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Vision: the case for cellular small cells for cloudlets

Published: 11 June 2014 Publication History

Abstract

Today's cellular networks are built with``macro cell'' basestations connected to the Internet via a rigid, complicated backhaul. Even with state-of-art technologies like LTE, users get limited throughput and high latency, with high variance. Performance enhancing IP boxes are deployed in the cellular operator's datacenters, far from the user. As a result, the most compelling cloudlet applications are difficult to realize on such networks and cloudlet researchers have thus far focused on Wi-Fi networks only.
We argue that the cloudlet community should consider small cell networks in addition to Wi-Fi networks. Small cells, such as femtocells and picocells, are relatively new additions to the cellular standards. By reducing the cell size compared to the traditional macro cells, they increase spatial reuse of precious licensed frequencies. Users get higher bandwidth and lower latency, with relatively less variance. This architecture, where small cells are deployed simply with power and Ethernet connectivity, lends itself well to cloudlet augmentation. In this position paper, we describe why deployed macro cell basestations are unsuitable for cloudlet deployment. In contrast, we describe why a small cell architecture is amenable for cloudlet deployments. Our experience from operating a small cell testbed in licensed frequencies matches that reported by equipment vendors. The applications we care about require high throughput and low latency. In a cellular network this can be achieved today by augmenting small cells with powerful cloudlets.

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      cover image ACM Conferences
      MCS '14: Proceedings of the fifth international workshop on Mobile cloud computing & services
      June 2014
      46 pages
      ISBN:9781450328241
      DOI:10.1145/2609908
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      Published: 11 June 2014

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      Author Tags

      1. cloudlets
      2. lte
      3. small cells

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      MCS '14 Paper Acceptance Rate 5 of 9 submissions, 56%;
      Overall Acceptance Rate 8 of 12 submissions, 67%

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      Cited By

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      • (2021)Hybrid Workflow Provisioning and Scheduling on Cooperative Edge Cloud Computing2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)10.1109/CCGrid51090.2021.00054(445-454)Online publication date: May-2021
      • (2020)Staleness Control for Edge Data AnalyticsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/33921564:2(1-24)Online publication date: 12-Jun-2020
      • (2020)A Truthful FPTAS Auction for the Edge-Cloud Pricing Problem2020 6th International Conference on Big Data Computing and Communications (BIGCOM)10.1109/BigCom51056.2020.00027(140-144)Online publication date: Jul-2020
      • (2020)A QoE-Aware Strategy for Supporting Service Continuity in an MCC EnvironmentWireless Personal Communications10.1007/s11277-020-07731-2Online publication date: 11-Aug-2020
      • (2019)A Distributed Coalition Game Approach to Femto-Cloud FormationIEEE Transactions on Cloud Computing10.1109/TCC.2016.25941757:1(129-140)Online publication date: 1-Jan-2019
      • (2019)Content Caching Strategy for Edge and Cloud Cooperation Computing2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)10.1109/IWCMC.2019.8766451(260-265)Online publication date: Jun-2019
      • (2019)Data Capsule: Representation of Heterogeneous Data in Cloud-Edge ComputingIEEE Access10.1109/ACCESS.2019.29105847(49558-49567)Online publication date: 2019
      • (2018)Delay-Sensitive Multiplayer Augmented Reality Game Planning in Mobile Edge ComputingProceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems10.1145/3242102.3242129(147-154)Online publication date: 25-Oct-2018
      • (2018)Distributed and Application-Aware Task Scheduling in Edge-Clouds2018 14th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)10.1109/MSN.2018.000-1(165-170)Online publication date: Dec-2018
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