Pub Date : 2012-06-04DOI: 10.1109/IWQoS.2012.6245990
Zhenhua Li, Tieying Zhang, Yan Huang, Zhi-Li Zhang, Yafei Dai
Hybrid cloud-P2P content distribution (“CloudP2P”) provides a promising alternative to the conventional cloud-based or peer-to-peer (P2P)-based large-scale content distribution. It addresses the potential limitations of these two conventional approaches while inheriting their advantages. A key strength of CloudP2P lies in the so-called bandwidth multiplier effect: by appropriately allocating a small portion of cloud (server) bandwidth Si to a peer swarm i (consisting of users interested in the same content) to seed the content, the users in the peer swarm - with an aggregate download bandwidth Di - can then distribute the content among themselves; we refer to the ratio Di/Si as the bandwidth multiplier (for peer swarm i). A major problem in the design of a CloudP2P content distribution system is therefore how to allocate cloud (server) bandwidth to peer swarms so as to maximize the overall bandwidth multiplier effect of the system. In this paper, using real-world measurements, we identify the key factors that affect the bandwidth multipliers of peer swarms and thus construct a fine-grained performance model for addressing the optimal bandwidth allocation problem (OBAP). Then we develop a fast-convergent iterative algorithm to solve OBAP. Both trace-driven simulations and prototype implementation confirm the efficacy of our solution.
{"title":"Maximizing the bandwidth multiplier effect for hybrid cloud-P2P content distribution","authors":"Zhenhua Li, Tieying Zhang, Yan Huang, Zhi-Li Zhang, Yafei Dai","doi":"10.1109/IWQoS.2012.6245990","DOIUrl":"https://doi.org/10.1109/IWQoS.2012.6245990","url":null,"abstract":"Hybrid cloud-P2P content distribution (“CloudP2P”) provides a promising alternative to the conventional cloud-based or peer-to-peer (P2P)-based large-scale content distribution. It addresses the potential limitations of these two conventional approaches while inheriting their advantages. A key strength of CloudP2P lies in the so-called bandwidth multiplier effect: by appropriately allocating a small portion of cloud (server) bandwidth Si to a peer swarm i (consisting of users interested in the same content) to seed the content, the users in the peer swarm - with an aggregate download bandwidth Di - can then distribute the content among themselves; we refer to the ratio Di/Si as the bandwidth multiplier (for peer swarm i). A major problem in the design of a CloudP2P content distribution system is therefore how to allocate cloud (server) bandwidth to peer swarms so as to maximize the overall bandwidth multiplier effect of the system. In this paper, using real-world measurements, we identify the key factors that affect the bandwidth multipliers of peer swarms and thus construct a fine-grained performance model for addressing the optimal bandwidth allocation problem (OBAP). Then we develop a fast-convergent iterative algorithm to solve OBAP. Both trace-driven simulations and prototype implementation confirm the efficacy of our solution.","PeriodicalId":178333,"journal":{"name":"2012 IEEE 20th International Workshop on Quality of Service","volume":"19 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125914468","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}
In explicit or delay-driven congestion control, a common objective is to sustain high throughput without long queues and large losses at the bottleneck link of the network path. Congestion control protocols strive to achieve this goal by transmitting smoothly in the steady state. The discovery of the appropriate steady-state transmission rates is a challenging task in itself and typically introduces additional queuing and losses. Seeking insights into the steady-state profiles of queuing and loss achievable by real protocols, this paper presents an AIST (Asynchronous arrivals with Ideally Smooth Transmission) model that abstracts away transient queuing and losses related to discovering the path capacity and redistributing it fairly among the packet flows on the bottleneck link. In AIST, the flows arrive asynchronously but transmit their packets at the same constant rate in the steady state. For the link with an overprovisioned buffer, our queuing-theoretic analysis and simulations for different smooth distributions of packet interarrival times agree that queuing under AIST with the target utilization of 1 is on the order of the square root of N, where N is the number of flows. With small buffers, our simulations of AIST show an ability to provide bounded loss rates regardless of the number of flows.
