In this paper, we address the problem of low-latency routing in a vehicular highway network. To cover long highways while minimizing the number of required roadside access points, we utilize vehicle-to-vehicle communication to propagate data in the network. Vehicular networks are highly dynamic, and hence routing algorithms that require global network state information or centralized coordination are not suitable for such networks. Instead, we develop a novel distributed routing algorithm that requires minimal coordination among vehicles, while achieving a highly efficient throughput-delay tradeoff. Specifically, we show that the proposed algorithm achieves a throughput that is within a factor of 1/e of the throughput of an algorithm that centrally coordinates vehicle transmissions in a highly dense network, and yet its end-to-end delay is approximately half of that of a widely studied ALOHA-based randomized routing algorithm. We evaluate our algorithm analytically and through simulations and compare its throughput-delay performance against the ALOHA-based randomized routing.
{"title":"Distributed Routing for Vehicular Ad Hoc Networks: Throughput-Delay Tradeoff","authors":"A. Abedi, Majid Ghaderi, C. Williamson","doi":"10.1109/MASCOTS.2010.14","DOIUrl":"https://doi.org/10.1109/MASCOTS.2010.14","url":null,"abstract":"In this paper, we address the problem of low-latency routing in a vehicular highway network. To cover long highways while minimizing the number of required roadside access points, we utilize vehicle-to-vehicle communication to propagate data in the network. Vehicular networks are highly dynamic, and hence routing algorithms that require global network state information or centralized coordination are not suitable for such networks. Instead, we develop a novel distributed routing algorithm that requires minimal coordination among vehicles, while achieving a highly efficient throughput-delay tradeoff. Specifically, we show that the proposed algorithm achieves a throughput that is within a factor of 1/e of the throughput of an algorithm that centrally coordinates vehicle transmissions in a highly dense network, and yet its end-to-end delay is approximately half of that of a widely studied ALOHA-based randomized routing algorithm. We evaluate our algorithm analytically and through simulations and compare its throughput-delay performance against the ALOHA-based randomized routing.","PeriodicalId":406889,"journal":{"name":"2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125103711","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 predominant portion of Internet services, like content delivery networks, news broadcasting, blogs sharing and social networks, etc., is data centric. A significant amount of new data is generated by these services each day. To efficiently store and maintain backups for such data is a challenging task for current data storage systems. Chunking based deduplication (dedup) methods are widely used to eliminate redundant data and hence reduce the required total storage space. In this paper, we propose a novel Frequency Based Chunking (FBC) algorithm. Unlike the most popular Content-Defined Chunking (CDC) algorithm which divides the data stream randomly according to the content, FBC explicitly utilizes the chunk frequency information in the data stream to enhance the data deduplication gain especially when the metadata overhead is taken into consideration. The FBC algorithm consists of two components, a statistical chunk frequency estimation algorithm for identifying the globally appeared frequent chunks, and a two-stage chunking algorithm which uses these chunk frequencies to obtain a better chunking result. To evaluate the effectiveness of the proposed FBC algorithm, we conducted extensive experiments on heterogeneous datasets. In all experiments, the FBC algorithm persistently outperforms the CDC algorithm in terms of achieving a better dedup gain or producing much less number of chunks. Particularly, our experiments show that FBC produces 2.5 ~ 4 times less number of chunks than that of a baseline CDC which achieving the same Duplicate Elimination Ratio (DER). Another benefit of FBC over CDC is that the FBC with average chunk size greater than or equal to that of CDC achieves up to 50% higher DER than that of a CDC algorithm.
