Proceedings of the Sixth ACM Symposium on Cloud Computing最新文献_第2页

Tarcil: reconciling scheduling speed and quality in large shared clusters 目标:协调大型共享集群的调度速度和质量

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2806779

引用次数: 174

Achieving cost-efficient, data-intensive computing in the cloud 在云中实现经济高效的数据密集型计算

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2806781

Michael Conley, Amin Vahdat, G. Porter

引用次数: 21

vFair: latency-aware fair storage scheduling via per-IO cost-based differentiation vFair:通过基于每个io成本的差异化实现延迟感知的公平存储调度

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2806943

Hui Lu, Brendan Saltaformaggio, R. Kompella, Dongyan Xu

{"title":"vFair: latency-aware fair storage scheduling via per-IO cost-based differentiation","authors":"Hui Lu, Brendan Saltaformaggio, R. Kompella, Dongyan Xu","doi":"10.1145/2806777.2806943","DOIUrl":"https://doi.org/10.1145/2806777.2806943","url":null,"abstract":"In virtualized data centers, multiple VMs are consolidated to access a shared storage system. Effective storage resource management, however, turns out to be challenging, as VM workloads exhibit various IO patterns and diverse loads. To multiplex the underlying hardware resources among VMs, providing fairness and isolation while maintaining high resource utilization becomes imperative for effective storage resource management. Existing schedulers such as Linux CFQ or SFQ can provide some fairness, but it has been observed that synchronous IO tends to lose fair shares significantly when competing with aggressive VMs. In this paper, we introduce vFair, a novel scheduling framework that achieves IO resource sharing fairness among VMs, regardless of their IO patterns and workloads. The design of vFair takes per-IO cost into consideration and strikes a balance between fairness and storage resource utilization. We have developed a Xen-based prototype of vFair and evaluated it with a wide range of storage workloads. Our results from both micro-benchmarks and real-world applications demonstrate the effectiveness of vFair, with significantly improved fairness and high resource utilization.","PeriodicalId":275158,"journal":{"name":"Proceedings of the Sixth ACM Symposium on Cloud Computing","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121300377","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}

引用次数: 20

Using data transformations for low-latency time series analysis 使用数据转换进行低延迟时间序列分析

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2806839

Henggang Cui, K. Keeton, Indrajit Roy, K. Viswanathan, G. Ganger

引用次数: 7

Reducing replication bandwidth for distributed document databases 减少分布式文档数据库的复制带宽

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2806840

Lianghong Xu, Andrew Pavlo, S. Sengupta, Jin Li, G. Ganger

{"title":"Reducing replication bandwidth for distributed document databases","authors":"Lianghong Xu, Andrew Pavlo, S. Sengupta, Jin Li, G. Ganger","doi":"10.1145/2806777.2806840","DOIUrl":"https://doi.org/10.1145/2806777.2806840","url":null,"abstract":"With the rise of large-scale, Web-based applications, users are increasingly adopting a new class of document-oriented database management systems (DBMSs) that allow for rapid prototyping while also achieving scalable performance. Like for other distributed storage systems, replication is important for document DBMSs in order to guarantee availability. The network bandwidth required to keep replicas synchronized is expensive and is often a performance bottleneck. As such, there is a strong need to reduce the replication bandwidth, especially for geo-replication scenarios where wide-area network (WAN) bandwidth is limited. This paper presents a deduplication system called sDedup that reduces the amount of data transferred over the network for replicated document DBMSs. sDedup uses similarity-based deduplication to remove redundancy in replication data by delta encoding against similar documents selected from the entire database. It exploits key characteristics of document-oriented workloads, including small item sizes, temporal locality, and the incremental nature of document edits. Our experimental evaluation of sDedup with three real-world datasets shows that it is able to achieve up to 38X reduction in data sent over the network, significantly outperforming traditional chunk-based deduplication techniques while incurring negligible performance overhead.","PeriodicalId":275158,"journal":{"name":"Proceedings of the Sixth ACM Symposium on Cloud Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123467641","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}

引用次数: 17

GraM: scaling graph computation to the trillions GraM:将图计算缩放到数万亿

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2806849

Ming Wu, Fan Yang, Jilong Xue, Wencong Xiao, Youshan Miao, Lan Wei, Haoxiang Lin, Yafei Dai, Lidong Zhou

