Cloud computing has become the dominant choice for hosting various systems and services, with substantial public cloud service spending growth. This trend has extended to blockchain technology, which offers decentralized solutions for diverse applications. Concurrently, there is an increasing focus on business models incorporating Environmental, Social and Governance (ESG) aspects. One such initiative is Carbono21, a platform generating tokens in response to reforestation actions and the carbon credit market. In this context, this article examines the performance aspects of generating Non-Fungible Tokens (NFTs) in a blockchain environment under stress conditions. The Hyperledger Caliper was the benchmark tool used in experiments conducted to analyze the blockchain’s resilience and stability. Linear regression models showed a strong positive correlation between memory usage and total transactions. These results highlight the need for precise resource sizing and robust monitoring mechanisms to prevent service degradation under high transaction loads.

The maturity of Cloud Computing (CC) and the advent of Edge Computing (EC) have paved the way to the concept of Cloud-Edge Continuum (CEC), where both of these domains are transparently available for applications to use. However, provisioning an application composed of many services in this Continuum can be challenging due to the dynamic nature of the environment, especially regarding the users’ geographical mobility. This requires a careful analysis from the perspectives of service providers, regarding application’s requirements and users’ Quality-of-Service (QoS) constraints. In this context, we propose, implement and analyze Mobility-aware Cloud-Edge Provisioning Heuristic (MCEPH), a mobility-aware orchestrator for placing service-composed applications atop CEC substrates. MCEPH relies on distances and delay metrics to dynamically provision services on CEC. In short, re-configurations are triggered to improve the overall performance perception of end users. For composing the simulation campaign, we developed a mobility model representing the citizens’ mobility in a Brazilian’s city. The model comprises points of interest, daily activity profiles, and multiple edge and cloud servers. The simulation campaign demonstrates how to configure MCEPH and discusses the overall benefits and drawbacks facing the specialized literature. Overall, the simulation campaign, aided by the more realistic mobility model, indicated great benefits to the users’ experienced quality of service from applications that fit the CEC pattern.

The use of private or consortium blockchains in organizations’ applications is growing. A relevant aspect of blockchains is the choice of consensus mechanism. This decision delimits which blockchain solutions are suitable for the private scenario. Once the consensus mechanism is chosen, more than one blockchain may be enabled. However, this decision making is not trivial and requires detailed experimental indicators about algorithms and blockchains performance. In this context, we provide a comprehensive performance analysis of the Raft consensus mechanism based on its implementation in Hyperledger Fabric and Ethereum blockchain solutions. We performed our experiments on an OpenStack private cloud using each blockchain developer’s default settings for virtual machines. Our findings show how the implementation of each solution can impact the application’s performance under certain conditions.

The popularization increase of MMORPGs demands new technological approaches to supply users’ requirements with a lower cost of computational resources. Designing these architectures, from the network point of view, is relevant and impacts these games’ success. We analyze and identify the computational resources consumed by the architectures Rudy, Salz, and Willson, which are microservices architectures elaborated for MMORPGs. These architectures were analyzed and tested using automated clients on the architectures deployed in our private computational cloud to identify resource bottlenecks. We conclude the performance, from the point of view of response time, is related to better use of CPU, either by data storage microservices or by data processing microservices. In addition, the application of queuing systems or barriers to manage access or minimize the consumption of an internal service proved feasible, directly impacting the flow of data through the architecture.

A contemporaneous data center (DC) hosts multiple competitive network data flows from different applications, sharing the intermediate switches capacities. In this context, congestion control and avoidance on Transmission Control Protocol (TCP) are critical tasks to ensure the quality of service for hosted applications. Specifically, Software Defined Networking (SDN) created an opportunity to avoid congestion once the centralized controller can gather ongoing and historical information from all network switches and flows. However, the data gathered is enormous, and fast-computing algorithms are crucial for decision-making. In this sense, this work proposes Reinforcement Learning- and SDN-aided Congestion Avoidance Tool (RSCAT), which uses data classification to determine if the network is congested and actor-critic reinforcement learning to find better TCP parameters. Our experimental analysis shows RSCAT could decrease the Flow Completion Time (FCT) of DCTCP and CUBIC variants in several cases without requiring any software update on DC end-points.

