Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems最新文献

{"title":"Using Burstable Instances in the Public Cloud: Why, When and How?","authors":"Cheng Wang, B. Urgaonkar, N. Nasiriani, G. Kesidis","doi":"10.1145/3078505.3078591","DOIUrl":"https://doi.org/10.1145/3078505.3078591","url":null,"abstract":"To attract more customers, public cloud providers offer virtual machine (instance) types that trade off lower prices for poorer capacities. As one salient approach, the providers employ aggressive statistical multiplexing of multiple cheaper instances on a single physical server, resulting in tenants experiencing higher dynamism in the resource capacity of these instances. Examples of this are EC2's \"type\" instances and GCE's \"shared-core\" instances.We collectively refer to these as burstable instances for their ability to dynamically \"burst\" (increase the capacity of) their resources. Burstable instances are significantly cheaper than the \"regular\" instances, and offer time-varying CPU capacity comprising a minimum guaranteed base capacity/rate, which is much smaller than a short-lived peak capacity that becomes available upon operating at lower than base rate for a sufficient duration. Table 1 summarizes our classification of resource capacity dynamism for GCE and EC2 instances along with the nature of disclosure made by the provider. To exploit burstable instances cost-effectively, a tenant would need to carefully understand the significant additional complexity of such instances beyond that disclosed by the providers.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124201349","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":"A Simple Yet Effective Balanced Edge Partition Model for Parallel Computing","authors":"Lingda Li, Robel Geda, Ari B. Hayes, Yan-Hao Chen, Pranav Chaudhari, E. Zhang, M. Szegedy","doi":"10.1145/3078505.3078520","DOIUrl":"https://doi.org/10.1145/3078505.3078520","url":null,"abstract":"Graph edge partition models have recently become an appealing alternative to graph vertex partition models for distributed computing due to both their flexibility in balancing loads and their performance in reducing communication cost. In this paper, we propose a simple yet effective graph edge partitioning algorithm. In practice, our algorithm provides good partition quality while maintaining low partition overhead. It also outperforms similar state-of-the-art edge partition approaches, especially for power-law graphs. In theory, previous work showed that an approximation guarantee of O(dmax√(log n log k)) apply to the graphs with m=Ω(k2) edges (n is the number of vertices, and k is the number of partitions). We further rigorously proved that this approximation guarantee hold for all graphs. We also demonstrate the applicability of the proposed edge partition algorithm in real parallel computing systems. We draw our example from GPU program locality enhancement and demonstrate that the graph edge partition model does not only apply to distributed computing with many computer nodes, but also to parallel computing in a single computer node with a many-core processor.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116816296","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":"Optimal Posted Prices for Online Cloud Resource Allocation","authors":"Zijun Zhang, Zongpeng Li, Chuan Wu","doi":"10.1145/3078505.3078529","DOIUrl":"https://doi.org/10.1145/3078505.3078529","url":null,"abstract":"We study online resource allocation in a cloud computing platform, through a posted pricing mechanism: The cloud provider publishes a unit price for each resource type, which may vary over time; upon arrival at the cloud system, a cloud user either takes the current prices, renting resources to execute its job, or refuses the prices without running its job there. We design pricing functions based on the current resource utilization ratios, in a wide array of demand-supply relationships and resource occupation durations, and prove worst-case competitive ratios of the pricing functions in terms of social welfare. In the basic case of a single-type, non-recycled resource (i.e., allocated resources are not later released for reuse), we prove that our pricing function design is optimal, in that any other pricing function can only lead to a worse competitive ratio. Insights obtained from the basic cases are then used to generalize the pricing functions to more realistic cloud systems with multiple types of resources, where a job occupies allocated resources for a number of time slots till completion, upon which time the resources are returned back to the cloud resource pool.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121925556","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":"Investigation of the 2016 Linux TCP Stack Vulnerability at Scale","authors":"Alan Quach, Zhongjie Wang, Zhiyun Qian","doi":"10.1145/3078505.3078510","DOIUrl":"https://doi.org/10.1145/3078505.3078510","url":null,"abstract":"To combat blind in-window attacks against TCP, changes proposed in RFC 5961 have been implemented by Linux since late 2012. While successfully eliminating the old vulnerabilities, the new TCP implementation was reported in August 2016 to have introduced a subtle yet serious security flaw. Assigned CVE-2016-5696, the flaw exploits the challenge ACK rate limiting feature that could allow an off-path attacker to infer the presence/absence of a TCP connection between two arbitrary hosts, terminate such a connection, and even inject malicious payload. In this work, we perform a comprehensive measurement of the impact of the new vulnerability. This includes (1) tracking the vulnerable Internet servers, (2) monitoring the patch behavior over time, (3) picturing the overall security status of TCP stacks at scale. Towards this goal, we design a scalable measurement methodology to scan the Alexa top 1 million websites for almost 6 months. We also present how notifications impact the patching behavior, and compare the result with the Heartbleed and the Debian PRNG vulnerability. The measurement represents a valuable data point in understanding how Internet servers react to serious security flaws in the operating system kernel.