Pub Date : 2011-04-11DOI: 10.1109/ICDE.2011.5767849
Keith B. Frikken, Kyle Kauffman, Aaron Steele
Sensor networks have the potential to allow users to “query the physical world” [1] by querying the sensor nodes for an aggregate result. However, a concern with such aggregation is that a few corrupt nodes in the network may manipulate the results that the querier sees. There has been a substantial amount of work on providing integrity for sensor network computations. However to the best of our knowledge, this prior work has one or more of the following two limitations: i) the methods only work for a specific aggregation function, or ii) the methods do not consider adversaries whose goal is to prevent the base station and the querier from receiving the result. In this paper we present the first scheme that provides general aggregation for sensor networks in the presence of malicious adversaries. The generality of the scheme results from the ability to securely evaluate any algorithm in the streaming model of computation. The main idea of this paper is to convert the commonly used aggregation tree into an aggregation list, and a process for doing this conversion is also presented. This result is an interesting and important first step towards achieving the realization of general secure querying of a sensor network.
{"title":"General secure sensor aggregation in the presence of malicious nodes","authors":"Keith B. Frikken, Kyle Kauffman, Aaron Steele","doi":"10.1109/ICDE.2011.5767849","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767849","url":null,"abstract":"Sensor networks have the potential to allow users to “query the physical world” [1] by querying the sensor nodes for an aggregate result. However, a concern with such aggregation is that a few corrupt nodes in the network may manipulate the results that the querier sees. There has been a substantial amount of work on providing integrity for sensor network computations. However to the best of our knowledge, this prior work has one or more of the following two limitations: i) the methods only work for a specific aggregation function, or ii) the methods do not consider adversaries whose goal is to prevent the base station and the querier from receiving the result. In this paper we present the first scheme that provides general aggregation for sensor networks in the presence of malicious adversaries. The generality of the scheme results from the ability to securely evaluate any algorithm in the streaming model of computation. The main idea of this paper is to convert the commonly used aggregation tree into an aggregation list, and a process for doing this conversion is also presented. This result is an interesting and important first step towards achieving the realization of general secure querying of a sensor network.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115420124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-04-11DOI: 10.1109/ICDE.2011.5767854
Julia Stoyanovich, S. Davidson, T. Milo, V. Tannen
Many real-world applications deal with uncertain or missing data, prompting a surge of activity in the area of probabilistic databases. A shortcoming of prior work is the assumption that an appropriate probabilistic model, along with the necessary probability distributions, is given. We address this shortcoming by presenting a framework for learning a set of inference ensembles, termed meta-rule semi-lattices, or MRSL, from the complete portion of the data. We use the MRSL to infer probability distributions for missing data, and demonstrate experimentally that high accuracy is achieved when a single attribute value is missing per tuple. We next propose an inference algorithm based on Gibbs sampling that accurately predicts the probability distribution for multiple missing values. We also develop an optimization that greatly improves performance of multi-attribute inference for collections of tuples, while maintaining high accuracy. Finally, we develop an experimental framework to evaluate the efficiency and accuracy of our approach.
{"title":"Deriving probabilistic databases with inference ensembles","authors":"Julia Stoyanovich, S. Davidson, T. Milo, V. Tannen","doi":"10.1109/ICDE.2011.5767854","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767854","url":null,"abstract":"Many real-world applications deal with uncertain or missing data, prompting a surge of activity in the area of probabilistic databases. A shortcoming of prior work is the assumption that an appropriate probabilistic model, along with the necessary probability distributions, is given. We address this shortcoming by presenting a framework for learning a set of inference ensembles, termed meta-rule semi-lattices, or MRSL, from the complete portion of the data. We use the MRSL to infer probability distributions for missing data, and demonstrate experimentally that high accuracy is achieved when a single attribute value is missing per tuple. We next propose an inference algorithm based on Gibbs sampling that accurately predicts the probability distribution for multiple missing values. We also develop an optimization that greatly improves performance of multi-attribute inference for collections of tuples, while maintaining high accuracy. Finally, we develop an experimental framework to evaluate the efficiency and accuracy of our approach.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117057017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-04-11DOI: 10.1109/ICDE.2011.5767955
B. Bustos, T. Skopal
This tutorial surveys domains employing non-metric functions for effective similarity search, and methods for efficient non-metric similarity search in very large collections.
