Pub Date : 2018-06-01DOI: 10.1109/IWQoS.2018.8624186
Ning Wang, Jie Wu
Commercial cloud providers, e.g., Amazon EC2, offer the volume discount for large instance reservation in a time slot, and the majority of cloud jobs are delay-tolerant and do not need to be processed intermittently. These two features create an opportunity for the cloud brokerage service which aggregates and schedules cloud users' rental requests to earn volume discounts from cloud providers and sell to cloud users at a cheap price. A challenge for the broker is to properly schedule delay-tolerant jobs in order to maximize the volume discount amount over time. The scheduling idea is to generate several job bundles so each job bundle can get discount. In this paper, we discuss this problem from the homogeneous model first, where each job has the same processing time and delay-tolerant time, and we propose a dynamic programming approach. Then, we extend the model into the heterogeneous model, where the job processing time and the job deadline can be arbitrary values. In the heterogeneous scenario, we prove that the proposed problem is NP-hard even when the job processing time is unit. Then, we propose a greedy approach which turns out to have an approximation of $O(ln n)$, where $n$ is the total job number. Extensive trace-driven experiments from Google cluster trace demonstrates that our schemes achieve good performances.
{"title":"Optimal Cloud Instance Acquisition via IaaS Cloud Brokerage with Volume Discount","authors":"Ning Wang, Jie Wu","doi":"10.1109/IWQoS.2018.8624186","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624186","url":null,"abstract":"Commercial cloud providers, e.g., Amazon EC2, offer the volume discount for large instance reservation in a time slot, and the majority of cloud jobs are delay-tolerant and do not need to be processed intermittently. These two features create an opportunity for the cloud brokerage service which aggregates and schedules cloud users' rental requests to earn volume discounts from cloud providers and sell to cloud users at a cheap price. A challenge for the broker is to properly schedule delay-tolerant jobs in order to maximize the volume discount amount over time. The scheduling idea is to generate several job bundles so each job bundle can get discount. In this paper, we discuss this problem from the homogeneous model first, where each job has the same processing time and delay-tolerant time, and we propose a dynamic programming approach. Then, we extend the model into the heterogeneous model, where the job processing time and the job deadline can be arbitrary values. In the heterogeneous scenario, we prove that the proposed problem is NP-hard even when the job processing time is unit. Then, we propose a greedy approach which turns out to have an approximation of $O(ln n)$, where $n$ is the total job number. Extensive trace-driven experiments from Google cluster trace demonstrates that our schemes achieve good performances.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130891415","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 : 2018-06-01DOI: 10.1109/IWQoS.2018.8624123
Xiaohui Wei, Yuanyuan Liu, Shang Gao, Xingwang Wang
With increasing real-time and resource-intensive requirements, approximate computing is widely adopted to improve the performance of query processing over data streams. However, existing works concentrate on simple queries with single-step operations, such as point or join queries. There are a large number of nested queries with selection or filtering operations before aggregation. In this poster, we focus on approximate nested stream queries. We first propose a novel approximate model, SCM-sketches, that makes two-stage approximation for nested query answering with guaranteed errors. In the first stage for nested filtering operations, we use the sampling method to compress the arriving data. Then in the second stage, a sketch is used for further aggregation or join operations. We also theoretically analyze the effect of error propagation on approximate errors. Compared with existing sketch-based methods, experiment results with real-life datasets verify the effectiveness of SCM-sketches.
