11,773 research outputs found

    DEPAS: A Decentralized Probabilistic Algorithm for Auto-Scaling

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    The dynamic provisioning of virtualized resources offered by cloud computing infrastructures allows applications deployed in a cloud environment to automatically increase and decrease the amount of used resources. This capability is called auto-scaling and its main purpose is to automatically adjust the scale of the system that is running the application to satisfy the varying workload with minimum resource utilization. The need for auto-scaling is particularly important during workload peaks, in which applications may need to scale up to extremely large-scale systems. Both the research community and the main cloud providers have already developed auto-scaling solutions. However, most research solutions are centralized and not suitable for managing large-scale systems, moreover cloud providers' solutions are bound to the limitations of a specific provider in terms of resource prices, availability, reliability, and connectivity. In this paper we propose DEPAS, a decentralized probabilistic auto-scaling algorithm integrated into a P2P architecture that is cloud provider independent, thus allowing the auto-scaling of services over multiple cloud infrastructures at the same time. Our simulations, which are based on real service traces, show that our approach is capable of: (i) keeping the overall utilization of all the instantiated cloud resources in a target range, (ii) maintaining service response times close to the ones obtained using optimal centralized auto-scaling approaches.Comment: Submitted to Springer Computin

    Technical Report: A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters

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    To improve customer experience, datacenter operators offer support for simplifying application and resource management. For example, running workloads of workflows on behalf of customers is desirable, but requires increasingly more sophisticated autoscaling policies, that is, policies that dynamically provision resources for the customer. Although selecting and tuning autoscaling policies is a challenging task for datacenter operators, so far relatively few studies investigate the performance of autoscaling for workloads of workflows. Complementing previous knowledge, in this work we propose the first comprehensive performance study in the field. Using trace-based simulation, we compare state-of-the-art autoscaling policies across multiple application domains, workload arrival patterns (e.g., burstiness), and system utilization levels. We further investigate the interplay between autoscaling and regular allocation policies, and the complexity cost of autoscaling. Our quantitative study focuses not only on traditional performance metrics and on state-of-the-art elasticity metrics, but also on time- and memory-related autoscaling-complexity metrics. Our main results give strong and quantitative evidence about previously unreported operational behavior, for example, that autoscaling policies perform differently across application domains and by how much they differ.Comment: Technical Report for the CCGrid 2018 submission "A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters

    CRAID: Online RAID upgrades using dynamic hot data reorganization

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    Current algorithms used to upgrade RAID arrays typically require large amounts of data to be migrated, even those that move only the minimum amount of data required to keep a balanced data load. This paper presents CRAID, a self-optimizing RAID array that performs an online block reorganization of frequently used, long-term accessed data in order to reduce this migration even further. To achieve this objective, CRAID tracks frequently used, long-term data blocks and copies them to a dedicated partition spread across all the disks in the array. When new disks are added, CRAID only needs to extend this process to the new devices to redistribute this partition, thus greatly reducing the overhead of the upgrade process. In addition, the reorganized access patterns within this partition improve the array’s performance, amortizing the copy overhead and allowing CRAID to offer a performance competitive with traditional RAIDs. We describe CRAID’s motivation and design and we evaluate it by replaying seven real-world workloads including a file server, a web server and a user share. Our experiments show that CRAID can successfully detect hot data variations and begin using new disks as soon as they are added to the array. Also, the usage of a dedicated partition improves the sequentiality of relevant data access, which amortizes the cost of reorganizations. Finally, we prove that a full-HDD CRAID array with a small distributed partition (<1.28% per disk) can compete in performance with an ideally restriped RAID-5 and a hybrid RAID-5 with a small SSD cache.Peer ReviewedPostprint (published version
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