Pub Date : 2017-06-14DOI: 10.1109/IWQoS.2017.7969117
K. Gao, Qiao Xiang, Xin Wang, Y. Yang, J. Bi
As many applications today migrate to distributed computing and cloud platforms, their user experience depends heavily on network performance. Software Defined Networking (SDN) makes it possible to obtain a global view of the network, introducing the new paradigm of developing adaptive applications with network views. A naive approach of realizing the paradigm, such as distributing the whole network view to applications, is not practical due to scalability and privacy concerns. Existing approaches providing network abstractions are limited to special cases, such as bottlenecks exist only at networks edges, resulting in potentially suboptimal or infeasible decisions. In this paper, we introduce a novel, on-demand network abstraction service that provides an abstract network view supporting not only accurate end-to-end QoS metrics, which satisfy the requirements of many peer-to-peer applications, but also multi-flow correlation, which is essential for bandwidth-sensitive applications containing many flows to conduct global network optimization. We prove that our abstract view is equivalent to the original network view, in the sense that applications can make the same optimal decision as with the complete information. Our evaluations demonstrate that the abstraction guarantees feasibility and optimality for network optimizations and protects the network service providers' privacy. Our evaluations also show that the service can be implemented efficiently; for example, for an extreme large network with 30,000 links and abstraction requests containing 3,000 flows, an abstract network view can be computed in less than one second.
{"title":"NOVA: Towards on-demand equivalent network view abstraction for network optimization","authors":"K. Gao, Qiao Xiang, Xin Wang, Y. Yang, J. Bi","doi":"10.1109/IWQoS.2017.7969117","DOIUrl":"https://doi.org/10.1109/IWQoS.2017.7969117","url":null,"abstract":"As many applications today migrate to distributed computing and cloud platforms, their user experience depends heavily on network performance. Software Defined Networking (SDN) makes it possible to obtain a global view of the network, introducing the new paradigm of developing adaptive applications with network views. A naive approach of realizing the paradigm, such as distributing the whole network view to applications, is not practical due to scalability and privacy concerns. Existing approaches providing network abstractions are limited to special cases, such as bottlenecks exist only at networks edges, resulting in potentially suboptimal or infeasible decisions. In this paper, we introduce a novel, on-demand network abstraction service that provides an abstract network view supporting not only accurate end-to-end QoS metrics, which satisfy the requirements of many peer-to-peer applications, but also multi-flow correlation, which is essential for bandwidth-sensitive applications containing many flows to conduct global network optimization. We prove that our abstract view is equivalent to the original network view, in the sense that applications can make the same optimal decision as with the complete information. Our evaluations demonstrate that the abstraction guarantees feasibility and optimality for network optimizations and protects the network service providers' privacy. Our evaluations also show that the service can be implemented efficiently; for example, for an extreme large network with 30,000 links and abstraction requests containing 3,000 flows, an abstract network view can be computed in less than one second.","PeriodicalId":422861,"journal":{"name":"2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122153113","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 the era of global-scale services, analytical queries are performed on datasets that span multiple data centers (DCs). Due to the scarce and expensive inter-DC bandwidth, various methods have been proposed to reduce either the traffic cost or the completion time for those analytics queries. However, current methods make no attempt to maximize the number of successfully served query requests. Moreover, most of them rely on unrealistic assumptions — such as analytical queries are repeated or known in advance. In this paper, we target at characterizing and optimizing the cost-performance tradeoff for geo-distributed data analytics. Our objectives are two-fold: (1) we minimize the inter-DC traffic cost when serving geo-distributed analytics with uncertain query demand, and (2) we maximize the system throughput, in terms of the number of query requests that can be successfully served with guaranteed queuing delay. To achieve these objectives, we take advantage of Lyapunov optimization techniques to design a two-timescale online control framework. Without prior knowledge of future query requests, this framework makes online decisions on input data placement and admission control of query requests. Extensive trace-driven simulation results demonstrate that our framework is capable of reducing inter-DC traffic cost, improving system throughput and guaranteeing a maximum delay for each query request.
