Proceedings of the 4th annual Symposium on Cloud Computing最新文献

{"title":"DEDIS","authors":"J. Paulo, J. Pereira","doi":"10.1145/2523616.2528936","DOIUrl":"https://doi.org/10.1145/2523616.2528936","url":null,"abstract":"Deduplication is now widely accepted as an efficient technique for reducing storage costs at the expense of some processing overhead, being increasingly sought in primary storage systems [7, 8] and cloud computing infrastructures holding Virtual Machine (VM) volumes [2, 1, 5]. Besides a large number of duplicates that can be found across static VM images [3], dynamic general purpose data from VM volumes allows space savings from 58% up to 80% if deduplicated in a cluster-wide fashion [1, 4]. However, some of these volumes persist latency sensitive data which limits the overhead that can be incurred in I/O operations. Therefore, this problem must be addressed by a cluster-wide distributed deduplication system for such primary storage volumes.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114439824","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":"Pico replication: a high availability framework for middleboxes","authors":"Shriram Rajagopalan, Dan Williams, H. Jamjoom","doi":"10.1145/2523616.2523635","DOIUrl":"https://doi.org/10.1145/2523616.2523635","url":null,"abstract":"Middleboxes are being rearchitected to be service oriented, composable, extensible, and elastic. Yet system-level support for high availability (HA) continues to introduce significant performance overhead. In this paper, we propose Pico Replication (PR), a system-level framework for middleboxes that exploits their flow-centric structure to achieve low overhead, fully customizable HA. Unlike generic (virtual machine level) techniques, PR operates at the flow level. Individual flows can be checkpointed at very high frequencies while the middlebox continues to process other flows. Furthermore, each flow can have its own checkpoint frequency, output buffer and target for backup, enabling rich and diverse policies that balance---per-flow---performance and utilization. PR leverages OpenFlow to provide near instant flow-level failure recovery, by dynamically rerouting a flow's packets to its replication target. We have implemented PR and a flow-based HA policy. In controlled experiments, PR sustains checkpoint frequencies of 1000Hz, an order of magnitude improvement over current VM replication solutions. As a result, PR drastically reduces the overhead on end-to-end latency from 280% to 15.5% and throughput overhead from 99.5% to 3.2%.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128217080","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":"Dynamic performance profiling of cloud caches","authors":"Hjörtur Björnsson, G. Chockler, T. Saemundsson, Ymir Vigfusson","doi":"10.1145/2523616.2527081","DOIUrl":"https://doi.org/10.1145/2523616.2527081","url":null,"abstract":"In-memory object caches, such as memcached, are critical to the success of popular web sites, such as Facebook [3], by reducing database load and improving scalability [2]. The prominence of caches implies that configuring their ideal memory size has the potential for significant savings on computation resources and energy costs, but unfortunately cache configuration is poorly understood. The modern practice of manually tweaking live caching systems takes significant effort and may both increase the variance for client request latencies and impose high load on the database backend.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130698173","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":"Decentralized privacy protection strategies for location-based services","authors":"Chih-Chun Chen, Yu-Ling Hsueh","doi":"10.1145/2523616.2525956","DOIUrl":"https://doi.org/10.1145/2523616.2525956","url":null,"abstract":"The rapid development of the integration of cloud computing and location-based services have drawn so much attention currently. With the increasing number of users who own smart phones, significant amount of data that describe user surrounding information and interests have become widely available. However, significant attentions have been raised on the privacy issues. The existing approaches mainly focus on a centralized approach which brings tremendous security concerns. To prevent a centralized query processor from being attached by malicious hackers, we propose a decentralized approach to protect the sensitive location information of users who request for location-based services. Our system provides an approximate computing and an exact computing mechanism for different scenarios and requirements.