互联网服务的主要部分，如内容交付网络、新闻广播、博客分享和社交网络等，都是以数据为中心的。这些服务每天都会产生大量的新数据。有效地存储和维护这些数据的备份对于当前的数据存储系统来说是一项具有挑战性的任务。基于分块的重复数据删除(dedup)方法被广泛用于消除冗余数据，从而减少所需的总存储空间。在本文中，我们提出了一种新的基于频率的分块算法。与最流行的CDC (content - defined Chunking)算法(根据内容随机划分数据流)不同，FBC明确地利用数据流中的块频率信息来提高重复数据删除的增益，特别是在考虑元数据开销的情况下。FBC算法由两个部分组成，一个是用于识别全局出现的频繁块的统计块频率估计算法，另一个是利用这些块频率获得更好的分块结果的两阶段分块算法。为了评估所提出的FBC算法的有效性，我们在异构数据集上进行了大量的实验。在所有实验中，FBC算法在获得更好的去噪增益或产生更少的块数量方面始终优于CDC算法。特别是，我们的实验表明，在达到相同的重复消除比(DER)的情况下，FBC产生的块数量比基线CDC少2.5 ~ 4倍。与CDC相比，FBC的另一个优点是，平均块大小大于或等于CDC的FBC算法的DER比CDC算法高50%。
{"title":"Frequency Based Chunking for Data De-Duplication","authors":"Guanlin Lu, Yu Jin, D. Du","doi":"10.1109/MASCOTS.2010.37","DOIUrl":"https://doi.org/10.1109/MASCOTS.2010.37","url":null,"abstract":"A predominant portion of Internet services, like content delivery networks, news broadcasting, blogs sharing and social networks, etc., is data centric. A significant amount of new data is generated by these services each day. To efficiently store and maintain backups for such data is a challenging task for current data storage systems. Chunking based deduplication (dedup) methods are widely used to eliminate redundant data and hence reduce the required total storage space. In this paper, we propose a novel Frequency Based Chunking (FBC) algorithm. Unlike the most popular Content-Defined Chunking (CDC) algorithm which divides the data stream randomly according to the content, FBC explicitly utilizes the chunk frequency information in the data stream to enhance the data deduplication gain especially when the metadata overhead is taken into consideration. The FBC algorithm consists of two components, a statistical chunk frequency estimation algorithm for identifying the globally appeared frequent chunks, and a two-stage chunking algorithm which uses these chunk frequencies to obtain a better chunking result. To evaluate the effectiveness of the proposed FBC algorithm, we conducted extensive experiments on heterogeneous datasets. In all experiments, the FBC algorithm persistently outperforms the CDC algorithm in terms of achieving a better dedup gain or producing much less number of chunks. Particularly, our experiments show that FBC produces 2.5 ~ 4 times less number of chunks than that of a baseline CDC which achieving the same Duplicate Elimination Ratio (DER). Another benefit of FBC over CDC is that the FBC with average chunk size greater than or equal to that of CDC achieves up to 50% higher DER than that of a CDC algorithm.","PeriodicalId":406889,"journal":{"name":"2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133190118","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}
Controlling energy usage in data centers, and storage in particular, continues to rise in importance. Many systems and models have examined energy efficiency through intelligent spin-down of disks and novel data layouts, yet little work has been done to examine how power usage over the course of months to years is impacted by the characteristics of the storage devices chosen for use. Long-term power usage is particularly important for archival storage systems, since it is a large contributor to overall system cost. In this work, we begin exploring the impact that broad policies (e.g. utilize high-bandwidth devices first) have upon the power efficiency of a disk based archival storage system of heterogeneous devices over the course of a year. Using a discrete event simulator, we found that even simple heuristic policies for allocating space can have significant impact on the power usage of a system. We show that our system growth policies can cause power usage to vary from 10% higher to 18% lower than a naive random data allocation scheme. We also found that under low read rates power is dominated by that used in standby modes. Most interestingly, we found cases where concentrating data on fewer devices yielded increased power usage.
{"title":"Examining Energy Use in Heterogeneous Archival Storage Systems","authors":"I. Adams, E. L. Miller, M. Storer","doi":"10.1109/MASCOTS.2010.38","DOIUrl":"https://doi.org/10.1109/MASCOTS.2010.38","url":null,"abstract":"Controlling energy usage in data centers, and storage in particular, continues to rise in importance. Many systems and models have examined energy efficiency through intelligent spin-down of disks and novel data layouts, yet little work has been done to examine how power usage over the course of months to years is impacted by the characteristics of the storage devices chosen for use. Long-term power usage is particularly important for archival storage systems, since it is a large contributor to overall system cost. In this work, we begin exploring the impact that broad policies (e.g. utilize high-bandwidth devices first) have upon the power efficiency of a disk based archival storage system of heterogeneous devices over the course of a year. Using a discrete event simulator, we found that even simple heuristic policies for allocating space can have significant impact on the power usage of a system. We show that our system growth policies can cause power usage to vary from 10% higher to 18% lower than a naive random data allocation scheme. We also found that under low read rates power is dominated by that used in standby modes. Most interestingly, we found cases where concentrating data on fewer devices yielded increased power usage.","PeriodicalId":406889,"journal":{"name":"2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114718268","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}
Faults in control flow-changing instructions are critical for correct execution because the faults could change the behavior of programs very differently from what they are expected to show. The conventional techniques to deal with control flow vulnerability typically add extra instructions to detect control flow-related faults, which increase both static and dynamic instructions, consequently, execution time and energy consumption. In contrast, we make our own control flow vulnerability model to evaluate the effects of different compiler optimizations. We find that different programs show very different degrees of control flow vulnerabilities and some compiler optimizations have high correlation to control flow vulnerability. The results observed in this work can be used to generate more resilient code against control flow-related faults.
