Pricing-based strategies for autonomic control of web servers for time-varying request arrivals

Chen, Yiyu; Das, Abhinav; Gautam, Natarajan; Wang, Qian; Sivasubramaniam, Anand

doi:10.1016/j.engappai.2004.09.001

Cited by 3 publications

(1 citation statement)

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“…Dynamic capacity provisioning is a useful technique for handling the multi-time scale variations seen in Internet workloads and has been a subject of extensive research Urgaonkar et al 2002Urgaonkar et al , 2005Doyle et al 2003;Benani and Menasce 2005;Xu and Xu 2004;Appleby et al 2001;Chen et al 2004Chen et al , 2005Rolia et al 2000;Kallahalla et al 2004;Zhu and Singhal 2001;Urgaonkar 2005;Ranjan et al 2002]. The goal of dynamic provisioning is to dynamically allocate sufficient capacity to the tiers of an application so that its response time needs can be met even in the presence of the peak workload.…”

Section: Dynamic Capacity Provisioning and Bottleneck Identificationmentioning

confidence: 98%

Analytic modeling of multitier Internet applications

et al. 2007

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Since many Internet applications employ a multitier architecture, in this article, we focus on the problem of analytically modeling the behavior of such applications. We present a model based on a network of queues where the queues represent different tiers of the application. Our model is sufficiently general to capture (i) the behavior of tiers with significantly different performance characteristics and (ii) application idiosyncrasies such as session-based workloads, tier replication, load imbalances across replicas, and caching at intermediate tiers. We validate our model using real multitier applications running on a Linux server cluster. Our experiments indicate that our model faithfully captures the performance of these applications for a number of workloads and configurations. Furthermore, our model successfully handles a comprehensive range of resource utilization-from 0 to near saturation for the CPU-for two separate tiers. For a variety of scenarios, including those with caching at one of the application tiers, the average response times predicted by our model were within the 95% confidence intervals of the observed average response times. Our experiments also demonstrate the utility of the model for dynamic capacity provisioning, performance prediction, bottleneck identification, and session policing. In one scenario, where the request arrival rate increased from less than 1500 to nearly 4200 requests/minute, a dynamic provisioning technique employing our model was able to maintain response time targets by increasing the capacity of two of the tiers by factors of 2 and 3.5, respectively.A preliminary version of this paper appeared in

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