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Creators/Authors contains: "Govindaraju, Madhusudhan"

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  1. Academic cloud infrastructures require users to specify an estimate of their resource requirements. The resource usage for applications often depends on the input file sizes, parameters, optimization flags, and attributes, specified for each run. Incorrect estimation can result in low resource utilization of the entire infrastructure and long wait times for jobs in the queue. We have designed a Resource Utilization based Migration (RUMIG) system to address the resource estimation problem. We present the overall architecture of the two-stage elastic cluster design, the Apache Mesos-specific container migration system, and analyze the performance for several scientific workloads on three different cloud/cluster environments. In this paper we (b) present a design and implementation for container migration in a Mesos environment, (c) evaluate the effect of right-sizing and cluster elasticity on overall performance, (d) analyze different profiling intervals to determine the best fit, (e) determine the overhead of our profiling mechanism. Compared to the default use of Apache Mesos, in the best cases, RUMIG provides a gain of 65% in runtime (local cluster), 51% in CPU utilization in the Chameleon cloud, and 27% in memory utilization in the Jetstream cloud. 
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  2. Apache Mesos, a two-level resource scheduler, provides resource sharing across multiple users in a multi-tenant clustered environment. Computational resources (i.e., CPU, memory, disk, etc.) are distributed according to the Dominant Resource Fairness (DRF) policy. Mesos frameworks (users) receive resources based on their current usage and are responsible for scheduling their tasks within the allocation. We have observed that multiple frameworks can cause fairness imbalance in a multi-user environment. For example, a greedy framework consuming more than its fair share of resources can deny resource fairness to others. The user with the least Dominant Share is considered first by the DRF module to get its resource allocation. However, the default DRF implementation, in Apache Mesos' Master allocation module, does not consider the overall resource demands of the tasks in the queue for each user/framework. This lack of awareness can lead to poor performance as users without any pending task may receive more resource offers, and users with a queue of pending tasks can starve due to their high dominant shares. In a multi-tenant environment, the characteristics of frameworks and workloads must be understood by cluster managers to be able to define fairness based on not only resource share but also resource demand and queue wait time. We have developed a policy driven queue manager, Tromino, for an Apache Mesos cluster where tasks for individual frameworks can be scheduled based on each framework's overall resource demands and current resource consumption. Dominant Share and demand awareness of Tromino and scheduling based on these attributes can reduce (1) the impact of unfairness due to a framework specific configuration, and (2) unfair waiting time due to higher resource demand in a pending task queue. In the best case, Tromino can significantly reduce the average waiting time of a framework by using the proposed Demand-DRF aware policy. 
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  3. Apache Mesos, a cluster-wide resource manager, is widely deployed in massive scale at several Clouds and Data Centers. Mesos aims to provide high cluster utilization via fine grained resource co-scheduling and resource fairness among multiple users through Dominant Resource Fairness (DRF) based allocation. DRF takes into account different resource types (CPU, Memory, Disk I/O) requested by each application and determines the share of each cluster resource that could be allocated to the applications. Mesos has adopted a two-level scheduling policy: (1) DRF to allocate resources to competing frameworks and (2) task level scheduling by each framework for the resources allocated during the previous step. We have conducted experiments in a local Mesos cluster when used with frameworks such as Apache Aurora, Marathon, and our own framework Scylla, to study resource fairness and cluster utilization. Experimental results show how informed decision regarding second level scheduling policy of frameworks and attributes like offer holding period, offer refusal cycle and task arrival rate can reduce unfair resource distribution. Bin-Packing scheduling policy on Scylla with Marathon can reduce unfair allocation from 38% to 3%. By reducing unused free resources in offers we bring down the unfairness from to 90% to 28%. We also show the effect of task arrival rate to reduce the unfairness from 23% to 7%. 
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  4. The HPC community is actively researching and evaluating tools to support execution of scientific applications in cloud-based environ- ments. Among the various technologies, containers have recently gained importance as they have significantly better performance compared to full-scale virtualization, support for microservices and DevOps, and work seamlessly with workflow and orchestration tools. Docker is currently the leader in containerization technology because it offers low overhead, flexibility, portability of applications, and reproducibility. Singularity is another container solution that is of interest as it is designed specifically for scientific applications. It is important to conduct performance and feature analysis of the container technologies to understand their applicability for each application and target execution environment. This paper presents a (1) performance evaluation of Docker and Singularity on bare metal nodes in the Chameleon cloud (2) mecha- nism by which Docker containers can be mapped with InfiniBand hardware with RDMA communication and (3) analysis of mapping elements of parallel workloads to the containers for optimal re- source management with container-ready orchestration tools. Our experiments are targeted toward application developers so that they can make informed decisions on choosing the container tech- nologies and approaches that are suitable for their HPC workloads on cloud infrastructure. Our performance analysis shows that sci- entific workloads for both Docker and Singularity based containers can achieve near-native performance. Singularity is designed specifically for HPC workloads. However, Docker still has advantages over Singularity for use in clouds as it provides overlay networking and an intuitive way to run MPI applications with one container per rank for fine-grained resources allocation. Both Docker and Singularity make it possible to directly use the underlying network fabric from the containers for coarse- grained resource allocation. 
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