{"title":"Optimizing the cost-performance tradeoff for geo-distributed data analytics with uncertain demand","authors":"Wenxin Li, Renhai Xu, Heng Qi, Keqiu Li, Xiaobo Zhou","doi":"10.1109/IWQoS.2017.7969120","DOIUrl":"https://doi.org/10.1109/IWQoS.2017.7969120","url":null,"abstract":"In the era of global-scale services, analytical queries are performed on datasets that span multiple data centers (DCs). Due to the scarce and expensive inter-DC bandwidth, various methods have been proposed to reduce either the traffic cost or the completion time for those analytics queries. However, current methods make no attempt to maximize the number of successfully served query requests. Moreover, most of them rely on unrealistic assumptions — such as analytical queries are repeated or known in advance. In this paper, we target at characterizing and optimizing the cost-performance tradeoff for geo-distributed data analytics. Our objectives are two-fold: (1) we minimize the inter-DC traffic cost when serving geo-distributed analytics with uncertain query demand, and (2) we maximize the system throughput, in terms of the number of query requests that can be successfully served with guaranteed queuing delay. To achieve these objectives, we take advantage of Lyapunov optimization techniques to design a two-timescale online control framework. Without prior knowledge of future query requests, this framework makes online decisions on input data placement and admission control of query requests. Extensive trace-driven simulation results demonstrate that our framework is capable of reducing inter-DC traffic cost, improving system throughput and guaranteeing a maximum delay for each query request.","PeriodicalId":422861,"journal":{"name":"2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126193719","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 : 2017-06-14DOI: 10.1109/IWQoS.2017.7969170
Joseph D. Beshay, A. T. Nasrabadi, R. Prakash, A. Francini
Applications are typically offered a single, generic option for end-to-end transport, which most commonly consists of the particular flavor of TCP that runs in the server host. This arrangement does not suit well the diversity of the requirements that individual applications pose on network metrics like throughput and delay. Link-Coupled TCP (LCTCP) is a new transport solution that leverages the architectural trends of 5G networks to enable accurate satisfaction of the unique requirements of each application. LCTCP first isolates the 5G access link as the only possible network bottleneck for the application flow, then establishes a lightweight signaling channel between the link buffer and the application server to convey critical information for flexible control of the data source. LCTCP can be deployed in the network without modification of the TCP clients. We use a Linux prototype to demonstrate its feasibility and effectiveness.
{"title":"Link-Coupled TCP for 5G networks","authors":"Joseph D. Beshay, A. T. Nasrabadi, R. Prakash, A. Francini","doi":"10.1109/IWQoS.2017.7969170","DOIUrl":"https://doi.org/10.1109/IWQoS.2017.7969170","url":null,"abstract":"Applications are typically offered a single, generic option for end-to-end transport, which most commonly consists of the particular flavor of TCP that runs in the server host. This arrangement does not suit well the diversity of the requirements that individual applications pose on network metrics like throughput and delay. Link-Coupled TCP (LCTCP) is a new transport solution that leverages the architectural trends of 5G networks to enable accurate satisfaction of the unique requirements of each application. LCTCP first isolates the 5G access link as the only possible network bottleneck for the application flow, then establishes a lightweight signaling channel between the link buffer and the application server to convey critical information for flexible control of the data source. LCTCP can be deployed in the network without modification of the TCP clients. We use a Linux prototype to demonstrate its feasibility and effectiveness.","PeriodicalId":422861,"journal":{"name":"2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129419707","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 : 2017-06-14DOI: 10.1109/IWQoS.2017.7969119
Shuo Wang, Jiao Zhang, Tao Huang, Tian Pan, Jiang Liu, Yun-jie Liu, Jin Li, Feng Li
Data transfer duration accounts for a great proportion of job completion time in big-data systems. To reduce the time spent on data transfer, some traffic scheduling mechanisms at coflow-level are proposed recently. Most of them abstract datacenter networks as an ideal non-blocking big-switch, and the bottleneck is located at egress or ingress ports of end-hosts instead of in networks. Thus, they mainly focus on how to allocate port capacities of end-hosts to jobs without considering in-network congestion. However, link congestion frequently occurs in datacenter networks due to network oversubscription and load imbalance. When link congestion occurs, bottleneck locations will move from the ports of end-hosts to network links. In this paper, we design and implement SkipL, a congestion-aware coflow scheduler which could detect congestion and schedules coflows at end-hosts to effectively reduce coflow completion time. In addition, to be easily deployed in cloud environments, SkipL does not require to control flow routes. SkipL prototype system is implemented in Linux. The results of experiments conducted in a real small testbed and simulations conducted in the flow-level simulator show that SkipL reduces the average Coflow Completion Time(CCT) compared to the per-flow fair sharing scheduling method and Varys.