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128837716","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":"Extending modern PaaS clouds with BSP to execute legacy MPI applications","authors":"Hiranya Jayathilaka, Michael Agun","doi":"10.1145/2523616.2525942","DOIUrl":"https://doi.org/10.1145/2523616.2525942","url":null,"abstract":"As the popularity of cloud computing continues to increase, a significant amount of legacy code implemented using older parallel computing standards is outdated and left behind. This forces the organizations to port the old applications into new cloud platforms. This, however, violates the \"develop once - run anywhere\" principle promised by utility computing. As a solution to this problem, we explore the possibility of executing unmodified MPI applications over a modern parallel computing platform. Using BSP as a bridging model between MPI and the Hadoop framework, we implement a prototype MPI runtime for today's computing clouds, which eliminates the overhead of porting legacy code.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121816334","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":"Fault-tolerant industrial automation as a cloud service","authors":"T. Hegazy, M. Hefeeda","doi":"10.1145/2523616.2525951","DOIUrl":"https://doi.org/10.1145/2523616.2525951","url":null,"abstract":"Cloud computing requires further research and development to accommodate more application areas [5]. We introduce a new application area: industrial automation. A current industrial automation (IA) system is a multi-tiered architecture entailing different layers from feedback control to enterprise management. If adopted in large-scale IA systems, cloud computing can offer over 40% cost saving and 25--85% time saving [4, 1]. However, IA requires tighter timeliness, reliability, and security than most other cloud applications. We propose a cloud-based IA architecture and focus on the timeliness and reliability requirements. Addressing such requirements for the lowest layer (feedback control) is the most challenging. We addressed the timeliness problem in [2]. To address reliability and further address timeliness, we propose a distributed fault tolerance algorithm for cloud-based controllers. We theoretically and practically prove that the proposed fault-tolerant, cloud-based controllers offer the same performance of the local ones.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131104782","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":"jVerbs: ultra-low latency for data center applications","authors":"Patrick Stuedi, B. Metzler, A. Trivedi","doi":"10.1145/2523616.2523631","DOIUrl":"https://doi.org/10.1145/2523616.2523631","url":null,"abstract":"Network latency has become increasingly important for data center applications. Accordingly, several efforts at both hardware and software level have been made to reduce the latency in data centers. Limited attention, however, has been paid to network latencies of distributed systems running inside an application container such as the Java Virtual Machine (JVM) or the .NET runtime. In this paper, we first highlight the latency overheads observed in several well-known Java-based distributed systems. We then present jVerbs, a networking framework for the JVM which achieves bare-metal latencies in the order of single digit microseconds using methods of Remote Direct Memory Access (RDMA). With jVerbs, applications are mapping the network device directly into the JVM, cutting through both the application virtual machine and the operating system. In the paper, we discuss the design and implementation of jVerbs and demonstrate how it can be used to improve latencies in some of the popular distributed systems running in data centers.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133182844","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":"COLO: COarse-grained LOck-stepping virtual machines for non-stop service","authors":"Yaozu Dong, W. Ye, Y. Jiang, I. Pratt, Shiqi Ma, Jian Li, Haibing Guan","doi":"10.1145/2523616.2523630","DOIUrl":"https://doi.org/10.1145/2523616.2523630","url":null,"abstract":"Virtual machine (VM) replication provides a software solution of for business continuity and disaster recovery through application-agnostic hardware fault tolerance by replicating the state of primary VM (PVM) to secondary VM (SVM) on a different physical node. Unfortunately, current VM replication approaches suffer from excessive overhead, which severely limit their applicability and suitability. In this paper, we leverage the practical effect of networked server-client system that PVM and SVM are considered as in the same state only if they can generate the same response from the clients' point of view, and this is exploited to optimize performance. To this end, we propose a generic and highly efficient non-stop service solution, named as \"COLO\" (COarse-grained LOck-stepping virtual machine) utilizing on-demand VM replication. COLO monitors the output responses of the PVM and SVM, and rules the SVM as a valid replica of the PVM according to the output similarity between PVM and SVM. If the responses do not match, the commit of network response is withheld until PVM's state has been synchronized to SVM. Hence, we ensure that the system is always capable of failover by SVM. Although non-determinism may mean a different internal state of SVM from that of the PVM, it is equally valid and remains consistent from external observations. Unlike earlier instruction level lock-stepping deterministic execution approaches, COLO can easily support Multi-Processors (MP) involving workloads with the satisfying performance. Results show that COLO significantly outperforms existing approaches, particularly on server-client workloads such as online databases and web server applications.