{"title":"Energy-Aware Allocation of Approximate Query Processing Over Data Streams with Error Guarantee","authors":"Xiaohui Wei, Yuanyuan Liu, Shang Gao, Xingwang Wang","doi":"10.1109/IWQoS.2018.8624123","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624123","url":null,"abstract":"With increasing real-time and resource-intensive requirements, approximate computing is widely adopted to improve the performance of query processing over data streams. However, existing works concentrate on simple queries with single-step operations, such as point or join queries. There are a large number of nested queries with selection or filtering operations before aggregation. In this poster, we focus on approximate nested stream queries. We first propose a novel approximate model, SCM-sketches, that makes two-stage approximation for nested query answering with guaranteed errors. In the first stage for nested filtering operations, we use the sampling method to compress the arriving data. Then in the second stage, a sketch is used for further aggregation or join operations. We also theoretically analyze the effect of error propagation on approximate errors. Compared with existing sketch-based methods, experiment results with real-life datasets verify the effectiveness of SCM-sketches.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134225280","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 : 2018-06-01DOI: 10.1109/IWQoS.2018.8624161
A. Erfanian, F. Tashtarian, M. Moghaddam
HTTP adaptive streaming (HAS) is quickly becoming the dominant video delivery technique for adaptive streaming over the Internet. Still considered as its primary challenges are determining the optimal rate adaptation and improving both the quality of experience (QoE) and QoE-fairness. Recent studies have shown that techniques providing a comprehensive and central view of the network resources can lead to greater gains in performance. By leveraging software defined networking (SDN), the current study proposes an SDN-based approach to maximize QoE metrics and QoE-fairness in AVC-based HTTP adaptive streaming. The proposed approach determines both the optimal adaptation and data paths for delivering the requested video files from HTTP-media servers to DASH clients. In fact, the proposed approach, which includes a set of application modules, is centrally executed by an SND controller in a time slot fashion. We formulate the problem as a mixed integer linear programming (MILP) optimization model in such a way that it applies defined policies, e.g. setting priorities for clients in obtaining video quality. We conduct experiments by emulating the proposed framework in Mininet using Floodlight as the SDN controller. In terms of improving QoE-fairness and QoE metrics, the effectiveness of the proposed approach is validated by a comparison with different approaches.
{"title":"On Maximizing QoE in AVC-Based HTTP Adaptive Streaming: An SDN Approach","authors":"A. Erfanian, F. Tashtarian, M. Moghaddam","doi":"10.1109/IWQoS.2018.8624161","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624161","url":null,"abstract":"HTTP adaptive streaming (HAS) is quickly becoming the dominant video delivery technique for adaptive streaming over the Internet. Still considered as its primary challenges are determining the optimal rate adaptation and improving both the quality of experience (QoE) and QoE-fairness. Recent studies have shown that techniques providing a comprehensive and central view of the network resources can lead to greater gains in performance. By leveraging software defined networking (SDN), the current study proposes an SDN-based approach to maximize QoE metrics and QoE-fairness in AVC-based HTTP adaptive streaming. The proposed approach determines both the optimal adaptation and data paths for delivering the requested video files from HTTP-media servers to DASH clients. In fact, the proposed approach, which includes a set of application modules, is centrally executed by an SND controller in a time slot fashion. We formulate the problem as a mixed integer linear programming (MILP) optimization model in such a way that it applies defined policies, e.g. setting priorities for clients in obtaining video quality. We conduct experiments by emulating the proposed framework in Mininet using Floodlight as the SDN controller. In terms of improving QoE-fairness and QoE metrics, the effectiveness of the proposed approach is validated by a comparison with different approaches.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128015437","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 : 2018-06-01DOI: 10.1109/IWQoS.2018.8624131
Omotayo Oshiga, Xiaowen Chu, Y. Leung, J. Ng
Many indoor localization systems rely on a set of reference anchors with known positions. A target's location is estimated from a set of distances between the target and its surrounding anchors, and hence the selection of anchors affects the localization accuracy. However, it remains a challenge to select the best set of anchors. In this paper, we study how to appropriately make use of the surrounding anchors for localizing a target. We first construct different candidate anchor clusters by selecting different number of anchors with the strongest received signals. Then for each candidate cluster, we propose a weighted min-max algorithm to provide a location estimation. Finally, we introduce a weighted geometric dilution of precision (w-GDOP) algorithm that combines the estimations from multiple clusters by quantifying their estimation accuracy. We evaluate the performance of our solution through simulations and real-world experiments. Our results show that the proposed anchor selection scheme and localization algorithm significantly improve the localization accuracy in large indoor environments.
