Computational geometry

Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey

技術解説 IEEE Computer

１ インフラを構築する（ＡＷＳにおけるインフラ；ＶＰＣを構成する；ＶＰＣとオンプレミス環境とを接続する） ２ ファイルオブジェクトを保存・共有・公開する（オブジェクトストレージＳ３の機能；ファイルストレージとして利用する；Ｗｅｂサーバーを構築する；信頼性とコストのバランスをとりたい） ３ アプリケーションサーバーを構築する（Ａｍａｚｏｎ ＥＣ２とＡＷＳ Ｌａｍｂｄａ；スケーラビリティーを高める；サーバーレスでプログラムを動かす；データベースサービスを活用する） ４ ＡＷＳシステムを管理する（リソース監視と異常検知・通報；耐障害性を高める仕組みとバックアップ＆リカバリー；構成管理）

Amazon Web Services完全ソリューションガイド

Some computational grid applications have very large resource requirements and need simultaneous access to resources from more than one parallel computer. Current scheduling systems do not provide mechanisms to gain such simultaneous access without the help of human administrators of the computer systems. In this work, we propose and evaluate several algorithms for supporting advanced reservation of resources in supercomputing scheduling systems. These advanced reservations allow users to request resources from scheduling systems at specific times. We find that the wait times of applications submitted to the queue increases when reservations are supported and the increase depends on how reservations are supported. Further, we find that the best performance is achieved when we assume that applications can be terminated and restarted, backfilling is performed, and relatively accurate run-time predictions are used.

Scheduling with advanced reservations

Automated tools for understanding application behavior and its changes during the application life-cycle are essential for many performance analysis and debugging tasks. Application performance issues have an immediate impact on customer experience and satisfaction. A sudden slowdown of enterprise-wide application can effect a large population of customers, lead to delayed projects and ultimately can result in company financial loss. We believe that online performance modeling should be a part of routine application monitoring. Early, informative warnings on significant changes in application performance should help service providers to timely identify and prevent performance problems and their negative impact on the service. We propose a novel framework for automated anomaly detection and application change analysis. It is based on integration of two complementary techniques: i) a regression-based transaction model that reflects a resource consumption model of the application, and ii) an application performance signature that provides a compact model of run-time behavior of the application. The proposed integrated framework provides a simple and powerful solution for anomaly detection and analysis of essential performance changes in application behavior. An additional benefit of the proposed approach is its simplicity: it is not intrusive and is based on monitoring data that is typically available in enterprise production environments.

/pdf/anomaly-application-change-or-workload-change-towards-2e5lg61mcl.pdf

Anomaly? application change? or workload change? towards automated detection of application performance anomaly and change

Workload flows in enterprise systems that use the multi-tier paradigm are often characterized as bursty, i.e., exhibit a form of temporal dependence. Burstiness often results in dramatic degradation of the perceived user performance, which is extremely difficult to capture with existing capacity planning models. The main reason behind this deficiency of traditional capacity planning models is that the user perceived performance is the result of the complex interaction of a very complex workload with a very complex system. In this paper, we propose a simple and effective methodology for detecting burstiness symptoms in multi-tier systems rather than identifying the low-level exact cause of burstiness as traditional models would require. We provide an effective way to incorporate this information into a surprisingly simple and effective modeling methodology. This new modeling methodology is based on the index of dispersion of the service process at a server, which is inferred by observing the number of completions within the concatenated busy periods of that server. The index of dispersion together with other measurements that reflect the "estimated" mean and the 95th percentile of service times are used to derive a Markov-modulated process that captures well burstiness and variability of the true service process, despite inevitable inaccuracies that result from inexact and limited measurements. Detailed experimentation on a TPC-W testbed where all measurements are obtained by HP (Mercury) Diagnostics, a commercially available tool, shows that the proposed technique offers a simple yet powerful solution to the difficult problem of inferring accurate descriptors of the service time process from coarse measurements of a given system. Experimental and model prediction results are in excellent agreement and argue strongly for the effectiveness of the proposed methodology under both bursty and non-bursty workloads.

