Intelligent data sampling is an innovative method that enhances conventional data sampling procedures by utilizing machine learning and artificial intelligence approaches. In this manuscript, we deep dive into the scientific and grey literature to find the main challenges faced by data sampling and elaborate on the various AI techniques utilized to mitigate them. We identify key issues such as class imbalance, overfitting, computational inefficiency, and bias, which often hinder traditional sampling methods. Furthermore, we explore AI-driven techniques that have been integrated into the sampling process to address these challenges effectively. As a result, we propose a novel framework for intelligent sampling that incorporates an AI-powered recommender system. This system dynamically selects the most appropriate sampling technique based on the specific characteristics of the data and the needs of the predictive model. By automating and optimizing the selection of sampling methods, our framework aims to enhance model performance, improve resource efficiency, and adapt to diverse real-world applications.

An increasingly common application for DLTs is their exploitation in enterprise systems operated by a consortium of organizations, who may assume different roles and whose interaction takes place over a blockchain network (in this scope, often permissioned), which holds a data state that they need to query frequently. In this regard, one of the main drawbacks of DLTs is their unsuitability for the efficient execution of complex queries on the data stored by the nodes comprising the network. Arising from this issue, many solutions propose to dump the ledger contents into databases, which, due to their own nature and purpose, are certainly optimized for the execution of such queries. However, most proposals on this area are not intended for querying the shared state held by the network, just the transaction history. Actually, through conducting a study on the state of the art, a lack of support for handling and querying complex structured data has been identified. DELTA provides a solution for the synchronization, with negligible overload, of the state of a DLT into a database, enabling (a) the operation with smart contracts whose data possess a complex structure and (b) the efficient execution of elaborate queries on these data. By using DELTA, query times decrease, at least, between one and five orders of magnitude, depending on the DLT, compared to queries directed to the ledger nodes.

Virtualization helps to deploy the functionality of expensive and rigid hardware appliances on scalable virtual resources running on commodity servers. However, optimal resource provisioning for non-trivial services is still an open problem. While there have been efforts to answer the questions of when to provision additional resources in a running service, and how many resources are needed, the question of what should be provisioned has not been investigated, in particular, for complex applications or services, which consist of a set of connected components, where each component in turn potentially consists of multiple component instances (e.g., VMs or containers). Each instance of a component can be run in different flavors (i.e., number of cores or amount of memory), while the service constructed by the combination of these component configurations must satisfy the customer Service Level Objective (SLO). In this work, we offer to service providers an answer to the what to deploy question by introducing Rconf, a system that automatically chooses the optimal combination of component instances for non-trivial network services. In particular, we propose an analytical model based on robust queuing theory that is able to accurately model arbitrary components, and develop an algorithm that finds the combination of their instances, such that the overall utilization of the running instances is maximized while meeting SLO requirements.

The virtualization of services with high-availability requirements calls to revisit traditional operation and provisioning processes. Providers are realizing services in software on virtual machines instead of using dedicated appliances to dynamically adjust service capacity to changing demands. Cloud orchestration systems control the number of service instances deployed to make sure each service has enough capacity to meet incoming workloads. However, determining the suitable build-out of a service is challenging as it takes time to install new instances and excessive re-configurations (i.e. scale in/out) can lead to decreased stability. In this paper we present AidOps, a cloud orchestration system that leverages machine learning and domain-specific knowledge to predict the traffic demand, optimizing service performance and cost. AidOps does not require a conservative provisioning of services to cover for the worst-case demand and significantly reduces operational costs while still fulfilling service quality expectations. We have evaluated our framework with real traffic using an enterprise application and a communication service in a private cloud. Our results show up to 4X improvement in service performance indicators compared to existing orchestration systems. AidOps achieves up to 99.985% availability levels while reducing operational costs at least by 20%.

Onboarding network functions onto current clouds requires labor-intensive configuration of the virtual environment. Developers need to dimension the resources available to each virtual machine such as CPU and memory, define thresholds for scaling dynamically and create configuration files that operators can use to execute the network services. This process is time consuming and dependent on the server architecture. As resources are managed on an individual virtual machine basis, services cannot be orchestrated end to end without significant expertise. In this paper, we argue that much of the manual configuration needed for onboarding services onto a cloud can be automated. Moreover, we can automatically generate abstractions that consider services end-to-end and enable their holistic orchestration. We propose a framework that benchmarks network services during the onboarding process and generates an elastic model which relates workload mixes to resource requirements, identifies component dependencies and automates service operation on heterogeneous stacks. We have evaluated our framework using a real-time communication service that handles multiple classes of workloads. Results show that underprovisioning can be eliminated for regular daily traffic, reducing resource provisioning time by at least 5X for the most stressing traffic surges, while improving key performance indicators by at least 40%.

In this work, we present an exhaustive empirical characterization of the power requirements of multiple components of data center servers. To do so, we devise different experiments to stress these components, taking into account the multiple available frequencies and the fact that we are working with multicore servers. In these experiments, we measure energy consumption of server components and identify their optimal operational points. Our study proves that the curve defining the minimal CPU power utilization, as a function of the load in active cycles per second, is neither concave nor purely convex. Instead, it definitively shows a super-linear dependence on the load. Similarly, we present results on how to improve the efficiency of network cards and disks. Finally, we validate the accuracy of the model derived from our characterization by comparing the real energy consumed by two Hadoop applications-PageRank and WordCount-with the estimation from our model, obtaining errors below 4.1% on average.

The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework.

The bisection width of interconnection networks has always been important in parallel computing, since it bounds the speed at which information can be moved from one side of a network to another, i.e., the bisection bandwidth. Finding its exact value has proven to be challenging for some network families. For instance, the problem of finding the exact bisection width of the multidimensional torus was posed by Leighton [1, Problem 1.281] and has remained open for almost 20 years. We provide two general results that allow us to obtain upper and lower bounds on the bisection width of any product graph as a function of some properties of its factor graphs. The power of these results is shown by deriving the exact value of the bisection width of the torus, as well as of several d-dimensional classical parallel topologies that can be obtained by the application of the Cartesian product of graphs. We also apply these results to data centers, by obtaining bounds for the bisection bandwidth of the d-dimensional BCube network, a recently proposed topology for data centers.

Despite some proposals for energy-efficient topologies, most of the studies for saving energy in data center networks are focused on traffic engineering, i.e., consolidating flows and switching off unnecessary network devices. The major weakness of this approach is network oscillation brought by the frequent change of network topology when traffic fluctuates very fast. In this paper, we propose to incorporate rate adaptation into green data center networks. With rate adaptive network devices, we aim at approaching network-wide energy proportionality by routing optimization. We formalize the problem with an integer program and propose an efficient approximation algorithm - TSRR, solving the problem quickly while guaranteeing a constant performance ratio. Extensive range of simulations confirm that more than 40% of the energy can be saved while introducing very slight stretch on network delay.