{"title":"Skipping congestion-links for coflow scheduling","authors":"Shuo Wang, Jiao Zhang, Tao Huang, Tian Pan, Jiang Liu, Yun-jie Liu, Jin Li, Feng Li","doi":"10.1109/IWQoS.2017.7969119","DOIUrl":"https://doi.org/10.1109/IWQoS.2017.7969119","url":null,"abstract":"Data transfer duration accounts for a great proportion of job completion time in big-data systems. To reduce the time spent on data transfer, some traffic scheduling mechanisms at coflow-level are proposed recently. Most of them abstract datacenter networks as an ideal non-blocking big-switch, and the bottleneck is located at egress or ingress ports of end-hosts instead of in networks. Thus, they mainly focus on how to allocate port capacities of end-hosts to jobs without considering in-network congestion. However, link congestion frequently occurs in datacenter networks due to network oversubscription and load imbalance. When link congestion occurs, bottleneck locations will move from the ports of end-hosts to network links. In this paper, we design and implement SkipL, a congestion-aware coflow scheduler which could detect congestion and schedules coflows at end-hosts to effectively reduce coflow completion time. In addition, to be easily deployed in cloud environments, SkipL does not require to control flow routes. SkipL prototype system is implemented in Linux. The results of experiments conducted in a real small testbed and simulations conducted in the flow-level simulator show that SkipL reduces the average Coflow Completion Time(CCT) compared to the per-flow fair sharing scheduling method and Varys.","PeriodicalId":422861,"journal":{"name":"2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127694663","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 : 2017-06-14DOI: 10.1109/IWQoS.2017.7969166
Xincai Fei, Fangming Liu, Hong Xu, Hai Jin
Network functions virtualization (NFV) is increasingly adopted by telecommunications (telecos) service providers for cost savings and flexible management. However, deploying virtual network functions (VNFs) in geo-distributed central offices (COs) is not straightforward. Unlike most existing centralized schemes in clouds, VNFs of a service chain usually need to be deployed in multiple COs due to limited resource capacity and uneven setup cost at various locations. To ensure the Quality of Service of service chains, a key problem for service providers is to determine where a VNF should go, in order to achieve cost-efficiency and load balancing of both computing and bandwidth resources, across all selected COs. To this end, we present a framework of CO Selection (CS) and VNF Assignment (VA) for distributed deployment of NFV. Specifically, we first select a set of COs that minimizes the communication cost among the selected COs. Then, we employ a shadow-routing based approach, which minimizes the maximum of appropriately defined CO utilizations, to jointly solve the VNF-CO and VNF-server assignment problem. Simulations demonstrate the effectiveness of CS algorithm, and asymptotic optimality, scalability and high adaptivity of the VNF assignment approach.
