Boyan Zhao, Rui Hou, Jianbo Dong, Michael C. Huang, S. Mckee, Qianlong Zhang, Yueji Liu, Ye Li, Lixin Zhang, Dan Meng
Consolidated server racks are quickly becoming the standard infrastructure for engineering, business, medicine, and science. Such servers are still designed much in the way when they were organized as individual, distributed systems. Given that many fields rely on big-data analytics substantially, its cost-effectiveness and performance should be improved, which can be achieved by flexibly allowing resources to be shared across nodes. Here we describe Venice, a family of data-center server architectures that includes a strong communication substrate as a first-class resource. Venice supports a diverse set of resource-joining mechanisms that enables applications to leverage non-local resources efficiently. We have constructed a hardware prototype to better understand the implications of design decisions about system support for resource sharing. We use it to measure the performance of at-scale applications and to explore performance, power, and resource-sharing transparency tradeoffs (i.e., how many programming changes are needed). We analyze these tradeoffs for sharing memory, accelerators, and NICs. We find that reducing/hiding latency is particularly important, the chosen communication channels should match the sharing access patterns of the applications, and of which we can improve performance by exploiting inter-channel collaboration.
{"title":"Venice","authors":"Boyan Zhao, Rui Hou, Jianbo Dong, Michael C. Huang, S. Mckee, Qianlong Zhang, Yueji Liu, Ye Li, Lixin Zhang, Dan Meng","doi":"10.1145/3310360","DOIUrl":"https://doi.org/10.1145/3310360","url":null,"abstract":"Consolidated server racks are quickly becoming the standard infrastructure for engineering, business, medicine, and science. Such servers are still designed much in the way when they were organized as individual, distributed systems. Given that many fields rely on big-data analytics substantially, its cost-effectiveness and performance should be improved, which can be achieved by flexibly allowing resources to be shared across nodes. Here we describe Venice, a family of data-center server architectures that includes a strong communication substrate as a first-class resource. Venice supports a diverse set of resource-joining mechanisms that enables applications to leverage non-local resources efficiently. We have constructed a hardware prototype to better understand the implications of design decisions about system support for resource sharing. We use it to measure the performance of at-scale applications and to explore performance, power, and resource-sharing transparency tradeoffs (i.e., how many programming changes are needed). We analyze these tradeoffs for sharing memory, accelerators, and NICs. We find that reducing/hiding latency is particularly important, the chosen communication channels should match the sharing access patterns of the applications, and of which we can improve performance by exploiting inter-channel collaboration.","PeriodicalId":318554,"journal":{"name":"ACM Transactions on Computer Systems (TOCS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122969121","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}
T. Hunt, Zhiting Zhu, Yuanzhong Xu, Simon Peter, E. Witchel
Users of modern data-processing services such as tax preparation or genomic screening are forced to trust them with data that the users wish to keep secret. Ryoan1 protects secret data while it is processed by services that the data owner does not trust. Accomplishing this goal in a distributed setting is difficult, because the user has no control over the service providers or the computational platform. Confining code to prevent it from leaking secrets is notoriously difficult, but Ryoan benefits from new hardware and a request-oriented data model. Ryoan provides a distributed sandbox, leveraging hardware enclaves (e.g., Intel’s software guard extensions (SGX) [40]) to protect sandbox instances from potentially malicious computing platforms. The protected sandbox instances confine untrusted data-processing modules to prevent leakage of the user’s input data. Ryoan is designed for a request-oriented data model, where confined modules only process input once and do not persist state about the input. We present the design and prototype implementation of Ryoan and evaluate it on a series of challenging problems including email filtering, health analysis, image processing and machine translation.
