Pub Date : 2018-05-01DOI: 10.1109/CCGRID.2018.00040
Brandon Posey, Christopher Gropp, Boyd Wilson, Boyd McGeachie, S. Padhi, Alexander Herzog, A. Apon
A major limitation for time-to-science can be the lack of available computing resources. Depending on the capacity of resources, executing an application suite with hundreds of thousands of jobs can take weeks when resources are in high demand. We describe how we dynamically provision a large scale high performance computing cluster of more than one million cores utilizing Amazon Web Services (AWS). We discuss the trade-offs, challenges, and solutions associated with creating such a large scale cluster with commercial cloud resources. We utilize our large scale cluster to study a parameter sweep workflow composed of message-passing parallel topic modeling jobs on multiple datasets. At peak, we achieve a simultaneous core count of 1,119,196 vCPUs across nearly 50,000 instances, and are able to execute almost half a million jobs within two hours utilizing AWS Spot Instances in a single AWS region. Our solutions to the challenges and trade-offs have broad application to the lifecycle management of similar clusters on other commercial clouds.
研究时间的一个主要限制可能是缺乏可用的计算资源。根据资源的容量，当资源需求量很大时，执行具有数十万个作业的应用程序套件可能需要数周时间。我们描述了如何利用Amazon Web Services (AWS)动态地提供超过一百万核的大规模高性能计算集群。我们将讨论与使用商业云资源创建如此大规模集群相关的权衡、挑战和解决方案。我们利用我们的大规模集群研究了一个由多个数据集上的消息传递并行主题建模作业组成的参数扫描工作流。在峰值时，我们在近50,000个实例中实现了1,119,196个vcpu的同时核心计数，并且能够在单个AWS区域中利用AWS Spot实例在两小时内执行近50万个作业。我们针对挑战和权衡的解决方案广泛应用于其他商业云上类似集群的生命周期管理。
{"title":"Addressing the Challenges of Executing a Massive Computational Cluster in the Cloud","authors":"Brandon Posey, Christopher Gropp, Boyd Wilson, Boyd McGeachie, S. Padhi, Alexander Herzog, A. Apon","doi":"10.1109/CCGRID.2018.00040","DOIUrl":"https://doi.org/10.1109/CCGRID.2018.00040","url":null,"abstract":"A major limitation for time-to-science can be the lack of available computing resources. Depending on the capacity of resources, executing an application suite with hundreds of thousands of jobs can take weeks when resources are in high demand. We describe how we dynamically provision a large scale high performance computing cluster of more than one million cores utilizing Amazon Web Services (AWS). We discuss the trade-offs, challenges, and solutions associated with creating such a large scale cluster with commercial cloud resources. We utilize our large scale cluster to study a parameter sweep workflow composed of message-passing parallel topic modeling jobs on multiple datasets. At peak, we achieve a simultaneous core count of 1,119,196 vCPUs across nearly 50,000 instances, and are able to execute almost half a million jobs within two hours utilizing AWS Spot Instances in a single AWS region. Our solutions to the challenges and trade-offs have broad application to the lifecycle management of similar clusters on other commercial clouds.","PeriodicalId":321027,"journal":{"name":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127410631","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}
Pub Date : 2018-05-01DOI: 10.1109/CCGRID.2018.00038
A. Segalini, Dino Lopez Pacheco, Quentin Jacquemart
In Data Centers (DCs), an abundance of virtual machines (VMs) remain idle due to network services awaiting for incoming connections, or due to established-and-idling sessions. These VMs lead to wastage of RAM – the scarcest resource in DCs – as they lock their allocated memory. In this paper, we introduce SEaMLESS, a solution designed to (i) transform fully-fledged idle VMs into lightweight and resourceless virtual network functions (VNFs), then (ii) reduces the allocated memory to those idle VMs. By replacing idle VMs with VNFs, SEaMLESS provides fast VM restoration upon user activity detection, thereby introducing limited impact on the Quality of Experience (QoE). Our results show that SEaMLESS can consolidate hundreds of VMs as VNFs onto one single machine. SEaMLESS is thus able to release the majority of the memory allocated to idle VMs. This freed memory can then be reassigned to new VMs, or lead to massive consolidation, to enable a better utilization of DC resources.