{"title":"AIST: Insights into queuing and loss on highly multiplexed links","authors":"Maxim Podlesny, Sergey Gorinsky, Balaji Rengarajan","doi":"10.1109/IWQoS.2012.6245974","DOIUrl":"https://doi.org/10.1109/IWQoS.2012.6245974","url":null,"abstract":"In explicit or delay-driven congestion control, a common objective is to sustain high throughput without long queues and large losses at the bottleneck link of the network path. Congestion control protocols strive to achieve this goal by transmitting smoothly in the steady state. The discovery of the appropriate steady-state transmission rates is a challenging task in itself and typically introduces additional queuing and losses. Seeking insights into the steady-state profiles of queuing and loss achievable by real protocols, this paper presents an AIST (Asynchronous arrivals with Ideally Smooth Transmission) model that abstracts away transient queuing and losses related to discovering the path capacity and redistributing it fairly among the packet flows on the bottleneck link. In AIST, the flows arrive asynchronously but transmit their packets at the same constant rate in the steady state. For the link with an overprovisioned buffer, our queuing-theoretic analysis and simulations for different smooth distributions of packet interarrival times agree that queuing under AIST with the target utilization of 1 is on the order of the square root of N, where N is the number of flows. With small buffers, our simulations of AIST show an ability to provide bounded loss rates regardless of the number of flows.","PeriodicalId":178333,"journal":{"name":"2012 IEEE 20th International Workshop on Quality of Service","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117214490","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}
Pub Date : 2012-06-04DOI: 10.1109/IWQoS.2012.6245978
Tong Yang, Bo Yuan, Shenjiang Zhang, Ting Zhang, Ruian Duan, Yi Wang, B. Liu
With the fast development of Internet, the size of routing tables in the backbone routers keeps a rapid growth in recent years. An effective solution to control the memory occupation of the ever-increased huge routing table is the Forwarding Information Base (FIB) compression. Existing optimal FIB compression algorithm ORTC suffers from high computational complexity and poor update performance, due to the loss of essential structure information during its compression process. To address this problem, we present two suboptimal FIB compression algorithms - EAR-fast and EAR-slow, respectively, based on our proposed Election and Representative (EAR) algorithm which is an optimal FIB compression algorithm. The two suboptimal algorithms preserve the structure information, and support fast incremental updates while reducing computational complexity. Experiments on an 18-month real data set show that compared with ORTC, the proposed EAR-fast algorithm requires only 9.8% compression time and 37.7% memory space, but supports faster update while prolonging the recompression interval remarkably. All these performance advantages come at a cost of merely a 1.5% loss in compression ratio compared with the theoretical optimal ratio.
近年来，随着互联网的快速发展，骨干路由器的路由表规模保持了快速增长。对于日益增长的庞大路由表，控制其内存占用的有效解决方案是转发信息库(Forwarding Information Base, FIB)压缩。现有最优FIB压缩算法ORTC由于在压缩过程中丢失了基本的结构信息，计算量大，更新性能差。为了解决这个问题，我们提出了两个次优的FIB压缩算法- EAR-fast和EAR-slow，分别基于我们提出的选举和代表(EAR)算法是最优的FIB压缩算法。这两种次优算法保留了结构信息，支持快速增量更新，同时降低了计算复杂度。在18个月的真实数据集上进行的实验表明，与ORTC相比，本文提出的ear快速算法只需要9.8%的压缩时间和37.7%的内存空间，但支持更快的更新，并且显著延长了重压缩间隔。所有这些性能优势的代价是压缩比与理论最佳比相比仅损失1.5%。
{"title":"Approaching optimal compression with fast update for large scale routing tables","authors":"Tong Yang, Bo Yuan, Shenjiang Zhang, Ting Zhang, Ruian Duan, Yi Wang, B. Liu","doi":"10.1109/IWQoS.2012.6245978","DOIUrl":"https://doi.org/10.1109/IWQoS.2012.6245978","url":null,"abstract":"With the fast development of Internet, the size of routing tables in the backbone routers keeps a rapid growth in recent years. An effective solution to control the memory occupation of the ever-increased huge routing table is the Forwarding Information Base (FIB) compression. Existing optimal FIB compression algorithm ORTC suffers from high computational complexity and poor update performance, due to the loss of essential structure information during its compression process. To address this problem, we present two suboptimal FIB compression algorithms - EAR-fast and EAR-slow, respectively, based on our proposed Election and Representative (EAR) algorithm which is an optimal FIB compression algorithm. The two suboptimal algorithms preserve the structure information, and support fast incremental updates while reducing computational complexity. Experiments on an 18-month real data set show that compared with ORTC, the proposed EAR-fast algorithm requires only 9.8% compression time and 37.7% memory space, but supports faster update while prolonging the recompression interval remarkably. All these performance advantages come at a cost of merely a 1.5% loss in compression ratio compared with the theoretical optimal ratio.","PeriodicalId":178333,"journal":{"name":"2012 IEEE 20th International Workshop on Quality of Service","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123614490","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}