{"title":"Anchor Selection for Localization in Large Indoor Venues","authors":"Omotayo Oshiga, Xiaowen Chu, Y. Leung, J. Ng","doi":"10.1109/IWQoS.2018.8624131","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624131","url":null,"abstract":"Many indoor localization systems rely on a set of reference anchors with known positions. A target's location is estimated from a set of distances between the target and its surrounding anchors, and hence the selection of anchors affects the localization accuracy. However, it remains a challenge to select the best set of anchors. In this paper, we study how to appropriately make use of the surrounding anchors for localizing a target. We first construct different candidate anchor clusters by selecting different number of anchors with the strongest received signals. Then for each candidate cluster, we propose a weighted min-max algorithm to provide a location estimation. Finally, we introduce a weighted geometric dilution of precision (w-GDOP) algorithm that combines the estimations from multiple clusters by quantifying their estimation accuracy. We evaluate the performance of our solution through simulations and real-world experiments. Our results show that the proposed anchor selection scheme and localization algorithm significantly improve the localization accuracy in large indoor environments.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130603226","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 : 2018-06-01DOI: 10.1109/IWQoS.2018.8624180
Qingyu Shi, F. Wang, D. Feng, Weibin Xie
In datacenter networks, multipath exists to facilitate parallel data transmission. Taking deployment challenges into account, some optimized alternatives (e.g. CLOVE, Hermes) to ECMP balance load at the virtual edge or hosts. However inaccuracies of congestion detection and reaction exist in these solutions. They either detect congestion through ECN and coarse-grained RTT measurements or are congestion-oblivious. These congestion feedbacks are not sufficient enough to indicate the accurate congestion status under asymmetry. And when rerouting events occur on multiple paths, ACKs with congestion feedback of other paths can improperly influence the current sending rate. Therefore, we explore how to balance load by solving above inaccuracy problems while ensuring good adaptation to commodity switches and existing network protocols. We propose ALB, an adaptive load-balancing mechanism based on accurate congestion feedback running at end hosts, which is resilient to asymmetry. ALB leverage a latency-based congestion detection to precisely route flowlets to lighter load paths, and an ACK correction method to avoid inaccurate flow rate adjustment. In large-scale simulations ALB achieves up to 7% and 40% better flow completion time (FCT) than CONGA and CLOVE-ECN under asymmetry.
{"title":"ALB: Adaptive Load Balancing Based on Accurate Congestion Feedback for Asymmetric Topologies","authors":"Qingyu Shi, F. Wang, D. Feng, Weibin Xie","doi":"10.1109/IWQoS.2018.8624180","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624180","url":null,"abstract":"In datacenter networks, multipath exists to facilitate parallel data transmission. Taking deployment challenges into account, some optimized alternatives (e.g. CLOVE, Hermes) to ECMP balance load at the virtual edge or hosts. However inaccuracies of congestion detection and reaction exist in these solutions. They either detect congestion through ECN and coarse-grained RTT measurements or are congestion-oblivious. These congestion feedbacks are not sufficient enough to indicate the accurate congestion status under asymmetry. And when rerouting events occur on multiple paths, ACKs with congestion feedback of other paths can improperly influence the current sending rate. Therefore, we explore how to balance load by solving above inaccuracy problems while ensuring good adaptation to commodity switches and existing network protocols. We propose ALB, an adaptive load-balancing mechanism based on accurate congestion feedback running at end hosts, which is resilient to asymmetry. ALB leverage a latency-based congestion detection to precisely route flowlets to lighter load paths, and an ACK correction method to avoid inaccurate flow rate adjustment. In large-scale simulations ALB achieves up to 7% and 40% better flow completion time (FCT) than CONGA and CLOVE-ECN under asymmetry.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125426798","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 : 2018-06-01DOI: 10.1109/IWQoS.2018.8624151
Chen Sun, J. Bi, Zili Meng, Xiao Zhang, Hongxin Hu
Network Function Virtualization (NFV) together with Software Defined Networking (SDN) offers the potential for enhancing service delivery flexibility and reducing overall costs. Based on the capability of dynamic creation and destruction of network function (NF) instances, NFV provides great elasticity in NF control, such as NF scaling out, scaling in, load balancing, etc. To realize NFV elasticity control, network traffic flows need to be redistributed across NF instances. However, deciding which flows are suitable for migration is a critical problem for efficient NFV elasticity control. In this paper, we propose to build an innovative flow migration controller, OFM Controller, to achieve optimized flow migration for NFV elasticity control. We identify the trigger conditions and control goals for different situations, and carefully design models and algorithms to address three major challenges including buffer overflow avoidance, migration cost calculation, and effective flow selection for migration. We implement the OFM Controller on top of NFV and SDN environments. Our evaluation results show that OFM Controller is efficient to support optimized flow migration in NFV elasticity control.
