Designing scalable and cost-effective data center interconnect architectures based on electrical packet switches is challenging. To overcome this challenge, researchers have tried to harness the advantages of optics in data center environment. This has resulted in exploration of hybrid switching architectures that contains an optical circuit switch to serve long bursts of traffic along with an electrical packet switch serving short bursts of traffic. The performance of such hybrid switching architectures in data center is dependent on the schedulers. Building hybrid schedulers is challenging because of varying properties of data center traffic, increasing network demands, requirements imposed by hybrid network architecture etc. Slow schedulers can negatively impact the performance of the data center network because of poor resource utilization. With future demands, this problem is going to escalate motivating the need for faster schedulers. One approach to do this would be to use a hardware based scheduler. In this paper we propose a framework that can be used to explore and evaluate hardware based hybrid schedulers.
{"title":"Extreme Data-rate Scheduling for the Data Center","authors":"N. M. Bojan, Noa Zilberman, G. Antichi, A. Moore","doi":"10.1145/2785956.2790019","DOIUrl":"https://doi.org/10.1145/2785956.2790019","url":null,"abstract":"Designing scalable and cost-effective data center interconnect architectures based on electrical packet switches is challenging. To overcome this challenge, researchers have tried to harness the advantages of optics in data center environment. This has resulted in exploration of hybrid switching architectures that contains an optical circuit switch to serve long bursts of traffic along with an electrical packet switch serving short bursts of traffic. The performance of such hybrid switching architectures in data center is dependent on the schedulers. Building hybrid schedulers is challenging because of varying properties of data center traffic, increasing network demands, requirements imposed by hybrid network architecture etc. Slow schedulers can negatively impact the performance of the data center network because of poor resource utilization. With future demands, this problem is going to escalate motivating the need for faster schedulers. One approach to do this would be to use a hardware based scheduler. In this paper we propose a framework that can be used to explore and evaluate hardware based hybrid schedulers.","PeriodicalId":268472,"journal":{"name":"Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114971451","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}
Liang Zheng, Carlee Joe-Wong, C. Tan, M. Chiang, Xinyu Wang
Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users to bid for cloud resources at a highly reduced rate. Amazon sets the spot price dynamically and accepts user bids above this price. Jobs with lower bids (including those already running) are interrupted and must wait for a lower spot price before resuming. Spot pricing thus raises two basic questions: how might the provider set the price, and what prices should users bid? Computing users' bidding strategies is particularly challenging: higher bid prices reduce the probability of, and thus extra time to recover from, interruptions, but may increase users' cost. We address these questions in three steps: (1) modeling the cloud provider's setting of the spot price and matching the model to historically offered prices, (2) deriving optimal bidding strategies for different job requirements and interruption overheads, and (3) adapting these strategies to MapReduce jobs with master and slave nodes having different interruption overheads. We run our strategies on EC2 for a variety of job sizes and instance types, showing that spot pricing reduces user cost by 90% with a modest increase in completion time compared to on-demand pricing.
{"title":"How to Bid the Cloud","authors":"Liang Zheng, Carlee Joe-Wong, C. Tan, M. Chiang, Xinyu Wang","doi":"10.1145/2785956.2787473","DOIUrl":"https://doi.org/10.1145/2785956.2787473","url":null,"abstract":"Amazon's Elastic Compute Cloud (EC2) uses auction-based spot pricing to sell spare capacity, allowing users to bid for cloud resources at a highly reduced rate. Amazon sets the spot price dynamically and accepts user bids above this price. Jobs with lower bids (including those already running) are interrupted and must wait for a lower spot price before resuming. Spot pricing thus raises two basic questions: how might the provider set the price, and what prices should users bid? Computing users' bidding strategies is particularly challenging: higher bid prices reduce the probability of, and thus extra time to recover from, interruptions, but may increase users' cost. We address these questions in three steps: (1) modeling the cloud provider's setting of the spot price and matching the model to historically offered prices, (2) deriving optimal bidding strategies for different job requirements and interruption overheads, and (3) adapting these strategies to MapReduce jobs with master and slave nodes having different interruption overheads. We run our strategies on EC2 for a variety of job sizes and instance types, showing that spot pricing reduces user cost by 90% with a modest increase in completion time compared to on-demand pricing.","PeriodicalId":268472,"journal":{"name":"Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134263864","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}
Arjun Roy, Hongyi Zeng, Jasmeet Bagga, G. Porter, A. Snoeren
Large cloud service providers have invested in increasingly larger datacenters to house the computing infrastructure required to support their services. Accordingly, researchers and industry practitioners alike have focused a great deal of effort designing network fabrics to efficiently interconnect and manage the traffic within these datacenters in performant yet efficient fashions. Unfortunately, datacenter operators are generally reticent to share the actual requirements of their applications, making it challenging to evaluate the practicality of any particular design. Moreover, the limited large-scale workload information available in the literature has, for better or worse, heretofore largely been provided by a single datacenter operator whose use cases may not be widespread. In this work, we report upon the network traffic observed in some of Facebook's datacenters. While Facebook operates a number of traditional datacenter services like Hadoop, its core Web service and supporting cache infrastructure exhibit a number of behaviors that contrast with those reported in the literature. We report on the contrasting locality, stability, and predictability of network traffic in Facebook's datacenters, and comment on their implications for network architecture, traffic engineering, and switch design.
