Several relaxed variants of Snapshot Isolation (SI) have been proposed for improved performance in distributed transactional key-value stores. These relaxed variants, however, provide no specification or control of the severity of the anomalies with respect to SI. They have also been designed to be used statically throughout the whole system life cycle. To overcome these drawbacks, we propose the idea of parameterized and runtime-tunable snapshot isolation. We first define a new transactional consistency model called Relaxed Version Snapshot Isolation (RVSI), which can formally and quantitatively specify the anomalies it may produce with respect to SI. To this end, we decompose SI into three "view properties", for each of which we introduce a parameter to quantify one of three kinds of possible anomalies: k1-BV (k1-version bounded backward view), k2-FV (k2-version bounded forward view), and k3-SV (k3-version bounded snapshot view). We then implement a prototype partitioned replicated distributed transactional key-value store called Chameleon across multiple data centers. While achieving RVSI, Chameleon allows each transaction to dynamically tune its consistency level at runtime. The experiments show that RVSI helps to reduce the transaction abort rates when applications are willing to tolerate certain anomalies. We also evaluate the individual impacts of k1-BV, k2-FV, and k3-SV on reducing the transaction abort rates in various scenarios. We find that it depends on the issue delays between clients and replicas which of k1 and k2 plays a major role in reducing transaction abort rates.
{"title":"Parameterized and Runtime-Tunable Snapshot Isolation in Distributed Transactional Key-Value Stores","authors":"Hengfeng Wei, Yu Huang, Jian Lu","doi":"10.1109/SRDS.2017.11","DOIUrl":"https://doi.org/10.1109/SRDS.2017.11","url":null,"abstract":"Several relaxed variants of Snapshot Isolation (SI) have been proposed for improved performance in distributed transactional key-value stores. These relaxed variants, however, provide no specification or control of the severity of the anomalies with respect to SI. They have also been designed to be used statically throughout the whole system life cycle. To overcome these drawbacks, we propose the idea of parameterized and runtime-tunable snapshot isolation. We first define a new transactional consistency model called Relaxed Version Snapshot Isolation (RVSI), which can formally and quantitatively specify the anomalies it may produce with respect to SI. To this end, we decompose SI into three \"view properties\", for each of which we introduce a parameter to quantify one of three kinds of possible anomalies: k1-BV (k1-version bounded backward view), k2-FV (k2-version bounded forward view), and k3-SV (k3-version bounded snapshot view). We then implement a prototype partitioned replicated distributed transactional key-value store called Chameleon across multiple data centers. While achieving RVSI, Chameleon allows each transaction to dynamically tune its consistency level at runtime. The experiments show that RVSI helps to reduce the transaction abort rates when applications are willing to tolerate certain anomalies. We also evaluate the individual impacts of k1-BV, k2-FV, and k3-SV on reducing the transaction abort rates in various scenarios. We find that it depends on the issue delays between clients and replicas which of k1 and k2 plays a major role in reducing transaction abort rates.","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":"89189458","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}
Filipe Freitas, J. Leitao, Nuno M. Preguiça, R. Rodrigues
Online services such as Facebook or Twitter have public APIs to enable an easy integration of these services with third party applications. However, the developers who design these applications have no information about the consistency provided by these services, which exacerbates the complexity of reasoning about the semantics of the applications they are developing. In this paper, we show that is possible to deploy a transparent middleware between the application and the service, which enables a fine-grained control over the session guarantees that comprise the consistency semantics provided by these APIs, without having to gain access to the implementation of the underlying services. We evaluated our middleware using the Facebook public API and the Redis datastore, and our results show that we are able to provide fine-grained control of the consistency semantics incurring in a small local storage and modest latency overhead.