{"title":"GraM: scaling graph computation to the trillions","authors":"Ming Wu, Fan Yang, Jilong Xue, Wencong Xiao, Youshan Miao, Lan Wei, Haoxiang Lin, Yafei Dai, Lidong Zhou","doi":"10.1145/2806777.2806849","DOIUrl":"https://doi.org/10.1145/2806777.2806849","url":null,"abstract":"GraM is an efficient and scalable graph engine for a large class of widely used graph algorithms. It is designed to scale up to multicores on a single server, as well as scale out to multiple servers in a cluster, offering significant, often over an order-of-magnitude, improvement over existing distributed graph engines on evaluated graph algorithms. GraM is also capable of processing graphs that are significantly larger than previously reported. In particular, using 64 servers (1,024 physical cores), it performs a PageRank iteration in 140 seconds on a synthetic graph with over one trillion edges, setting a new milestone for graph engines. GraM's efficiency and scalability comes from a judicious architectural design that exploits the benefits of multi-core and RDMA. GraM uses a simple message-passing based scaling architecture for both scaling up and scaling out to expose inherent parallelism. It further benefits from a specially designed multi-core aware RDMA-based communication stack that preserves parallelism in a balanced way and allows overlapping of communication and computation. A high degree of parallelism often comes at the cost of lower efficiency due to resource fragmentation. GraM is equipped with an adaptive mechanism that evaluates the cost and benefit of parallelism to decide the appropriate configuration. Combined, these mechanisms allow GraM to scale up and out with high efficiency.","PeriodicalId":275158,"journal":{"name":"Proceedings of the Sixth ACM Symposium on Cloud Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128372829","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}

引用次数: 133

ShardFS vs. IndexFS: replication vs. caching strategies for distributed metadata management in cloud storage systems ShardFS与IndexFS:云存储系统中分布式元数据管理的复制与缓存策略

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2806844

Lin Xiao, Kai Ren, Qing Zheng, Garth A. Gibson

{"title":"ShardFS vs. IndexFS: replication vs. caching strategies for distributed metadata management in cloud storage systems","authors":"Lin Xiao, Kai Ren, Qing Zheng, Garth A. Gibson","doi":"10.1145/2806777.2806844","DOIUrl":"https://doi.org/10.1145/2806777.2806844","url":null,"abstract":"The rapid growth of cloud storage systems calls for fast and scalable namespace processing. While few commercial file systems offer anything better than federating individually non-scalable namespace servers, a recent academic file system, IndexFS, demonstrates scalable namespace processing based on client caching of directory entries and permissions (directory lookup state) with no per-client state in servers. In this paper we explore explicit replication of directory lookup state in all servers as an alternative to caching this information in all clients. Both eliminate most repeated RPCs to different servers in order to resolve hierarchical permission tests. Our realization for server replicated directory lookup state, ShardFS, employs a novel file system specific hybrid optimistic and pessimistic concurrency control favoring single object transactions over distributed transactions. Our experimentation suggests that if directory lookup state mutation is a fixed fraction of operations (strong scaling for metadata), server replication does not scale as well as client caching, but if directory lookup state mutation is proportional to the number of jobs, not the number of processes per job, (weak scaling for metadata), then server replication can scale more linearly than client caching and provide lower 70 percentile response times as well.","PeriodicalId":275158,"journal":{"name":"Proceedings of the Sixth ACM Symposium on Cloud Computing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121659471","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}

引用次数: 36

CoolProvision: underprovisioning datacenter cooling CoolProvision:数据中心冷却不足

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2806938

I. Manousakis, Íñigo Goiri, S. Sankar, Thu D. Nguyen, R. Bianchini

{"title":"CoolProvision: underprovisioning datacenter cooling","authors":"I. Manousakis, Íñigo Goiri, S. Sankar, Thu D. Nguyen, R. Bianchini","doi":"10.1145/2806777.2806938","DOIUrl":"https://doi.org/10.1145/2806777.2806938","url":null,"abstract":"Cloud providers have made significant strides in reducing the cooling capital and operational costs of their datacenters, for example, by leveraging outside air (\"free\") cooling where possible. Despite these advances, cooling costs still represent a significant expense mainly because cloud providers typically provision their cooling infrastructure for the worst-case scenario (i.e., very high load and outside temperature at the same time). Thus, in this paper, we propose to reduce cooling costs by underprovisioning the cooling infrastructure. When the cooling is underprovisioned, there might be (rare) periods when the cooling infrastructure cannot cool down the IT equipment enough. During these periods, we can either (1) reduce the processing capacity and potentially degrade the quality of service, or (2) let the IT equipment temperature increase in exchange for a controlled degradation in reliability. To determine the ideal amount of underprovisioning, we introduce CoolProvision, an optimization and simulation framework for selecting the cheapest provisioning within performance constraints defined by the provider. CoolProvision leverages an abstract trace of the expected workload, as well as cooling, performance, power, reliability, and cost models to explore the space of potential provisionings. Using data from a real small free-cooled datacenter, our results suggest that CoolProvision can reduce the cost of cooling by up to 55%. We extrapolate our experience and results to larger cloud datacenters as well.","PeriodicalId":275158,"journal":{"name":"Proceedings of the Sixth ACM Symposium on Cloud Computing","volume":"227 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131184548","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}

引用次数: 35

Kubernetes and the path to cloud native Kubernetes和通往云原生的道路

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777.2809955

E. Brewer

引用次数: 140

Proceedings of the Sixth ACM Symposium on Cloud Computing 第六届ACM云计算研讨会论文集

Proceedings of the Sixth ACM Symposium on Cloud Computing Pub Date : 2015-08-27 DOI: 10.1145/2806777

Shahram Ghandeharizadeh, M. Balazinska, M. Freedman, Sumita Barahmand

引用次数: 5