Pub Date : 2012-06-04DOI: 10.1109/IWQoS.2012.6245966
J. Gross
The concept of the effective service capacity is an analytical framework for evaluating QoS-constrained queuing performance of communication systems. Recently, it has been applied to the analysis of different wireless systems like point-to-point systems or multi-user systems. In contrast to previous work, we consider in this work slot-based systems where a scheduler determines a packet size to be transmitted at the beginning of the slot. For this, the scheduler can utilize outdated channel state information. Based on a threshold error model, we derive the effective service capacity for different scheduling strategies that the scheduler might apply. We show that even slightly outdated channel state information leads to a significant loss in capacity in comparison to an ideal system with perfect channel state information available at the transmitter. This loss depends on the `risk-level' the scheduler is willing to take which is represented by an SNR margin. We show that for any QoS target and average link state there exists an optimal SNR margin improving the maximum sustainable rate. Typically, this SNR margin is around 3 dB but is sensible to the QoS target and average link quality. Finally, we can also show that adapting to the instantaneous channel state only pays off if the correlation between the channel estimate and the channel state is relatively high (with a coefficient above 0.9).
{"title":"Scheduling with outdated CSI: Effective service capacities of optimistic vs. pessimistic policies","authors":"J. Gross","doi":"10.1109/IWQoS.2012.6245966","DOIUrl":"https://doi.org/10.1109/IWQoS.2012.6245966","url":null,"abstract":"The concept of the effective service capacity is an analytical framework for evaluating QoS-constrained queuing performance of communication systems. Recently, it has been applied to the analysis of different wireless systems like point-to-point systems or multi-user systems. In contrast to previous work, we consider in this work slot-based systems where a scheduler determines a packet size to be transmitted at the beginning of the slot. For this, the scheduler can utilize outdated channel state information. Based on a threshold error model, we derive the effective service capacity for different scheduling strategies that the scheduler might apply. We show that even slightly outdated channel state information leads to a significant loss in capacity in comparison to an ideal system with perfect channel state information available at the transmitter. This loss depends on the `risk-level' the scheduler is willing to take which is represented by an SNR margin. We show that for any QoS target and average link state there exists an optimal SNR margin improving the maximum sustainable rate. Typically, this SNR margin is around 3 dB but is sensible to the QoS target and average link quality. Finally, we can also show that adapting to the instantaneous channel state only pays off if the correlation between the channel estimate and the channel state is relatively high (with a coefficient above 0.9).","PeriodicalId":178333,"journal":{"name":"2012 IEEE 20th International Workshop on Quality of Service","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131597571","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}
Pub Date : 2012-06-04DOI: 10.1109/IWQoS.2012.6245977
T. Yamazaki, T. Miyoshi, Masato Eguchi, K. Yamori
Both of Quality of Service (QoS) and Quality of Experience (QoE) are defined to specify the degree of service quality. Although they are dealt with in different layers in multi-layered models, collaboration of these is necessary to improve the user satisfaction for telecommunication services. In this paper, after sorting out the concepts and specification of QoS and QoE, a service quality coordination model combining these is proposed. The model is applied to a video-sharing service and its coordination model is derived based on subjective experiments. The structural equation modeling is used to compute the user satisfaction from QoS and QoE.