在数据中心(dc)中，由于网络服务等待传入的连接，或者由于建立和空闲会话，大量虚拟机(vm)处于空闲状态。这些vm会导致RAM(数据中心中最稀缺的资源)的浪费，因为它们会锁定已分配的内存。在本文中，我们介绍了SEaMLESS，一个旨在(i)将完全空闲的vm转换为轻量级和无资源的虚拟网络功能(VNFs)的解决方案，然后(ii)减少分配给这些空闲vm的内存。通过将空闲的虚拟机替换为VNFs, SEaMLESS可以在检测到用户活动时快速恢复虚拟机，从而减少对QoE (Quality of Experience)的影响。我们的结果表明，SEaMLESS可以将数百个vm作为VNFs整合到一台机器上。因此，SEaMLESS能够释放分配给空闲vm的大部分内存。然后可以将释放的内存重新分配给新的vm，或者进行大规模整合，以便更好地利用DC资源。
{"title":"Towards Massive Consolidation in Data Centers with SEaMLESS","authors":"A. Segalini, Dino Lopez Pacheco, Quentin Jacquemart","doi":"10.1109/CCGRID.2018.00038","DOIUrl":"https://doi.org/10.1109/CCGRID.2018.00038","url":null,"abstract":"In Data Centers (DCs), an abundance of virtual machines (VMs) remain idle due to network services awaiting for incoming connections, or due to established-and-idling sessions. These VMs lead to wastage of RAM – the scarcest resource in DCs – as they lock their allocated memory. In this paper, we introduce SEaMLESS, a solution designed to (i) transform fully-fledged idle VMs into lightweight and resourceless virtual network functions (VNFs), then (ii) reduces the allocated memory to those idle VMs. By replacing idle VMs with VNFs, SEaMLESS provides fast VM restoration upon user activity detection, thereby introducing limited impact on the Quality of Experience (QoE). Our results show that SEaMLESS can consolidate hundreds of VMs as VNFs onto one single machine. SEaMLESS is thus able to release the majority of the memory allocated to idle VMs. This freed memory can then be reassigned to new VMs, or lead to massive consolidation, to enable a better utilization of DC resources.","PeriodicalId":321027,"journal":{"name":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131989413","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}
Pub Date : 2018-05-01DOI: 10.1109/CCGRID.2018.00025
K. Uehara, Yu Xiang, Y. Chen, M. Hiltunen, Kaustubh R. Joshi, R. Schlichting
The explosive growth of data due to the increasing adoption of cloud technologies in the enterprise has created a strong demand for more flexible, cost-effective, and scalable storage solutions. Many storage systems, however, are not well matched to the workloads they service due to the difficulty of configuring the storage system optimally a priori with only approximate knowledge of the workload characteristics. This paper shows how cloud-based orchestration can be leveraged to create flexible storage solutions that use continuous adaptation to tailor themselves to their target application workloads, and in doing so, provide superior performance, cost, and scalability over traditional fixed designs. To demonstrate this approach, we have built "SuperCell," a Ceph-based distributed storage solution with a recommendation engine for the storage configuration. SuperCell provides storage operators with real-time recommendations on how to reconfigure the storage system to optimize its performance, cost, and efficiency based on statistical storage modeling and data analysis of the actual workload. Using real cloud storage workloads, we experimentally demonstrate that SuperCell reduces the cost of storage systems by up to 48%, while meeting service level agreement (SLA) 99% of the time, a level that any static design fails to meet for the workloads.