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133074351","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":"CloudLEGO: scalable cross-VM-type application performance prediction","authors":"S. Meng, A. Iyengar, Ling Liu, Ting Wang, Jian Tan, I. Silva-Lepe, I. Rouvellou","doi":"10.1145/2523616.2525948","DOIUrl":"https://doi.org/10.1145/2523616.2525948","url":null,"abstract":"Understanding the performance difference of a multi-tier Cloud application between different provisioning plans and workloads is difficult to achieve. A typical IaaS provider offers a variety of virtual server instances with different performance capacities and rental rates. Such instances are often marked with a high level description of their hardware/software configuration (e.g. 1 or 2 vC-PUs) which provides insufficient information on the performance of the virtual server instances. Furthermore, as each tier of an application can be independently provisioned with different types and numbers of VMs, the number of possible provisioning plans grows exponentially with each additional tier. Previous work [10] proposed to perform automatic experiments to evaluate candidate provisioning plans, which leads to high cost due to the exponential increase of candidate provisioning plans with the number of tiers and available VM types. While several existing works [8, 6, 7] studied a variety of performance models for multi-tier applications, these works assume that an application runs on a fixed deployment (with fixed machine type and number for each tier). We present CloudLEGO, an efficient cross-VM-type performance learning and prediction approach. Since building a model for each possible deployment is clearly not scalable, instead of treating each candidate deployment separately, CloudLEGO views them as derivatives from a single, fixed deployment. Accordingly, the task of learning the performance of a targeted deployment can be decoupled into learning the performance of the original fixed deployment and learning the performance difference between the original deployment and the targeted one. The key to efficiently capture performance difference between deployments is to find multiple independent changes that can be used to derive any deployment from the original deployment. CloudLEGO formulates such \"modular\" changes as VM type changes at a given tier. To capture changes of performance at a tier caused by VM type changes, CloudLEGO uses relative performance models [5] which predict the performance difference between a pair of VMs (rather than the absolute performance of a VM) for a given workload. Moreover, training relative performance models requires only performance data from Cloud monitoring services [1, 4] rather than fine-grain data such as per-tier response time which requires application instrumentation. Training relative performance models with traditional passive learning techniques would require a large amount of training data as performance data are collected uniformly in a single batch. We find that different types of VMs often share similar performance for many \"regions\" of workloads. To leverage this characteristic and guide the profiling to regions with high performance differences, CloudLEGO uses active learning techniques [2, 3, 9] that split the profiling process into multiple stages where data collected in one stage are used to ident","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121379581","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":"Leveraging sharding in the design of scalable replication protocols","authors":"Hussam Abu-Libdeh, R. V. Renesse, Ymir Vigfusson","doi":"10.1145/2523616.2523623","DOIUrl":"https://doi.org/10.1145/2523616.2523623","url":null,"abstract":"Most if not all datacenter services use sharding and replication for scalability and reliability. Shards are more-or-less independent of one another and individually replicated. In this paper, we challenge this design philosophy and present a replication protocol where the shards interact with one another: A protocol running within shards ensures linearizable consistency, while the shards interact in order to improve availability. We provide a specification for the protocol, prove its safety, analyze its liveness and availability properties, and evaluate a working implementation.","PeriodicalId":298547,"journal":{"name":"Proceedings of the 4th annual Symposium on Cloud Computing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131852607","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}