{"title":"OFM: Optimized Flow Migration for NFV Elasticity Control","authors":"Chen Sun, J. Bi, Zili Meng, Xiao Zhang, Hongxin Hu","doi":"10.1109/IWQoS.2018.8624151","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624151","url":null,"abstract":"Network Function Virtualization (NFV) together with Software Defined Networking (SDN) offers the potential for enhancing service delivery flexibility and reducing overall costs. Based on the capability of dynamic creation and destruction of network function (NF) instances, NFV provides great elasticity in NF control, such as NF scaling out, scaling in, load balancing, etc. To realize NFV elasticity control, network traffic flows need to be redistributed across NF instances. However, deciding which flows are suitable for migration is a critical problem for efficient NFV elasticity control. In this paper, we propose to build an innovative flow migration controller, OFM Controller, to achieve optimized flow migration for NFV elasticity control. We identify the trigger conditions and control goals for different situations, and carefully design models and algorithms to address three major challenges including buffer overflow avoidance, migration cost calculation, and effective flow selection for migration. We implement the OFM Controller on top of NFV and SDN environments. Our evaluation results show that OFM Controller is efficient to support optimized flow migration in NFV elasticity control.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"52 s37","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120839401","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 : 2018-06-01DOI: 10.1109/IWQoS.2018.8624166
Yiming Zeng, Pengzhan Zhou, Ji Liu, Yuanyuan Yang
This paper studies a data gathering problem in a wireless sensor network containing multiple private residual subnetworks. The interaction between the wireless sensor network operator and the owners of residual sub-networks is modeled by a Stackelberg game, which forms a novel framework for jointly analyzing the pricing, gathering data, and planning routes. It is shown that the game has a unique Stackelberg equilibrium at which the wireless sensor network operator sets prices to minimize total cost, while owners of residual sub-networks respond accordingly to maximize their utilities subject to their bandwidth constraints. An algorithm and theoretical analyses are provided for the corresponding strategies of the operator and owners, and validated by extensive simulations. It is demonstrated that the algorithm achieves lower network cost compared with existing data gathering strategies.
{"title":"A Stackelberg Game Framework for Mobile Data Gathering in Leasing Residential Sensor Networks","authors":"Yiming Zeng, Pengzhan Zhou, Ji Liu, Yuanyuan Yang","doi":"10.1109/IWQoS.2018.8624166","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624166","url":null,"abstract":"This paper studies a data gathering problem in a wireless sensor network containing multiple private residual subnetworks. The interaction between the wireless sensor network operator and the owners of residual sub-networks is modeled by a Stackelberg game, which forms a novel framework for jointly analyzing the pricing, gathering data, and planning routes. It is shown that the game has a unique Stackelberg equilibrium at which the wireless sensor network operator sets prices to minimize total cost, while owners of residual sub-networks respond accordingly to maximize their utilities subject to their bandwidth constraints. An algorithm and theoretical analyses are provided for the corresponding strategies of the operator and owners, and validated by extensive simulations. It is demonstrated that the algorithm achieves lower network cost compared with existing data gathering strategies.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134418004","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 : 2018-06-01DOI: 10.1109/IWQoS.2018.8624185
Juan Li, Jie Wu, Yanmin Zhu
With the increasingly wide adoption of crowdsensing services, we can leverage the crowd to obtain labeled data instances for training machine learning models. In this paper, we focus on the critical problem that which data instances should be collected to maximize the performance of the trained model under the budget limit. Solving this problem is nontrivial because of the unclear relationship between the performance of the trained model and the data collection process, NP-hardness of the problem and the online arrival of workers. To overcome these challenges, we first propose a crowdsensing framework with multiple rounds of data collecting and model training. The framework is based on the stream-based batch-mode active learning. According to the framework, we come up with a novel data utility model to measure the contribution of a data batch to the performance of the learning model. The data utility model combines uncertainty and weighted density to measure the contribution of one instance. Finally, we propose an online algorithm to select a data batch in each round. The algorithm achieves fairness, computational efficiency and a competitive ratio 0.1218 when the ratio of the largest contribution of one data instance to the optimal offline total data utility is infinitely small. Through evaluations based on a real data set, we demonstrate the efficiency of our data utility model and our online algorithm.