{"title":"Modeling and Evaluation of Control Flow Vulnerability in the Embedded System","authors":"M. Rouf, Soontae Kim","doi":"10.1109/MASCOTS.2010.71","DOIUrl":"https://doi.org/10.1109/MASCOTS.2010.71","url":null,"abstract":"Faults in control flow-changing instructions are critical for correct execution because the faults could change the behavior of programs very differently from what they are expected to show. The conventional techniques to deal with control flow vulnerability typically add extra instructions to detect control flow-related faults, which increase both static and dynamic instructions, consequently, execution time and energy consumption. In contrast, we make our own control flow vulnerability model to evaluate the effects of different compiler optimizations. We find that different programs show very different degrees of control flow vulnerabilities and some compiler optimizations have high correlation to control flow vulnerability. The results observed in this work can be used to generate more resilient code against control flow-related faults.","PeriodicalId":406889,"journal":{"name":"2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems","volume":"28 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129418251","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}
Caroline Collange, M. Daumas, D. Defour, David Parello
We present Barra, a simulator of Graphics Processing Units (GPU) tuned for general purpose processing (GPGPU). It is based on the UNISIM framework and it simulates the native instruction set of the Tesla architecture at the functional level. The inputs are CUDA executables produced by NVIDIA tools. No alterations are needed to perform simulations. As it uses parallelism, Barra generates detailed statistics on executions in about the time needed by CUDA to operate in emulation mode. We use it to understand and explore the micro-architecture design spaces of GPUs.
{"title":"Barra: A Parallel Functional Simulator for GPGPU","authors":"Caroline Collange, M. Daumas, D. Defour, David Parello","doi":"10.1109/MASCOTS.2010.43","DOIUrl":"https://doi.org/10.1109/MASCOTS.2010.43","url":null,"abstract":"We present Barra, a simulator of Graphics Processing Units (GPU) tuned for general purpose processing (GPGPU). It is based on the UNISIM framework and it simulates the native instruction set of the Tesla architecture at the functional level. The inputs are CUDA executables produced by NVIDIA tools. No alterations are needed to perform simulations. As it uses parallelism, Barra generates detailed statistics on executions in about the time needed by CUDA to operate in emulation mode. We use it to understand and explore the micro-architecture design spaces of GPUs.","PeriodicalId":406889,"journal":{"name":"2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129757901","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}
Execution and communication traces are central to performance modeling and analysis. Since the traces can be very long, meaningful compression and extraction of representative behavior is important. Commonly used compression procedures identify repeating patterns in sections of the input string and replace each instance with a representative symbol. This can prevent the identification of long repeating sequences corresponding to outer loops in a trace. This paper introduces and analyzes a framework for identifying the maximal loop nest from a trace. The discovery of loop nests makes construction of compressed representative traces straightforward. The paper also introduces a greedy algorithm for fast ``near optimal'' loop nest discovery with well defined bounds. Results of compressing MPI communication traces of NAS parallel benchmarks show that both algorithms identified the basic loop structures correctly. The greedy algorithm was also very efficient with an average processing time of 16.5 seconds for an average trace length of 71695 MPI events.