{"title":"Inside the Social Network's (Datacenter) Network","authors":"Arjun Roy, Hongyi Zeng, Jasmeet Bagga, G. Porter, A. Snoeren","doi":"10.1145/2785956.2787472","DOIUrl":"https://doi.org/10.1145/2785956.2787472","url":null,"abstract":"Large cloud service providers have invested in increasingly larger datacenters to house the computing infrastructure required to support their services. Accordingly, researchers and industry practitioners alike have focused a great deal of effort designing network fabrics to efficiently interconnect and manage the traffic within these datacenters in performant yet efficient fashions. Unfortunately, datacenter operators are generally reticent to share the actual requirements of their applications, making it challenging to evaluate the practicality of any particular design. Moreover, the limited large-scale workload information available in the literature has, for better or worse, heretofore largely been provided by a single datacenter operator whose use cases may not be widespread. In this work, we report upon the network traffic observed in some of Facebook's datacenters. While Facebook operates a number of traditional datacenter services like Hadoop, its core Web service and supporting cache infrastructure exhibit a number of behaviors that contrast with those reported in the literature. We report on the contrasting locality, stability, and predictability of network traffic in Facebook's datacenters, and comment on their implications for network architecture, traffic engineering, and switch design.","PeriodicalId":268472,"journal":{"name":"Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132939556","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":"Session details: Wide Area Networks and Traffic","authors":"A. Feldmann","doi":"10.1145/3261009","DOIUrl":"https://doi.org/10.1145/3261009","url":null,"abstract":"","PeriodicalId":268472,"journal":{"name":"Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128432328","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}
Matthew K. Mukerjee, David Naylor, Junchen Jiang, Dongsu Han, S. Seshan, Hui Zhang
Live video delivery is expected to reach a peak of 50 Tbps this year. This surging popularity is fundamentally changing the Internet video delivery landscape. CDNs must meet users' demands for fast join times, high bitrates, and low buffering ratios, while minimizing their own cost of delivery and responding to issues in real-time. Wide-area latency, loss, and failures, as well as varied workloads ("mega-events" to long-tail), make meeting these demands challenging. An analysis of video sessions concluded that a centralized controller could improve user experience, but CDN systems have shied away from such designs due to the difficulty of quickly handling failures, a requirement of both operators and users. We introduce VDN, a practical approach to a video delivery network that uses a centralized algorithm for live video optimization. VDN provides CDN operators with real-time, fine-grained control. It does this in spite of challenges resulting from the wide-area (e.g., state inconsistency, partitions, failures) by using a hybrid centralized+distributed control plane, increasing average bitrate by 1.7x and decreasing cost by 2x in different scenarios.