{"title":"Non-metric similarity search problems in very large collections","authors":"B. Bustos, T. Skopal","doi":"10.1109/ICDE.2011.5767955","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767955","url":null,"abstract":"This tutorial surveys domains employing non-metric functions for effective similarity search, and methods for efficient non-metric similarity search in very large collections.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127122678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-04-11DOI: 10.1109/ICDE.2011.5767923
Maria Kontaki, A. Gounaris, A. Papadopoulos, K. Tsichlas, Y. Manolopoulos
Anomaly detection is considered an important data mining task, aiming at the discovery of elements (also known as outliers) that show significant diversion from the expected case. More specifically, given a set of objects the problem is to return the suspicious objects that deviate significantly from the typical behavior. As in the case of clustering, the application of different criteria lead to different definitions for an outlier. In this work, we focus on distance-based outliers: an object x is an outlier if there are less than k objects lying at distance at most R from x. The problem offers significant challenges when a stream-based environment is considered, where data arrive continuously and outliers must be detected on-the-fly. There are a few research works studying the problem of continuous outlier detection. However, none of these proposals meets the requirements of modern stream-based applications for the following reasons: (i) they demand a significant storage overhead, (ii) their efficiency is limited and (iii) they lack flexibility. In this work, we propose new algorithms for continuous outlier monitoring in data streams, based on sliding windows. Our techniques are able to reduce the required storage overhead, run faster than previously proposed techniques and offer significant flexibility. Experiments performed on real-life as well as synthetic data sets verify our theoretical study.
{"title":"Continuous monitoring of distance-based outliers over data streams","authors":"Maria Kontaki, A. Gounaris, A. Papadopoulos, K. Tsichlas, Y. Manolopoulos","doi":"10.1109/ICDE.2011.5767923","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767923","url":null,"abstract":"Anomaly detection is considered an important data mining task, aiming at the discovery of elements (also known as outliers) that show significant diversion from the expected case. More specifically, given a set of objects the problem is to return the suspicious objects that deviate significantly from the typical behavior. As in the case of clustering, the application of different criteria lead to different definitions for an outlier. In this work, we focus on distance-based outliers: an object x is an outlier if there are less than k objects lying at distance at most R from x. The problem offers significant challenges when a stream-based environment is considered, where data arrive continuously and outliers must be detected on-the-fly. There are a few research works studying the problem of continuous outlier detection. However, none of these proposals meets the requirements of modern stream-based applications for the following reasons: (i) they demand a significant storage overhead, (ii) their efficiency is limited and (iii) they lack flexibility. In this work, we propose new algorithms for continuous outlier monitoring in data streams, based on sliding windows. Our techniques are able to reduce the required storage overhead, run faster than previously proposed techniques and offer significant flexibility. Experiments performed on real-life as well as synthetic data sets verify our theoretical study.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116119019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-04-11DOI: 10.1109/ICDE.2011.5767963
J. Gehrke
Scalability is a fundamental problem in the development of computer games and massively multiplayer online games (MMOs). Players always demand more — more polygons, more physics particles, more interesting AI behavior, more monsters, more simultaneous players and interactions, and larger virtual worlds.
{"title":"Playing games with databases","authors":"J. Gehrke","doi":"10.1109/ICDE.2011.5767963","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767963","url":null,"abstract":"Scalability is a fundamental problem in the development of computer games and massively multiplayer online games (MMOs). Players always demand more — more polygons, more physics particles, more interesting AI behavior, more monsters, more simultaneous players and interactions, and larger virtual worlds.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122495475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-04-11DOI: 10.1109/ICDE.2011.5767962
M. Tamer Özsu, P. Valduriez, S. Abiteboul, Bettina Kemme, R. Jiménez-Peris, B. Ooi
Work on distributed data management commenced shortly after the introduction of the relational model in the mid-1970's. 1970's and 1980's were very active periods for the development of distributed relational database technology, and claims were made that in the following ten years centralized databases will be an “antique curiosity” and most organizations will move toward distributed database managers [1]. That prediction has certainly become true, and all commercial DBMSs today are distributed.
{"title":"Distributed data management in 2020?","authors":"M. Tamer Özsu, P. Valduriez, S. Abiteboul, Bettina Kemme, R. Jiménez-Peris, B. Ooi","doi":"10.1109/ICDE.2011.5767962","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767962","url":null,"abstract":"Work on distributed data management commenced shortly after the introduction of the relational model in the mid-1970's. 1970's and 1980's were very active periods for the development of distributed relational database technology, and claims were made that in the following ten years centralized databases will be an “antique curiosity” and most organizations will move toward distributed database managers [1]. That prediction has certainly become true, and all commercial DBMSs today are distributed.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121847213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-04-11DOI: 10.1109/ICDE.2011.5767881
Yu Cao, Chun Chen, Fei Guo, Dawei Jiang, Yuting Lin, B. Ooi, Hoang Tam Vo, Sai Wu, Quanqing Xu
Cloud computing represents a paradigm shift driven by the increasing demand of Web based applications for elastic, scalable and efficient system architectures that can efficiently support their ever-growing data volume and large-scale data analysis. A typical data management system has to deal with real-time updates by individual users, and as well as periodical large scale analytical processing, indexing, and data extraction. While such operations may take place in the same domain, the design and development of the systems have somehow evolved independently for transactional and periodical analytical processing. Such a system-level separation has resulted in problems such as data freshness as well as serious data storage redundancy. Ideally, it would be more efficient to apply ad-hoc analytical processing on the same data directly. However, to the best of our knowledge, such an approach has not been adopted in real implementation. Intrigued by such an observation, we have designed and implemented epiC, an elastic power-aware data-itensive Cloud platform for supporting both data intensive analytical operations (ref. as OLAP) and online transactions (ref. as OLTP). In this paper, we present ES2 - the elastic data storage system of epiC, which is designed to support both functionalities within the same storage. We present the system architecture and the functions of each system component, and experimental results which demonstrate the efficiency of the system.