{"title":"Ryoan","authors":"T. Hunt, Zhiting Zhu, Yuanzhong Xu, Simon Peter, E. Witchel","doi":"10.1145/3231594","DOIUrl":"https://doi.org/10.1145/3231594","url":null,"abstract":"Users of modern data-processing services such as tax preparation or genomic screening are forced to trust them with data that the users wish to keep secret. Ryoan1 protects secret data while it is processed by services that the data owner does not trust. Accomplishing this goal in a distributed setting is difficult, because the user has no control over the service providers or the computational platform. Confining code to prevent it from leaking secrets is notoriously difficult, but Ryoan benefits from new hardware and a request-oriented data model. Ryoan provides a distributed sandbox, leveraging hardware enclaves (e.g., Intel’s software guard extensions (SGX) [40]) to protect sandbox instances from potentially malicious computing platforms. The protected sandbox instances confine untrusted data-processing modules to prevent leakage of the user’s input data. Ryoan is designed for a request-oriented data model, where confined modules only process input once and do not persist state about the input. We present the design and prototype implementation of Ryoan and evaluate it on a series of challenging problems including email filtering, health analysis, image processing and machine translation.","PeriodicalId":318554,"journal":{"name":"ACM Transactions on Computer Systems (TOCS)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115830931","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}
Irene Zhang, Naveen Kr. Sharma, Adriana Szekeres, A. Krishnamurthy, Dan R. K. Ports
Application programmers increasingly prefer distributed storage systems with strong consistency and distributed transactions (e.g., Google’s Spanner) for their strong guarantees and ease of use. Unfortunately, existing transactional storage systems are expensive to use—in part, because they require costly replication protocols, like Paxos, for fault tolerance. In this article, we present a new approach that makes transactional storage systems more affordable: We eliminate consistency from the replication protocol, while still providing distributed transactions with strong consistency to applications. We present the Transactional Application Protocol for Inconsistent Replication (TAPIR), the first transaction protocol to use a novel replication protocol, called inconsistent replication, that provides fault tolerance without consistency. By enforcing strong consistency only in the transaction protocol, TAPIR can commit transactions in a single round-trip and order distributed transactions without centralized coordination. We demonstrate the use of TAPIR in a transactional key-value store, TAPIR-KV. Compared to conventional systems, TAPIR-KV provides better latency and better throughput.
{"title":"Building Consistent Transactions with Inconsistent Replication","authors":"Irene Zhang, Naveen Kr. Sharma, Adriana Szekeres, A. Krishnamurthy, Dan R. K. Ports","doi":"10.1145/3269981","DOIUrl":"https://doi.org/10.1145/3269981","url":null,"abstract":"Application programmers increasingly prefer distributed storage systems with strong consistency and distributed transactions (e.g., Google’s Spanner) for their strong guarantees and ease of use. Unfortunately, existing transactional storage systems are expensive to use—in part, because they require costly replication protocols, like Paxos, for fault tolerance. In this article, we present a new approach that makes transactional storage systems more affordable: We eliminate consistency from the replication protocol, while still providing distributed transactions with strong consistency to applications. We present the Transactional Application Protocol for Inconsistent Replication (TAPIR), the first transaction protocol to use a novel replication protocol, called inconsistent replication, that provides fault tolerance without consistency. By enforcing strong consistency only in the transaction protocol, TAPIR can commit transactions in a single round-trip and order distributed transactions without centralized coordination. We demonstrate the use of TAPIR in a transactional key-value store, TAPIR-KV. Compared to conventional systems, TAPIR-KV provides better latency and better throughput.","PeriodicalId":318554,"journal":{"name":"ACM Transactions on Computer Systems (TOCS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130775268","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}
Monitoring and troubleshooting distributed systems is notoriously difficult; potential problems are complex, varied, and unpredictable. The monitoring and diagnosis tools commonly used today—logs, counters, and metrics—have two important limitations: what gets recorded is defined a priori, and the information is recorded in a component- or machine-centric way, making it extremely hard to correlate events that cross these boundaries. This article presents Pivot Tracing, a monitoring framework for distributed systems that addresses both limitations by combining dynamic instrumentation with a novel relational operator: the happened-before join. Pivot Tracing gives users, at runtime, the ability to define arbitrary metrics at one point of the system, while being able to select, filter, and group by events meaningful at other parts of the system, even when crossing component or machine boundaries. We have implemented a prototype of Pivot Tracing for Java-based systems and evaluate it on a heterogeneous Hadoop cluster comprising HDFS, HBase, MapReduce, and YARN. We show that Pivot Tracing can effectively identify a diverse range of root causes such as software bugs, misconfiguration, and limping hardware. We show that Pivot Tracing is dynamic, extensible, and enables cross-tier analysis between inter-operating applications, with low execution overhead.