{"title":"Practical, Real-time Centralized Control for CDN-based Live Video Delivery","authors":"Matthew K. Mukerjee, David Naylor, Junchen Jiang, Dongsu Han, S. Seshan, Hui Zhang","doi":"10.1145/2785956.2787475","DOIUrl":"https://doi.org/10.1145/2785956.2787475","url":null,"abstract":"Live video delivery is expected to reach a peak of 50 Tbps this year. This surging popularity is fundamentally changing the Internet video delivery landscape. CDNs must meet users' demands for fast join times, high bitrates, and low buffering ratios, while minimizing their own cost of delivery and responding to issues in real-time. Wide-area latency, loss, and failures, as well as varied workloads (\"mega-events\" to long-tail), make meeting these demands challenging. An analysis of video sessions concluded that a centralized controller could improve user experience, but CDN systems have shied away from such designs due to the difficulty of quickly handling failures, a requirement of both operators and users. We introduce VDN, a practical approach to a video delivery network that uses a centralized algorithm for live video optimization. VDN provides CDN operators with real-time, fine-grained control. It does this in spite of challenges resulting from the wide-area (e.g., state inconsistency, partitions, failures) by using a hybrid centralized+distributed control plane, increasing average bitrate by 1.7x and decreasing cost by 2x in different scenarios.","PeriodicalId":268472,"journal":{"name":"Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133342128","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}
S. Biswas, John C. Bicket, Edmund Wong, Raluca Musaloiu-E, Apurv Bhartia, Daniel Aguayo
Meraki is a cloud-based network management system which provides centralized configuration, monitoring, and network troubleshooting tools across hundreds of thousands of sites worldwide. As part of its architecture, the Meraki system has built a database of time-series measurements of wireless link, client, and application behavior for monitoring and debugging purposes. This paper studies an anonymized subset of measurements, containing data from approximately ten thousand radio access points, tens of thousands of links, and 5.6 million clients from one-week periods in January 2014 and January 2015 to provide a deeper understanding of real-world network behavior. This paper observes the following phenomena: wireless network usage continues to grow quickly, driven most by growth in the number of devices connecting to each network. Intermediate link delivery rates are common indoors across a wide range of deployment environments. Typical access points share spectrum with dozens of nearby networks, but the presence of a network on a channel does not predict channel utilization. Most access points see 2.4 GHz channel utilization of 20% or more, with the top decile seeing greater than 50%, and the majority of the channel use contains decodable 802.11 headers.
{"title":"Large-scale Measurements of Wireless Network Behavior","authors":"S. Biswas, John C. Bicket, Edmund Wong, Raluca Musaloiu-E, Apurv Bhartia, Daniel Aguayo","doi":"10.1145/2785956.2787489","DOIUrl":"https://doi.org/10.1145/2785956.2787489","url":null,"abstract":"Meraki is a cloud-based network management system which provides centralized configuration, monitoring, and network troubleshooting tools across hundreds of thousands of sites worldwide. As part of its architecture, the Meraki system has built a database of time-series measurements of wireless link, client, and application behavior for monitoring and debugging purposes. This paper studies an anonymized subset of measurements, containing data from approximately ten thousand radio access points, tens of thousands of links, and 5.6 million clients from one-week periods in January 2014 and January 2015 to provide a deeper understanding of real-world network behavior. This paper observes the following phenomena: wireless network usage continues to grow quickly, driven most by growth in the number of devices connecting to each network. Intermediate link delivery rates are common indoors across a wide range of deployment environments. Typical access points share spectrum with dozens of nearby networks, but the presence of a network on a channel does not predict channel utilization. Most access points see 2.4 GHz channel utilization of 20% or more, with the top decile seeing greater than 50%, and the majority of the channel use contains decodable 802.11 headers.","PeriodicalId":268472,"journal":{"name":"Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121789618","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}
Manikanta Kotaru, K. Joshi, Dinesh Bharadia, S. Katti
This paper presents the design and implementation of SpotFi, an accurate indoor localization system that can be deployed on commodity WiFi infrastructure. SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems. SpotFi makes two key technical contributions. First, SpotFi incorporates super-resolution algorithms that can accurately compute the angle of arrival (AoA) of multipath components even when the access point (AP) has only three antennas. Second, it incorporates novel filtering and estimation techniques to identify AoA of direct path between the localization target and AP by assigning values for each path depending on how likely the particular path is the direct path. Our experiments in a multipath rich indoor environment show that SpotFi achieves a median accuracy of 40 cm and is robust to indoor hindrances such as obstacles and multipath.
