Executing performance experiments in Kubernetes clusters is a resource- and time-intensive task, especially in large-scale environments typical of production systems. However, such experiments are essential for understanding system behavior and supporting operational decisions that improve efficiency and reliability. This paper introduces KLUE (Kubernetes Lite execUtion Environment), a lightweight framework that enables performance experimentation in emulated Kubernetes clusters. KLUE provides a practical, cost-effective approach for testing and validating configurations, policies, and workloads without the need for extensive physical infrastructure. Using KLUE, we successfully reproduced an experiment originally conducted in a real Kubernetes cluster, achieving a 93.5% reduction in execution cost. We also leveraged the framework to study the impact of multiple application spreading strategies using a 24-hour production-scale trace from a large technology company by spending only 0.14% of the estimated cost required to run the same study on real infrastructure—an analysis that would be economically infeasible in real environments. These results highlight KLUE's potential to accelerate experimentation, reduce costs, and improve decision-making in Kubernetes-based environments, offering a valuable tool for both research and industry settings.

Cloud providers offer multiple pricing models for acquiring virtual machines (VMs), such as on-demand, with payper-use flexibility, and reserved instances, which reduce costs under long-term commitments. Choosing between these models is challenging in IaaS capacity planning under uncertain demand. Traditional online heuristics use the current demand and purchase history, offering performance guarantees but ignoring future workload patterns, which influence the quality of decisions in a capacity planning context. This paper investigates the integration of time series forecasting with online heuristics to improve VM acquisition strategies across pricing models. We evaluate heuristics that incorporate demand predictions from state-of-the-art models—Prophet, XGBoost, and FFT-into the decision-making process. Using real-world data from a global e-commerce platform, we simulate one year of hourly VM allocation. Results show that the predictive heuristic consistently reduces costs compared to both on-demand-only and traditional online heuristics. However, we also observed that forecast ac-curacy and stability significantly influence decision quality, as prediction errors-especially overestimations-may cause idle reservations and higher costs. Our findings demonstrate that predictive heuristics can enhance IaaS cost efficiency and support more accurate capacity planning, especially when aligned with consistent and robust forecasting models.

Modern cloud environments leverage hybrid and multi-infrastructure deployments, distributing cloud-native applications across multiple Kubernetes clusters. This approach increases the complexity of dynamic workload management, as migration decisions must reconcile conflicting factors, including performance, infrastructure costs, and availability. Existing optimization strategies lack adaptive mechanisms for autonomous, dynamic workload migration across multi-cluster environments using real-time data. We introduce an AI-based framework designed as an adaptive decision-maker for workload migration to address this. This solution leverages real-time monitored metrics from all clusters to generate context-aware recommendations for intelligent workload distribution. Experimental validation in a simulated hybrid infrastructure confirms the framework's consistent behavior, achieving well-expected workload distribution and producing reasonable, intelligent migration suggestions, thereby advancing the state of AI-driven resource orchestration.

Caching is a classic technique for improving system performance by reducing client-perceived latency and server load. However, cache management still needs to be improved and is even more difficult in multi-tenant systems. To shed light on these problems and discuss possible solutions, we performed a workload characterization of a multi-tenant cache operated by a large ecommerce platform. In this platform, each one of thousands of tenants operates independently. We found that the workload patterns of the tenants could be very different. Also, the characteristics of the tenants change over time. Based on these findings, we highlight strategies to improve the management of multi-tenant cache systems.

Automatic risk estimation is paramount to prioritizing public contracts auditing efforts, and Machine Learning Risk Prediction (MLRP) models are a promising solution to the classification task of identifying high-risk contracts. Current approaches are focused on the federal level of government, at the same time, face limiting challenges such as the absence of exhaustive ground truth, the difficulty of gaining access to critical databases to build model features, as well as the absence of public literature on the relevance of proposed features. In this work, we attempt to propose MLRP models at the municipal level and overcome those issues by exploring the space of challenges and opportunities in applying MLRP to a setting of Brazilian public contracts. With grounds on the prosecutors' practical experience, we combine three data sources to produce a novel dataset that is more detailed and precise than those used in previous works, first to establish a baseline measuring the gains of applying MLRP to Brazilian Public contracts at the municipal level, second to compare the performance of MLRP and a sample of ad-hoc state-of-the-practice data. Next, we leverage semantic features from contract descriptions in order to evaluate the impact of the contract area on the model's prediction. Also, we experiment using urban and economic characteristics to improve model performance. We measure the impact of access to each datasource on model performance, quantifying the importance of non-open data for this task. Our results suggest that the ad-hoc approach at the firm level has little practical efficacy when evaluated through a more granular/actionable perspective. Contract-level MLRP may be a promising approach, especially when using economic indicators to characterize municipalities, such as GDP per capita. Also, we found no difference between the impact of each feature set on the models' predictions.