{"title":"ES2: A cloud data storage system for supporting both OLTP and OLAP","authors":"Yu Cao, Chun Chen, Fei Guo, Dawei Jiang, Yuting Lin, B. Ooi, Hoang Tam Vo, Sai Wu, Quanqing Xu","doi":"10.1109/ICDE.2011.5767881","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767881","url":null,"abstract":"Cloud computing represents a paradigm shift driven by the increasing demand of Web based applications for elastic, scalable and efficient system architectures that can efficiently support their ever-growing data volume and large-scale data analysis. A typical data management system has to deal with real-time updates by individual users, and as well as periodical large scale analytical processing, indexing, and data extraction. While such operations may take place in the same domain, the design and development of the systems have somehow evolved independently for transactional and periodical analytical processing. Such a system-level separation has resulted in problems such as data freshness as well as serious data storage redundancy. Ideally, it would be more efficient to apply ad-hoc analytical processing on the same data directly. However, to the best of our knowledge, such an approach has not been adopted in real implementation. Intrigued by such an observation, we have designed and implemented epiC, an elastic power-aware data-itensive Cloud platform for supporting both data intensive analytical operations (ref. as OLAP) and online transactions (ref. as OLTP). In this paper, we present ES2 - the elastic data storage system of epiC, which is designed to support both functionalities within the same storage. We present the system architecture and the functions of each system component, and experimental results which demonstrate the efficiency of the system.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122158401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-04-11DOI: 10.1109/ICDE.2011.5767943
Ioannis K. Paparrizos, Hoyoung Jeung, K. Aberer
As sensors continue to proliferate, the capabilities of effectively querying not only sensor data but also its metadata becomes important in a wide range of applications. This paper demonstrates a search system that utilizes various techniques and tools for querying sensor metadata and visualizing the results. Our system provides an easy-to-use query interface, built upon semantic technologies where users can freely store and query their metadata. Going beyond basic keyword search, the system provides a variety of advanced functionalities tailored for sensor metadata search; ordering search results according to our ranking mechanism based on the PageRank algorithm, recommending pages that contain relevant metadata information to given search conditions, presenting search results using various visualization tools, and offering dynamic hypergraphs and tag clouds of metadata. The system has been running as a real application and its effectiveness has been proved by a number of users.
{"title":"Advanced search, visualization and tagging of sensor metadata","authors":"Ioannis K. Paparrizos, Hoyoung Jeung, K. Aberer","doi":"10.1109/ICDE.2011.5767943","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767943","url":null,"abstract":"As sensors continue to proliferate, the capabilities of effectively querying not only sensor data but also its metadata becomes important in a wide range of applications. This paper demonstrates a search system that utilizes various techniques and tools for querying sensor metadata and visualizing the results. Our system provides an easy-to-use query interface, built upon semantic technologies where users can freely store and query their metadata. Going beyond basic keyword search, the system provides a variety of advanced functionalities tailored for sensor metadata search; ordering search results according to our ranking mechanism based on the PageRank algorithm, recommending pages that contain relevant metadata information to given search conditions, presenting search results using various visualization tools, and offering dynamic hypergraphs and tag clouds of metadata. The system has been running as a real application and its effectiveness has been proved by a number of users.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114073013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-04-11DOI: 10.1109/ICDE.2011.5767911
James Cheng, Yiping Ke, Shumo Chu, M. Tamer Özsu
The k-core of a graph is the largest subgraph in which every vertex is connected to at least k other vertices within the subgraph. Core decomposition finds the k-core of the graph for every possible k. Past studies have shown important applications of core decomposition such as in the study of the properties of large networks (e.g., sustainability, connectivity, centrality, etc.), for solving NP-hard problems efficiently in real networks (e.g., maximum clique finding, densest subgraph approximation, etc.), and for large-scale network fingerprinting and visualization. The k-core is a well accepted concept partly because there exists a simple and efficient algorithm for core decomposition, by recursively removing the lowest degree vertices and their incident edges. However, this algorithm requires random access to the graph and hence assumes the entire graph can be kept in main memory. Nevertheless, real-world networks such as online social networks have become exceedingly large in recent years and still keep growing at a steady rate. In this paper, we propose the first external-memory algorithm for core decomposition in massive graphs. When the memory is large enough to hold the graph, our algorithm achieves comparable performance as the in-memory algorithm. When the graph is too large to be kept in the memory, our algorithm requires only O(kmax) scans of the graph, where kmax is the largest core number of the graph. We demonstrate the efficiency of our algorithm on real networks with up to 52.9 million vertices and 1.65 billion edges.