{"title":"SpotFi: Decimeter Level Localization Using WiFi","authors":"Manikanta Kotaru, K. Joshi, Dinesh Bharadia, S. Katti","doi":"10.1145/2785956.2787487","DOIUrl":"https://doi.org/10.1145/2785956.2787487","url":null,"abstract":"This paper presents the design and implementation of SpotFi, an accurate indoor localization system that can be deployed on commodity WiFi infrastructure. SpotFi only uses information that is already exposed by WiFi chips and does not require any hardware or firmware changes, yet achieves the same accuracy as state-of-the-art localization systems. SpotFi makes two key technical contributions. First, SpotFi incorporates super-resolution algorithms that can accurately compute the angle of arrival (AoA) of multipath components even when the access point (AP) has only three antennas. Second, it incorporates novel filtering and estimation techniques to identify AoA of direct path between the localization target and AP by assigning values for each path depending on how likely the particular path is the direct path. Our experiments in a multipath rich indoor environment show that SpotFi achieves a median accuracy of 40 cm and is robust to indoor hindrances such as obstacles and multipath.","PeriodicalId":268472,"journal":{"name":"Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122600760","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}
Balázs Sonkoly, János Czentye, R. Szabó, Dávid Jocha, János Elek, Sahel Sahhaf, W. Tavernier, Fulvio Risso
End-to-end service delivery often includes transparently inserted Network Functions (NFs) in the path. Flexible service chaining will require dynamic instantiation of both NFs and traffic forwarding overlays. Virtualization techniques in compute and networking, like cloud and Software Defined Networking (SDN), promise such flexibility for service providers. However, patching together existing cloud and network control mechanisms necessarily puts one over the above, e.g., OpenDaylight under an OpenStack controller. We designed and implemented a joint cloud and network resource virtualization and programming API. In this demonstration, we show that our abstraction is capable for flexible service chaining control over any technology domains.
{"title":"Multi-Domain Service Orchestration Over Networks and Clouds: A Unified Approach","authors":"Balázs Sonkoly, János Czentye, R. Szabó, Dávid Jocha, János Elek, Sahel Sahhaf, W. Tavernier, Fulvio Risso","doi":"10.1145/2785956.2790041","DOIUrl":"https://doi.org/10.1145/2785956.2790041","url":null,"abstract":"End-to-end service delivery often includes transparently inserted Network Functions (NFs) in the path. Flexible service chaining will require dynamic instantiation of both NFs and traffic forwarding overlays. Virtualization techniques in compute and networking, like cloud and Software Defined Networking (SDN), promise such flexibility for service providers. However, patching together existing cloud and network control mechanisms necessarily puts one over the above, e.g., OpenDaylight under an OpenStack controller. We designed and implemented a joint cloud and network resource virtualization and programming API. In this demonstration, we show that our abstraction is capable for flexible service chaining control over any technology domains.","PeriodicalId":268472,"journal":{"name":"Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication","volume":"42 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123408550","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":"Session details: Network Algorithmics and Economics","authors":"L. Rizzo","doi":"10.1145/3260999","DOIUrl":"https://doi.org/10.1145/3260999","url":null,"abstract":"","PeriodicalId":268472,"journal":{"name":"Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125349736","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}
The need for fault tolerance and scalability is leading to the development of distributed SDN operating systems and applications. But how can you develop such systems and applications reliably without access to an expensive testbed? We continue to observe SDN development practices using full system virtualization or heavyweight containers, increasing complexity and overhead while decreasing usability. We demonstrate a simpler and more efficient approach: using Mininet's cluster mode to easily deploy a virtual testbed of lightweight containers on a single machine, an ad hoc cluster, or a dedicated hardware testbed. By adding an open source, distributed network operating system such as ONOS, we can create a flexible and scalable open source development platform for distributed SDN system and application software development.
{"title":"A Mininet-based Virtual Testbed for Distributed SDN Development","authors":"Bob Lantz, Brian O'Connor","doi":"10.1145/2785956.2790030","DOIUrl":"https://doi.org/10.1145/2785956.2790030","url":null,"abstract":"The need for fault tolerance and scalability is leading to the development of distributed SDN operating systems and applications. But how can you develop such systems and applications reliably without access to an expensive testbed? We continue to observe SDN development practices using full system virtualization or heavyweight containers, increasing complexity and overhead while decreasing usability. We demonstrate a simpler and more efficient approach: using Mininet's cluster mode to easily deploy a virtual testbed of lightweight containers on a single machine, an ad hoc cluster, or a dedicated hardware testbed. By adding an open source, distributed network operating system such as ONOS, we can create a flexible and scalable open source development platform for distributed SDN system and application software development.","PeriodicalId":268472,"journal":{"name":"Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123265243","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}
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