Ce Chi, Kaixuan Ji, Avinab Marahatta, Fa Zhang, Youshi Wang, Zhiyong Liu
{"title":"An Energy Saving-Oriented Incentive Mechanism in Colocation Data Centers","authors":"Ce Chi, Kaixuan Ji, Avinab Marahatta, Fa Zhang, Youshi Wang, Zhiyong Liu","doi":"10.1109/ICCCN49398.2020.9209724","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209724","url":null,"abstract":"The size and amount of colocation data centers (colocations, for short) have been growing rapidly with the increasing popularity of cloud services, which ultimately causes a heavy burden on the power grid and the environment. However, even a colocation owner wishes to reduce its energy consumption, it may not be able to apply some effective energy saving techniques directly to the servers, since the servers belong to and are operated by its tenants. To solve the \"uncoordinated relationship\" issue between owners and tenants and achieve the energy reduction with a limited cost budget, an energy saving-oriented incentive mechanism, ESCo is proposed in this paper. Different from existing mechanisms that emphasize on minimizing the cost for the owners, our mechanism emphasizes on the maximization of the amount of energy saved by the tenants given a limited budget that the owner wants to pay for the energy saving. An algorithm is developed to realize a stable assignment in the mechanism. Trace-driven simulations based on real-world data are performed to verify the effectiveness of ESCo. The results show that ESCo can achieve 14.47% more energy saving than existing colocation incentive mechanisms.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126528135","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}
{"title":"HGO: Hierarchical Graph Optimization for Accurate, Efficient, and Robust Network Localization","authors":"Haodi Ping, Yongcai Wang, Deying Li","doi":"10.1109/ICCCN49398.2020.9209620","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209620","url":null,"abstract":"Inferring nodes’ locations by inter-node measurements is a crucial problem in the IoT era. Despite the various approaches to this problem, obtaining accurate results is still challenging when the measurements are noisy, sparse, or uneven. Such unsatisfactory measurements are, however, inevitable for the general consideration of the deployment cost and the limited sensing scope.This paper proposes a Hierarchical Graph Optimization (HGO) framework to address the network localization problem when the measurements are sparse and noisy. It firstly efficiently extracts the dense sub-graphs and realizes their local structures in local coordinate systems. The local structures of dense components are rather accurate for the local sufficiency of the measurements. Then, the noises of the inter-edges that sparsely connect the dense sub-graphs are found as the main course of the network localization errors. A close-loop condition is derived and two denoising algorithms are proposed to set up linear equation arrays to correct the noises of these critical edges. After that, a projection algorithm is proposed to realize a smoothed backbone graph using the corrected critical edges, and finally, a hierarchical registration method is proposed to register the realized backbone and the dense sub-components to produce the global network structure. A parallel implementation is further developed, which speeds up HGO in large scale networks. Extensive simulations verify that HGO consistently outperforms existing network localization algorithms in terms of accuracy, efficiency, and reliability under various measurement settings.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125699758","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}
{"title":"Reliability-Aware Service Function Chain Provisioning in Mobile Edge-Cloud Networks","authors":"Shouxu Lin, W. Liang, Jing Li","doi":"10.1109/ICCCN49398.2020.9209732","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209732","url":null,"abstract":"Mobile Edge Computing (MEC) has been envisioning as a promising technology to address limited computing and storage resources in mobile devices. The virtual services provided by the MEC platform are implemented as instances of Virtual Network Functions (VNFs). However, these VNF instances as pieces of software that run in virtual machines (VMs) are not always reliable. To provide reliable services for their users while meeting user service reliability requirements, the service providers of MEC usually adopt the replica policy that deploy a certain number of service replicas for each VNF instance. In this paper, we study reliable service provisioning in an MEC network through redundant placement of instances of VNFs. We assume that each service request consists of a Service Function Chain (SFC) requirement and a service reliability requirement. We formulate a novel reliability-aware service function chain provisioning problem with the aim to maximize the number of requests admitted, while meeting the specified reliability requirement of each admitted request. We first show that the problem is NP-hard, and formulate an ILP solution for the problem when the problem size is small. We then develop a randomized algorithm with a provable approximation ratio and high probability for the problem when the problem size is large, and the achieved approximation ratio is at the expense of moderate computing capacity and reliability constraint violations. We also devise an efficient heuristic for the problem without any resource and requirement constraint violations. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122256916","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}
