition assumption. However, real-world network state varies greatly depending on the context, and DNN partitioning only has limited strategy space. In this paper, we explore the structural flexibility of DNN to fit the edge model to varying network contexts and different deployment platforms. Specifically, we designed a reinforcement learning-based decision engine to search for model transformation strategies in response to a combined objective of model accuracy and computation latency. The engine generates a context-aware model tree so that the DNN can decide the model branch to switch to at runtime. By the emulation and field experimental results, our approach enjoys a 30% − 50% latency reduction while retaining the model accuracy.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084819","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

Serverless Straggler Mitigation using Error-Correcting Codes 使用纠错码的无服务器离散器缓解

2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2020-11-01 DOI: 10.1109/ICDCS47774.2020.00019

Vipul Gupta, Dominic Carrano, Yaoqing Yang, Vaishaal Shankar, T. Courtade, K. Ramchandran

{"title":"Serverless Straggler Mitigation using Error-Correcting Codes","authors":"Vipul Gupta, Dominic Carrano, Yaoqing Yang, Vaishaal Shankar, T. Courtade, K. Ramchandran","doi":"10.1109/ICDCS47774.2020.00019","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00019","url":null,"abstract":"Inexpensive cloud services, such as serverless computing, are often vulnerable to straggling nodes that increase the end-to-end latency for distributed computation. We propose and implement simple yet principled approaches for straggler mitigation in serverless systems for matrix multiplication and evaluate them on several common applications from machine learning and high-performance computing. The proposed schemes are inspired by error-correcting codes and employ parallel encoding and decoding over the data stored in the cloud using serverless workers. This creates a fully distributed computing framework without using a master node to conduct encoding or decoding, which removes the computation, communication and storage bottleneck at the master. On the theory side, we establish that our proposed scheme is asymptotically optimal in terms of decoding time and provide a lower bound on the number of stragglers it can tolerate with high probability. Through extensive experiments, we show that our scheme outperforms existing schemes such as speculative execution and other coding theoretic methods by at least 25%.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"1961 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129363302","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: 3

Communication-efficient k-Means for Edge-based Machine Learning 基于边缘的机器学习的高效通信k均值

2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2020-11-01 DOI: 10.1109/ICDCS47774.2020.00062

Hanlin Lu, T. He, Shiqiang Wang, Changchang Liu, M. Mahdavi, V. Narayanan, Kevin S. Chan, Stephen Pasteris

{"title":"Communication-efficient k-Means for Edge-based Machine Learning","authors":"Hanlin Lu, T. He, Shiqiang Wang, Changchang Liu, M. Mahdavi, V. Narayanan, Kevin S. Chan, Stephen Pasteris","doi":"10.1109/ICDCS47774.2020.00062","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00062","url":null,"abstract":"We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine learning computation to nearby edge servers. k-Means computation is fundamental to many data analytics, and the capability of computing provably accurate k-means centers by leveraging the computation power of the edge servers, at a low communication and computation cost to the data sources, will greatly improve the performance of these analytics. We propose to let the data sources send small summaries, generated by joint dimensionality reduction (DR) and cardinality reduction (CR), to support approximate k-means computation at reduced complexity and communication cost. By analyzing the complexity, the communication cost, and the approximation error of k-means algorithms based on state-of-the-art DR/CR methods, we show that: (i) in the single-source case, it is possible to achieve a near-optimal approximation at a near-linear complexity and a constant communication cost, (ii) in the multiple-source case, it is possible to achieve similar performance at a logarithmic communication cost, and (iii) the order of applying DR and CR significantly affects the complexity and the communication cost. Our findings are validated through experiments based on real datasets.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117350266","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

Refining Micro Services Placement over Multiple Kubernetes-orchestrated Clusters employing Resource Monitoring 使用资源监控在多个kubernetes编排的集群上优化微服务布局

2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS) Pub Date : 2020-11-01 DOI: 10.1109/ICDCS47774.2020.00173

Seunghyun Lee, Seokho Son, Jungsu Han, JongWon Kim

{"title":"Refining Micro Services Placement over Multiple Kubernetes-orchestrated Clusters employing Resource Monitoring","authors":"Seunghyun Lee, Seokho Son, Jungsu Han, JongWon Kim","doi":"10.1109/ICDCS47774.2020.00173","DOIUrl":"https://doi.org/10.1109/ICDCS47774.2020.00173","url":null,"abstract":"In the cloud field, there is an increasing demand for globalized services and corresponding execution environments that overcome local limitations and selectively utilize optimal resources. Utilizing multi-cloud deployments and operations rather than using a single cloud is an effective way to satisfy the increasing demand. In particular, we need to provide cloud-native environment to organically support services based on a microservices architecture. In this paper, we propose a cloud-native workload profiling system with Kubernetes-orchestrated multi-cluster configuration. The contributions of this paper are as follows. (i) We design the operating software over multiple cloud-native cluster to select optimal resources by monitoring. (ii) For operating the multiple clusters through the design, we define and design specific general service workloads. Also, we implement the workloads in application software (iii) To seek optimal resources, we deployed the general workloads and monitored resource usage repeatedly in detail. We calculate resource variation in comparison with initial resource usage and average resource usage after deploying the service workloads. Also, we analyze the resource monitoring result. We expect this methodology can find proper resources for service workload types.","PeriodicalId":158630,"journal":{"name":"2020 IEEE 40th International Conference on Distributed Computing Systems (ICDCS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115773452","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