{"title":"Efficient Discovery of Loop Nests in Execution Traces","authors":"Qiang Xu, J. Subhlok, Nathaniel Hammen","doi":"10.1109/MASCOTS.2010.28","DOIUrl":"https://doi.org/10.1109/MASCOTS.2010.28","url":null,"abstract":"Execution and communication traces are central to performance modeling and analysis. Since the traces can be very long, meaningful compression and extraction of representative behavior is important. Commonly used compression procedures identify repeating patterns in sections of the input string and replace each instance with a representative symbol. This can prevent the identification of long repeating sequences corresponding to outer loops in a trace. This paper introduces and analyzes a framework for identifying the maximal loop nest from a trace. The discovery of loop nests makes construction of compressed representative traces straightforward. The paper also introduces a greedy algorithm for fast ``near optimal'' loop nest discovery with well defined bounds. Results of compressing MPI communication traces of NAS parallel benchmarks show that both algorithms identified the basic loop structures correctly. The greedy algorithm was also very efficient with an average processing time of 16.5 seconds for an average trace length of 71695 MPI events.","PeriodicalId":406889,"journal":{"name":"2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130755557","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}
G. Kunz, O. Landsiedel, S. Götz, Klaus Wehrle, J. Gross, Farshad Naghibi
The simulation models of wireless networks rapidly increase in complexity to accurately model wireless channel characteristics and the properties of advanced transmission technologies. Such detailed models typically lead to a high computational load per simulation event that accumulates to extensive simulation runtimes. Reducing runtimes through parallelization is challenging since it depends on detecting causally independent events that can execute concurrently. Most existing approaches base this detection on lookaheads derived from channel propagation latency or protocol characteristics. In wireless networks, these lookaheads are typically short, causing the potential for parallelization and the achievable speedup to remain small. This paper presents Horizon, which unlocks a substantial portion of a simulation model's workload for parallelization by going beyond the traditional lookahead. We show how to augment discrete events with durations to identify a much larger horizon of independent simulation events and efficiently schedule them on multi-core systems. Our evaluation shows that this approach can significantly cut down the runtime of simulations, in particular for complex and accurate models of wireless networks.
{"title":"Expanding the Event Horizon in Parallelized Network Simulations","authors":"G. Kunz, O. Landsiedel, S. Götz, Klaus Wehrle, J. Gross, Farshad Naghibi","doi":"10.1109/MASCOTS.2010.26","DOIUrl":"https://doi.org/10.1109/MASCOTS.2010.26","url":null,"abstract":"The simulation models of wireless networks rapidly increase in complexity to accurately model wireless channel characteristics and the properties of advanced transmission technologies. Such detailed models typically lead to a high computational load per simulation event that accumulates to extensive simulation runtimes. Reducing runtimes through parallelization is challenging since it depends on detecting causally independent events that can execute concurrently. Most existing approaches base this detection on lookaheads derived from channel propagation latency or protocol characteristics. In wireless networks, these lookaheads are typically short, causing the potential for parallelization and the achievable speedup to remain small. This paper presents Horizon, which unlocks a substantial portion of a simulation model's workload for parallelization by going beyond the traditional lookahead. We show how to augment discrete events with durations to identify a much larger horizon of independent simulation events and efficiently schedule them on multi-core systems. Our evaluation shows that this approach can significantly cut down the runtime of simulations, in particular for complex and accurate models of wireless networks.","PeriodicalId":406889,"journal":{"name":"2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126265454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present two new cooperative caching algorithms that allow a cluster of file system clients to cache chunks of files instead of directly accessing them from origin file servers. The first algorithm, called C-LRU (Cooperative-LRU), is based on the simple D-LRU (Distributed-LRU) algorithm, but moves a chunk's position closer to the tail of its local LRU list when the number of copies of the chunk increases. The second algorithm, called RobinHood, is based on the N-Chance algorithm, but targets chunks cached at many clients for replacement when forwarding a singlet to a peer. We evaluate these algorithms on a variety of workloads, including several publicly available traces, and find that the new algorithms significantly outperform their predecessors.
{"title":"New Algorithms for File System Cooperative Caching","authors":"Eric Anderson, Christopher Hoover, Xiaozhou Li","doi":"10.1109/MASCOTS.2010.59","DOIUrl":"https://doi.org/10.1109/MASCOTS.2010.59","url":null,"abstract":"We present two new cooperative caching algorithms that allow a cluster of file system clients to cache chunks of files instead of directly accessing them from origin file servers. The first algorithm, called C-LRU (Cooperative-LRU), is based on the simple D-LRU (Distributed-LRU) algorithm, but moves a chunk's position closer to the tail of its local LRU list when the number of copies of the chunk increases. The second algorithm, called RobinHood, is based on the N-Chance algorithm, but targets chunks cached at many clients for replacement when forwarding a singlet to a peer. We evaluate these algorithms on a variety of workloads, including several publicly available traces, and find that the new algorithms significantly outperform their predecessors.","PeriodicalId":406889,"journal":{"name":"2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133965049","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}
Rui Zhang, D. Chambliss, P. Pandey, William Shearman, J. Ruiz, Yan Xu, Joseph Hyde
Data storage is an integral part of IT infrastructures, where Quality of Service (QoS) differentiation amongst customers and their applications is essential for many. Achieving this objective in a production environment is nontrivial, because these environments are complex and dynamic. Numerous practical and engineering constraints render the task even more challenging. This paper presents SLED-2, a QoS differentiation solution that meets these challenges in offering effective protection to the performance of important workloads at the expense of less important workloads when needed. SLED-2 uses a customized feedback heuristic that rate-limits selected I/O streams. This approach is unique in that it accounts for a number of important practical considerations, including fine-grained controls, errors in storage systems models, and inexpensive and safe QoS management. SLED-2 has been implemented for the IBM DS8000 series storage servers and shown to be highly effective in a set of hostile and practical scenarios using test facilities for IBM storage products.
