Sophie Cerf, Vincent Primault, A. Boutet, Sonia Ben Mokhtar, R. Birke, S. Bouchenak, L. Chen, N. Marchand, B. Robu
Leveraging location information in location-based services leads to improving service utility through geocontextualization. However, this raises privacy concerns as new knowledge can be inferred from location records, such as user's home and work places, or personal habits. Although Location Privacy Protection Mechanisms (LPPMs) provide a means to tackle this problem, they often require manual configuration posing significant challenges to service providers and users. Moreover, their impact on data privacy and utility is seldom assessed. In this paper, we present PULP, a model-driven system which automatically provides user-specific privacy protection and contributes to service utility via choosing adequate LPPM and configuring it. At the heart of PULP is nonlinear models that can capture the complex dependency of data privacy and utility for each individual user under given LPPM considered, i.e., Geo-Indistinguishability and Promesse. According to users' preferences on privacy and utility, PULP efficiently recommends suitable LPPM and corresponding configuration. We evaluate the accuracy of PULP's models and its effectiveness to achieve the privacy-utility trade-off per user, using four real-world mobility traces of 770 users in total. Our extensive experimentation shows that PULP ensures the contribution to location service while adhering to privacy constraints for a great percentage of users, and is orders of magnitude faster than non-model based alternatives.
{"title":"PULP: Achieving Privacy and Utility Trade-Off in User Mobility Data","authors":"Sophie Cerf, Vincent Primault, A. Boutet, Sonia Ben Mokhtar, R. Birke, S. Bouchenak, L. Chen, N. Marchand, B. Robu","doi":"10.1109/SRDS.2017.25","DOIUrl":"https://doi.org/10.1109/SRDS.2017.25","url":null,"abstract":"Leveraging location information in location-based services leads to improving service utility through geocontextualization. However, this raises privacy concerns as new knowledge can be inferred from location records, such as user's home and work places, or personal habits. Although Location Privacy Protection Mechanisms (LPPMs) provide a means to tackle this problem, they often require manual configuration posing significant challenges to service providers and users. Moreover, their impact on data privacy and utility is seldom assessed. In this paper, we present PULP, a model-driven system which automatically provides user-specific privacy protection and contributes to service utility via choosing adequate LPPM and configuring it. At the heart of PULP is nonlinear models that can capture the complex dependency of data privacy and utility for each individual user under given LPPM considered, i.e., Geo-Indistinguishability and Promesse. According to users' preferences on privacy and utility, PULP efficiently recommends suitable LPPM and corresponding configuration. We evaluate the accuracy of PULP's models and its effectiveness to achieve the privacy-utility trade-off per user, using four real-world mobility traces of 770 users in total. Our extensive experimentation shows that PULP ensures the contribution to location service while adhering to privacy constraints for a great percentage of users, and is orders of magnitude faster than non-model based alternatives.","PeriodicalId":6475,"journal":{"name":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82720834","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}
I. Weber, V. Gramoli, A. Ponomarev, M. Staples, Ralph Holz, An Binh Tran, Paul Rimba
Blockchain has recently gained momentum. Startups, enterprises, banks, and government agencies around the world are exploring the use of blockchain for broad applications including public registries, supply chains, health records, and voting. Dependability properties, like availability, are critical for many of these applications, but the guarantees offered by the blockchain technology remain unclear, especially from an application perspective. In this paper, we identify the availability limitations of two mainstream blockchains, Ethereum and Bitcoin. We demonstrate that while read availability of blockchains is typically high, write availability - for transaction management - is actually low. For Ethereum, we collected 6 million transactions over a period of 97 days. First, we measured the time for transactions to commit as required by the applications. Second, we observed that some transactions never commit, due to the inherent blockchain design. Third and perhaps even more dramatically, we identify the consequences of the lack of built-in options for explicit abort or retry that can maintain the application in an uncertain state, where transactions remain pending (neither aborted nor committed) for an unknown duration. Finally we propose techniques to mitigate the availability limitations of existing blockchains, and experimentally test the efficacy of these techniques.