{"title":"SuperCell: Adaptive Software-Defined Storage for Cloud Storage Workloads","authors":"K. Uehara, Yu Xiang, Y. Chen, M. Hiltunen, Kaustubh R. Joshi, R. Schlichting","doi":"10.1109/CCGRID.2018.00025","DOIUrl":"https://doi.org/10.1109/CCGRID.2018.00025","url":null,"abstract":"The explosive growth of data due to the increasing adoption of cloud technologies in the enterprise has created a strong demand for more flexible, cost-effective, and scalable storage solutions. Many storage systems, however, are not well matched to the workloads they service due to the difficulty of configuring the storage system optimally a priori with only approximate knowledge of the workload characteristics. This paper shows how cloud-based orchestration can be leveraged to create flexible storage solutions that use continuous adaptation to tailor themselves to their target application workloads, and in doing so, provide superior performance, cost, and scalability over traditional fixed designs. To demonstrate this approach, we have built \"SuperCell,\" a Ceph-based distributed storage solution with a recommendation engine for the storage configuration. SuperCell provides storage operators with real-time recommendations on how to reconfigure the storage system to optimize its performance, cost, and efficiency based on statistical storage modeling and data analysis of the actual workload. Using real cloud storage workloads, we experimentally demonstrate that SuperCell reduces the cost of storage systems by up to 48%, while meeting service level agreement (SLA) 99% of the time, a level that any static design fails to meet for the workloads.","PeriodicalId":321027,"journal":{"name":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131582213","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}
Pub Date : 2018-05-01DOI: 10.1109/CCGRID.2018.00027
Thomas B. Rolinger, T. Simon, Christopher D. Krieger
Applications for deep learning and big data analytics have compute and memory requirements that exceed the limits of a single GPU. However, effectively scaling out an application to multiple GPUs is challenging due to the complexities of communication between the GPUs, particularly for collective communication with irregular message sizes. In this work, we provide a performance evaluation of the Allgatherv routine on multi-GPU systems, focusing on GPU network topology and the communication library used. We present results from the OSU-micro benchmark as well as conduct a case study for sparse tensor factorization, one application that uses Allgatherv with highly irregular message sizes. We extend our existing tensor factorization tool to run on systems with different node counts and varying number of GPUs per node. We then evaluate the communication performance of our tool when using traditional MPI, CUDA-aware MVAPICH and NCCL across a suite of real-world data sets on three different systems: a 16-node cluster with one GPU per node, NVIDIA's DGX-1 with 8 GPUs and Cray's CS-Storm with 16 GPUs. Our results show that irregularity in the tensor data sets produce trends that contradict those in the OSU micro-benchmark, as well as trends that are absent from the benchmark.
{"title":"An Empirical Evaluation of Allgatherv on Multi-GPU Systems","authors":"Thomas B. Rolinger, T. Simon, Christopher D. Krieger","doi":"10.1109/CCGRID.2018.00027","DOIUrl":"https://doi.org/10.1109/CCGRID.2018.00027","url":null,"abstract":"Applications for deep learning and big data analytics have compute and memory requirements that exceed the limits of a single GPU. However, effectively scaling out an application to multiple GPUs is challenging due to the complexities of communication between the GPUs, particularly for collective communication with irregular message sizes. In this work, we provide a performance evaluation of the Allgatherv routine on multi-GPU systems, focusing on GPU network topology and the communication library used. We present results from the OSU-micro benchmark as well as conduct a case study for sparse tensor factorization, one application that uses Allgatherv with highly irregular message sizes. We extend our existing tensor factorization tool to run on systems with different node counts and varying number of GPUs per node. We then evaluate the communication performance of our tool when using traditional MPI, CUDA-aware MVAPICH and NCCL across a suite of real-world data sets on three different systems: a 16-node cluster with one GPU per node, NVIDIA's DGX-1 with 8 GPUs and Cray's CS-Storm with 16 GPUs. Our results show that irregularity in the tensor data sets produce trends that contradict those in the OSU micro-benchmark, as well as trends that are absent from the benchmark.","PeriodicalId":321027,"journal":{"name":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134572638","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}
Pub Date : 2018-05-01DOI: 10.1109/CCGRID.2018.00041
N. V. Bozdog, M. Makkes, A. V. Halteren, H. Bal
The daily home-office commute of millions of people in crowded cities puts a strain on air quality, traveling time and noise pollution. This is especially problematic in western cities, where cars and taxis have low occupancy with daily commuters. To reduce these issues, authorities often encourage commuters to share their rides, also known as carpooling or ridesharing. To increase the ridesharing usage it is essential that commuters are efficiently matched. In this paper we present RideMatcher, a novel peer-to-peer system for matching car rides based on their routes and travel times. Unlike other ridesharing systems, RideMatcher is completely decentralized, which makes it possible to deploy it on distributed infrastructures, using fog and edge computing. Despite being decentralized, our system is able to efficiently match ridesharing users in near real-time. Our evaluations performed on a dataset with 34,837 real taxi trips from New York show that RideMatcher is able to reduce the number of taxi trips by up to 65%, the distance traveled by taxi cabs by up to 64%, and the cost of the trips by up to 66%.
