Capacity planning tools for web and grid environments

Bagchi, Sugato; Hung, Eugene; Iyengar, Arun; Vogl, Norbert; Wadia, Noshir

doi:10.1145/1190095.1190127

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Jiang et al 23 proposed cloud capacity prediction in the face of frequent provisioning and deprovisioning by modeling over-estimation and under-estimation of capacity. Bagchi et al 24 used analytical models and event simulations to perform capacity and load planning of customer workloads while satisfying site-level service level objectives. Goher et al 25 described a novel capacity planning technique that cloud providers can use when confronted with demand spikes that they cannot instantly fulfill.…”

Section: Related Workmentioning

confidence: 99%

Architecture of a time‐sensitive provisioning system for cloud‐native software

2021

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Application development paradigms and composition of technology services are decisively moving in the direction of hybrid and multi clouds. Enterprises are stitching new cloud-native business models that leverage containerized multi-tier microservice architecture, heterogeneity of cloud deployment models, and diversity of cloud providers. Scalability and resiliency are key components of this new world architecture, but these are also functions of the predictability of provisioning the underpinning compute instances on cloud. Thus, a major challenge to surmount before complex multicloud aware applications can be designed is the problem of unpredictable latencies associated with the provisioning of compute services on cloud. In the first part of this article, we develop a technique for time-sensitive provisioning of virtual compute on demand, while also allowing deprovisioning on demand. Using the technique we propose, a cloud broker will be able to operate on a pool of reserved instances sourced from cloud providers and multiplex them profitably across cloud customers with associated provisioning time guarantees, but without usage commitment restrictions. We articulate this challenge in the form of the Reserved Instance Allocation Problem (RIAP), which we first prove to be NP-hard. We then design a heuristic-based method to solve the intractable RIAP in polynomial time. We evaluate the effectiveness of our heuristic-based mechanism through a combination of deep simulations and practical validation on mainstream public clouds. We demonstrate that our algorithm consistently yields a high profit-to-investment ratio for a broker who seeks to operate a commerce of virtual machines with time-sensitive provisioning. In the second part of this article, we tackle the problem of unpredictable provisioning latencies in bare metal commerce. We build and evaluate an allocation model to calculate optimal supporting bare metal inventory to maximize cost-sensitive fulfillment of bare metal provisioning requests in a time-sensitive manner.

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Section: Related Workmentioning

confidence: 99%

Architecture of a time‐sensitive provisioning system for cloud‐native software

2021

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“…Calculations of resources required for an instance supporting multi-tenants with multi-users in each tenant are new and complicated (Kwok and Mohindra, 2007). A hierarchical and extensible resource management system has been built to allow the execution of multiple scheduling paradigms concurrently (Bagchi et al, 2006). A number of commercial software tools for capacity planning, resource allocation and performance analysis for multiple applications on a set of servers are also available (Kwok and Mohindra, 2007;Bagchi et al, 2006;Weissman and Bobrowski, 2009).…”

Section: Literature Reviewmentioning

confidence: 99%

“…A hierarchical and extensible resource management system has been built to allow the execution of multiple scheduling paradigms concurrently (Bagchi et al, 2006). A number of commercial software tools for capacity planning, resource allocation and performance analysis for multiple applications on a set of servers are also available (Kwok and Mohindra, 2007;Bagchi et al, 2006;Weissman and Bobrowski, 2009). However, these tools do not apply to the data server of for multitenant SaaS applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-tenant Data Migration Strategy in SaaS Platform

Zheng¹,

Ren²,

Kong³

2014

RJASET

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On demand migration of tenants' data is a critical technology for elastic load balancing in SaaS application. However, different tenants have different required resource with various SLA requirements. Moreover, previously reported migration techniques are not tenant aware. To study the elastic load balancing of tenants' data, we mainly focus on the problem of when to migrate which tenants' data in a muti-tenant shared DB server and build a new framework that aims to monitor load and minimize migration cost. We establish a demand resource estimation model to decide when to migration and then create a cost model to guide the choice of possible migration tenants. The simulation experimental results show the high effectiveness and the good efficiency of our model.

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“…Performance modeling is a natural approach for accomplishing such provisioning and planning tasks in multi-tier cluster-based server systems. It forms the basis for many commercial tools [1,19], and contemporary models incorporate sophisticated techniques including detailed resource profiling of tiered services [18], long-term forecasting of workloads in enterprise systems [7], comprehensive queueing models of multiple service tiers [20], and the modeling of nonstationarity in workload mixes [17].…”

Section: Introductionmentioning

confidence: 99%

Automatic request categorization in internet services

Sharma

Bhagwan

Choudhury

et al. 2008

SIGMETRICS Perform. Eval. Rev.

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Modeling system performance and workload characteristics has become essential for efficiently provisioning Internet services and for accurately predicting future resource requirements on anticipated workloads. The accuracy of these models benefits substantially by differentiating among categories of requests based on their resource usage characteristics. However, categorizing requests and their resource demands often requires significantly more monitoring infrastructure. In this paper, we describe a method to automatically differentiate and categorize requests without requiring sophisticated monitoring techniques. Using machine learning, our method requires only aggregate measures such as total number of requests and the total CPU and network demands, and does not assume prior knowledge of request categories or their individual resource demands. We explore the feasibility of our method on the .Net PetShop 4.0 benchmark application, and show that it works well while being lightweight, generic, and easily deployable.

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Capacity planning tools for web and grid environments

Cited by 6 publications

References 16 publications

Architecture of a time‐sensitive provisioning system for cloud‐native software

Architecture of a time‐sensitive provisioning system for cloud‐native software

Multi-tenant Data Migration Strategy in SaaS Platform

Automatic request categorization in internet services

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