{"title":"On Availability for Blockchain-Based Systems","authors":"I. Weber, V. Gramoli, A. Ponomarev, M. Staples, Ralph Holz, An Binh Tran, Paul Rimba","doi":"10.1109/SRDS.2017.15","DOIUrl":"https://doi.org/10.1109/SRDS.2017.15","url":null,"abstract":"Blockchain has recently gained momentum. Startups, enterprises, banks, and government agencies around the world are exploring the use of blockchain for broad applications including public registries, supply chains, health records, and voting. Dependability properties, like availability, are critical for many of these applications, but the guarantees offered by the blockchain technology remain unclear, especially from an application perspective. In this paper, we identify the availability limitations of two mainstream blockchains, Ethereum and Bitcoin. We demonstrate that while read availability of blockchains is typically high, write availability - for transaction management - is actually low. For Ethereum, we collected 6 million transactions over a period of 97 days. First, we measured the time for transactions to commit as required by the applications. Second, we observed that some transactions never commit, due to the inherent blockchain design. Third and perhaps even more dramatically, we identify the consequences of the lack of built-in options for explicit abort or retry that can maintain the application in an uncertain state, where transactions remain pending (neither aborted nor committed) for an unknown duration. Finally we propose techniques to mitigate the availability limitations of existing blockchains, and experimentally test the efficacy of these techniques.","PeriodicalId":6475,"journal":{"name":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87899882","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 field of multi-robot system, the problem of pattern formation has attracted considerable attention. However, the faulty sensor input of each robot is crucial for such system to act reliably in practice. Existing works focus on assuming certain noise model and reducing the noise impact. In this work, we propose to use a learning-based method to overcome this kind of barrier. By interacting with the environment, each robot learns to adapt its behavior to eliminate the malfunctions in the sensors and the actuators. Moreover, we plan to evaluate the proposed algorithms by deploying it into the multi-robot platform developed in our research lab
{"title":"Fault-Tolerant Pattern Formation by Multiple Robots: A Learning Approach","authors":"Jia Wang, Jiannong Cao, Shan Jiang","doi":"10.1109/SRDS.2017.42","DOIUrl":"https://doi.org/10.1109/SRDS.2017.42","url":null,"abstract":"In the field of multi-robot system, the problem of pattern formation has attracted considerable attention. However, the faulty sensor input of each robot is crucial for such system to act reliably in practice. Existing works focus on assuming certain noise model and reducing the noise impact. In this work, we propose to use a learning-based method to overcome this kind of barrier. By interacting with the environment, each robot learns to adapt its behavior to eliminate the malfunctions in the sensors and the actuators. Moreover, we plan to evaluate the proposed algorithms by deploying it into the multi-robot platform developed in our research lab","PeriodicalId":6475,"journal":{"name":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77433726","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}
H. Sukhwani, J. M. Martínez, Xiaolin Chang, Kishor S. Trivedi, A. Rindos
While Blockchain network brings tremendous benefits, there are concerns whether their performance would match up with the mainstream IT systems. This paper aims to investigate whether the consensus process using Practical Byzantine Fault Tolerance (PBFT) could be a performance bottleneck for networks with a large number of peers. We model the PBFT consensus process using Stochastic Reward Nets (SRN) to compute the mean time to complete consensus for networks up to 100 peers. We create a blockchain network using IBM Bluemix service, running a production-grade IoT application and use the data to parameterize and validate our models. We also conduct sensitivity analysis over a variety of system parameters and examine the performance of larger networks
{"title":"Performance Modeling of PBFT Consensus Process for Permissioned Blockchain Network (Hyperledger Fabric)","authors":"H. Sukhwani, J. M. Martínez, Xiaolin Chang, Kishor S. Trivedi, A. Rindos","doi":"10.1109/SRDS.2017.36","DOIUrl":"https://doi.org/10.1109/SRDS.2017.36","url":null,"abstract":"While Blockchain network brings tremendous benefits, there are concerns whether their performance would match up with the mainstream IT systems. This paper aims to investigate whether the consensus process using Practical Byzantine Fault Tolerance (PBFT) could be a performance bottleneck for networks with a large number of peers. We model the PBFT consensus process using Stochastic Reward Nets (SRN) to compute the mean time to complete consensus for networks up to 100 peers. We create a blockchain network using IBM Bluemix service, running a production-grade IoT application and use the data to parameterize and validate our models. We also conduct sensitivity analysis over a variety of system parameters and examine the performance of larger networks","PeriodicalId":6475,"journal":{"name":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74218686","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}
Shengtuo Hu, Xiaobo Ma, Muhui Jiang, Xiapu Luo, M. Au
By hiding messages inside existing network protocols, anti-censorship tools could empower censored users to visit blocked websites. However, existing solutions generally suffer from two limitations. First, they usually need the support of ISP or the deployment of many customized hosts to conceal the communication between censored users and blocked websites. Second, their manipulations of normal network traffic may result in detectable features, which could be captured by the censorship system. In this paper, to tackle these limitations, we propose a novel framework that exploits the publicly available automation services and the plenty of web services and contents to circumvent web censorship, and realize it in a practical tool named AutoFlowLeaker. Moreover, we conduct extensive experiments to evaluate AutoFlowLeaker, and the results show that it has promising performance and can effectively evade realworld web censorship.