/pdf/burstiness-in-multi-tier-applications-symptoms-causes-and-59wpz026f2.pdf

Burstiness in multi-tier applications: symptoms, causes, and new models

Automated tools for understanding application behavior and its changes during the application lifecycle are essential for many performance analysis and debugging tasks. Application performance issues have an immediate impact on customer experience and satisfaction. A sudden slowdown of enterprise-wide application can effect a large population of customers, lead to delayed projects, and ultimately can result in company financial loss. Significantly shortened time between new software releases further exacerbates the problem of thoroughly evaluating the performance of an updated application. Our thesis is that online performance modeling should be a part of routine application monitoring. Early, informative warnings on significant changes in application performance should help service providers to timely identify and prevent performance problems and their negative impact on the service. We propose a novel framework for automated anomaly detection and application change analysis. It is based on integration of two complementary techniques: (i) a regression-based transaction model that reflects a resource consumption model of the application, and (ii) an application performance signature that provides a compact model of runtime behavior of the application. The proposed integrated framework provides a simple and powerful solution for anomaly detection and analysis of essential performance changes in application behavior. An additional benefit of the proposed approach is its simplicity: It is not intrusive and is based on monitoring data that is typically available in enterprise production environments. The introduced solution further enables the automation of capacity planning and resource provisioning tasks of multitier applications in rapidly evolving IT environments.

Automated anomaly detection and performance modeling of enterprise applications

The design of autonomic systems often relies on representative benchmarks for evaluating system performance and scalability. Despite the fact that experimental observations have established that burstiness is a common workload characteristic that has deleterious effects on user-perceived performance, existing client-server benchmarks do not provide mechanisms for injecting burstiness into the workload. In this paper, we introduce a new methodology for generating workloads that emulate the temporal surge phenomenon in a controllable way, thus provide a mechanism that enables testing and evaluation of client-server system performance under reproducible bursty workloads. This new methodology allows to inject different amounts of burstiness into the arrival stream using the index of dispersion, a single parameter that is as simple to use as a turnable knob.We exemplify the effectiveness of this new methodology by introducing a new module into the TPC-W, a benchmark that is routinely used for capacity planning of e-commerce systems. This new module injects burstiness into the arrival process of clients in a controllable manner, and hence, enables understanding system performance degradation due to burstiness. Detailed experimentation on a real system shows that this benchmark modification can stress the system under different degrees of burstiness, making a strong case for the usefulness of this modification for capacity planning of autonomic systems.

/pdf/injecting-realistic-burstiness-to-a-traditional-client-43vy9j1x47.pdf

Injecting realistic burstiness to a traditional client-server benchmark

Cloud computing nowadays becomes quite popular among a community of cloud users by offering a variety of resources. However, burstiness in user demands often dramatically degrades the application performance. In order to satisfy peak user demands and meet Service Level Agreement (SLA), efficient resource allocation schemes are highly demanded in the cloud. However, we find that conventional load balancers unfortunately neglect cases of bursty arrivals and thus experience significant performance degradation. Motivated by this problem, we propose new burstiness-aware algorithms to balance bursty workloads across all computing sites, and thus to improve overall system performance. We present a smart load balancer, which leverages the knowledge of burstiness to predict the changes in user demands and on-the-fly shifts between the schemes that are “greedy” (i.e., always select the best site) and “random” (i.e., randomly select one) based on the predicted information. Both simulation and real experimental results show that this new load balancer can adapt quickly to the changes in user demands and thus improve performance by making a smart site selection for cloud users under both bursty and non-bursty workloads.

/pdf/ara-adaptive-resource-allocation-for-cloud-computing-2y77hxgjsu.pdf

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Papers

Anomaly? application change? or workload change? towards automated detection of application performance anomaly and change

Burstiness in multi-tier applications: symptoms, causes, and new models

Automated anomaly detection and performance modeling of enterprise applications

Injecting realistic burstiness to a traditional client-server benchmark

ArA: Adaptive resource allocation for cloud computing environments under bursty workloads