服务质量(Quality of Service, QoS)和体验质量(Quality of Experience, QoE)都是用来表示服务质量的程度。尽管在多层模型中它们是在不同的层中处理的，但为了提高用户对电信服务的满意度，它们之间的协作是必要的。本文在对QoS和QoE的概念和规范进行梳理后，提出了一种结合两者的服务质量协调模型。将该模型应用于视频分享服务，并在主观实验的基础上推导出其协调模型。采用结构方程模型从QoS和QoE两方面计算用户满意度。
{"title":"A service quality coordination model bridging QoS and QoE","authors":"T. Yamazaki, T. Miyoshi, Masato Eguchi, K. Yamori","doi":"10.1109/IWQoS.2012.6245977","DOIUrl":"https://doi.org/10.1109/IWQoS.2012.6245977","url":null,"abstract":"Both of Quality of Service (QoS) and Quality of Experience (QoE) are defined to specify the degree of service quality. Although they are dealt with in different layers in multi-layered models, collaboration of these is necessary to improve the user satisfaction for telecommunication services. In this paper, after sorting out the concepts and specification of QoS and QoE, a service quality coordination model combining these is proposed. The model is applied to a video-sharing service and its coordination model is derived based on subjective experiments. The structural equation modeling is used to compute the user satisfaction from QoS and QoE.","PeriodicalId":178333,"journal":{"name":"2012 IEEE 20th International Workshop on Quality of Service","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122032597","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}
Pub Date : 2012-06-04DOI: 10.1109/IWQoS.2012.6245989
Chengdi Lai, Ka-Cheong Leung, V. Li
In order to maintain a small, stable backlog at the router buffer, active queue management (AQM) algorithms drop packets probabilistically at the onset of congestion, leading to backoffs by Transmission Control Protocol (TCP) flows. However, wireless losses may be misinterpreted as congestive losses and induce spurious backoffs. In this paper, we raise the basic question: Can AQM maintain a stable, small backlog under wireless losses? We find that the representative AQM, random early detection (RED), fails to maintain a stable backlog under time-varying wireless losses. We find that the key to resolving the problem is to robustly track the backlog to a preset reference level, and apply the control-theoretic vehicle, internal model principle, to realize such tracking. We further devise the integral controller (IC) as an embodiment of the principle. Our simulation results show that IC is robust against time-varying wireless losses under various network scenarios.
{"title":"Enhancing AQM to combat wireless losses","authors":"Chengdi Lai, Ka-Cheong Leung, V. Li","doi":"10.1109/IWQoS.2012.6245989","DOIUrl":"https://doi.org/10.1109/IWQoS.2012.6245989","url":null,"abstract":"In order to maintain a small, stable backlog at the router buffer, active queue management (AQM) algorithms drop packets probabilistically at the onset of congestion, leading to backoffs by Transmission Control Protocol (TCP) flows. However, wireless losses may be misinterpreted as congestive losses and induce spurious backoffs. In this paper, we raise the basic question: Can AQM maintain a stable, small backlog under wireless losses? We find that the representative AQM, random early detection (RED), fails to maintain a stable backlog under time-varying wireless losses. We find that the key to resolving the problem is to robustly track the backlog to a preset reference level, and apply the control-theoretic vehicle, internal model principle, to realize such tracking. We further devise the integral controller (IC) as an embodiment of the principle. Our simulation results show that IC is robust against time-varying wireless losses under various network scenarios.","PeriodicalId":178333,"journal":{"name":"2012 IEEE 20th International Workshop on Quality of Service","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123994958","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}
Pub Date : 2012-06-04DOI: 10.1109/IWQoS.2012.6245995
A. Tam, Kang Xi, Yang Xu, H. Jonathan Chao
Incast applications have grown in popularity with the advancement of data center technology. It is found that the TCP incast may suffer from the throughput collapse problem, as a consequence of TCP retransmission timeouts when the bottleneck buffer is overwhelmed and causes the packet losses. This is critical to the Quality of Service of cloud computing applications. While some previous literature has proposed solutions, we still see the problem not completely solved. In this paper, we investigate the three root causes for the poor performance of TCP incast flows and propose three solutions, one for each at the beginning, the middle and the end of a TCP connection. The three solutions are: admission control to TCP flows so that the flow population would not exceed the network's capacity; retransmission based on timestamp to detect loss of retransmitted packets; and reiterated FIN packets to keep the TCP connection active until the the termination of a session is acknowledged. The orchestration of these solutions prevents the throughput collapse. The main idea of these solutions is to ensure all the on-going TCP incast flows can maintain the self-clocking, thus eliminates the need to resort to retransmission timeout for recovery. We evaluate these solutions and find them work well in preventing the retransmission timeout of TCP incast flows, hence also preventing the throughput collapse.