{"title":"AutoFlowLeaker: Circumventing Web Censorship through Automation Services","authors":"Shengtuo Hu, Xiaobo Ma, Muhui Jiang, Xiapu Luo, M. Au","doi":"10.1109/SRDS.2017.30","DOIUrl":"https://doi.org/10.1109/SRDS.2017.30","url":null,"abstract":"By hiding messages inside existing network protocols, anti-censorship tools could empower censored users to visit blocked websites. However, existing solutions generally suffer from two limitations. First, they usually need the support of ISP or the deployment of many customized hosts to conceal the communication between censored users and blocked websites. Second, their manipulations of normal network traffic may result in detectable features, which could be captured by the censorship system. In this paper, to tackle these limitations, we propose a novel framework that exploits the publicly available automation services and the plenty of web services and contents to circumvent web censorship, and realize it in a practical tool named AutoFlowLeaker. Moreover, we conduct extensive experiments to evaluate AutoFlowLeaker, and the results show that it has promising performance and can effectively evade realworld web censorship.","PeriodicalId":6475,"journal":{"name":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89476600","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}
Peng Li, Jiaxiang Dong, Xueda Liu, G. Wang, Zhongwei Li, X. Liu
In cloud storage systems, the use of erasure coding results in high read latency and long recovery time when drive or node failure happens. In this paper, we design a parity independent array codes (PIT), a variation of STAR code, which is triple fault tolerant and nearly space-optimal, and also propose an efficient single-failure recovery scheme (PITR) for them to mitigate the problem. In addition, we present a "shortened" version of PIT (SPIT) to further reduce the recovery cost. In this way, less disk I/O and network resources are used, thereby reducing the recovery time and achieving a high system reliability and availability.
{"title":"PITR: An Efficient Single-Failure Recovery Scheme for PIT-Coded Cloud Storage Systems","authors":"Peng Li, Jiaxiang Dong, Xueda Liu, G. Wang, Zhongwei Li, X. Liu","doi":"10.1109/SRDS.2017.38","DOIUrl":"https://doi.org/10.1109/SRDS.2017.38","url":null,"abstract":"In cloud storage systems, the use of erasure coding results in high read latency and long recovery time when drive or node failure happens. In this paper, we design a parity independent array codes (PIT), a variation of STAR code, which is triple fault tolerant and nearly space-optimal, and also propose an efficient single-failure recovery scheme (PITR) for them to mitigate the problem. In addition, we present a \"shortened\" version of PIT (SPIT) to further reduce the recovery cost. In this way, less disk I/O and network resources are used, thereby reducing the recovery time and achieving a high system reliability and availability.","PeriodicalId":6475,"journal":{"name":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89083723","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}
Atul Bohara, Mohammad A. Noureddine, Ahmed M. Fawaz, W. Sanders
Lateral movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and lateral movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.
{"title":"An Unsupervised Multi-Detector Approach for Identifying Malicious Lateral Movement","authors":"Atul Bohara, Mohammad A. Noureddine, Ahmed M. Fawaz, W. Sanders","doi":"10.1109/SRDS.2017.31","DOIUrl":"https://doi.org/10.1109/SRDS.2017.31","url":null,"abstract":"Lateral movement-based attacks are increasingly leading to compromises in large private and government networks, often resulting in information exfiltration or service disruption. Such attacks are often slow and stealthy and usually evade existing security products. To enable effective detection of such attacks, we present a new approach based on graph-based modeling of the security state of the target system and correlation of diverse indicators of anomalous host behavior. We believe that irrespective of the specific attack vectors used, attackers typically establish a command and control channel to operate, and move in the target system to escalate their privileges and reach sensitive areas. Accordingly, we identify important features of command and control and lateral movement activities and extract them from internal and external communication traffic. Driven by the analysis of the features, we propose the use of multiple anomaly detection techniques to identify compromised hosts. These methods include Principal Component Analysis, k-means clustering, and Median Absolute Deviation-based outlier detection. We evaluate the accuracy of identifying compromised hosts by using injected attack traffic in a real enterprise network dataset, for various attack communication models. Our results show that the proposed approach can detect infected hosts with high accuracy and a low false positive rate.","PeriodicalId":6475,"journal":{"name":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86193107","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}
Erasure coding has been widely adopted to protect data storage against failures in production data centers. Given the hierarchical nature of data centers, characterizing the effects of erasure coding and redundancy placement on the reliability of erasure-coded data centers is critical yet largely unexplored. This paper presents a comprehensive simulation analysis of reliability on erasure-coded data centers. We conduct the analysis by building a discrete-event simulator called SIMEDC, which reports reliability metrics of an erasure-coded data center based on the configurable inputs of the data center topology, erasure codes, redundancy placement, and failure/repair patterns of different subsystems obtained from statistical models or production traces. Our simulation results show that placing erasure-coded data in fewer racks generally improves reliability by reducing cross-rack repair traffic, even though it sacrifices rack-level fault tolerance in the face of correlated failures.