{"title":"Data Utility Maximization When Leveraging Crowdsensing in Machine Learning","authors":"Juan Li, Jie Wu, Yanmin Zhu","doi":"10.1109/IWQoS.2018.8624185","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624185","url":null,"abstract":"With the increasingly wide adoption of crowdsensing services, we can leverage the crowd to obtain labeled data instances for training machine learning models. In this paper, we focus on the critical problem that which data instances should be collected to maximize the performance of the trained model under the budget limit. Solving this problem is nontrivial because of the unclear relationship between the performance of the trained model and the data collection process, NP-hardness of the problem and the online arrival of workers. To overcome these challenges, we first propose a crowdsensing framework with multiple rounds of data collecting and model training. The framework is based on the stream-based batch-mode active learning. According to the framework, we come up with a novel data utility model to measure the contribution of a data batch to the performance of the learning model. The data utility model combines uncertainty and weighted density to measure the contribution of one instance. Finally, we propose an online algorithm to select a data batch in each round. The algorithm achieves fairness, computational efficiency and a competitive ratio 0.1218 when the ratio of the largest contribution of one data instance to the optimal offline total data utility is infinitely small. Through evaluations based on a real data set, we demonstrate the efficiency of our data utility model and our online algorithm.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124316912","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 : 2018-06-01DOI: 10.1109/IWQoS.2018.8624120
Yueyue Chen, Deke Guo, Ming Xu
A mobile crowdsensing (MCS) platform motivates to employ participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit of the platform, i.e., the charge of a sensing task minus the payments to participants that execute the task. Recently, the appearance of data reconstruction method makes it possible to improve the platform's profit with a limited amount of sensing results in Compressive MCS (CMCS). However, It is of great challenge to the maximal profit for the CMCS platform, since it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In response to such challenges, we propose two profit-driven online participant selection mechanisms for the given task model and participant model. In ProSC, the sub-profit in each slot is maximized during the sensing period of a task, by combing a statistical-based quality prediction method and a repetitive cross-validation algorithm. In ProSC+, we jointly optimize the number of required participants and their spatial distribution to further improve the converging property. Finally, we conduct comprehensive evaluations, the results indicate the effectiveness and efficiency of our mechanisms.
{"title":"ProSC+: Profit-Driven Online Participant Selection in Compressive Mobile Crowdsensing","authors":"Yueyue Chen, Deke Guo, Ming Xu","doi":"10.1109/IWQoS.2018.8624120","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624120","url":null,"abstract":"A mobile crowdsensing (MCS) platform motivates to employ participants from the crowd to complete sensing tasks. A crucial problem is to maximize the profit of the platform, i.e., the charge of a sensing task minus the payments to participants that execute the task. Recently, the appearance of data reconstruction method makes it possible to improve the platform's profit with a limited amount of sensing results in Compressive MCS (CMCS). However, It is of great challenge to the maximal profit for the CMCS platform, since it is hard to predict the reconstruction quality due to the dynamic features and mobility of participants. In response to such challenges, we propose two profit-driven online participant selection mechanisms for the given task model and participant model. In ProSC, the sub-profit in each slot is maximized during the sensing period of a task, by combing a statistical-based quality prediction method and a repetitive cross-validation algorithm. In ProSC+, we jointly optimize the number of required participants and their spatial distribution to further improve the converging property. Finally, we conduct comprehensive evaluations, the results indicate the effectiveness and efficiency of our mechanisms.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122173594","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 : 2018-06-01DOI: 10.1109/IWQoS.2018.8624130
Hao Wang, Haoyun Shen, P. Wieder, R. Yahyapour
For many application scenarios, interconnected data centers provide high service flexibility, reduce response time, and facilitate timely data backup. Many data center system parameters might have variant impact on the interconnection performance. Despite many studies on data center network performance, there exist few analytical work that reveal insightful knowledge with wide range of system parameters as input, especially focusing on data center interconnects (DCI). This paper creates analytical models for representative data center network architectures and provides the performance calculus aiming to apply for data center interconnects. By parameterising the number of devices, the arriving traffics, the switch link capacities, and the traffic locality, we derive the relationship among the DCI bandwidth, inter-DC latency, and these parameters. Based on this, further discussion and numerical examples investigate and evaluate the modelling and calculus from multiple angles and show the possibility how this calculus assists DC/DCI design and operation.
{"title":"A Data Center Interconnects Calculus","authors":"Hao Wang, Haoyun Shen, P. Wieder, R. Yahyapour","doi":"10.1109/IWQoS.2018.8624130","DOIUrl":"https://doi.org/10.1109/IWQoS.2018.8624130","url":null,"abstract":"For many application scenarios, interconnected data centers provide high service flexibility, reduce response time, and facilitate timely data backup. Many data center system parameters might have variant impact on the interconnection performance. Despite many studies on data center network performance, there exist few analytical work that reveal insightful knowledge with wide range of system parameters as input, especially focusing on data center interconnects (DCI). This paper creates analytical models for representative data center network architectures and provides the performance calculus aiming to apply for data center interconnects. By parameterising the number of devices, the arriving traffics, the switch link capacities, and the traffic locality, we derive the relationship among the DCI bandwidth, inter-DC latency, and these parameters. Based on this, further discussion and numerical examples investigate and evaluate the modelling and calculus from multiple angles and show the possibility how this calculus assists DC/DCI design and operation.","PeriodicalId":222290,"journal":{"name":"2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116745106","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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