Computer networks are becoming increasingly complex today and thus prone to various network faults. Traditional testing tools (e.g., ping, traceroute) that often involve substantial manual effort to uncover faults are inefficient. This paper focuses on fault detection of the network data plane using test packets. Existing solutions of test packet generation either take very long time (e.g., more than one hour) to complete or generate too many test packets that may hurt regular traffic. In this paper, we present Pronto, an automated test packet generation tool that generates test packets to exercise data plane rules in the entire network in a short time (e.g., several seconds) and can quickly react to rule changes due to network dynamics. In addition, Pronto minimizes the number of test packets by allowing a packet to test multiple rules at different switches. The performance evaluation using two real network data plane rule sets shows that Pronto is faster than a recently developed tool by more than two orders of magnitude. Pronto can update the probes for rule changes using less than 1ms while existing methods have no such update function.
{"title":"Pronto: Efficient Test Packet Generation for Dynamic Network Data Planes","authors":"Yu Zhao, Huazhe Wang, Xiaoze Lin, Tingting Yu, Chen Qian","doi":"10.1109/ICDCS.2017.55","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.55","url":null,"abstract":"Computer networks are becoming increasingly complex today and thus prone to various network faults. Traditional testing tools (e.g., ping, traceroute) that often involve substantial manual effort to uncover faults are inefficient. This paper focuses on fault detection of the network data plane using test packets. Existing solutions of test packet generation either take very long time (e.g., more than one hour) to complete or generate too many test packets that may hurt regular traffic. In this paper, we present Pronto, an automated test packet generation tool that generates test packets to exercise data plane rules in the entire network in a short time (e.g., several seconds) and can quickly react to rule changes due to network dynamics. In addition, Pronto minimizes the number of test packets by allowing a packet to test multiple rules at different switches. The performance evaluation using two real network data plane rule sets shows that Pronto is faster than a recently developed tool by more than two orders of magnitude. Pronto can update the probes for rule changes using less than 1ms while existing methods have no such update function.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125981351","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}
Ankur Sarker, Zhuozhao Li, William Kolodzey, Haiying Shen
Electric vehicles (EVs) have great potential to reduce dependency on fossil fuels. The recent surge in the development of online EV (OLEV) will help to address the drawbacks associated with current generation EVs, such as the heavy and expensive batteries. OLEVs are integrated with the smart grid of power infrastructure through a wireless power transfer system (WPT) to increase the driving range of the OLEV. However, the integration of OLEVs with the grid creates a tremendous load for the smart grid. The demand of a power grid changes over time and the price of power is not fixed throughout the day. There should be some congestion avoidance and load balancing policy implications to ensure quality of services for OLEVs. In this paper, first, we conduct an analysis to show the existence of unpredictable power load and congestion because of OLEVs. We use the Simulation for Urban MObility tool and hourly traffic counts of a road section of the New York City to analyze the amount of energy OLEVs can receive at different times of the day. Then, we present a game theory based on a distributed power schedule framework to find the optimal schedule between OLEVs and smart grid. In the proposed framework, OLEVs receive the amount of power charging from the smart grid based on a power payment function which is updated using best response strategy. We prove that the updated power requests converge to the optimal power schedule. In this way, the smart grid maximizes the social welfare of OLEVs, which is defined as mixed consideration of total satisfaction and its power charging cost. Finally, we verify the performance of our proposed pricing policy under different scenarios in a simulation study.
