Cloud computing environments provide on-demand resource provisioning, allowing applications to elastically scale. However, application benchmarks currently being used to test cloud management systems are not designed for this purpose. This results in resource underprovisioning and quality-of-service (QoS) violations when systems tested using these benchmarks are deployed in production environments. We present C-MART, a benchmark designed to emulate a modern web application running in a cloud computing environment. It is designed using the cloud computing paradigm of elastic scalability at every application tier and utilizes modern web-based technologies such as HTML5, AJAX, jQuery, and SQLite. C-MART consists of a web application, client emulator, deployment server, and scaling API. The deployment server automatically deploys and configures the test environment in orders of magnitude less time than current benchmarks. The scaling API allows users to define and provision their own customized datacenter. The client emulator generates the web workload for the application by emulating complex and varied client behaviors, including decisions based on page content and prior history. We show that C-MART can detect problems in management systems that previous benchmarks fail to identify, such as an increase from 4.4 to 50 percent error in predicting server CPU utilization and resource underprovisioning in 22 percent of QoS measurements.

Next-generation wireless cellular networks are likely to be multi-tiered, consisting of a macro-cell tier based on the traditional operator-deployed macro-cell structure, and a lower tier of femto-cells that are arbitrarily deployed by end-users. The need for such architectures is driven by increasing demands for higher data rates as well as to provide capacity to serve increasing numbers of subscribers. This paradigm shift in cellular architecture may therefore imply changes in the way cellular networks are managed today. In this paper, we examine operation costs of a two-tier macro-femto-cellular network in a capacity-limited scenario. We identify the key parameters, including nomadic-to-mobile user ratio, non-linear femto-cell capacity growth, femto-cell subscription rate, and QoS provisioning costs, which will impact recurring annual management costs of these networks. We then identify a model which captures these cost components and key parameters, allowing an operator to characterize the management cost of their access networks. We also identify through evaluations key situations where adopting these femto-cells may in fact add to an operator's overall costs, and provide key insights into the financial feasibility of femto-cells.