Towards Multi-Tenant Performance SLOs

Lang, Willis; Shankar, Srinath; Patel, Jignesh M.; Kalhan, Ajay

doi:10.1109/tkde.2013.74

Cited by 24 publications

(18 citation statements)

References 23 publications

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“…There is also some research work about managing other types of cloud resources [18] [19]. Nandi et al in [ 18 ] propose a license allocation algorithm for tenants by considering task deadline and application license demand specified in tenant dynamic SLA model.…”

Section: A Tenant-oriented Resource Managementmentioning

confidence: 99%

“…Nandi et al in [ 18 ] propose a license allocation algorithm for tenants by considering task deadline and application license demand specified in tenant dynamic SLA model. Lang et al in [19] present a framework to generate a cost-effective recipe of hardware allocation and scheduling, which takes as input the tenant workloads, their performance Service Level Objectives (SLOs) and Data-as-a-Service (DaaS) provider owned server hardware.…”

Section: A Tenant-oriented Resource Managementmentioning

confidence: 99%

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An SLA-Driven Cache Optimization Approach for Multi-tenant Application on PaaS

Chen

et al. 2014

2014 IEEE 38th Annual Computer Software and Applications Conference

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as multi-tenant applications spring up in clouds, more and more people advocate using Service Level Agreement (SLA) in service delivery to fit tenants' non-functional needs e.g. response time and budget limit. However, most of the present application optimizations based on SLA focuses on virtual machine-based (VM-based) computing service, while other services such as storage and cache are often neglected. In this paper, we propose an SLA-driven application optimization for cache service to help to meet tenants' needs better and improve cost-effectiveness, which can be taken as complementary to the existing work. The proposed approach, built on top of Platform-as-a-Service (PaaS), pays attention to evicted data. It considers both tenant SLA-evaluated status and data performance when weighting the evicted data with re-cache likelihoods, and then adjusts their re-cache priorities. At the beginning of every cycle it predicts tenant status and evicted data performance for the coming cycle by Holt-Winters double exponential smoothing. Our simulation experiments demonstrate the optimization effectiveness in improving cache cost-effectiveness and satisfying tenant SLAs.

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Section: A Tenant-oriented Resource Managementmentioning

confidence: 99%

Section: A Tenant-oriented Resource Managementmentioning

confidence: 99%

An SLA-Driven Cache Optimization Approach for Multi-tenant Application on PaaS

Chen

et al. 2014

2014 IEEE 38th Annual Computer Software and Applications Conference

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“…The authors argue that analytical models for performance and consolidation are hard due to complex component interactions and shifting bottlenecks. Lang et al [14] propose a SLOfocused framework for static provisioning and placement where tenant workloads are known. In general, existing approaches do not target the problem of continuous tenant modeling, dynamic tenant placement, variable and unknown tenant workloads, and performance crisis mitigation in the shared process multitenancy model, which is critical for deploying shared database services.…”

Section: Challenges In Multitenancymentioning

confidence: 99%

“…Additionally, Pythia learns tenant behavior without any assumptions or in-depth understanding of the underlying systems. In addition, unlike workload driven techniques [1,6,14], Pythia does not require advanced knowledge of the tenants' workload or limit the workload types. Moreover, Pythia does not require profiling tenants in a sandbox, a dedicated node for running tenants in isolation, thus making it applicable even in scenarios where production workloads cannot be replayed due to operational or privacy considerations [2].…”

Section: Controller For a Multitenant Dbmsmentioning

confidence: 99%

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Characterizing tenant behavior for placement and crisis mitigation in multitenant DBMSs

Elmore

Das

Pucher

et al. 2013

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

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A multitenant database management system (DBMS) in the cloud must continuously monitor the trade-off between efficient resource sharing among multiple application databases (tenants) and their performance. Considering the scale of hundreds to thousands of tenants in such multitenant DBMSs, manual approaches for continuous monitoring are not tenable. A self-managing controller of a multitenant DBMS faces several challenges. For instance, how to characterize a tenant given its variety of workloads, how to reduce the impact of tenant colocation, and how to detect and mitigate a performance crisis where one or more tenants' desired service level objective (SLO) is not achieved. We present Delphi, a self-managing system controller for a multitenant DBMS, and Pythia, a technique to learn behavior through observation and supervision using DBMS-agnostic database level performance measures. Pythia accurately learns tenant behavior even when multiple tenants share a database process, learns good and bad tenant consolidation plans (or packings), and maintains a per-tenant history to detect behavior changes. Delphi detects performance crises, and leverages Pythia to suggests remedial actions using a hill-climbing search algorithm to identify a new tenant placement strategy to mitigate violating SLOs. Our evaluation using a variety of tenant types and workloads shows that Pythia can learn a tenant's behavior with more than 92% accuracy and learn the quality of packings with more than 86% accuracy. During a performance crisis, Delphi is able to reduce 99th percentile latencies by 80%, and can consolidate 45% more tenants than a greedy baseline, which balances tenant load without modeling tenant behavior.

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