{"title":"DeepTxFinder: Multiple Transmitter Localization by Deep Learning in Crowdsourced Spectrum Sensing","authors":"A. Zubow, S. Bayhan, P. Gawłowicz, F. Dressler","doi":"10.1109/ICCCN49398.2020.9209727","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209727","url":null,"abstract":"As the radio spectrum has become the bottleneck resource with increasing volume of mobile data and ultra-dense network deployments, it is crucial to use spectrum more flexibly in time, space, and frequency dimensions. However, higher efficiency in spectrum usage facilitated by flexible spectrum allocation comes with a cost, namely the increased complexity of spectrum monitoring and management. Identifying the transmitters is at the interest of particularly spectrum enforcement authorities to ensure that spectrum is used as intended by the legitimate users of the spectrum. For a scalable, efficient, and highly-accurate operation, we propose a crowd-sensing based solution where sensing devices report their measured receive power levels to a central entity which later fuses the collected information for localizing an unknown number of transmitters. Our solution, referred to as DeepTxFinder, leverages deep learning to handle many sources of uncertainty in the operation environment: namely number of transmitters, their transmission power levels, and channel conditions (shadowing). Using deep-learning, DeepTxFinder distinguishes itself from the prior state-of-the art which requires knowledge of the number and transmission power of transmitters or require the transmitters to be well separated in space by tens to hundreds of meters making them ill-suited for application in expected ultra-dense deployment of small-cells. Moreover, we propose a tiling-based approach to increase the scalability of our proposal by reducing the computational complexity. Our simulation studies show that DeepTxFinder can provide a high detection accuracy even only by collecting data from a very small number of sensors. More specifically, with 1 %–2 % sensor density DeepTxFinder can estimate the number of transmitters and their locations with high probability which proves that sparse sensing is feasible.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130231919","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}
{"title":"Blockchain-based Distributed Banking for Permissioned and Accountable Financial Transaction Processing","authors":"Wenjun Fan, Sang-Yoon Chang, Shawn Emery, Xiaobo Zhou","doi":"10.1109/ICCCN49398.2020.9209687","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209687","url":null,"abstract":"Distributed banking platforms and services forgo centralized banks to process financial transactions. For example, M-Pesa provides distributed banking service in the developing regions so that the people without a bank account can deposit, withdraw, or transfer money. The current distributed banking systems lack the transparency in monitoring and tracking of distributed banking transactions and thus do not support auditing of distributed banking transactions for accountability. To address this issue, this paper proposes a blockchain-based distributed banking (BDB) scheme, which uses blockchain technology to leverage its built-in properties to record and track immutable transactions. BDB supports distributed financial transaction processing but is significantly different from cryptocurrencies in its design properties, simplicity, and computational efficiency. We implement a prototype of BDB using smart contract and conduct experiments to show BDB’s effectiveness and performance. We further compare our prototype with the Ethereum cryptocurrency to highlight the fundamental differences and demonstrate the BDB’s superior computational efficiency.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134156881","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}
{"title":"Network Testing Using a Novel Framework for Traffic Modeling and Generation*","authors":"O. A. Adeleke, Nicholas Bastin, D. Gurkan","doi":"10.1109/ICCCN49398.2020.9209685","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209685","url":null,"abstract":"Network traffic modeling plays an important role in the generation of realistic network traffic in test environments. Especially in cases where researchers carry out experiments with real production-like traffic, as seen in specific home, enterprise, campus, LAN, or WAN networks. We present our ongoing work on a new framework that enables the methodical creation of application-agnostic traffic models from given network traces of a known network topology. The framework then uses these models to generate realistic traffic on a given network topology. We share a preliminary evaluation of the framework based on repeatable experiments where we model a typical web application traffic and then regenerate the traffic based on the model in a test network on our VTS (Virtual Topology Services) testbed.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115459253","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}
{"title":"Multi-User Distributed Spectrum Access Method for 802.11ax Stations","authors":"Dheeraj Kotagiri, Koichi Nihei, Tansheng Li","doi":"10.1109/ICCCN49398.2020.9209737","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209737","url":null,"abstract":"The 802.11ax MAC incorporates Multi-User (MU) Orthogonal Frequency Division Multiple Access (OFDMA) based uplink communication where stations access spectrum by randomly selecting one of the available sub-channel, called Resource Unit (RU). This paper proposes a distributed RU selection method using Convolution Neural Network (CNN) based Deep Reinforcement Learning (C-DRL). The proposed method works in tandem with the standard CSMA/CA protocol where CSMA/CA determines when a transmission opportunity is available to a station while the proposed method is used to select the RU for transmitting the data. Specifically, each station locally trains its CNN in an online manner solely based on energy detection and acknowledgment packets. The proposed method achieves 81:18% higher average throughput and 42:37% lower latency compared to standard MU-OFDMA MAC protocol for a single Access Point network with a variable number of stations.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128163968","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}