{"title":"Towards load-balanced VNF assignment in geo-distributed NFV Infrastructure","authors":"Xincai Fei, Fangming Liu, Hong Xu, Hai Jin","doi":"10.1109/IWQoS.2017.7969166","DOIUrl":"https://doi.org/10.1109/IWQoS.2017.7969166","url":null,"abstract":"Network functions virtualization (NFV) is increasingly adopted by telecommunications (telecos) service providers for cost savings and flexible management. However, deploying virtual network functions (VNFs) in geo-distributed central offices (COs) is not straightforward. Unlike most existing centralized schemes in clouds, VNFs of a service chain usually need to be deployed in multiple COs due to limited resource capacity and uneven setup cost at various locations. To ensure the Quality of Service of service chains, a key problem for service providers is to determine where a VNF should go, in order to achieve cost-efficiency and load balancing of both computing and bandwidth resources, across all selected COs. To this end, we present a framework of CO Selection (CS) and VNF Assignment (VA) for distributed deployment of NFV. Specifically, we first select a set of COs that minimizes the communication cost among the selected COs. Then, we employ a shadow-routing based approach, which minimizes the maximum of appropriately defined CO utilizations, to jointly solve the VNF-CO and VNF-server assignment problem. Simulations demonstrate the effectiveness of CS algorithm, and asymptotic optimality, scalability and high adaptivity of the VNF assignment approach.","PeriodicalId":422861,"journal":{"name":"2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122341977","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 : 2017-06-14DOI: 10.1109/IWQoS.2017.7969109
Lin Yao, Dong Liu, Xin Wang, Guowei Wu
With the constant increase of social-network data published, the privacy preservation becomes more and more important. Although some literature algorithms apply K-anonymity to the relational data to prevent an adversary from significantly perpetrating privacy breaches, the inappropriate choice of K has a big impact on the quality of privacy protection and data utility. We propose a technique named Relationship Privacy Preservation based on Compressive Sensing (RPPCS) in this paper to anonymize the relationship data of social networks. The network links are randomized from the recovery of the random measurements of the sparse relationship matrix to both preserve the privacy and data utility. Two comprehensive sets of real-world relationship data on social networks are applied to evaluate the performance of our anonymization technique. Our performance evaluations based on Collaboration Network and Gnutella Network demonstrate that our scheme can better preserve the utility of the anonymized data compared to peer schemes. Privacy analysis shows that our scheme can resist the background knowledge attack.
{"title":"Preserving the Relationship Privacy of the published social-network data based on Compressive Sensing","authors":"Lin Yao, Dong Liu, Xin Wang, Guowei Wu","doi":"10.1109/IWQoS.2017.7969109","DOIUrl":"https://doi.org/10.1109/IWQoS.2017.7969109","url":null,"abstract":"With the constant increase of social-network data published, the privacy preservation becomes more and more important. Although some literature algorithms apply K-anonymity to the relational data to prevent an adversary from significantly perpetrating privacy breaches, the inappropriate choice of K has a big impact on the quality of privacy protection and data utility. We propose a technique named Relationship Privacy Preservation based on Compressive Sensing (RPPCS) in this paper to anonymize the relationship data of social networks. The network links are randomized from the recovery of the random measurements of the sparse relationship matrix to both preserve the privacy and data utility. Two comprehensive sets of real-world relationship data on social networks are applied to evaluate the performance of our anonymization technique. Our performance evaluations based on Collaboration Network and Gnutella Network demonstrate that our scheme can better preserve the utility of the anonymized data compared to peer schemes. Privacy analysis shows that our scheme can resist the background knowledge attack.","PeriodicalId":422861,"journal":{"name":"2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116661281","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 : 2017-06-14DOI: 10.1109/IWQoS.2017.7969147
Yang Wang, Zhenyu Li, Gaogang Xie, Kave Salamatian
NFV together with SDN promises to provide more flexible and efficient service provision methods by decoupling the network functions (NFs) from the physical network topology and devices, but requires the real-time and automatic composition and verification for service function chain (SFC). However, most of SFCs today are still typically built through manual configuration processes, which are slow and error prone. In this paper, we present a novel SFC composition framework, called Automatic Composition Toolkit (ACT). It aims to automatically detect the dependencies and conflicts between NFs, so as to compose and verify SFCs before they are enforced on the physical infrastructure.