{"title":"RideMatcher: Peer-to-Peer Matching of Passengers for Efficient Ridesharing","authors":"N. V. Bozdog, M. Makkes, A. V. Halteren, H. Bal","doi":"10.1109/CCGRID.2018.00041","DOIUrl":"https://doi.org/10.1109/CCGRID.2018.00041","url":null,"abstract":"The daily home-office commute of millions of people in crowded cities puts a strain on air quality, traveling time and noise pollution. This is especially problematic in western cities, where cars and taxis have low occupancy with daily commuters. To reduce these issues, authorities often encourage commuters to share their rides, also known as carpooling or ridesharing. To increase the ridesharing usage it is essential that commuters are efficiently matched. In this paper we present RideMatcher, a novel peer-to-peer system for matching car rides based on their routes and travel times. Unlike other ridesharing systems, RideMatcher is completely decentralized, which makes it possible to deploy it on distributed infrastructures, using fog and edge computing. Despite being decentralized, our system is able to efficiently match ridesharing users in near real-time. Our evaluations performed on a dataset with 34,837 real taxi trips from New York show that RideMatcher is able to reduce the number of taxi trips by up to 65%, the distance traveled by taxi cabs by up to 64%, and the cost of the trips by up to 66%.","PeriodicalId":321027,"journal":{"name":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114329401","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}
Pub Date : 2018-05-01DOI: 10.1109/CCGRID.2018.00065
Yi Zhou, Shubbhi Taneja, Mohammed I. Alghamdi, X. Qin
The goal of this study is to optimize energy efficiency of database clusters through prefetching and caching strategies. We design a workload-skewness scheme to collectively manage a set of hot and cold nodes in a database cluster system. The prefetching mechanism fetches popular data tables to the hot nodes while keeping unpopular data in cold nodes. We leverage a power management module to aggressively turn cold nodes in the low-power mode to conserve energy consumption. We construct a prefetching model and an energy-saving model to govern the power management module in database lusters. The energy-efficient prefetching and caching mechanism is conducive to cutting back the number of power-state transitions, thereby offering high energy efficiency. We systematically evaluate energy conservation technique in the process of managing, fetching, and storing data on clusters supporting database applications. Our experimental results show that our prefetching/caching solution significantly improves energy efficiency of the existing PostgreSQL system.
{"title":"Improving Energy Efficiency of Database Clusters Through Prefetching and Caching","authors":"Yi Zhou, Shubbhi Taneja, Mohammed I. Alghamdi, X. Qin","doi":"10.1109/CCGRID.2018.00065","DOIUrl":"https://doi.org/10.1109/CCGRID.2018.00065","url":null,"abstract":"The goal of this study is to optimize energy efficiency of database clusters through prefetching and caching strategies. We design a workload-skewness scheme to collectively manage a set of hot and cold nodes in a database cluster system. The prefetching mechanism fetches popular data tables to the hot nodes while keeping unpopular data in cold nodes. We leverage a power management module to aggressively turn cold nodes in the low-power mode to conserve energy consumption. We construct a prefetching model and an energy-saving model to govern the power management module in database lusters. The energy-efficient prefetching and caching mechanism is conducive to cutting back the number of power-state transitions, thereby offering high energy efficiency. We systematically evaluate energy conservation technique in the process of managing, fetching, and storing data on clusters supporting database applications. Our experimental results show that our prefetching/caching solution significantly improves energy efficiency of the existing PostgreSQL system.","PeriodicalId":321027,"journal":{"name":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114874299","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}
Pub Date : 2018-05-01DOI: 10.1109/CCGRID.2018.00083
Sreekrishnan Venkateswaran, S. Sarkar
Cloud service brokerage is an emerging technology that attempts to simplify the consumption and operation of hybrid clouds. Today's cloud brokers attempt to insulate consumers from the vagaries of multiple clouds. To achieve the insulation, the modern cloud broker needs to disguise itself as the end-provider to consumers by creating and operating a virtual data center construct that we call a "meta-cloud", which is assembled on top of a set of participating supplier clouds. It is crucial for such a cloud broker to be considered a trusted partner both by cloud consumers and by the underpinning cloud suppliers. A fundamental tenet of brokerage trust is vendor neutrality. On the one hand, cloud consumers will be comfortable if a cloud broker guarantees that they will not be led through a preferred path. And on the other hand, cloud suppliers would be more interested in partnering with a cloud broker who promises a fair apportioning of client provisioning requests. Because consumer and supplier trust on a meta-cloud broker stems from the assumption of being agnostic to supplier clouds, there is a need for a test strategy that verifies the fairness of cloud brokerage. In this paper, we propose a calculus of fairness that defines the rules to determine the operational behavior of a cloud broker. The calculus uses temporal logic to model the fact that fairness is a trait that has to be ascertained over time; it is not a characteristic that can be judged at a per-request fulfillment level. Using our temporal calculus of fairness as the basis, we propose an algorithm to determine the fairness of a broker probabilistically, based on its observed request apportioning policies. Our model for the fairness of cloud broker behavior also factors in inter-provider variables such as cost divergence and capacity variance. We empirically validate our approach by constructing a meta-cloud from AWS, Azure and IBM, in addition to leveraging a cloud simulator. Our industrial engagements with large enterprises also validate the need for such cloud brokerage with verifiable fairness.