{"title":"Preventing TCP incast throughput collapse at the initiation, continuation, and termination","authors":"A. Tam, Kang Xi, Yang Xu, H. Jonathan Chao","doi":"10.1109/IWQoS.2012.6245995","DOIUrl":"https://doi.org/10.1109/IWQoS.2012.6245995","url":null,"abstract":"Incast applications have grown in popularity with the advancement of data center technology. It is found that the TCP incast may suffer from the throughput collapse problem, as a consequence of TCP retransmission timeouts when the bottleneck buffer is overwhelmed and causes the packet losses. This is critical to the Quality of Service of cloud computing applications. While some previous literature has proposed solutions, we still see the problem not completely solved. In this paper, we investigate the three root causes for the poor performance of TCP incast flows and propose three solutions, one for each at the beginning, the middle and the end of a TCP connection. The three solutions are: admission control to TCP flows so that the flow population would not exceed the network's capacity; retransmission based on timestamp to detect loss of retransmitted packets; and reiterated FIN packets to keep the TCP connection active until the the termination of a session is acknowledged. The orchestration of these solutions prevents the throughput collapse. The main idea of these solutions is to ensure all the on-going TCP incast flows can maintain the self-clocking, thus eliminates the need to resort to retransmission timeout for recovery. We evaluate these solutions and find them work well in preventing the retransmission timeout of TCP incast flows, hence also preventing the throughput collapse.","PeriodicalId":178333,"journal":{"name":"2012 IEEE 20th International Workshop on Quality of Service","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124469733","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}
Pub Date : 2012-06-04DOI: 10.1109/IWQoS.2012.6245964
Cong Ding, Yang Chen, Tianyin Xu, Xiaoming Fu
In order to minimize user perceived latency while ensuring high data availability, cloud applications desire to select servers from one of the multiple data centers (i.e., server clusters) in different geographical locations, which are able to provide desired services with low latency and low cost. This paper presents CloudGPS, a new server selection scheme of the cloud computing environment that achieves high scalability and ISP-friendliness. CloudGPS proposes a configurable global performance function that allows Internet service providers (ISPs) and cloud service providers (CSPs) to leverage the cost in terms of inter-domain transit traffic and the quality of service in terms of network latency. CloudGPS bounds the overall burden to be linear with the number of end users. Moreover, compared with traditional approaches, CloudGPS significantly reduces network distance measurement cost (i.e., from O(N) to O(1) for each end user in an application using N data centers). Furthermore, CloudGPS achieves ISP-friendliness by significantly decreasing inter-domain transit traffic.
{"title":"CloudGPS: A scalable and ISP-friendly server selection scheme in cloud computing environments","authors":"Cong Ding, Yang Chen, Tianyin Xu, Xiaoming Fu","doi":"10.1109/IWQoS.2012.6245964","DOIUrl":"https://doi.org/10.1109/IWQoS.2012.6245964","url":null,"abstract":"In order to minimize user perceived latency while ensuring high data availability, cloud applications desire to select servers from one of the multiple data centers (i.e., server clusters) in different geographical locations, which are able to provide desired services with low latency and low cost. This paper presents CloudGPS, a new server selection scheme of the cloud computing environment that achieves high scalability and ISP-friendliness. CloudGPS proposes a configurable global performance function that allows Internet service providers (ISPs) and cloud service providers (CSPs) to leverage the cost in terms of inter-domain transit traffic and the quality of service in terms of network latency. CloudGPS bounds the overall burden to be linear with the number of end users. Moreover, compared with traditional approaches, CloudGPS significantly reduces network distance measurement cost (i.e., from O(N) to O(1) for each end user in an application using N data centers). Furthermore, CloudGPS achieves ISP-friendliness by significantly decreasing inter-domain transit traffic.","PeriodicalId":178333,"journal":{"name":"2012 IEEE 20th International Workshop on Quality of Service","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126046747","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}
Pub Date : 2012-06-04DOI: 10.1109/IWQoS.2012.6245965
Min Ding, V. Singh, Yueping Zhang, Guofei Jiang
Driven by the large-scale growth of applications deployment in data centers and complicated interactions between service components, automated application dependency discovery becomes essential to daily system management and operation. In this paper, we present ADD, which extracts dependency paths for each application by decomposing the application-layer connectivity graph inferred from passive network monitoring data. ADD utilizes a series of statistical techniques and is based on the combination of global observation of application traffic matrix in the data center and local observation of traffic volumes at small time scales on each server. Compared to existing approaches, ADD is especially effective in the presence of overlapping and multi-hop applications and resilient to data loss and estimation errors.
