The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to * Corresponding

Finding optimal configurations for Stream Processing Systems (SPS) is a challenging problem due to the large number of parameters that can influence their performance and the lack of analytical models to anticipate the effect of a change. To tackle this issue, we consider tuning methods where an experimenter is given a limited budget of experiments and needs to carefully allocate this budget to find optimal configurations. We propose in this setting Bayesian Optimization for Configuration Optimization (BO4CO), an auto-tuning algorithm that leverages Gaussian Processes (GPs) to iteratively capture posterior distributions of the configuration spaces and sequentially drive the experimentation. Validation based on Apache Storm demonstrates that our approach locates optimal configurations within a limited experimental budget, with an improvement of SPS performance typically of at least an order of magnitude compared to existing configuration algorithms.

Cloud computing is emerging as a major trend in the ICT industry. While most of the attention of the research community is focused on considering the perspective of the Cloud providers, offering mechanisms to support scaling of resources and interoperability and federation between Clouds, the perspective of developers and operators willing to choose the Cloud without being strictly bound to a specific solution is mostly neglected. We argue that Model-Driven Development can be helpful in this context as it would allow developers to design software systems in a cloud-agnostic way and to be supported by model transformation techniques into the process of instantiating the system into specific, possibly, multiple Clouds. The MODA-CLOUDS (MOdel-Driven Approach for the design and execution of applications on multiple Clouds) approach we present here is based on these principles and aims at supporting system developers and operators in exploiting multiple Clouds for the same system and in migrating (part of) their systems from Cloud to Cloud as needed. MODACLOUDS offers a qualitydriven design, development and operation method and features a Decision Support System to enable risk analysis for the selection of Cloud providers and for the evaluation of the Cloud adoption impact on internal business processes. Furthermore, MODACLOUDS offers a run-time environment for observing the system under execution and for enabling a feedback loop with the design environment. This allows system developers to react to performance fluctuations and to redeploy applications on different Clouds on the long term.

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.

Abstract. Workload flows in enterprise systems that use the multi-tier paradigm are often characterized as bursty, i.e., exhibit a form of temporal dependence. Burstiness often results in dramatic degradation of the perceived user performance, which is extremely difficult to capture with existing capacity planning models. The main reason behind this deficiency of traditional capacity planning models is that the user perceived performance is the result of the complex interaction of a very complex workload with a very complex system. In this paper, we propose a simple and effective methodology for detecting burstiness symptoms in multi-tier systems rather than identifying the low-level exact cause of burstiness as traditional models would require. We provide an effective way to incorporate this information into a surprisingly simple and effective modeling methodology. This new modeling methodology is based on the index of dispersion of the service process at a server, which is inferred by observing the number of completions within the concatenated busy periods of that server. The index of dispersion together with other measurements that reflect the "estimated" mean and the 95th percentile of service times are used to derive a Markov-modulated process that captures well burstiness and variability of the true service process, despite inevitable inaccuracies that result from inexact and limited measurements. Detailed experimentation on a TPC-W testbed where all measurements are obtained by HP (Mercury) Diagnostics, a commercially available tool, shows that the proposed technique offers a simple yet powerful solution to the difficult problem of inferring accurate descriptors of the service time process from coarse measurements of a given system. Experimental and model prediction results are in excellent agreement and argue strongly for the effectiveness of the proposed methodology under both bursty and non-bursty workloads.