{"title":"Modeling Operational Fairness of Hybrid Cloud Brokerage","authors":"Sreekrishnan Venkateswaran, S. Sarkar","doi":"10.1109/CCGRID.2018.00083","DOIUrl":"https://doi.org/10.1109/CCGRID.2018.00083","url":null,"abstract":"Cloud service brokerage is an emerging technology that attempts to simplify the consumption and operation of hybrid clouds. Today's cloud brokers attempt to insulate consumers from the vagaries of multiple clouds. To achieve the insulation, the modern cloud broker needs to disguise itself as the end-provider to consumers by creating and operating a virtual data center construct that we call a \"meta-cloud\", which is assembled on top of a set of participating supplier clouds. It is crucial for such a cloud broker to be considered a trusted partner both by cloud consumers and by the underpinning cloud suppliers. A fundamental tenet of brokerage trust is vendor neutrality. On the one hand, cloud consumers will be comfortable if a cloud broker guarantees that they will not be led through a preferred path. And on the other hand, cloud suppliers would be more interested in partnering with a cloud broker who promises a fair apportioning of client provisioning requests. Because consumer and supplier trust on a meta-cloud broker stems from the assumption of being agnostic to supplier clouds, there is a need for a test strategy that verifies the fairness of cloud brokerage. In this paper, we propose a calculus of fairness that defines the rules to determine the operational behavior of a cloud broker. The calculus uses temporal logic to model the fact that fairness is a trait that has to be ascertained over time; it is not a characteristic that can be judged at a per-request fulfillment level. Using our temporal calculus of fairness as the basis, we propose an algorithm to determine the fairness of a broker probabilistically, based on its observed request apportioning policies. Our model for the fairness of cloud broker behavior also factors in inter-provider variables such as cost divergence and capacity variance. We empirically validate our approach by constructing a meta-cloud from AWS, Azure and IBM, in addition to leveraging a cloud simulator. Our industrial engagements with large enterprises also validate the need for such cloud brokerage with verifiable fairness.","PeriodicalId":321027,"journal":{"name":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129260733","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}
Pub Date : 2018-05-01DOI: 10.1109/CCGRID.2018.00028
Aleksandra Kuzmanovska, H. V. D. Bogert, R. H. Mak, D. Epema
When multiple data-processing frameworks with time-varying workloads are simultaneously present in a single cluster or data-center, an apparent goal is to have them experience equal performance, expressed in whatever performance metrics are applicable. In modern data-center environments, Two-Level Schedulers (TLSs) that leave the scheduling of individual jobs to the schedulers within the data-processing frameworks are typically used for managing the resources of data-processing frameworks. Two such TLSs with opposite designs are Mesos and Koala-F. Mesos employs fine-grained resource allocation and aims at Dominant Resource Fairness (DRF) among framework instances by offering resources to them for the duration of a single task. In contrast, Koala-F aims at performance fairness among framework instances by employing dynamic coarse-grained resource allocation of sets of complete nodes based on performance feedback from individual instances. The goal of this paper is to explore the trade-offs between these two TLS designs when trying to achieve performance balance among frameworks. We select Apache Spark as a representative of data-processing frameworks, and perform experiments on a modest-sized cluster, using jobs chosen from commonly used data-processing benchmarks. Our results reveal that achieving performance balance among framework instances is a challenge for both TLS designs, despite their opposite design choices. Moreover, we exhibit design flaws in the DRF allocation policy that prevent Mesos from achieving performance balance. Finally, to remedy these flaws, we propose a feedback controller for Mesos that dynamically adapts framework weights, as used in Weighted DRF (W-DRF), based on their performance.