Currently available open-source cloud management middlewares provide a single service class to allocate computing resources on-demand. The allocation of resources is constrained only by the actual capacity of the infrastructure and usage quotas applied on a per-user basis. Typically, quotas are defined for different users allowing oversubscribed resources, potentially impacting the Quality of Service (QoS) delivered. This is particularly undesirable when mixed workloads with different QoS requirements are submitted to the cloud. We propose a new scheduler for the popular OpenStack middleware which supports multiple service classes. We compared the performance of the proposed scheduler with that of OpenStack's standard scheduler that supports a single service class. Different from the latter, our scheduler handles mixed workloads in a way that the resource utilization is increased, without significantly affecting the QoS guarantees offered to the different service classes.

Efficiently scheduling the workloads that are submitted to warehouse-scale clusters is not a trivial task. In these systems, the scheduler needs to deal with the heterogeneity in both the jobs that compose the workload, as well as in the servers that comprise the clusters. Moreover, placement constraints that either prevent or force jobs to be allocated in particular servers, makes the scheduler's task even harder. A number of strategies have been proposed to increase the efficiency of these schedulers, however, all of them assume the nowadays prevalent server-based architecture to build clusters. In this paper we assess the possible efficiency gains that can be attained considering that the underlying infrastructure is based on a disaggregated hardware (DH) architecture. This novel paradigm allows the dynamic assembling of logical servers from pools of system-widere sources, providing a way to shape the computing infrastructure while the workload is being allocated to it. Our simulation results, fed with publicly available data from relevant production systems, show that, on average, 5% more CPU demand, and 6% more RAM demand can be allocated, when we compare the fraction of a workload that a state-of-the-art scheduler is able to schedule on a server-based infrastructure with the one that it can allocate on an equivalent DH-based infrastructure. Moreover, in addition to allocating a larger workload, it can do so using less resources. For the workload we studied, on average, 8% of RAM capacity may be kept unused in the resource pools, where it can be switched off to save energy. Although at first sight these numbers might seem small, given the size of these systems, even small percentage improvements can lead to a very large economical impact.

Horizontally scalable applications can potentially run very efficiently over IaaS environments. For that, application providers need to appropriately plan the resource capacity that is to be acquired from the cloud providers, such that, at any point in time, they allocate the smallest infrastructure that is needed to provide the required quality of service for their applications. Since the workload of these applications typically vary widely over time, proactive auto-scaling of the infrastructure is a must. In this paper, we study the impact that an efficient instance type selection based on demands of multidimensional resources has on the performance of proactive auto-scaling. This issue has been mostly overlooked in the related literature. Our results show that suitable selection of the most cost-effective instance type yields a potential cost saving of as much as 50% when compared to the case where the auto-scaling mechanism is oblivious to the instance type selection. We also show evidences that a large portion of applications can benefit of this selection. Finally, we propose a simple selection mechanism that can lead to reasonable cost savings at the expenses of a small number of SLO violations.

Elasticity is a key property to reduce the costs associated with running services in cloud systems that employ an infrastructure-as-a-service (IaaS) deployment model. However, to be able to exploit this property, users of IaaS systems need to be able to anticipate the short-term future demand of their own services, so that only the required infrastructure is requested at any instant in time. This guarantees that service level objectives (SLOs) are always honored by the users of IaaS systems, while over provisioning is avoided. The process of automatically change the amount of resources used to run a service in an IaaS system is named auto-scaling, and the state-of-the-practice uses simple reactive approaches. Although these approaches can successfully reduce the costs due to over provisioning, they are frequently insufficient to minimize the costs due to SLO violations. For that, proactive approaches are required. In this paper we propose a framework for the implementation of auto-scaling services that follows both reactive and proactive approaches. The latter is based on the use of a set of predictors of the future demand of services deployed over IaaS resources, and a selection mechanism that chooses, over time, what is the best predictor to be used. We have also proposed a correction method that uses historical data on the predictors' errors to reduce the probability of under provisioning the services, thus further diminishing the number of SLO violations. We have evaluated the performance of the proposed approach using production traces of HP customers. Our results show that costs savings of as much as 37% can be achieved, while the probability of an SLO violation can be kept, on average, as small as 0.008%, and never larger than 0.036%.

We present a functional verification methodology that employs hierarchical coloured petri nets (HCPN) to describe the testbench. By this way, we are avoiding the absence of formal techniques concerning the testbench description and keeping a high-level of abstraction that is required in this phase of the project. The hierarchical (de)composition is the solution to deal with large designs. The methodology prescribes a way to (de)compose the testbench that promotes incremental development and reuse of testbench elements. Furthermore, our methodology provides tool support for the testbench creation. Experimental results concerning the functional verification of the MPEG 4 video decoder are presented.