Abstract. This demo shows how we have used a stream reasoning mechanism, C-SPARQL, as the main component of a Monitoring Platform for multi-Clouds applications, that is, applications replicated or distributed on multiple Clouds. The C-SPARQL engine executes monitoring queries on data gathered both

Abstract—In the landscape of cloud computing, the compet-ition between providers has led to an ever growing number of cloud solutions offered to consumers. The ability to run and manage multi-cloud systems (i.e., applications on multiple clouds) allows exploiting the peculiarities of each cloud

and providers are increasing, the complexity of the overall system and the number of potential attacks will also increase. Preserving the security among multi-cloud is very important and trust among the providers is essential as the user often store sensitive information in cloud. Different providers will have

platforms to choose. A better choice is to use more than one data center, which is called multi-cloud. In this paper a closed-loop approach is proposed for optimizing QoS and cost. Modules of monitoring and controlling data centers are required as well as the application feedback such as video streaming

Multi-tenant cloud, which usually leases resources in the form of virtual machines, has been commercially available for years. Unfortunately, with the adoption of commodity virtualized infrastructures, software stacks in typical multi-tenant clouds are non-trivially large and complex, and thus

-to-end behavior of their systems. This paper presents AjaxScope, a dynamic instrumentation platform that enables cross-user monitoring and just-in-time control of web application behavior on end-user desktops. AjaxScope is a proxy that performs on-the-fly parsing and instrumentation of JavaScript code

; however, abstractions provided by cloud computing are often inadequate to provide major cost savings across the IT infrastructure life-cycle. Multi-tenancy, which allows a single application to emu-late multiple application instances, has been proposed as a solution to this problem. By sharing one

Performance monitoring is fundamental to track cloud application health and service-level agreement compliance, but with the emergence of multi-cloud deployments, it may become increasingly important also to create a feedback loop between runtime operation in multi-clouds and design-time reasoning

run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS—Cross-Layer Multi-Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments

applications is not a trivial task, requiring for both the applications and platform, to be constantly monitored, capturing information at various levels and time granularity. In this paper we describe the challenges that occur when monitoring elastically adaptive Cloud applications and to address these issues