{"title":"Enabling automatic composition and verification of service function chain","authors":"Yang Wang, Zhenyu Li, Gaogang Xie, Kave Salamatian","doi":"10.1109/IWQoS.2017.7969147","DOIUrl":"https://doi.org/10.1109/IWQoS.2017.7969147","url":null,"abstract":"NFV together with SDN promises to provide more flexible and efficient service provision methods by decoupling the network functions (NFs) from the physical network topology and devices, but requires the real-time and automatic composition and verification for service function chain (SFC). However, most of SFCs today are still typically built through manual configuration processes, which are slow and error prone. In this paper, we present a novel SFC composition framework, called Automatic Composition Toolkit (ACT). It aims to automatically detect the dependencies and conflicts between NFs, so as to compose and verify SFCs before they are enforced on the physical infrastructure.","PeriodicalId":422861,"journal":{"name":"2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116669736","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 : 2017-06-14DOI: 10.1109/IWQoS.2017.7969135
S. Tesfatsion, Luis Tomás, Johan Tordsson
A lack of energy proportionality, low resource utilization, and interference in virtualized infrastructure make the cloud a challenging target environment for improving energy efficiency. In this paper we present OptiBook, a system that improves energy proportionality and/or resource utilization to optimize performance and energy efficiency. OptiBook shares servers between latency-sensitive services and batch jobs, overbooks the system in a controllable manner, uses vertical (CPU and DVFS) scaling for prioritized virtual machines, and applies performance isolation techniques such as CPU pinning and quota enforcement as well as online resource tuning to effectively improve energy efficiency. Our evaluations show that on average, OptiBook improves performance per watt by 20% and reduces energy consumption by 9% while minimizing SLO violations.
{"title":"OptiBook: Optimal resource booking for energy-efficient datacenters","authors":"S. Tesfatsion, Luis Tomás, Johan Tordsson","doi":"10.1109/IWQoS.2017.7969135","DOIUrl":"https://doi.org/10.1109/IWQoS.2017.7969135","url":null,"abstract":"A lack of energy proportionality, low resource utilization, and interference in virtualized infrastructure make the cloud a challenging target environment for improving energy efficiency. In this paper we present OptiBook, a system that improves energy proportionality and/or resource utilization to optimize performance and energy efficiency. OptiBook shares servers between latency-sensitive services and batch jobs, overbooks the system in a controllable manner, uses vertical (CPU and DVFS) scaling for prioritized virtual machines, and applies performance isolation techniques such as CPU pinning and quota enforcement as well as online resource tuning to effectively improve energy efficiency. Our evaluations show that on average, OptiBook improves performance per watt by 20% and reduces energy consumption by 9% while minimizing SLO violations.","PeriodicalId":422861,"journal":{"name":"2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126958054","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 : 2017-06-14DOI: 10.1109/IWQoS.2017.7969112
Yifan Zhang, Yunxin Liu, Xuanzhe Liu, Qun A. Li
CPU is one of the most significant sources of power consumption on smartphones. Power modeling is a key technique and important tool for power estimation and management, both of which are critical for providing good QoS for smartphones. However, we find that existing CPU power models for smartphones are ill-suited for modern multicore CPUs: they can give high estimation errors (up to 34%) and high estimation accuracy variation (more than 30%) for different types of workloads on mainstream multicore smartphones. The cause is that the existing approaches do not appropriately consider the effects of CPU idle power states on smartphones CPU power modeling. Based on our extensive measurement experiments, we develop a new CPU power modeling approach that carefully considers the effects of CPU idle power states. We present the detailed design of our power modeling approach, and a prototype CPU power estimation system on commercial multicore smartphones. Evaluation results show that our approach consistently achieves higher power estimation accuracy and stability for various benchmarks programs and real apps than the existing approaches.