{"title":"Fine-Grained Consistency Upgrades for Online Services","authors":"Filipe Freitas, J. Leitao, Nuno M. Preguiça, R. Rodrigues","doi":"10.1109/SRDS.2017.9","DOIUrl":"https://doi.org/10.1109/SRDS.2017.9","url":null,"abstract":"Online services such as Facebook or Twitter have public APIs to enable an easy integration of these services with third party applications. However, the developers who design these applications have no information about the consistency provided by these services, which exacerbates the complexity of reasoning about the semantics of the applications they are developing. In this paper, we show that is possible to deploy a transparent middleware between the application and the service, which enables a fine-grained control over the session guarantees that comprise the consistency semantics provided by these APIs, without having to gain access to the implementation of the underlying services. We evaluated our middleware using the Facebook public API and the Redis datastore, and our results show that we are able to provide fine-grained control of the consistency semantics incurring in a small local storage and modest latency overhead.","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":"86379853","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 extensively employed for data availability protection in production storage systems by maintaining a low degree of data redundancy. However, how to mitigate the parity update overhead of partial stripe writes in erasure-coded storage systems is still a critical concern. In this paper, we reconsider this problem from two new perspectives: data correlation and stripe organization, and propose CASO, a correlation-aware stripe organization algorithm. CASO captures data correlation of a data access stream. It packs correlated data into a small number of stripes to reduce the incurred I/Os in partial stripe writes, and further organizes uncorrelated data into stripes to leverage the spatial locality in later accesses. By differentiating correlated and uncorrelated data in stripe organization, we show via extensive trace-driven evaluation that CASO reduces up to 25.1% of parity updates and accelerates the write speed by up to 28.4%.
{"title":"Correlation-Aware Stripe Organization for Efficient Writes in Erasure-Coded Storage Systems","authors":"Zhirong Shen, P. Lee, J. Shu, Wenzhong Guo","doi":"10.1109/SRDS.2017.18","DOIUrl":"https://doi.org/10.1109/SRDS.2017.18","url":null,"abstract":"Erasure coding has been extensively employed for data availability protection in production storage systems by maintaining a low degree of data redundancy. However, how to mitigate the parity update overhead of partial stripe writes in erasure-coded storage systems is still a critical concern. In this paper, we reconsider this problem from two new perspectives: data correlation and stripe organization, and propose CASO, a correlation-aware stripe organization algorithm. CASO captures data correlation of a data access stream. It packs correlated data into a small number of stripes to reduce the incurred I/Os in partial stripe writes, and further organizes uncorrelated data into stripes to leverage the spatial locality in later accesses. By differentiating correlated and uncorrelated data in stripe organization, we show via extensive trace-driven evaluation that CASO reduces up to 25.1% of parity updates and accelerates the write speed by up to 28.4%.","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":"86971347","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}
Jordan Adamek, Mikhail Nesterenko, J. Robinson, S. Tixeuil
We present two geometric routing algorithms that reliably deliver messages to all devices in a geocast region. One algorithm is based on flooding, the other on concurrent geometric routing. They are the fist known stateless geocasting algorithms. We formally prove the algorithms correct, evaluate their performance through abstract and concrete simulation and estimate their message complexity.
{"title":"Stateless Reliable Geocasting","authors":"Jordan Adamek, Mikhail Nesterenko, J. Robinson, S. Tixeuil","doi":"10.1109/SRDS.2017.13","DOIUrl":"https://doi.org/10.1109/SRDS.2017.13","url":null,"abstract":"We present two geometric routing algorithms that reliably deliver messages to all devices in a geocast region. One algorithm is based on flooding, the other on concurrent geometric routing. They are the fist known stateless geocasting algorithms. We formally prove the algorithms correct, evaluate their performance through abstract and concrete simulation and estimate their message complexity.","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":"91220080","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}
Ricardo Macedo, J. Paulo, Rogério Pontes, Bernardo Portela, Tiago Oliveira, M. Matos, R. Oliveira
Cloud infrastructures provide database services as cost-efficient and scalable solutions for storing and processing large amounts of data. To maximize performance, these services require users to trust sensitive information to the cloud provider, which raises privacy and legal concerns. This represents a major obstacle to the adoption of the cloud computing paradigm. Recent work addressed this issue by extending databases to compute over encrypted data. However, these approaches usually support a single and strict combination of cryptographic techniques invariably making them application specific. To assess and broaden the applicability of cryptographic techniques in secure cloud storage and processing, these techniques need to be thoroughly evaluated in a modular and configurable database environment. This is even more noticeable for NoSQL data stores where data privacy is still mostly overlooked. In this paper, we present a generic NoSQL framework and a set of libraries supporting data processing cryptographic techniques that can be used with existing NoSQL engines and composed to meet the privacy and performance requirements of different applications. This is achieved through a modular and extensible design that enables data processing over multiple cryptographic techniques applied on the same database. For each technique, we provide an overview of its security model, along with an extensive set of experiments. The framework is evaluated with the YCSB benchmark, where we assess the practicality and performance tradeoffs for different combinations of cryptographic techniques. The results for a set of macro experiments show that the average overhead in NoSQL operations performance is below 15%, when comparing our system with a baseline database without privacy guarantees.