{"title":"Pivot Tracing","authors":"Jonathan Mace, Ryan Roelke, Rodrigo Fonseca","doi":"10.1145/3208104","DOIUrl":"https://doi.org/10.1145/3208104","url":null,"abstract":"Monitoring and troubleshooting distributed systems is notoriously difficult; potential problems are complex, varied, and unpredictable. The monitoring and diagnosis tools commonly used today—logs, counters, and metrics—have two important limitations: what gets recorded is defined a priori, and the information is recorded in a component- or machine-centric way, making it extremely hard to correlate events that cross these boundaries. This article presents Pivot Tracing, a monitoring framework for distributed systems that addresses both limitations by combining dynamic instrumentation with a novel relational operator: the happened-before join. Pivot Tracing gives users, at runtime, the ability to define arbitrary metrics at one point of the system, while being able to select, filter, and group by events meaningful at other parts of the system, even when crossing component or machine boundaries. We have implemented a prototype of Pivot Tracing for Java-based systems and evaluate it on a heterogeneous Hadoop cluster comprising HDFS, HBase, MapReduce, and YARN. We show that Pivot Tracing can effectively identify a diverse range of root causes such as software bugs, misconfiguration, and limping hardware. We show that Pivot Tracing is dynamic, extensible, and enables cross-tier analysis between inter-operating applications, with low execution overhead.","PeriodicalId":318554,"journal":{"name":"ACM Transactions on Computer Systems (TOCS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126592927","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. Belay, G. Prekas, Mia Primorac, Ana Klimovic, Samuel Grossman, C. Kozyrakis, Edouard Bugnion
On page 21 of “The IX Operating System: Combining Low Latency, High Throughput and Efficiency in a Protected Dataplane” we describe our use of the tool mutilate to evaluate the latency and throughput of memcached. We discovered an error in our setup: we did not load the initial key-value state into memcached before the start of the experiment. Instead, memcached started in an empty state, causing some GET requests to require less computation than intended. Table 1 shows the performance differences between our original and corrected memcached results.
{"title":"Corrigendum to “The IX Operating System: Combining Low Latency, High Throughput and Efficiency in a Protected Dataplane”","authors":"A. Belay, G. Prekas, Mia Primorac, Ana Klimovic, Samuel Grossman, C. Kozyrakis, Edouard Bugnion","doi":"10.1145/3154292","DOIUrl":"https://doi.org/10.1145/3154292","url":null,"abstract":"On page 21 of “The IX Operating System: Combining Low Latency, High Throughput and Efficiency in a Protected Dataplane” we describe our use of the tool mutilate to evaluate the latency and throughput of memcached. We discovered an error in our setup: we did not load the initial key-value state into memcached before the start of the experiment. Instead, memcached started in an empty state, causing some GET requests to require less computation than intended. Table 1 shows the performance differences between our original and corrected memcached results.","PeriodicalId":318554,"journal":{"name":"ACM Transactions on Computer Systems (TOCS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124445504","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}
Hari Cherupalli, Henry Duwe, Weidong Ye, Rakesh Kumar, J. Sartori
Many emerging applications such as the Internet of Things, wearables, implantables, and sensor networks are constrained by power and energy. These applications rely on ultra-low-power processors that have rapidly become the most abundant type of processor manufactured today. In the ultra-low-power embedded systems used by these applications, peak power and energy requirements are the primary factors that determine critical system characteristics, such as size, weight, cost, and lifetime. While the power and energy requirements of these systems tend to be application specific, conventional techniques for rating peak power and energy cannot accurately bound the power and energy requirements of an application running on a processor, leading to overprovisioning that increases system size and weight. In this article, we present an automated technique that performs hardware–software coanalysis of the application and ultra-low-power processor in an embedded system to determine application-specific peak power and energy requirements. Our technique provides more accurate, tighter bounds than conventional techniques for determining peak power and energy requirements. Also, unlike conventional approaches, our technique reports guaranteed bounds on peak power and energy independent of an application’s input set. Tighter bounds on peak power and energy can be exploited to reduce system size, weight, and cost.
