{"title":"Archie: a speculative replicated transactional system","authors":"Sachin Hirve, R. Palmieri, B. Ravindran","doi":"10.1145/2663165.2666090","DOIUrl":"https://doi.org/10.1145/2663165.2666090","url":null,"abstract":"We present Archie, a high performance fault-tolerant transactional system. Archie complies with the State Machine Approach, where the transactional state is fully replicated and total ordered transactions are executed on the replicas. Archie avoids the serial execution after transactions get ordered, which is the typical bottleneck of those protocols, by anticipating the work and using speculation to process transactions in parallel, enforcing a predefined order. The key feature of Archie is to avoid any non-trivial operation to perform post total order's notification, in case the sequencer node remains stable (only a single timestamp increment is needed for committing a transaction). This approach significantly shortens the transaction's critical path. The contention of speculative execution is always kept limited by activating a fixed number of transactions at a time. A comprehensive evaluation, using three competitors and three well known benchmarks, shows that Archie outperforms competitors in all medium/high contention scenarios.","PeriodicalId":262822,"journal":{"name":"International Middleware Conference","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126555207","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}
{"title":"Tiera: towards flexible multi-tiered cloud storage instances","authors":"A. Raghavan, A. Chandra, J. Weissman","doi":"10.1145/2663165.2663333","DOIUrl":"https://doi.org/10.1145/2663165.2663333","url":null,"abstract":"Cloud providers offer an array of storage services that represent different points along the performance, cost, and durability spectrum. If an application desires the composite benefits of multiple storage tiers, then it must manage the complexity of different interfaces to these storage services and their diverse policies. We believe that it is possible to provide the benefits of customized tiered cloud storage to applications without compromising simplicity using a lightweight middleware. In this paper, we introduce Tiera, a middleware that enables the provision of multi-tiered cloud storage instances that are easy to specify, flexible, and enable a rich array of storage policies and desired metrics to be realized. Tiera's novelty lies in the first-class support for encapsulated tiered cloud storage, ease of programmability of data management policies, and support for runtime replacement and addition of policies and tiers. Tiera enables an application to realize a desired metric (e.g., low latency or low cost) by selecting different storage services that constitute a Tiera instance, and easily specifying a policy, using event and response pairs, to manage the life cycle of data stored in the instance. We illustrate the benefits of Tiera through a prototype implemented on the Amazon cloud. By deploying unmodified MySQL database engine and a TPC-W Web bookstore application on Tiera, we are able to improve their respective throughputs by 47% -- 125% and 46% -- 69%, over standard deployments. We further show the flexibility of Tiera in achieving different desired application metrics with minimal overhead.","PeriodicalId":262822,"journal":{"name":"International Middleware Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125730002","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}
Pramod Bhatotia, Umut A. Acar, F. Junqueira, R. Rodrigues
{"title":"Slider: incremental sliding window analytics","authors":"Pramod Bhatotia, Umut A. Acar, F. Junqueira, R. Rodrigues","doi":"10.1145/2663165.2663334","DOIUrl":"https://doi.org/10.1145/2663165.2663334","url":null,"abstract":"Sliding window analytics is often used in distributed data-parallel computing for analyzing large streams of continuously arriving data. When pairs of consecutive windows overlap, there is a potential to update the output incrementally, more efficiently than recomputing from scratch. However, in most systems, realizing this potential requires programmers to explicitly manage the intermediate state for overlapping windows, and devise an application-specific algorithm to incrementally update the output.\u0000 In this paper, we present self-adjusting contraction trees, a set of data structures and algorithms for transparently updating the output of a sliding window computation as the window moves, while reusing, to the extent possible, results from prior computations. Self-adjusting contraction trees structure sub-computations of a data-parallel computation in the form of a shallow (logarithmic depth) balanced data dependence graph, through which input changes are efficiently propagated in asymptotically sub-linear time.\u0000 We implemented self-adjusting contraction trees in a system called Slider. The design of Slider incorporates several novel techniques, most notably: (i) a set of self balancing trees tuned for different variants of sliding window computation (append-only, fixed-width, or variable-width slides); (ii) a split processing mode, where a background pre-processing stage leverages the predictability of input changes to pave the way for a more efficient foreground processing when the window slides; and (iii) an extension of the data structures to handle multiple-job workflows such as data-flow query processing. We evaluated Slider using a variety of applications and real-world case studies. Our results show significant performance gains without requiring any changes to the existing application code used for non-incremental data processing.","PeriodicalId":262822,"journal":{"name":"International Middleware Conference","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121573613","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}
{"title":"Stage-aware anomaly detection through tracking log points","authors":"Saeed Ghanbari, Ali B. Hashemi, C. Amza","doi":"10.1145/2663165.2663319","DOIUrl":"https://doi.org/10.1145/2663165.2663319","url":null,"abstract":"We introduce Stage-aware Anomaly Detection (SAAD), a low-overhead real-time solution for detecting runtime anomalies in storage systems. Modern storage server architectures are multi-threaded and structured as a set of modules, which we call stages. SAAD leverages this to collect stage-level log summaries at runtime and to perform statistical analysis across stage instances. Stages that generate rare execution flows and/or register unusually high duration for regular flows at run-time indicate anomalies. SAAD makes two key contributions: i) limits the search space for root causes, by pinpointing specific anomalous code stages, and ii) reduces compute and storage requirements for log analysis, while preserving accuracy, through a novel technique based on log summarization. We evaluate SAAD on three distributed storage systems: HBase, Hadoop Distributed File System (HDFS), and Cassandra. We show that, with practically zero overhead, we uncover various anomalies in real-time.","PeriodicalId":262822,"journal":{"name":"International Middleware Conference","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114920292","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}
D. Hagimont, Christine Mayap Kamga, L. Broto, A. Tchana, N. D. Palma
{"title":"DVFS Aware CPU Credit Enforcement in a Virtualized System","authors":"D. Hagimont, Christine Mayap Kamga, L. Broto, A. Tchana, N. D. Palma","doi":"10.1007/978-3-642-45065-5_7","DOIUrl":"https://doi.org/10.1007/978-3-642-45065-5_7","url":null,"abstract":"","PeriodicalId":262822,"journal":{"name":"International Middleware Conference","volume":"211 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123387729","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}
{"title":"DynaSoRe: Efficient In-Memory Store for Social Applications","authors":"Xiao Bai, Arnaud Jégou, F. Junqueira, V. Leroy","doi":"10.1007/978-3-642-45065-5_22","DOIUrl":"https://doi.org/10.1007/978-3-642-45065-5_22","url":null,"abstract":"","PeriodicalId":262822,"journal":{"name":"International Middleware Conference","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124215457","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}
{"title":"Back to the Future: Using Magnetic Tapes in Cloud Based Storage Infrastructures","authors":"Varun Prakash, Xi Zhao, Y. Wen, W. Shi","doi":"10.1007/978-3-642-45065-5_17","DOIUrl":"https://doi.org/10.1007/978-3-642-45065-5_17","url":null,"abstract":"","PeriodicalId":262822,"journal":{"name":"International Middleware Conference","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128255803","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}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
