An important problem when moving an application to the cloud consists in selecting the most suitable cloud plan (among those available from cloud providers) for the application deployment, with the goal of finding the best match between application requirements and plan characteristics. If a user wishes to move multiple applications at the same time, this task can be complicated by the fact that different applications might have different (and possibly contrasting) requirements. In this paper, we propose an approach enabling users to select a cloud plan that best balances the satisfaction of the requirements of multiple applications. Our solution operates by first ranking the available plans for each application (matching plan characteristics and application requirements) and then by selecting, through a consensus-based process, the one that is considered more acceptable by all applications.
Clouds provide an illusion of an infinite amount of resources and enable elastic services and applications that are capable to scale up and down (grow and shrink by requesting and releasing resources) in response to changes in its environment, workload, and Quality of Service (QoS) requirements. Elasticity allows to achieve required QoS at a minimal cost in a Cloud environment with its pay-as-you-go pricing model.In this paper, we present our experience in designing a feedback elastically controller for a key-value store. The goal of our research is to investigate the feasibility of the control theoretic approach to the automation of elasticity of Cloud-based key-value stores. We describe design steps necessary to build a feedback controller for a real system, namely Voldemort, which we use as a case study in this work. The design steps include defining touchpoints (sensors and actuators), system identification, and controller design. We have designed, developed, and implemented a prototype of the feedback elasticity controller for Voldemort. Our initial evaluation results show the feasibility of using feedback control to automate elasticity of distributed keyvalue stores.
Abstract-Cloud users usually have different preferences over their applications that outsource to the cloud, based on the financial pro fit of each application's execution. Moreover, various types of virtual machines are offered by a cloud service provider with distinct characteristics , such as rental prices, availab ility levels , each with a different probability of occurrence and a penalty, which is paid to the user in case the virtual mach ine is not available. Therefore, the problem o f applicat ion scheduling in cloud computing environments, considering the risk of financial loss of application-to-VM assignment becomes a challenging issue. In this paper, we propose a riskaware scheduling model, using risk analysis to allocate the applications to the virtual machines , so that, the expected total pay-off o f an application is maximized, by taking into account of the priority of applications. A running examp le is used through the paper to better illustrate the model and its application to imp rove the efficiency of resource assignment in cloud computing scenarios.
A main key success for public transportation networks is their tuning by the analysis of mobility demand with respect to the offer in terms of public transportation means. Most of the solutions at the state of the art have strong limitations in taking into account: multiple contextual information as attractors/motivations for people movements, modalities of travel means, multiple operators, and a range of key performance indicators. For these reasons, a model for analyzing the demand with respect to the offer of mobility has been studied, and the corresponding tool DORAM developed. DORAM allows to perform the analysis of alternative scenarios, as what-if analyses, when the transport service offer and the mobility demand changed in the scenario, adopting a fast-computation strategy to compare scenarios with the aim of detecting/identifying motivations of crowded conditions on stops and on the vehicles. The analysis can exploit a wide range of data sources when computing a set of key performance indicators. The DORAM solution has been defined and developed in the MOSAIC research and development project with ALSTOM and other companies. The DORAM solution is validated by using real data and conditions in the Tuscany region.
Modularization is one of the important subjects in the software design area which leads to increasing the level of quality attributes such as maintainability, portability, reusability, interoperability and flexibility. Therefore, measuring the modularity of a designed architecture is a vital issue to obtain software with a high quality level. Moreover, low coupling between modules, high cohesion of a fine-grained module is two major criteria that could lead to more advanced standard design. In this paper, we introduce an analytical method to calculate modularity considering coupling, granularity and cohesion. To assess the comprehensiveness of the proposed method, the degree of modularity is calculated in a case study using two different architectural designs which shows the architecture's desired quality characteristics in designing the software. The assessment implies that our approach offers a holistic, flexible method considering the type of software application.
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