{"title":"Achieving Performance Balance Among Spark Frameworks with Two-Level Schedulers","authors":"Aleksandra Kuzmanovska, H. V. D. Bogert, R. H. Mak, D. Epema","doi":"10.1109/CCGRID.2018.00028","DOIUrl":"https://doi.org/10.1109/CCGRID.2018.00028","url":null,"abstract":"When multiple data-processing frameworks with time-varying workloads are simultaneously present in a single cluster or data-center, an apparent goal is to have them experience equal performance, expressed in whatever performance metrics are applicable. In modern data-center environments, Two-Level Schedulers (TLSs) that leave the scheduling of individual jobs to the schedulers within the data-processing frameworks are typically used for managing the resources of data-processing frameworks. Two such TLSs with opposite designs are Mesos and Koala-F. Mesos employs fine-grained resource allocation and aims at Dominant Resource Fairness (DRF) among framework instances by offering resources to them for the duration of a single task. In contrast, Koala-F aims at performance fairness among framework instances by employing dynamic coarse-grained resource allocation of sets of complete nodes based on performance feedback from individual instances. The goal of this paper is to explore the trade-offs between these two TLS designs when trying to achieve performance balance among frameworks. We select Apache Spark as a representative of data-processing frameworks, and perform experiments on a modest-sized cluster, using jobs chosen from commonly used data-processing benchmarks. Our results reveal that achieving performance balance among framework instances is a challenge for both TLS designs, despite their opposite design choices. Moreover, we exhibit design flaws in the DRF allocation policy that prevent Mesos from achieving performance balance. Finally, to remedy these flaws, we propose a feedback controller for Mesos that dynamically adapts framework weights, as used in Weighted DRF (W-DRF), based on their performance.","PeriodicalId":321027,"journal":{"name":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116725946","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}
Pub Date : 2018-05-01DOI: 10.1109/CCGRID.2018.00056
Dung Nguyen, André Luckow, Edward B. Duffy, Ken E. Kennedy, A. Apon
This paper presents a systematic evaluation of Amazon Kinesis and Apache Kafka for meeting highly demanding application requirements. Results show that Kinesis and Kafka can provide high reliability, performance and scalability. Cost and performance trade-offs of Kinesis and Kafka are presented for a variety of application data rates, resource utilization, and resource configurations.
{"title":"Evaluation of Highly Available Cloud Streaming Systems for Performance and Price","authors":"Dung Nguyen, André Luckow, Edward B. Duffy, Ken E. Kennedy, A. Apon","doi":"10.1109/CCGRID.2018.00056","DOIUrl":"https://doi.org/10.1109/CCGRID.2018.00056","url":null,"abstract":"This paper presents a systematic evaluation of Amazon Kinesis and Apache Kafka for meeting highly demanding application requirements. Results show that Kinesis and Kafka can provide high reliability, performance and scalability. Cost and performance trade-offs of Kinesis and Kafka are presented for a variety of application data rates, resource utilization, and resource configurations.","PeriodicalId":321027,"journal":{"name":"2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121050171","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}
Pub Date : 2018-05-01DOI: 10.1109/CCGRID.2018.00071
Vito Giovanni Castellana, Marco Minutoli
The unprecedented amount of data that needs to be processed in emerging data analytics applications poses novel challenges to industry and academia. Scalability and high performance become more than a desirable feature because, due to the scale and the nature of the problems, they draw the line between what is achievable and what is unfeasible. In this paper, we propose SHAD, the Scalable High-performance Algorithms and Data-structures library. SHAD adopts a modular design that confines low level details and promotes reuse. SHAD's core is built on an Abstract Runtime Interface which enhances portability and identifies the minimal set of features of the underlying system required by the framework. The core library includes common data-structures such as: Array, Vector, Map and Set. These are designed to accommodate significant amount of data which can be accessed in massively parallel environments, and used as building blocks for SHAD extensions, i.e. higher level software libraries. We have validated and evaluated our design with a performance and scalability study of the core components of the library. We have validated the design flexibility by proposing a Graph Library as an example of SHAD extension, which implements two different graph data-structures; we evaluate their performance with a set of graph applications. Experimental results show that the approach is promising in terms of both performance and scalability. On a distributed system with 320 cores, SHAD Arrays are able to sustain a throughput of 65 billion operations per second, while SHAD Maps sustain 1 billion of operations per second. Algorithms implemented using the Graph Library exhibit performance and scalability comparable to a custom solution, but with smaller development effort.
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