{"title":"Opportunistic Energy Sharing Between Power Grid and Electric Vehicles: A Game Theory-Based Pricing Policy","authors":"Ankur Sarker, Zhuozhao Li, William Kolodzey, Haiying Shen","doi":"10.1109/ICDCS.2017.219","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.219","url":null,"abstract":"Electric vehicles (EVs) have great potential to reduce dependency on fossil fuels. The recent surge in the development of online EV (OLEV) will help to address the drawbacks associated with current generation EVs, such as the heavy and expensive batteries. OLEVs are integrated with the smart grid of power infrastructure through a wireless power transfer system (WPT) to increase the driving range of the OLEV. However, the integration of OLEVs with the grid creates a tremendous load for the smart grid. The demand of a power grid changes over time and the price of power is not fixed throughout the day. There should be some congestion avoidance and load balancing policy implications to ensure quality of services for OLEVs. In this paper, first, we conduct an analysis to show the existence of unpredictable power load and congestion because of OLEVs. We use the Simulation for Urban MObility tool and hourly traffic counts of a road section of the New York City to analyze the amount of energy OLEVs can receive at different times of the day. Then, we present a game theory based on a distributed power schedule framework to find the optimal schedule between OLEVs and smart grid. In the proposed framework, OLEVs receive the amount of power charging from the smart grid based on a power payment function which is updated using best response strategy. We prove that the updated power requests converge to the optimal power schedule. In this way, the smart grid maximizes the social welfare of OLEVs, which is defined as mixed consideration of total satisfaction and its power charging cost. Finally, we verify the performance of our proposed pricing policy under different scenarios in a simulation study.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128956203","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}
Y. Taleb, Shadi Ibrahim, Gabriel Antoniu, Toni Cortes
Most large popular web applications, like Facebook and Twitter, have been relying on large amounts of in-memory storage to cache data and offer a low response time. As the main memory capacity of clusters and clouds increases, it becomes possible to keep most of the data in the main memory. This motivates the introduction of in-memory storage systems. While prior work has focused on how to exploit the low-latency of in-memory access at scale, there is very little visibility into the energy-efficiency of in-memory storage systems. Even though it is known that main memory is a fundamental energy bottleneck in computing systems (i.e., DRAM consumes up to 40% of a server's power). In this paper, by the means of experimental evaluation, we have studied the performance and energy-efficiency of RAMCloud - a well-known in-memory storage system. We reveal that although RAMCloud is scalable for read-only applications, it exhibits non-proportional power consumption. We also find that the current replication scheme implemented in RAMCloud limits the performance and results in high energy consumption. Surprisingly, we show that replication can also play a negative role in crash-recovery.
{"title":"Characterizing Performance and Energy-Efficiency of the RAMCloud Storage System","authors":"Y. Taleb, Shadi Ibrahim, Gabriel Antoniu, Toni Cortes","doi":"10.1109/ICDCS.2017.51","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.51","url":null,"abstract":"Most large popular web applications, like Facebook and Twitter, have been relying on large amounts of in-memory storage to cache data and offer a low response time. As the main memory capacity of clusters and clouds increases, it becomes possible to keep most of the data in the main memory. This motivates the introduction of in-memory storage systems. While prior work has focused on how to exploit the low-latency of in-memory access at scale, there is very little visibility into the energy-efficiency of in-memory storage systems. Even though it is known that main memory is a fundamental energy bottleneck in computing systems (i.e., DRAM consumes up to 40% of a server's power). In this paper, by the means of experimental evaluation, we have studied the performance and energy-efficiency of RAMCloud - a well-known in-memory storage system. We reveal that although RAMCloud is scalable for read-only applications, it exhibits non-proportional power consumption. We also find that the current replication scheme implemented in RAMCloud limits the performance and results in high energy consumption. Surprisingly, we show that replication can also play a negative role in crash-recovery.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114718790","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 large-scale computing platforms, jobs are prone to interruptions and premature terminations, limiting their usability and leading to significant waste in cluster resources. In this paper, we tackle this problem in three steps. First, we provide a comprehensive study based on log data from multiple large-scale production systems to identify patterns in the behaviour of unsuccessful jobs across different clusters and investigate possible root causes behind job termination. Our results reveal several interesting properties that distinguish unsuccessful jobs from others, particularly w.r.t. resource consumption patterns and job configuration settings. Secondly, we design a machine learning-based framework for predicting job and task terminations. We show that job failures can be predicted relatively early with high precision and recall, and also identify attributes that have strong predictive power of job failure. Finally, we demonstrate in a concrete use case how our prediction framework can be used to mitigate the effect of unsuccessful execution using an effective task-cloning policy that we propose.