{"title":"Application dependency discovery using matrix factorization","authors":"Min Ding, V. Singh, Yueping Zhang, Guofei Jiang","doi":"10.1109/IWQoS.2012.6245965","DOIUrl":"https://doi.org/10.1109/IWQoS.2012.6245965","url":null,"abstract":"Driven by the large-scale growth of applications deployment in data centers and complicated interactions between service components, automated application dependency discovery becomes essential to daily system management and operation. In this paper, we present ADD, which extracts dependency paths for each application by decomposing the application-layer connectivity graph inferred from passive network monitoring data. ADD utilizes a series of statistical techniques and is based on the combination of global observation of application traffic matrix in the data center and local observation of traffic volumes at small time scales on each server. Compared to existing approaches, ADD is especially effective in the presence of overlapping and multi-hop applications and resilient to data loss and estimation errors.","PeriodicalId":178333,"journal":{"name":"2012 IEEE 20th International Workshop on Quality of Service","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132629603","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}
Pub Date : 2012-06-04DOI: 10.1109/IWQoS.2012.6245987
Rafael Huysegems, Bart De Vleeschauwer, Koen De Schepper, Chris Hawinkel, Tingyao Wu, K. Laevens, W. V. Leekwijck
HTTP Adaptive Streaming (HAS) is rapidly becoming a key video delivery technology for fixed and mobile networks. However, today there is no solution that allows network operators or CDN providers to perform network-based QoE monitoring for HAS sessions. We present a HAS QoE monitoring system, based on data collected in the network, without monitoring information from the client. To retrieve the major QoE parameters such as average quality, quality variation, rebuffering events and interactivity delay, we propose a technique called session reconstruction. We define a number of iterative steps and developed algorithms that can be used to perform HAS session reconstruction. Finally, we present the results of a working prototype for the reconstruction and monitoring of Microsoft Smooth Streaming HAS sessions that is capable of dealing with intermediate caching and user interactivity. We describe the main observations when using the platform to analyze more than a hundred HAS sessions.
{"title":"Session reconstruction for HTTP adaptive streaming: Laying the foundation for network-based QoE monitoring","authors":"Rafael Huysegems, Bart De Vleeschauwer, Koen De Schepper, Chris Hawinkel, Tingyao Wu, K. Laevens, W. V. Leekwijck","doi":"10.1109/IWQoS.2012.6245987","DOIUrl":"https://doi.org/10.1109/IWQoS.2012.6245987","url":null,"abstract":"HTTP Adaptive Streaming (HAS) is rapidly becoming a key video delivery technology for fixed and mobile networks. However, today there is no solution that allows network operators or CDN providers to perform network-based QoE monitoring for HAS sessions. We present a HAS QoE monitoring system, based on data collected in the network, without monitoring information from the client. To retrieve the major QoE parameters such as average quality, quality variation, rebuffering events and interactivity delay, we propose a technique called session reconstruction. We define a number of iterative steps and developed algorithms that can be used to perform HAS session reconstruction. Finally, we present the results of a working prototype for the reconstruction and monitoring of Microsoft Smooth Streaming HAS sessions that is capable of dealing with intermediate caching and user interactivity. We describe the main observations when using the platform to analyze more than a hundred HAS sessions.","PeriodicalId":178333,"journal":{"name":"2012 IEEE 20th International Workshop on Quality of Service","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114443497","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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