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  • 1.
    Liu, Ying
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. Université Catholique de Louvain, Belgium.
    Rameshan, Navaneeth
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. Universitat Politècnica de Catalunya, Spain.
    Monte, E.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Navarro, L.
    ProRenaTa: Proactive and reactive tuning to scale a distributed storage system2015In: Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015, Institute of Electrical and Electronics Engineers (IEEE), 2015, 453-464 p.Conference paper (Refereed)
    Abstract [en]

    Provisioning tasteful services in the Cloud that guarantees high quality of service with reduced hosting cost is challenging to achieve. There are two typical auto-scaling approaches: predictive and reactive. A prediction based controller leaves the system enough time to react to workload changes while a feedback based controller scales the system with better accuracy. In this paper, we show the limitations of using a proactive or reactive approach in isolation to scale a tasteful system and the overhead involved. To overcome the limitations, we implement an elasticity controller, ProRenaTa, which combines both reactive and proactive approaches to leverage on their respective advantages and also implements a data migration model to handle the scaling overhead. We show that the combination of reactive and proactive approaches outperforms the state of the art approaches. Our experiments with Wikipedia workload trace indicate that ProRenaTa guarantees a high level of SLA commitments while improving the overall resource utilization.

  • 2.
    Rameshan, Navaneeth
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    On the Role of Performance Interference in Consolidated Environments2016Doctoral thesis, monograph (Other academic)
    Abstract [en]

    With the advent of resource shared environments such as the Cloud, virtualization has become the de facto standard for server consolidation. While consolidation improves utilization, it causes performance-interference between Virtual Machines (VMs) from contention in shared resources such as CPU, Last Level Cache (LLC) and memory bandwidth. Over-provisioning resources for performance sensitive applications can guarantee Quality of Service (QoS), however, it results in low machine utilization. Thus, assuring QoS for performance sensitive applications while allowing co-location has been a challenging problem. In this thesis, we identify ways to mitigate performance interference without undue over-provisioning and also point out the need to model and account for performance interference to improve the reliability and accuracy of elastic scaling. The end goal of this research is to leverage on the observations to provide efficient resource management that is both performance and cost aware. Our main contributions are threefold; first, we improve the overall machine utilization by executing best-effort applications along side latency critical applications without violating its performance requirements. Our solution is able to dynamically adapt and leverage on the changing workload/phase behaviour to execute best-effort applications without causing excessive interference on performance; second, we identify that certain performance metrics used for elastic scaling decisions may become unreliable if performance interference is unaccounted. By modelling performance interference, we show that these performance metrics become reliable in a multi-tenant environment; and third, we identify and demonstrate the impact of interference on the accuracy of elastic scaling and propose a solution to significantly minimise performance violations at a reduced cost.

  • 3.
    Rameshan, Navaneeth
    et al.
    KTH. Universitat Politècnica de Catalunya, Spain.
    Birke, R.
    Navarro, L.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Urgaonkar, B.
    Kesidis, G.
    Schmatz, M.
    Chen, L. Y.
    Profiling Memory Vulnerability of Big-data Applications2016In: 2016 46TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOPS (DSN-W), IEEE, 2016, 258-261 p.Conference paper (Refereed)
    Abstract [en]

    Motivated by the increasing popularity of hosting in-memory big-data analytics in cloud, we present a profiling methodology that can understand how different memory subsystems, i.e., cache and memory bandwidth, are susceptible to the impact of interference from co-located applications. We first describe the design of the proposed tool and demonstrate a case study consisting of five Spark applications on real-life data set.

  • 4.
    Rameshan, Navaneeth
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Liu, Ying
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Navarro, Leandro
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Augmenting Elasticity Controllers for Improved Accuracy2016In: 2016 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC), IEEE Computer Society, 2016, 117-126 p.Conference paper (Refereed)
    Abstract [en]

    Elastic resource provisioning is used to guarantee service level objectives (SLO) at reduced cost in a Cloud platform. However, performance interference in the hosting platform introduces uncertainty in the performance guarantees of provisioned services. Existing elasticity controllers are either unaware of this interference or over-provision resources to meet the SLO. In this paper, we show that assuming predictable performance of VMs in a multi-tenant environment to scale, will result in long periods of SLO violations. We augment the elasticity controller to be aware of interference and improve the convergence time of scaling without over provisioning. We perform experiments with Memcached and compare our solution against a baseline elasticity controller that is unaware of performance interference. Our results show that augmentation can reduce SLO violations by 65% or more and also save provisioning costs compared to an interference oblivious controller.

  • 5.
    Rameshan, Navaneeth
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Liu, Ying
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Navarro, Leandro
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Hubbub-Scale: Towards Reliable Elastic Scaling under Multi-tenancy2016Conference paper (Refereed)
    Abstract [en]

    Elastic resource provisioning is used to guarantee service level objective (SLO) with reduced cost in a Cloud platform. However, performance interference in the hosting platform introduces uncertainty in the performance guarantees of provisioned services. Existing elasticity controllers are either unaware of this interference or over-provision resources to meet the SLO. In this paper, we show that assuming predictable performance of VMs to build an elasticity controller will fail if interference is not modelled. We identify and control the different sources of unpredictability and build Hubbub-Scale, an elasticity controller that is reliable in the presence of performance interference. Our evaluation with Redis and Memcached show that Hubbub-Scale efficiently conforms to the SLO requirements under scenarios where standard modelling approaches fail.

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