{"title":"Enabling accurate and efficient modeling-based CPU power estimation for smartphones","authors":"Yifan Zhang, Yunxin Liu, Xuanzhe Liu, Qun A. Li","doi":"10.1109/IWQoS.2017.7969112","DOIUrl":"https://doi.org/10.1109/IWQoS.2017.7969112","url":null,"abstract":"CPU is one of the most significant sources of power consumption on smartphones. Power modeling is a key technique and important tool for power estimation and management, both of which are critical for providing good QoS for smartphones. However, we find that existing CPU power models for smartphones are ill-suited for modern multicore CPUs: they can give high estimation errors (up to 34%) and high estimation accuracy variation (more than 30%) for different types of workloads on mainstream multicore smartphones. The cause is that the existing approaches do not appropriately consider the effects of CPU idle power states on smartphones CPU power modeling. Based on our extensive measurement experiments, we develop a new CPU power modeling approach that carefully considers the effects of CPU idle power states. We present the detailed design of our power modeling approach, and a prototype CPU power estimation system on commercial multicore smartphones. Evaluation results show that our approach consistently achieves higher power estimation accuracy and stability for various benchmarks programs and real apps than the existing approaches.","PeriodicalId":422861,"journal":{"name":"2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128358019","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 : 2017-06-14DOI: 10.1109/IWQoS.2017.7969138
Jiamin Liu, Peng Zhang, Huanzhao Wang, Chengchen Hu
Traffic statistics are fundamental for many network measurement tasks like heavy hitter identification, traffic matrix estimator, anomaly detection, etc. However, traditional techniques like NetFlow and sFlow only provide coarse-grained statistics due to packet or flow sampling. Even Software Defined Networking (SDN) offers fine-grained traffic statistics collection, most of existing methods focus on specific applications and thus lack generality. To this end, we propose CounterMap, a generic traffic statistics collection and query platform. CounterMap maintains a full map of flow counters by actively polling switches and passively monitoring flow timeouts. For efficient storage and query, CounterMap stores the counters in fast off-the-shelf in-memory data store, and offers a generic SQL-like query language. With the CounterMap language, applications can gain visibility into both existing and historical flows, without querying the dataplane devices themselves. We show how network applications benefit from CounterMap, with higher measurement accuracy and lower dataplane overhead.
{"title":"CounterMap: Towards generic traffic statistics collection and query in Software Defined Network","authors":"Jiamin Liu, Peng Zhang, Huanzhao Wang, Chengchen Hu","doi":"10.1109/IWQoS.2017.7969138","DOIUrl":"https://doi.org/10.1109/IWQoS.2017.7969138","url":null,"abstract":"Traffic statistics are fundamental for many network measurement tasks like heavy hitter identification, traffic matrix estimator, anomaly detection, etc. However, traditional techniques like NetFlow and sFlow only provide coarse-grained statistics due to packet or flow sampling. Even Software Defined Networking (SDN) offers fine-grained traffic statistics collection, most of existing methods focus on specific applications and thus lack generality. To this end, we propose CounterMap, a generic traffic statistics collection and query platform. CounterMap maintains a full map of flow counters by actively polling switches and passively monitoring flow timeouts. For efficient storage and query, CounterMap stores the counters in fast off-the-shelf in-memory data store, and offers a generic SQL-like query language. With the CounterMap language, applications can gain visibility into both existing and historical flows, without querying the dataplane devices themselves. We show how network applications benefit from CounterMap, with higher measurement accuracy and lower dataplane overhead.","PeriodicalId":422861,"journal":{"name":"2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129712802","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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