{"title":"Effective Quality of Service Differentiation for Real-world Storage Systems","authors":"Rui Zhang, D. Chambliss, P. Pandey, William Shearman, J. Ruiz, Yan Xu, Joseph Hyde","doi":"10.1109/MASCOTS.2010.63","DOIUrl":"https://doi.org/10.1109/MASCOTS.2010.63","url":null,"abstract":"Data storage is an integral part of IT infrastructures, where Quality of Service (QoS) differentiation amongst customers and their applications is essential for many. Achieving this objective in a production environment is nontrivial, because these environments are complex and dynamic. Numerous practical and engineering constraints render the task even more challenging. This paper presents SLED-2, a QoS differentiation solution that meets these challenges in offering effective protection to the performance of important workloads at the expense of less important workloads when needed. SLED-2 uses a customized feedback heuristic that rate-limits selected I/O streams. This approach is unique in that it accounts for a number of important practical considerations, including fine-grained controls, errors in storage systems models, and inexpensive and safe QoS management. SLED-2 has been implemented for the IBM DS8000 series storage servers and shown to be highly effective in a set of hostile and practical scenarios using test facilities for IBM storage products.","PeriodicalId":406889,"journal":{"name":"2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114537393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose Clasas (from the Castilian “Claves seguras” for “secure keys”), a key-store for distributed storage such in the Cloud. The security of Clasas derives from breaking keys into K shares and storing the key shares at many different sites. This provides both a probabilistic and a deterministic guarantee against an adversary trying to obtain keys. The probabilistic guarantee is based on a combinatorial explosion, which forces an adversary to subvert a very large portion of the storage sites for even a minute chance of obtaining a key. The deterministic guarantee stems from the use of LH* distributed linear hashing. Our use of the LH* addressing rules insures that no two key shares (belonging to the same key) are ever, even in transit, stored at the same site. Consequentially, an adversary has to subvert at least K sites. In addition, even an insider with extensive administrative privileges over many of the sites used for key storage is prevented from obtaining access to any key. Our key-store uses LH* or its scalable availability derivate, LH*RS to distribute key shares among a varying number of storage sites in a manner transparent to its users. While an adversary faces very high obstacles in obtaining a key, clients or authorized entities acting on their behalf can access keys with a very small number of messages, even if they do not know all sites where key shares are stored. This allows easy sharing of keys, rekeying, and key revocation.
{"title":"Clasas: A Key-Store for the Cloud","authors":"T. Schwarz, D. Long","doi":"10.1109/MASCOTS.2010.35","DOIUrl":"https://doi.org/10.1109/MASCOTS.2010.35","url":null,"abstract":"We propose Clasas (from the Castilian “Claves seguras” for “secure keys”), a key-store for distributed storage such in the Cloud. The security of Clasas derives from breaking keys into K shares and storing the key shares at many different sites. This provides both a probabilistic and a deterministic guarantee against an adversary trying to obtain keys. The probabilistic guarantee is based on a combinatorial explosion, which forces an adversary to subvert a very large portion of the storage sites for even a minute chance of obtaining a key. The deterministic guarantee stems from the use of LH* distributed linear hashing. Our use of the LH* addressing rules insures that no two key shares (belonging to the same key) are ever, even in transit, stored at the same site. Consequentially, an adversary has to subvert at least K sites. In addition, even an insider with extensive administrative privileges over many of the sites used for key storage is prevented from obtaining access to any key. Our key-store uses LH* or its scalable availability derivate, LH*RS to distribute key shares among a varying number of storage sites in a manner transparent to its users. While an adversary faces very high obstacles in obtaining a key, clients or authorized entities acting on their behalf can access keys with a very small number of messages, even if they do not know all sites where key shares are stored. This allows easy sharing of keys, rekeying, and key revocation.","PeriodicalId":406889,"journal":{"name":"2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114851870","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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