{"title":"Learning from Failure Across Multiple Clusters: A Trace-Driven Approach to Understanding, Predicting, and Mitigating Job Terminations","authors":"Nosayba El-Sayed, Hongyu Zhu, Bianca Schroeder","doi":"10.1109/ICDCS.2017.317","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.317","url":null,"abstract":"In large-scale computing platforms, jobs are prone to interruptions and premature terminations, limiting their usability and leading to significant waste in cluster resources. In this paper, we tackle this problem in three steps. First, we provide a comprehensive study based on log data from multiple large-scale production systems to identify patterns in the behaviour of unsuccessful jobs across different clusters and investigate possible root causes behind job termination. Our results reveal several interesting properties that distinguish unsuccessful jobs from others, particularly w.r.t. resource consumption patterns and job configuration settings. Secondly, we design a machine learning-based framework for predicting job and task terminations. We show that job failures can be predicted relatively early with high precision and recall, and also identify attributes that have strong predictive power of job failure. Finally, we demonstrate in a concrete use case how our prediction framework can be used to mitigate the effect of unsuccessful execution using an effective task-cloning policy that we propose.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116890112","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}
Xing Gao, Dachuan Liu, Daiping Liu, Haining Wang, A. Stavrou
As the limited battery lifetime remains a major factor restricting the applicability of a smartphone, significant research efforts have been devoted to understand the energy consumption in smartphones. Existing energy modeling methods can account energy drain in a fine-grained manner and provide well designed human-battery interfaces for users to characterize energy usage of every app in smartphones. However, in this paper, we demonstrate that there are still pitfalls in current Android energy modeling approaches, leaving collateral energy consumption unaccounted. The existence of collateral energy consumption becomes a serious energy bug. In particular, those energy bugs could be exploited to launch a new class of energy attacks, which deplete battery life and sidestep the supervision of current energy accounting. To unveil collateral energy bugs, we propose E-Android to accurately profile energy consumption of a smartphone in a comprehensive manner. E-Android monitors collateral energy related events and maintains energy consumption maps for relevant apps. We evaluate the effectiveness of E-Android under six different collateral energy attacks and two normal scenarios, and compare the results with those of Android. While Android fails to disclose collateral energy bugs, E-Android can accurately profile energy consumption and reveal the existence of energy bugs with minor overhead.
{"title":"E-Android: A New Energy Profiling Tool for Smartphones","authors":"Xing Gao, Dachuan Liu, Daiping Liu, Haining Wang, A. Stavrou","doi":"10.1109/ICDCS.2017.218","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.218","url":null,"abstract":"As the limited battery lifetime remains a major factor restricting the applicability of a smartphone, significant research efforts have been devoted to understand the energy consumption in smartphones. Existing energy modeling methods can account energy drain in a fine-grained manner and provide well designed human-battery interfaces for users to characterize energy usage of every app in smartphones. However, in this paper, we demonstrate that there are still pitfalls in current Android energy modeling approaches, leaving collateral energy consumption unaccounted. The existence of collateral energy consumption becomes a serious energy bug. In particular, those energy bugs could be exploited to launch a new class of energy attacks, which deplete battery life and sidestep the supervision of current energy accounting. To unveil collateral energy bugs, we propose E-Android to accurately profile energy consumption of a smartphone in a comprehensive manner. E-Android monitors collateral energy related events and maintains energy consumption maps for relevant apps. We evaluate the effectiveness of E-Android under six different collateral energy attacks and two normal scenarios, and compare the results with those of Android. While Android fails to disclose collateral energy bugs, E-Android can accurately profile energy consumption and reveal the existence of energy bugs with minor overhead.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117049876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The security issue of public WiFi is gaining more and more concern. By listening to probe requests, an adversary can obtain the SSID list of the APs to which a smartphone previously connected, and utilizes this information to trick the smartphone into associating to it. However, with the enhancement of security level, most smartphones now do not proactively disclose their SSID lists, making these attacks obsolete. In this paper, we propose City-Hunter, an attacker that can lure nearby smartphones without knowing their SSID information. City-Hunter establishes and maintains an SSID database by integrating both offline and online information. Meanwhile, it smartly chooses some SSIDs to hit a smartphone according to the past record and freshness. We evaluate the performance of City-Hunter in different public places. The results demonstrate that City-Hunter is able to successfully hit 12% ∼ 18% smartphones without knowing their SSID information, which is about 4 ∼ 8 times improvement compared to the similar attacks like KARMA and MANA.