{"title":"A Practical Framework for Privacy-Preserving NoSQL Databases","authors":"Ricardo Macedo, J. Paulo, Rogério Pontes, Bernardo Portela, Tiago Oliveira, M. Matos, R. Oliveira","doi":"10.1109/SRDS.2017.10","DOIUrl":"https://doi.org/10.1109/SRDS.2017.10","url":null,"abstract":"Cloud infrastructures provide database services as cost-efficient and scalable solutions for storing and processing large amounts of data. To maximize performance, these services require users to trust sensitive information to the cloud provider, which raises privacy and legal concerns. This represents a major obstacle to the adoption of the cloud computing paradigm. Recent work addressed this issue by extending databases to compute over encrypted data. However, these approaches usually support a single and strict combination of cryptographic techniques invariably making them application specific. To assess and broaden the applicability of cryptographic techniques in secure cloud storage and processing, these techniques need to be thoroughly evaluated in a modular and configurable database environment. This is even more noticeable for NoSQL data stores where data privacy is still mostly overlooked. In this paper, we present a generic NoSQL framework and a set of libraries supporting data processing cryptographic techniques that can be used with existing NoSQL engines and composed to meet the privacy and performance requirements of different applications. This is achieved through a modular and extensible design that enables data processing over multiple cryptographic techniques applied on the same database. For each technique, we provide an overview of its security model, along with an extensive set of experiments. The framework is evaluated with the YCSB benchmark, where we assess the practicality and performance tradeoffs for different combinations of cryptographic techniques. The results for a set of macro experiments show that the average overhead in NoSQL operations performance is below 15%, when comparing our system with a baseline database without privacy guarantees.","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":"82829222","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}
During software regression testing, the code coverage of target program is a crucial factor while we perform test case reduction and prioritization. Modified Condition/ Decision Coverage (MC/DC) is one of the most strict and high-accuracy criterion in code coverage and it is usually considered necessary for adequate testing of critical software. In the past, Hayhurst et al proposed a method to implement the MC/DC criterion that complies with regulatory guidance for DO-178B level A software. Hayhurst's MC/DC approach was to find some test cases which are satisfied by MC/DC criterion for each operator (and, or, not, or xor) in the Boolean expression. However, there could be some problems when using Hayhurst's MC/DC approach to select test cases. In this paper, we discuss how to improve and/or enhance Hayhurst's MC/DC approach by using a greedy-based method. Some experiments are performed based on real programs to evaluate as well as compare the performance of our proposed and Hayhurst's approaches.
在软件回归测试期间，当我们执行测试用例减少和优先级排序时，目标程序的代码覆盖率是一个至关重要的因素。修改条件/决策覆盖率(MC/DC)是代码覆盖率中最严格、精度最高的标准之一，通常被认为是对关键软件进行充分测试的必要条件。过去，Hayhurst等人针对DO-178B a级软件提出了一种符合法规指导的MC/DC标准的实现方法。Hayhurst的MC/DC方法是为布尔表达式中的每个操作符(and, or, not, or xor)找到一些满足MC/DC标准的测试用例。然而，当使用Hayhurst的MC/DC方法来选择测试用例时，可能会有一些问题。在本文中，我们讨论了如何通过使用基于贪婪的方法来改进和/或增强Hayhurst的MC/DC方法。基于实际程序进行了一些实验，以评估和比较我们提出的方法和Hayhurst方法的性能。
{"title":"A Greedy-Based Method for Modified Condition/Decision Coverage Testing Criterion","authors":"Bo-His Li, Chin-Yu Huang","doi":"10.1109/SRDS.2017.33","DOIUrl":"https://doi.org/10.1109/SRDS.2017.33","url":null,"abstract":"During software regression testing, the code coverage of target program is a crucial factor while we perform test case reduction and prioritization. Modified Condition/ Decision Coverage (MC/DC) is one of the most strict and high-accuracy criterion in code coverage and it is usually considered necessary for adequate testing of critical software. In the past, Hayhurst et al proposed a method to implement the MC/DC criterion that complies with regulatory guidance for DO-178B level A software. Hayhurst's MC/DC approach was to find some test cases which are satisfied by MC/DC criterion for each operator (and, or, not, or xor) in the Boolean expression. However, there could be some problems when using Hayhurst's MC/DC approach to select test cases. In this paper, we discuss how to improve and/or enhance Hayhurst's MC/DC approach by using a greedy-based method. Some experiments are performed based on real programs to evaluate as well as compare the performance of our proposed and Hayhurst's approaches.","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":"85838847","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}
Saba Aflaki, Matthias Volk, Borzoo Bonakdarpour, J. Katoen, A. Storjohann
Although randomized algorithms have widely been used in distributed computing as a means to tackle impossibility results, it is currently unclear what type of randomization leads to the best performance in such algorithms. This paper proposes three automated techniques to find the probability distribution that achieves minimum average recovery time for an input randomized distributed self-stabilizing protocol without changing the behavior of the algorithm. Our first technique is based on solving symbolic linear algebraic equations in order to identify fastest state reachability in parametric discrete-time Markov chains. The second approach applies parameter synthesis techniques from probabilistic model checking to compute the rational function describing the average recovery time and then uses dedicated solvers to find the optimal parameter valuation. The third approach computes over- and under-approximations of the result for a given parameter region and iteratively refines the regions with minimal recovery time up to the desired precision. The latter approach finds sub-optimal solutions with negligible errors, but it is significantly more scalable in orders of magnitude as compared to the other approaches.