{"title":"Efficient core decomposition in massive networks","authors":"James Cheng, Yiping Ke, Shumo Chu, M. Tamer Özsu","doi":"10.1109/ICDE.2011.5767911","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767911","url":null,"abstract":"The k-core of a graph is the largest subgraph in which every vertex is connected to at least k other vertices within the subgraph. Core decomposition finds the k-core of the graph for every possible k. Past studies have shown important applications of core decomposition such as in the study of the properties of large networks (e.g., sustainability, connectivity, centrality, etc.), for solving NP-hard problems efficiently in real networks (e.g., maximum clique finding, densest subgraph approximation, etc.), and for large-scale network fingerprinting and visualization. The k-core is a well accepted concept partly because there exists a simple and efficient algorithm for core decomposition, by recursively removing the lowest degree vertices and their incident edges. However, this algorithm requires random access to the graph and hence assumes the entire graph can be kept in main memory. Nevertheless, real-world networks such as online social networks have become exceedingly large in recent years and still keep growing at a steady rate. In this paper, we propose the first external-memory algorithm for core decomposition in massive graphs. When the memory is large enough to hold the graph, our algorithm achieves comparable performance as the in-memory algorithm. When the graph is too large to be kept in the memory, our algorithm requires only O(kmax) scans of the graph, where kmax is the largest core number of the graph. We demonstrate the efficiency of our algorithm on real networks with up to 52.9 million vertices and 1.65 billion edges.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121629143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-04-11DOI: 10.1109/ICDE.2011.5767928
Pengcheng Xiong, Yun Chi, Shenghuo Zhu, H. J. Moon, C. Pu, Hakan Hacıgümüş
In a cloud computing environment, resources are shared among different clients. Intelligently managing and allocating resources among various clients is important for system providers, whose business model relies on managing the infrastructure resources in a cost-effective manner while satisfying the client service level agreements (SLAs). In this paper, we address the issue of how to intelligently manage the resources in a shared cloud database system and present SmartSLA, a cost-aware resource management system. SmartSLA consists of two main components: the system modeling module and the resource allocation decision module. The system modeling module uses machine learning techniques to learn a model that describes the potential profit margins for each client under different resource allocations. Based on the learned model, the resource allocation decision module dynamically adjusts the resource allocations in order to achieve the optimum profits. We evaluate SmartSLA by using the TPC-W benchmark with workload characteristics derived from real-life systems. The performance results indicate that SmartSLA can successfully compute predictive models under different hardware resource allocations, such as CPU and memory, as well as database specific resources, such as the number of replicas in the database systems. The experimental results also show that SmartSLA can provide intelligent service differentiation according to factors such as variable workloads, SLA levels, resource costs, and deliver improved profit margins.
{"title":"Intelligent management of virtualized resources for database systems in cloud environment","authors":"Pengcheng Xiong, Yun Chi, Shenghuo Zhu, H. J. Moon, C. Pu, Hakan Hacıgümüş","doi":"10.1109/ICDE.2011.5767928","DOIUrl":"https://doi.org/10.1109/ICDE.2011.5767928","url":null,"abstract":"In a cloud computing environment, resources are shared among different clients. Intelligently managing and allocating resources among various clients is important for system providers, whose business model relies on managing the infrastructure resources in a cost-effective manner while satisfying the client service level agreements (SLAs). In this paper, we address the issue of how to intelligently manage the resources in a shared cloud database system and present SmartSLA, a cost-aware resource management system. SmartSLA consists of two main components: the system modeling module and the resource allocation decision module. The system modeling module uses machine learning techniques to learn a model that describes the potential profit margins for each client under different resource allocations. Based on the learned model, the resource allocation decision module dynamically adjusts the resource allocations in order to achieve the optimum profits. We evaluate SmartSLA by using the TPC-W benchmark with workload characteristics derived from real-life systems. The performance results indicate that SmartSLA can successfully compute predictive models under different hardware resource allocations, such as CPU and memory, as well as database specific resources, such as the number of replicas in the database systems. The experimental results also show that SmartSLA can provide intelligent service differentiation according to factors such as variable workloads, SLA levels, resource costs, and deliver improved profit margins.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124026298","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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