{"title":"City-Hunter: Hunting Smartphones in Urban Areas","authors":"Xuefeng Liu, Jiaqi Wen, Shaojie Tang, Jiannong Cao, Jiaxing Shen","doi":"10.1109/ICDCS.2017.148","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.148","url":null,"abstract":"The security issue of public WiFi is gaining more and more concern. By listening to probe requests, an adversary can obtain the SSID list of the APs to which a smartphone previously connected, and utilizes this information to trick the smartphone into associating to it. However, with the enhancement of security level, most smartphones now do not proactively disclose their SSID lists, making these attacks obsolete. In this paper, we propose City-Hunter, an attacker that can lure nearby smartphones without knowing their SSID information. City-Hunter establishes and maintains an SSID database by integrating both offline and online information. Meanwhile, it smartly chooses some SSIDs to hit a smartphone according to the past record and freshness. We evaluate the performance of City-Hunter in different public places. The results demonstrate that City-Hunter is able to successfully hit 12% ∼ 18% smartphones without knowing their SSID information, which is about 4 ∼ 8 times improvement compared to the similar attacks like KARMA and MANA.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127218115","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}
T. Abdelzaher, Md. Tanvir Al Amin, A. Bar-Noy, William Dron, R. Govindan, Reginald L. Hobbs, Shaohan Hu, Jung-Eun Kim, Jongdeog Lee, K. Marcus, Shuochao Yao, Yiran Zhao
This paper introduces a novel paradigm for resource management in distributed systems, called decision-driven execution. The paradigm is appropriate for mission-driven systems, where the goal is to enable faster, leaner, and more effective decision making. All resource consumption, in this paradigm, is tied to the needs of making decisions on alternative courses of action. A point of departure from traditional architectures lies in interfaces that allow applications to specify their underlying decision logic. This specification, in turn, allows the system to reason about most effective means to meet information needs of decisions, resulting in simultaneous optimization of decision accuracy, cost, and speed. The paper discusses the overall vision of decision-driven execution, outlining preliminary work and novel challenges.
{"title":"Decision-Driven Execution: A Distributed Resource Management Paradigm for the Age of IoT","authors":"T. Abdelzaher, Md. Tanvir Al Amin, A. Bar-Noy, William Dron, R. Govindan, Reginald L. Hobbs, Shaohan Hu, Jung-Eun Kim, Jongdeog Lee, K. Marcus, Shuochao Yao, Yiran Zhao","doi":"10.1109/ICDCS.2017.318","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.318","url":null,"abstract":"This paper introduces a novel paradigm for resource management in distributed systems, called decision-driven execution. The paradigm is appropriate for mission-driven systems, where the goal is to enable faster, leaner, and more effective decision making. All resource consumption, in this paradigm, is tied to the needs of making decisions on alternative courses of action. A point of departure from traditional architectures lies in interfaces that allow applications to specify their underlying decision logic. This specification, in turn, allows the system to reason about most effective means to meet information needs of decisions, resulting in simultaneous optimization of decision accuracy, cost, and speed. The paper discusses the overall vision of decision-driven execution, outlining preliminary work and novel challenges.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"189 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131654245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The paper proposes a new approach to model checking Chandy-Lamport Distributed Snapshot Algorithm (CLDSA). The essential of the approach is that CLDSA is specified as a meta-program in Maude such that the meta-program takes a specification of an underlying distributed system (UDS) and generates the specification of the UDS on which CLDSA is superimposed (UDS-CLDSA). To model check that a UDS-CLDSA enjoys a desired property, it suffices that human users specify the UDS for the proposed approach, while human users need to specify the UDS-CLDSA for the existing approach for each UDS. Since the proposed approach conducts model checking at meta-level, it produces a counterexample if a UDS-CLDSA does not enjoy the property, while the existing approach does not. Our method specifying CLDSA as a meta-program can be applied to formal specification of the class of distributed algorithms that are superimposed on UDSs.
{"title":"Specifying a Distributed Snapshot Algorithm as a Meta-Program and Model Checking it at Meta-Level","authors":"Ha Thi Thu Doan, K. Ogata, François Bonnet","doi":"10.1109/ICDCS.2017.176","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.176","url":null,"abstract":"The paper proposes a new approach to model checking Chandy-Lamport Distributed Snapshot Algorithm (CLDSA). The essential of the approach is that CLDSA is specified as a meta-program in Maude such that the meta-program takes a specification of an underlying distributed system (UDS) and generates the specification of the UDS on which CLDSA is superimposed (UDS-CLDSA). To model check that a UDS-CLDSA enjoys a desired property, it suffices that human users specify the UDS for the proposed approach, while human users need to specify the UDS-CLDSA for the existing approach for each UDS. Since the proposed approach conducts model checking at meta-level, it produces a counterexample if a UDS-CLDSA does not enjoy the property, while the existing approach does not. Our method specifying CLDSA as a meta-program can be applied to formal specification of the class of distributed algorithms that are superimposed on UDSs.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120956839","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}
We investigate the resource allocation problem, including time slot allocation, channel allocation, and power adaptation, in a millimeter Wave (mmWave) network with multiple transmission links, multiple channels, and a PicoNet Coordinator (PNC). Each link has a video session to transmit from the transmitter to the receiver. The objective is to minimize the number of time slots to finish the video sessions of all links by jointly optimizing channel allocation and time slot allocation for links, while considering the possible interference between different links on the same channel. The optimal solution for the formulated problem is computationally prohibitive to obtain due to the exponential complexity. We developed a column generation based method to reformulate the original problem into a main problem along with a series of sub-problems, with greatly reduced complexity. We prove that the optimal solution for the reformulated problem converges to the optimal solution of the original problem, and we derived a lower bound for the performance of the reformulated problem at each iteration, which will finally converge to the global optimal solution. The proposed scheme is validated with simulations with its superior performance over existing work is observed.