{"title":"NFEH: An SDN Framework for Containerized Network Function-enabled End Hosts","authors":"Rahil Gandotra, Levi Perigo","doi":"10.1109/ICCCN49398.2020.9209701","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209701","url":null,"abstract":"Network infrastructure operation and management are becoming increasingly complex. This complexity is a result of multi-vendor devices, distributed platforms, and the numerous protocols for control and management present in the network. Previous studies indicate that shifting network functions from the core network infrastructure to the end hosts offers network benefits and reduces the dependencies on the core infrastructure. In this research, we propose a novel framework to enable network functions on end hosts by utilizing container virtualization technologies. The lightweight and flexible nature of containers facilitate simple deployment and management while enabling application-specific intelligence to reside on the end host relieving the core network infrastructure of these complexities. To achieve this, we implement software-defined networking (SDN) concepts and technologies to enable centralized control of end-host network functions. Functional validation of the proposed framework is performed using Voice over IP (VoIP), with the Session Initiation Protocol (SIP), as the network function and the experiment results indicate that the containerized VoIP functions can operate on the end host, simplifying network management and enabling a simple network core.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127306378","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}
Anirudh Ganji, Anandeshwar Singh, Muhammad Shahzad
{"title":"Choosing TCP Variants for Cloud Tenants – A Measurement based Approach","authors":"Anirudh Ganji, Anandeshwar Singh, Muhammad Shahzad","doi":"10.1109/ICCCN49398.2020.9209622","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209622","url":null,"abstract":"Cloud computing has become the de-facto paradigm for fulfilling the computing needs of a myriad of applications like streaming, e-commerce, data analytics, etc. While a significant amount of work exists on how cloud providers can improve the networking performance of their cloud platforms, very little has been done to explore how a cloud tenant can achieve the best performance for their applications. In this paper, we study how the choice of the TCP variant impacts the performance achieved by tenant applications. We present a generic measurementbased approach to identify the best TCP variant for any given application in a given cloud environment. Our approach is comprised of first measuring several performance metrics including throughput, latency, and loss in the given cloud platform for several TCP variants, and then identifying the best TCP variant based on three things: observations from the measurements, nature of the traffic of the given application, and application requirements such as high throughput or low latency. We study the effectiveness of our approach by implementing it in two large public clouds, Amazon’s AWS and Google’s GCP, and present our observations from several case studies using three common cloud applications, namely streaming, distributed input-output, and sort, and four common TCP variants, namely Cubic, New Reno, BBR, and DCTCP. From our observations, we found that just by changing the TCP variant that an application uses, the average throughput can be increased by up to 13.7% and the round trip time can be decreased by up to 5 times.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127436819","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}
{"title":"Grid Partition: an Efficient Greedy Approach for Outdoor Camera IoT Deployments in 2.5D Terrain","authors":"K. Veenstra, K. Obraczka","doi":"10.1109/ICCCN49398.2020.9209624","DOIUrl":"https://doi.org/10.1109/ICCCN49398.2020.9209624","url":null,"abstract":"In this paper, we introduce Distributed Grid Partition, a distributed greedy deployment algorithm for outdoor IoT camera networks. The proposed algorithm optimizes visual network coverage over 2.5D terrain. The main idea behind Distributed Grid Partition is that each deployment node tries to find the best vantage point in its neighborhood that will maximize the network’s overall visual coverage. It does so by using information from its immediate neighbors. In order to achieve a favorable cost-performance trade-off, Distributed Grid Partition uses height as a proxy for visual coverage, or fitness, avoiding expensive fitness computations. In addition, each node’s contribution to network fitness is determined without knowledge of the overall network using the concept of \"Wonderful Life Utility\". Our experimental results show that Distributed Grid Partition results in deployments with superior coverage-cost performance when compared to other distributed optimization algorithms as well as a centralized greedy set cover heuristic.","PeriodicalId":137835,"journal":{"name":"2020 29th International Conference on Computer Communications and Networks (ICCCN)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127488063","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}