{"title":"Determining Application-Specific Peak Power and Energy Requirements for Ultra-Low-Power Processors","authors":"Hari Cherupalli, Henry Duwe, Weidong Ye, Rakesh Kumar, J. Sartori","doi":"10.1145/3148052","DOIUrl":"https://doi.org/10.1145/3148052","url":null,"abstract":"Many emerging applications such as the Internet of Things, wearables, implantables, and sensor networks are constrained by power and energy. These applications rely on ultra-low-power processors that have rapidly become the most abundant type of processor manufactured today. In the ultra-low-power embedded systems used by these applications, peak power and energy requirements are the primary factors that determine critical system characteristics, such as size, weight, cost, and lifetime. While the power and energy requirements of these systems tend to be application specific, conventional techniques for rating peak power and energy cannot accurately bound the power and energy requirements of an application running on a processor, leading to overprovisioning that increases system size and weight. In this article, we present an automated technique that performs hardware–software coanalysis of the application and ultra-low-power processor in an embedded system to determine application-specific peak power and energy requirements. Our technique provides more accurate, tighter bounds than conventional techniques for determining peak power and energy requirements. Also, unlike conventional approaches, our technique reports guaranteed bounds on peak power and energy independent of an application’s input set. Tighter bounds on peak power and energy can be exploited to reduce system size, weight, and cost.","PeriodicalId":318554,"journal":{"name":"ACM Transactions on Computer Systems (TOCS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127002214","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}
Rajiv Nishtala, P. Carpenter, V. Petrucci, X. Martorell
In 2013, U.S. data centers accounted for 2.2% of the country’s total electricity consumption, a figure that is projected to increase rapidly over the next decade. Many important data center workloads in cloud computing are interactive, and they demand strict levels of quality-of-service (QoS) to meet user expectations, making it challenging to optimize power consumption along with increasing performance demands. This article introduces Hipster, a technique that combines heuristics and reinforcement learning to improve resource efficiency in cloud systems. Hipster explores heterogeneous multi-cores and dynamic voltage and frequency scaling for reducing energy consumption while managing the QoS of the latency-critical workloads. To improve data center utilization and make best usage of the available resources, Hipster can dynamically assign remaining cores to batch workloads without violating the QoS constraints for the latency-critical workloads. We perform experiments using a 64-bit ARM big.LITTLE platform and show that, compared to prior work, Hipster improves the QoS guarantee for Web-Search from 80% to 96%, and for Memcached from 92% to 99%, while reducing the energy consumption by up to 18%. Hipster is also effective in learning and adapting automatically to specific requirements of new incoming workloads just enough to meet the QoS and optimize resource consumption.
{"title":"The Hipster Approach for Improving Cloud System Efficiency","authors":"Rajiv Nishtala, P. Carpenter, V. Petrucci, X. Martorell","doi":"10.1145/3144168","DOIUrl":"https://doi.org/10.1145/3144168","url":null,"abstract":"In 2013, U.S. data centers accounted for 2.2% of the country’s total electricity consumption, a figure that is projected to increase rapidly over the next decade. Many important data center workloads in cloud computing are interactive, and they demand strict levels of quality-of-service (QoS) to meet user expectations, making it challenging to optimize power consumption along with increasing performance demands. This article introduces Hipster, a technique that combines heuristics and reinforcement learning to improve resource efficiency in cloud systems. Hipster explores heterogeneous multi-cores and dynamic voltage and frequency scaling for reducing energy consumption while managing the QoS of the latency-critical workloads. To improve data center utilization and make best usage of the available resources, Hipster can dynamically assign remaining cores to batch workloads without violating the QoS constraints for the latency-critical workloads. We perform experiments using a 64-bit ARM big.LITTLE platform and show that, compared to prior work, Hipster improves the QoS guarantee for Web-Search from 80% to 96%, and for Memcached from 92% to 99%, while reducing the energy consumption by up to 18%. Hipster is also effective in learning and adapting automatically to specific requirements of new incoming workloads just enough to meet the QoS and optimize resource consumption.","PeriodicalId":318554,"journal":{"name":"ACM Transactions on Computer Systems (TOCS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132185960","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}
The ubiquity of multicore processors has led programmers to write parallel and concurrent applications to take advantage of the underlying hardware and speed up their executions. In this context, Transactional Memory (TM) has emerged as a simple and effective synchronization paradigm, via the familiar abstraction of atomic transactions. After many years of intense research, major processor manufacturers (including Intel) have recently released mainstream processors with hardware support for TM (HTM). In this work, we study a relevant issue with great impact on the performance of HTM. Due to the optimistic and inherently limited nature of HTM, transactions may have to be aborted and restarted numerous times, without any progress guarantee. As a result, it is up to the software library that regulates the HTM usage to ensure progress and optimize performance. Transaction scheduling is probably one of the most well-studied and effective techniques to achieve these goals. However, these recent mainstream HTMs have some technical limitations that prevent the adoption of known scheduling techniques: unlike software implementations of TM used in the past, existing HTMs provide limited or no information on which memory regions or contending transactions caused the abort. To address this crucial issue for HTMs, we propose Seer, a software scheduler that addresses precisely this restriction of HTM by leveraging on an online probabilistic inference technique that identifies the most likely conflict relations and establishes a dynamic locking scheme to serialize transactions in a fine-grained manner. The key idea of our solution is to constrain the portions of parallelism that are affecting negatively the whole system. As a result, this not only prevents performance reduction but also in fact unveils further scalability and performance for HTM. Via an extensive evaluation study, we show that Seer improves the performance of the Intel’s HTM by up to 3.6×, and by 65% on average across all concurrency degrees and benchmarks on a large processor with 28 cores.
