An Empirical Study of the Impact of Cloud Patterns on Quality of Service (QoS)

International audienceCloud patterns are described as good solutions to recurring design problems in a cloud context. These patterns are often inherited from Service Oriented Architectures or Object-Oriented Architectures where they are considered good practices. However, there is a lack of studies that assess the benefits of these patterns for cloud applications. In this paper, we conduct an empirical study on a RESTful application deployed in the cloud, to investigate the individual and the combined impact of three cloud patterns (i.e., Local Database proxy, Local Sharding-Based Router and Priority Queue Patterns) on Quality of Service (QoS). We measure the QoS using the application's response time, average, and maximum number of requests processed per seconds. Results show that cloud patterns doesn't always improve the response time of an application. In the case of the Local Database proxy pattern, the choice of algorithm used to route requests has an impact on response time, as well as the average and maximum number of requests processed per second. Combinations of patterns can significantly affect the QoS of applications. Developers and software architects can make use of these results to guide their design decisions

SLO-aware Colocation of Data Center Tasks Based on Instantaneous Processor Requirements

In a cloud data center, a single physical machine simultaneously executes dozens of highly heterogeneous tasks. Such colocation results in more efficient utilization of machines, but, when tasks' requirements exceed available resources, some of the tasks might be throttled down or preempted. We analyze version 2.1 of the Google cluster trace that shows short-term (1 second) task CPU usage. Contrary to the assumptions taken by many theoretical studies, we demonstrate that the empirical distributions do not follow any single distribution. However, high percentiles of the total processor usage (summed over at least 10 tasks) can be reasonably estimated by the Gaussian distribution. We use this result for a probabilistic fit test, called the Gaussian Percentile Approximation (GPA), for standard bin-packing algorithms. To check whether a new task will fit into a machine, GPA checks whether the resulting distribution's percentile corresponding to the requested service level objective, SLO is still below the machine's capacity. In our simulation experiments, GPA resulted in colocations exceeding the machines' capacity with a frequency similar to the requested SLO.Comment: Author's version of a paper published in ACM SoCC'1

Practical service placement approach for microservices architecture

Community networks (CNs) have gained momentum in the last few years with the increasing number of spontaneously deployed WiFi hotspots and home networks. These networks, owned and managed by volunteers, offer various services to their members and to the public. To reduce the complexity of service deployment, community micro-clouds have recently emerged as a promising enabler for the delivery of cloud services to community users. By putting services closer to consumers, micro-clouds pursue not only a better service performance, but also a low entry barrier for the deployment of mainstream Internet services within the CN. Unfortunately, the provisioning of the services is not so simple. Due to the large and irregular topology, high software and hardware diversity of CNs, it requires of aPeer ReviewedPostprint (author's final draft