{"title":"Automated Fine Tuning of Probabilistic Self-Stabilizing Algorithms","authors":"Saba Aflaki, Matthias Volk, Borzoo Bonakdarpour, J. Katoen, A. Storjohann","doi":"10.1109/SRDS.2017.22","DOIUrl":"https://doi.org/10.1109/SRDS.2017.22","url":null,"abstract":"Although randomized algorithms have widely been used in distributed computing as a means to tackle impossibility results, it is currently unclear what type of randomization leads to the best performance in such algorithms. This paper proposes three automated techniques to find the probability distribution that achieves minimum average recovery time for an input randomized distributed self-stabilizing protocol without changing the behavior of the algorithm. Our first technique is based on solving symbolic linear algebraic equations in order to identify fastest state reachability in parametric discrete-time Markov chains. The second approach applies parameter synthesis techniques from probabilistic model checking to compute the rational function describing the average recovery time and then uses dedicated solvers to find the optimal parameter valuation. The third approach computes over- and under-approximations of the result for a given parameter region and iteratively refines the regions with minimal recovery time up to the desired precision. The latter approach finds sub-optimal solutions with negligible errors, but it is significantly more scalable in orders of magnitude as compared to the other approaches.","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":"83206307","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 this paper, we propose Auto-vGPU, a framework of automated resource sharing for virtualized GPU with self-configuration, to reduce manual intervention in system management while ensuring Service Level Agreement (SLA) targets. Auto-vGPU automatically collects the measurements of system metrics and learns a linear model for each application with dimension reduction. In order to fulfill the automated configuration of controller parameters, we propose a self-control-configuration method featuring the theory of automatic tuning of proportional-integral (PI) regulators. The experimental results of cloud gaming implementation demonstrate that Auto-vGPU is able to automatically build the low-dimension model and configure the control parameters without any manual interventions and the derived controller can adaptively allocate virtualized GPU resource to ensure the high performance of cloud applications.
{"title":"Automated Resource Sharing for Virtualized GPU with Self-Configuration","authors":"Jianguo Yao, Q. Lu, Zhengwei Qi","doi":"10.1109/SRDS.2017.35","DOIUrl":"https://doi.org/10.1109/SRDS.2017.35","url":null,"abstract":"In this paper, we propose Auto-vGPU, a framework of automated resource sharing for virtualized GPU with self-configuration, to reduce manual intervention in system management while ensuring Service Level Agreement (SLA) targets. Auto-vGPU automatically collects the measurements of system metrics and learns a linear model for each application with dimension reduction. In order to fulfill the automated configuration of controller parameters, we propose a self-control-configuration method featuring the theory of automatic tuning of proportional-integral (PI) regulators. The experimental results of cloud gaming implementation demonstrate that Auto-vGPU is able to automatically build the low-dimension model and configure the control parameters without any manual interventions and the derived controller can adaptively allocate virtualized GPU resource to ensure the high performance of cloud applications.","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":"88251798","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}
Jiahui Jiao, Benjun Ye, Yue Zhao, Rebecca J. Stones, G. Wang, X. Liu, Shaoyan Wang, Gu-Ya Xie
Cloud computing data centers have become one of the most important infrastructures in the big-data era. When considering the security of data centers, distributed denial of service (DDoS) attacks are one of the most serious problems. Here we consider DDoS attacks leveraging TCP traffic, which are increasingly rampant but are difficult to detect. To detect DDoS attacks, we identify two attack modes: fixed source IP attacks (FSIA) and random source IP attacks (RSIA), based on the source IP address used by attackers. We also propose a real-time TCP-based DDoS detection approach, which extracts effective features of TCP traffic and distinguishes malicious traffic from normal traffic by two decision tree classifiers. We evaluate the proposed approach using a simulated dataset and real datasets, including the ISCX IDS dataset, the CAIDA DDoS Attack 2007 dataset, and a Baidu Cloud Computing Platform dataset. Experimental results show that the proposed approach can achieve attack detection rate higher than 99% with a false alarm rate less than 1%. This approach will be deployed to the victim-end DDoS defense system in Baidu cloud computing data center.