{"title":"Seer","authors":"Nuno Diegues, P. Romano, Stoyan Garbatov","doi":"10.1145/3132036","DOIUrl":"https://doi.org/10.1145/3132036","url":null,"abstract":"The ubiquity of multicore processors has led programmers to write parallel and concurrent applications to take advantage of the underlying hardware and speed up their executions. In this context, Transactional Memory (TM) has emerged as a simple and effective synchronization paradigm, via the familiar abstraction of atomic transactions. After many years of intense research, major processor manufacturers (including Intel) have recently released mainstream processors with hardware support for TM (HTM). In this work, we study a relevant issue with great impact on the performance of HTM. Due to the optimistic and inherently limited nature of HTM, transactions may have to be aborted and restarted numerous times, without any progress guarantee. As a result, it is up to the software library that regulates the HTM usage to ensure progress and optimize performance. Transaction scheduling is probably one of the most well-studied and effective techniques to achieve these goals. However, these recent mainstream HTMs have some technical limitations that prevent the adoption of known scheduling techniques: unlike software implementations of TM used in the past, existing HTMs provide limited or no information on which memory regions or contending transactions caused the abort. To address this crucial issue for HTMs, we propose Seer, a software scheduler that addresses precisely this restriction of HTM by leveraging on an online probabilistic inference technique that identifies the most likely conflict relations and establishes a dynamic locking scheme to serialize transactions in a fine-grained manner. The key idea of our solution is to constrain the portions of parallelism that are affecting negatively the whole system. As a result, this not only prevents performance reduction but also in fact unveils further scalability and performance for HTM. Via an extensive evaluation study, we show that Seer improves the performance of the Intel’s HTM by up to 3.6×, and by 65% on average across all concurrency degrees and benchmarks on a large processor with 28 cores.","PeriodicalId":318554,"journal":{"name":"ACM Transactions on Computer Systems (TOCS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127623785","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}
Byung-Gon Chun, Tyson Condie, Yingda Chen, Brian Cho, Andrew Chung, C. Curino, C. Douglas, Matteo Interlandi, Beomyeol Jeon, Joo Seong Jeong, Gyewon Lee, Yunseong Lee, Tony Majestro, D. Malkhi, Sergiy Matusevych, Brandon Myers, M. Mykhailova, Shravan M. Narayanamurthy, Joseph Noor, R. Ramakrishnan, Sriram Rao, R. Sears, B. Sezgin, Taegeon Um, Julia Wang, Markus Weimer, Youngseok Yang
Resource Managers like YARN and Mesos have emerged as a critical layer in the cloud computing system stack, but the developer abstractions for leasing cluster resources and instantiating application logic are very low level. This flexibility comes at a high cost in terms of developer effort, as each application must repeatedly tackle the same challenges (e.g., fault tolerance, task scheduling and coordination) and reimplement common mechanisms (e.g., caching, bulk-data transfers). This article presents REEF, a development framework that provides a control plane for scheduling and coordinating task-level (data-plane) work on cluster resources obtained from a Resource Manager. REEF provides mechanisms that facilitate resource reuse for data caching and state management abstractions that greatly ease the development of elastic data processing pipelines on cloud platforms that support a Resource Manager service. We illustrate the power of REEF by showing applications built atop: a distributed shell application, a machine-learning framework, a distributed in-memory caching system, and a port of the CORFU system. REEF is currently an Apache top-level project that has attracted contributors from several institutions and it is being used to develop several commercial offerings such as the Azure Stream Analytics service.