{"title":"Optimal Resource Allocation for Multi-user Video Streaming over mmWave Networks","authors":"Zhifeng He, S. Mao","doi":"10.1109/ICDCS.2017.159","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.159","url":null,"abstract":"We investigate the resource allocation problem, including time slot allocation, channel allocation, and power adaptation, in a millimeter Wave (mmWave) network with multiple transmission links, multiple channels, and a PicoNet Coordinator (PNC). Each link has a video session to transmit from the transmitter to the receiver. The objective is to minimize the number of time slots to finish the video sessions of all links by jointly optimizing channel allocation and time slot allocation for links, while considering the possible interference between different links on the same channel. The optimal solution for the formulated problem is computationally prohibitive to obtain due to the exponential complexity. We developed a column generation based method to reformulate the original problem into a main problem along with a series of sub-problems, with greatly reduced complexity. We prove that the optimal solution for the reformulated problem converges to the optimal solution of the original problem, and we derived a lower bound for the performance of the reformulated problem at each iteration, which will finally converge to the global optimal solution. The proposed scheme is validated with simulations with its superior performance over existing work is observed.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133474095","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}
Crowdsourcing allows requesters to allocate tasks to a group of workers on the Internet to make use of their collective intelligence. Quality control is a key design objective in incentive mechanisms for crowdsourcing as requesters aim at obtaining answers of high quality under a given budget. However, when measuring workers' long-term quality, existing mechanisms either fail to utilize workers' historical information, or treat workers' quality as stable and ignore its temporal characteristics, hence performing poorly in a long run. In this paper we propose MELODY, a long-term dynamic quality-aware incentive mechanism for crowdsourcing. MELODY models interaction between requesters and workers as reverse auctions that run continuously. In each run of MELODY, we design a truthful, individual rational, budget feasible and quality-aware algorithm for task allocation with polynomial-time computation complexity and O(1) performance ratio. Moreover, taking into consideration the long-term characteristics of workers' quality, we propose a novel framework in MELODY for quality inference and parameters learning based on Linear Dynamical Systems at the end of each run, which takes full advantage of workers' historical information and predicts their quality accurately. Through extensive simulations, we demonstrate that MELODY outperforms existing work in terms of both quality estimation (reducing estimation error by 17.6% ~ 24.2%) and social performance (increasing requester's utility by 18.2% ~ 46.6%) in long-term scenarios.
{"title":"MELODY: A Long-Term Dynamic Quality-Aware Incentive Mechanism for Crowdsourcing","authors":"Hongwei Wang, Song Guo, Jiannong Cao, M. Guo","doi":"10.1109/ICDCS.2017.28","DOIUrl":"https://doi.org/10.1109/ICDCS.2017.28","url":null,"abstract":"Crowdsourcing allows requesters to allocate tasks to a group of workers on the Internet to make use of their collective intelligence. Quality control is a key design objective in incentive mechanisms for crowdsourcing as requesters aim at obtaining answers of high quality under a given budget. However, when measuring workers' long-term quality, existing mechanisms either fail to utilize workers' historical information, or treat workers' quality as stable and ignore its temporal characteristics, hence performing poorly in a long run. In this paper we propose MELODY, a long-term dynamic quality-aware incentive mechanism for crowdsourcing. MELODY models interaction between requesters and workers as reverse auctions that run continuously. In each run of MELODY, we design a truthful, individual rational, budget feasible and quality-aware algorithm for task allocation with polynomial-time computation complexity and O(1) performance ratio. Moreover, taking into consideration the long-term characteristics of workers' quality, we propose a novel framework in MELODY for quality inference and parameters learning based on Linear Dynamical Systems at the end of each run, which takes full advantage of workers' historical information and predicts their quality accurately. Through extensive simulations, we demonstrate that MELODY outperforms existing work in terms of both quality estimation (reducing estimation error by 17.6% ~ 24.2%) and social performance (increasing requester's utility by 18.2% ~ 46.6%) in long-term scenarios.","PeriodicalId":127689,"journal":{"name":"2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS)","volume":"12 45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114755992","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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