Abstract Disk bandwidth is a critical resource for I/O-intensive applications that must transfer large volumes of data to and from persistent storage. Most multi-tenant infrastructures efficiently allocate CPU and memory resources to concurrent workloads, but typically lack mechanisms for allocating I/O bandwidth. As a result, users often resort to exclusive node reservations to avoid disk contention, which can lead to underutilisation of other node resources if not fully exploited. Another common issue is that users do not know the exact resource requirements of their applications. Even when this is known, applications rarely maintain peak resource usage throughout their entire execution, resulting in wasted resources that could otherwise benefit other users. Today, many users prefer cloud serverless platforms because of their ease of use and flexible billing. However, these platforms have inherent limitations and may not be suitable for workloads with specific requirements. In this paper, we present a serverless scaling mechanism that dynamically adjusts disk I/O bandwidth for containerised applications by scaling their allocation up or down based on real-time usage and configurable weights. In addition, the system incorporates automatic extension management capabilities for virtual disk devices, such as logical volumes. Our approach can be integrated with other serverless scaling mechanisms, such as CPU and memory management, to provide a comprehensive resource scaling solution. The experimental results have shown significant performance improvements, with overall runtime reductions of up to 53% for concurrent I/O-intensive workloads compared to running them without serverless capabilities.

Power supply is a limiting factor when increasing the computing capacity of supercomputers. As a consequence, power consumption has become one of the biggest challenges in the field of High Performance Computing (HPC). In order to develop energy-efficient tools (e.g., frameworks, applications), it is essential to have an accurate power consumption modelling. Al-though previous works proposed a wide variety of approaches to model CPU power consumption, building models in an automated and adaptable way to changing scenarios and predicting power with high precision remains complex due to multiple factors (e.g., training and test workloads, model variables). In this paper, we present a set of tools to fully automate the process of modelling power consumption using CPU time series data. More specifically, our proposal includes two tools: (1) CPUPowerWatcher, which gathers CPU metrics during the execution of user-configurable workloads; and (2) CPUPowerSeer, which builds models to predict CPU power consumption (e.g., polynomial regression) from different CPU variables (e.g., usage, clock frequency) using time series data. Thus, multiple models can be created and evaluated easily, allowing the selection of an optimal model for a specific workload. The experiments conducted by combining these tools allow analysing the impact of novel factors on CPU power consumption, such as the type of CPU usage generated by different workloads or how the CPU cores are allocated to them. In addition, the accuracy of six regression models is compared when predicting CPU- and I/O-intensive workloads using two different core allocations.

Serverless computing is an emerging paradigm that has gained a lot of relevance in recent years, as it allows users to consume computing resources without worrying about the underlying infrastructure and pay only for what they actually use. Most current services that implement this paradigm typically rely on the Function-as-a-Service (FaaS) model, which works perfectly for simple applications based on stateless functions triggered by specific events. However, these services are not designed to run more complex applications with intricate interactions, usually presenting a significant degree of configuration difficulty and/or low ability to customise the execution environment. They also tend to be designed for short and simple workloads, with some services even limiting their maximum runtime to just a few minutes. In this paper, we present a platform based on Hadoop YARN oriented to the execution of Big Data workloads in a containerised and serverless way, so that the resources allocated to such containers are automatically and dynamically scaled according to their actual usage. An experimental evaluation has been carried out to compare our serverless-like platform with a standard YARN deployment when executing Big Data workloads concurrently. Our results have shown experimental evidence of enhancing both performance and overall resource efficiency, providing runtime reductions and resource usage improvements of up to 41% and 50%, respectively.

Energy consumption is currently highly regarded on computing systems for many reasons, such as improving the environmental impact and reducing operational costs considering the rising price of energy. Previous works have analysed how to improve energy efficiency from the entire infrastructure down to individual computing instances (e.g., virtual machines). However, the research is more scarce when it comes to controlling energy consumption, specially in real time and at the software level. This paper presents a platform that manages a power budget to cap the energy consumed from users to applications and down to individual instances. Using containers as virtualization technology, the energy limitation is implemented thanks to the platform's ability to monitor container energy consumption and dynamically adjust its CPU resources via vertical scaling as required. Representative Big Data applications have been deployed on the platform to prove the feasibility of this approach for energy control, showing that it is possible to distribute and enforce a power budget among users and applications.