{"title":"Apache REEF","authors":"Byung-Gon Chun, Tyson Condie, Yingda Chen, Brian Cho, Andrew Chung, C. Curino, C. Douglas, Matteo Interlandi, Beomyeol Jeon, Joo Seong Jeong, Gyewon Lee, Yunseong Lee, Tony Majestro, D. Malkhi, Sergiy Matusevych, Brandon Myers, M. Mykhailova, Shravan M. Narayanamurthy, Joseph Noor, R. Ramakrishnan, Sriram Rao, R. Sears, B. Sezgin, Taegeon Um, Julia Wang, Markus Weimer, Youngseok Yang","doi":"10.1145/3132037","DOIUrl":"https://doi.org/10.1145/3132037","url":null,"abstract":"Resource Managers like YARN and Mesos have emerged as a critical layer in the cloud computing system stack, but the developer abstractions for leasing cluster resources and instantiating application logic are very low level. This flexibility comes at a high cost in terms of developer effort, as each application must repeatedly tackle the same challenges (e.g., fault tolerance, task scheduling and coordination) and reimplement common mechanisms (e.g., caching, bulk-data transfers). This article presents REEF, a development framework that provides a control plane for scheduling and coordinating task-level (data-plane) work on cluster resources obtained from a Resource Manager. REEF provides mechanisms that facilitate resource reuse for data caching and state management abstractions that greatly ease the development of elastic data processing pipelines on cloud platforms that support a Resource Manager service. We illustrate the power of REEF by showing applications built atop: a distributed shell application, a machine-learning framework, a distributed in-memory caching system, and a port of the CORFU system. REEF is currently an Apache top-level project that has attracted contributors from several institutions and it is being used to develop several commercial offerings such as the Azure Stream Analytics service.","PeriodicalId":318554,"journal":{"name":"ACM Transactions on Computer Systems (TOCS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131453046","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}
Zhiming Shen, Qin Jia, Gur-Eyal Sela, Weijia Song, Hakim Weatherspoon, R. Van Renesse
Infrastructure-as-a-Service (IaaS) cloud providers hide available interfaces for virtual machine (VM) placement and migration, CPU capping, memory ballooning, page sharing, and I/O throttling, limiting the ways in which applications can optimally configure resources or respond to dynamically shifting workloads. Given these interfaces, applications could migrate VMs in response to diurnal workloads or changing prices, adjust resources in response to load changes, and so on. This article proposes a new abstraction that we call a Library Cloud and that allows users to customize the diverse available cloud resources to best serve their applications. We built a prototype of a Library Cloud that we call the Supercloud. The Supercloud encapsulates applications in a virtual cloud under users’ full control and can incorporate one or more availability zones within a cloud provider or across different providers. The Supercloud provides virtual machine, storage, and networking complete with a full set of management operations, allowing applications to optimize performance. In this article, we demonstrate various innovations enabled by the Library Cloud.
{"title":"Supercloud","authors":"Zhiming Shen, Qin Jia, Gur-Eyal Sela, Weijia Song, Hakim Weatherspoon, R. Van Renesse","doi":"10.1145/3132038","DOIUrl":"https://doi.org/10.1145/3132038","url":null,"abstract":"Infrastructure-as-a-Service (IaaS) cloud providers hide available interfaces for virtual machine (VM) placement and migration, CPU capping, memory ballooning, page sharing, and I/O throttling, limiting the ways in which applications can optimally configure resources or respond to dynamically shifting workloads. Given these interfaces, applications could migrate VMs in response to diurnal workloads or changing prices, adjust resources in response to load changes, and so on. This article proposes a new abstraction that we call a Library Cloud and that allows users to customize the diverse available cloud resources to best serve their applications. We built a prototype of a Library Cloud that we call the Supercloud. The Supercloud encapsulates applications in a virtual cloud under users’ full control and can incorporate one or more availability zones within a cloud provider or across different providers. The Supercloud provides virtual machine, storage, and networking complete with a full set of management operations, allowing applications to optimize performance. In this article, we demonstrate various innovations enabled by the Library Cloud.","PeriodicalId":318554,"journal":{"name":"ACM Transactions on Computer Systems (TOCS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121537953","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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