{"title":"A Simulation Analysis of Reliability in Erasure-Coded Data Centers","authors":"Mi Zhang, Shujie Han, P. Lee","doi":"10.1109/SRDS.2017.19","DOIUrl":"https://doi.org/10.1109/SRDS.2017.19","url":null,"abstract":"Erasure coding has been widely adopted to protect data storage against failures in production data centers. Given the hierarchical nature of data centers, characterizing the effects of erasure coding and redundancy placement on the reliability of erasure-coded data centers is critical yet largely unexplored. This paper presents a comprehensive simulation analysis of reliability on erasure-coded data centers. We conduct the analysis by building a discrete-event simulator called SIMEDC, which reports reliability metrics of an erasure-coded data center based on the configurable inputs of the data center topology, erasure codes, redundancy placement, and failure/repair patterns of different subsystems obtained from statistical models or production traces. Our simulation results show that placing erasure-coded data in fewer racks generally improves reliability by reducing cross-rack repair traffic, even though it sacrifices rack-level fault tolerance in the face of correlated failures.","PeriodicalId":6475,"journal":{"name":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90815099","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}
This paper considers the statistical approach to model software degradation process from time series data of system attributes. We first develop the continuous-time Markov chain (CTMC) model to represent the degradation level of system. By combining the CTMC with system attributes distributions, a continuous-time hidden Markov model (CT-HMM) is proposed as the basic model to represent the degradation level of system. To estimate model parameters, we develop the EM algorithm for CT-HMM. The advantage of this modeling is that the estimated model is directly applied to existing CTMC-based software aging and rejuvenation models. In numerical experiments, we exhibit the performance of our method by simulated data and also demonstrate estimating the software degradation process with experimental data in MySQL database system.
{"title":"A Statistical Framework on Software Aging Modeling with Continuous-Time Hidden Markov Model","authors":"H. Okamura, Junjun Zheng, T. Dohi","doi":"10.1109/SRDS.2017.24","DOIUrl":"https://doi.org/10.1109/SRDS.2017.24","url":null,"abstract":"This paper considers the statistical approach to model software degradation process from time series data of system attributes. We first develop the continuous-time Markov chain (CTMC) model to represent the degradation level of system. By combining the CTMC with system attributes distributions, a continuous-time hidden Markov model (CT-HMM) is proposed as the basic model to represent the degradation level of system. To estimate model parameters, we develop the EM algorithm for CT-HMM. The advantage of this modeling is that the estimated model is directly applied to existing CTMC-based software aging and rejuvenation models. In numerical experiments, we exhibit the performance of our method by simulated data and also demonstrate estimating the software degradation process with experimental data in MySQL database system.","PeriodicalId":6475,"journal":{"name":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85658432","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}
A. S. Sabyasachi, H. M. D. Kabir, A. Abdelmoniem, S. Mondal
Public cloud providers, such as Amazon EC2, offer idle computing resources known as spot instances at a much cheaper rate compared to On-Demand instances. Spot instance prices are set dynamically according to market demand. Cloud users request spot instances by submitting their bid, and if user's bid price exceeds current spot price then a spot instance is assigned to that user. The problem however is that while spot instances are executing their jobs, they can be revoked whenever the spot price rises above the current bid of the user. In such scenarios and to complete jobs reliably, we propose a set of improvements for the cloud spot market which benefits both the provider and users. Typically, the new framework allows users to bid different prices depending on their perceived urgency and nature of the running job. Hence, it practically allow them to negotiate the current bid price in a way that guarantees the timely completion of their jobs. To complement our intuition, we have conducted an empirical study using real cloud spot price traces to evaluate our framework strategies which aim to achieve a resilient deadline-aware auction framework.
{"title":"A Resilient Auction Framework for Deadline-Aware Jobs in Cloud Spot Market","authors":"A. S. Sabyasachi, H. M. D. Kabir, A. Abdelmoniem, S. Mondal","doi":"10.1109/SRDS.2017.34","DOIUrl":"https://doi.org/10.1109/SRDS.2017.34","url":null,"abstract":"Public cloud providers, such as Amazon EC2, offer idle computing resources known as spot instances at a much cheaper rate compared to On-Demand instances. Spot instance prices are set dynamically according to market demand. Cloud users request spot instances by submitting their bid, and if user's bid price exceeds current spot price then a spot instance is assigned to that user. The problem however is that while spot instances are executing their jobs, they can be revoked whenever the spot price rises above the current bid of the user. In such scenarios and to complete jobs reliably, we propose a set of improvements for the cloud spot market which benefits both the provider and users. Typically, the new framework allows users to bid different prices depending on their perceived urgency and nature of the running job. Hence, it practically allow them to negotiate the current bid price in a way that guarantees the timely completion of their jobs. To complement our intuition, we have conducted an empirical study using real cloud spot price traces to evaluate our framework strategies which aim to achieve a resilient deadline-aware auction framework.","PeriodicalId":6475,"journal":{"name":"2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90503337","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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