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":" 649","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131977099","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":"Session details: SIGMETRICS Achievement Award: Sem Borst","authors":"L. Golubchik","doi":"10.1145/3248534","DOIUrl":"https://doi.org/10.1145/3248534","url":null,"abstract":"","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"858 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133872251","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":"Design-Induced Latency Variation in Modern DRAM Chips: Characterization, Analysis, and Latency Reduction Mechanisms","authors":"Donghyuk Lee, S. Khan, Lavanya Subramanian, Saugata Ghose, Rachata Ausavarungnirun, Gennady Pekhimenko, V. Seshadri, O. Mutlu","doi":"10.1145/3078505.3078533","DOIUrl":"https://doi.org/10.1145/3078505.3078533","url":null,"abstract":"Variation has been shown to exist across the cells within a modern DRAM chip. Prior work has studied and exploited several forms of variation, such as manufacturing-process- or temperature-induced variation. We empirically demonstrate a new form of variation that exists within a real DRAM chip, induced by the design and placement of different components in the DRAM chip: different regions in DRAM, based on their relative distances from the peripheral structures, require different minimum access latencies for reliable operation. In particular, we show that in most real DRAM chips, cells closer to the peripheral structures can be accessed much faster than cells that are farther. We call this phenomenon design-induced variation in DRAM. Our goals are to i) understand design-induced variation that exists in real, state-of-the-art DRAM chips, ii) exploit it to develop low-cost mechanisms that can dynamically find and use the lowest latency at which to operate a DRAM chip reliably, and, thus, iii) improve overall system performance while ensuring reliable system operation. To this end, we first experimentally demonstrate and analyze designed-induced variation in modern DRAM devices by testing and characterizing 96 DIMMs (768 DRAM chips). Our experimental study shows that i) modern DRAM chips exhibit design-induced latency variation in both row and column directions, ii) access latency gradually increases in the row direction within a DRAM cell array (mat) and this pattern repeats in every mat, and iii) some columns require higher latency than others due to the internal hierarchical organization of the DRAM chip. Our characterization identifies DRAM regions that are vulnerable to errors, if operated at lower latency, and finds consistency in their locations across a given DRAM chip generation, due to design-induced variation. Variations in the vertical and horizontal dimensions, together, divide the cell array into heterogeneous-latency regions, where cells in some regions require longer access latencies for reliable operation. Reducing the latency uniformly across all regions in DRAM would improve performance, but can introduce failures in the inherently slower regions that require longer access latencies for correct operation. We refer to these inherently slower regions of DRAM as design-induced vulnerable regions. Based on our extensive experimental analysis, we develop two mechanisms that reliably reduce DRAM latency. First, DIVI Profiling uses runtime profiling to dynamically identify the lowest DRAM latency that does not introduce failures. DIVA Profiling exploits design-induced variation and periodically profiles only the vulnerable regions to determine the lowest DRAM latency at low cost. It is the first mechanism to dynamically determine the lowest latency that can be used to operate DRAM reliably. DIVA Profiling reduces the latency of read/write requests by 35.1%/57.8%, respectively, at 55C. Our second mechanism, DIVA Shuffling, shuffles data ","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132430603","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":"Session details: Session 9: Accurate and Efficient Performance Measurement","authors":"G. Casale","doi":"10.1145/3248546","DOIUrl":"https://doi.org/10.1145/3248546","url":null,"abstract":"","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122204480","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":"Characterizing 3D Floating Gate NAND Flash","authors":"Qin Xiong, Fei Wu, Zhonghai Lu, Yue Zhu, You Zhou, Yibing Chu, C. Xie, Ping Huang","doi":"10.1145/3078505.3078550","DOIUrl":"https://doi.org/10.1145/3078505.3078550","url":null,"abstract":"In this paper, we characterize a state-of-the-art 3D floating gate NAND flash memory through comprehensive experiments on an FPGA platform. Then, we present distinct observations on performance and reliability, such as operation latencies and various error patterns. We believe that through our work, novel 3D NAND flash-oriented designs can be developed to achieve better performance and reliability.","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117223785","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":"Session details: Session 2: Algorithms for Massive Processing Applications","authors":"Y. Tay","doi":"10.1145/3248536","DOIUrl":"https://doi.org/10.1145/3248536","url":null,"abstract":"","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121127161","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":"Session details: SIGMETRICS Keynote Talk: Michael Jordan","authors":"Zhi-Li Zhang","doi":"10.1145/3248542","DOIUrl":"https://doi.org/10.1145/3248542","url":null,"abstract":"","PeriodicalId":133673,"journal":{"name":"Proceedings of the 2017 ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123157221","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}