{"title":"Detecting TCP-Based DDoS Attacks in Baidu Cloud Computing Data Centers","authors":"Jiahui Jiao, Benjun Ye, Yue Zhao, Rebecca J. Stones, G. Wang, X. Liu, Shaoyan Wang, Gu-Ya Xie","doi":"10.1109/SRDS.2017.37","DOIUrl":"https://doi.org/10.1109/SRDS.2017.37","url":null,"abstract":"Cloud computing data centers have become one of the most important infrastructures in the big-data era. When considering the security of data centers, distributed denial of service (DDoS) attacks are one of the most serious problems. Here we consider DDoS attacks leveraging TCP traffic, which are increasingly rampant but are difficult to detect. To detect DDoS attacks, we identify two attack modes: fixed source IP attacks (FSIA) and random source IP attacks (RSIA), based on the source IP address used by attackers. We also propose a real-time TCP-based DDoS detection approach, which extracts effective features of TCP traffic and distinguishes malicious traffic from normal traffic by two decision tree classifiers. We evaluate the proposed approach using a simulated dataset and real datasets, including the ISCX IDS dataset, the CAIDA DDoS Attack 2007 dataset, and a Baidu Cloud Computing Platform dataset. Experimental results show that the proposed approach can achieve attack detection rate higher than 99% with a false alarm rate less than 1%. This approach will be deployed to the victim-end DDoS defense system in Baidu cloud computing data center.","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":"75875644","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}
Causal consistency is an intermediate consistency model that can be achieved together with high availability and high-performance requirements even in presence of network partitions. In the context of partitioned data stores, it has been shown that implicit dependency tracking using clocks is more efficient than explicit dependency tracking by sending dependency check messages. Existing clock-based solutions depend on monotonic psychical clocks that are closely synchronized. These requirements make current protocols vulnerable to clock anomalies. In this paper, we propose a new clock-based algorithm, CausalSpartan, that instead of physical clocks, utilizes Hybrid Logical Clocks (HLCs). We show that using HLCs, without any overhead, we make the system robust on physical clock anomalies. This improvement is more significant in the context of query amplification, where a single query results in multiple GET/PUT operations. We also show that CausalSpartan decreases the visibility latency for a given data item comparing to existing clock-based approaches. In turn, this reduces the completion time of collaborative applications where two clients accessing two different replicas edit same items of the data store. Like previous protocols, CausalSpartan assumes that a given client does not access more than one replica. We show that in presence of network partitions, this assumption (made in several other works) is essential if one were to provide causal consistency as well as immediate availability to local updates.
{"title":"CausalSpartan: Causal Consistency for Distributed Data Stores Using Hybrid Logical Clocks","authors":"Mohammad Roohitavaf, M. Demirbas, S. Kulkarni","doi":"10.1109/SRDS.2017.27","DOIUrl":"https://doi.org/10.1109/SRDS.2017.27","url":null,"abstract":"Causal consistency is an intermediate consistency model that can be achieved together with high availability and high-performance requirements even in presence of network partitions. In the context of partitioned data stores, it has been shown that implicit dependency tracking using clocks is more efficient than explicit dependency tracking by sending dependency check messages. Existing clock-based solutions depend on monotonic psychical clocks that are closely synchronized. These requirements make current protocols vulnerable to clock anomalies. In this paper, we propose a new clock-based algorithm, CausalSpartan, that instead of physical clocks, utilizes Hybrid Logical Clocks (HLCs). We show that using HLCs, without any overhead, we make the system robust on physical clock anomalies. This improvement is more significant in the context of query amplification, where a single query results in multiple GET/PUT operations. We also show that CausalSpartan decreases the visibility latency for a given data item comparing to existing clock-based approaches. In turn, this reduces the completion time of collaborative applications where two clients accessing two different replicas edit same items of the data store. Like previous protocols, CausalSpartan assumes that a given client does not access more than one replica. We show that in presence of network partitions, this assumption (made in several other works) is essential if one were to provide causal consistency as well as immediate availability to local updates.","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":"74438773","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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