Network management on multi-tenant container-based data center has critical impact on performance. Tenants encapsulate applications in containers abstracting away details on hosting infrastructures, and entrust data center management framework with the provisioning of network Quality-of-Service requirements. In this paper, we propose a network-aware multi-criteria container scheduler to jointly process containers and network requirements. We introduce a new Mixed Integer Linear Programming formulation for network-aware scheduling encompassing both tenants and providers metrics. We describe two GPU-accelerated modules to address the complexity barrier of the problem and efficiently process scheduling requests. Our experiments show that our scheduling approach accounting for both network and containers outperforms traditional algorithms used by containers orchestrators.

Summary The adoption of infrastructure as a Service (IaaS) is a reality for academic, industrial, and governmental institutions. Cloud tenants request dynamically provisioned virtual infrastructures (VIs) tailored to their application requirements, detailing not only the virtual compute/storage resources but also the network components, topology, and services. The creation of a large number of cloud providers came along with the widespread use of VIs. The selection of an appropriate provider is a challenging task due to the diversity of the IaaS market and formally is a multicriteria analysis (NP‐hard). Notwithstanding the provider selection complexity, the mobility of VI‐hosted applications is limited due to the optimization anchors introduced by providers. Although the existing IaaS cloud brokers can indicate a hosting provider, they lack on conceptual and technical skills to migrate a VI and all its internal components between providers. This work enhances the state‐of‐the‐art on IaaS cloud brokerage by proposing virtual infrastructure multicriteria allocation and migration–based broker (VIMAM), which performs a multicriteria analysis of providers and VI migration. VIMAM is driven by an analytic hierarchy process (AHP) to select an IaaS provider, offering a set of predefined weighting schemas to represent distinct tenant perspectives. Moreover, to migrate a VI, VIMAM takes into account the virtual machines, containers, switches, and other topology elements. In addition to discussing the AHP ranking weights and frequency of providers selection, the experimental analysis details the implementation of an OpenStack and Docker–based prototype for VI migration.

The installation and configuration of cloud environments has increasingly become automated and therefore simple. For instance, solutions such as RedHat RDO and Mirantis Fuel facilitate the deployment of popular computational clouds like OpenStack. Despite the advances in usability, effort is still required to create and manage multiple users. This is of particular relevance when dealing with sensitive information, a somewhat common case for private clouds. To alleviate this burden, many clouds have adopted federated Single Sign-On (SSO) mechanisms for authenticating their users in a more transparent manner. In this work we analyze the practical security of an OpenStack IaaS cloud when combined with either OpenID Connect (using Google as IdP) or Facebook Connect (using Facebook as IdP). The criteria used in the analysis comprise the ability to provide data encryption, the risks involved in the use of an external IdP, and improper access control. We identify potential issues regarding these solutions and we propose approaches to fix them.

The cloud computing paradigm consolidated the on-demand provisioning of virtual resources. However, the diversity of services, prices, data centers, and geographical footprints, have turned the clouds into a complex and heterogeneous environment. There are several IaaS providers differentiated by the provisioning costs and availability figures. Due to management complexity, the survivability and reliability aspects are often disregarded by tenants, eventually resulting in heavy losses due to unavailability of services that are hosted on Virtual Infrastructures (VIs). We present an alternative to improve VIs survivability and reliability, which considers the use of replicas and the spreading of virtual resources atop providers, regions, and zones. Replicas are used to achieve a user-defined reliability level while the controlled spreading of VI components decrease the probability of full outages. In addition, our proposal performs a cost-effective allocation. We formulate the VI allocation with survivability and reliability requirements as a Mixed Integer Program (MIP). Following, binary constraints are relaxed to obtain a Linear Program (LP). The LP solution is given as input for the simulated annealing technique, composing the Reliable and Survivable Virtual Infrastructure Allocation (RS-VIA) mechanism. Simulation results with different reliability requests indicate an increasing in survivability without inflating costs.

Allocating IT resources to Virtual Infrastructures (VIs) (i.e.groups of VMs, virtual switches, and their network interconnections) is an NP-hard problem. Most allocation algorithms designed to run on CPUs face scalability issues when considering current cloud data centers comprising thousands of servers. This work offers and evaluates a set of allocation algorithms refactored for Graphic Processing Units (GPUs). Experimental results demonstrate their ability to handle three large-scale data center topologies.

The cloud computing model enables users to view computational resources as unlimited and that can be dynamically reserved. In addition, users are charged according to a contracted configuration and effective reservation time. Thus, it became possible to dynamically provision Virtual Infrastructures (VIs) composed of virtual machines, containers and virtualized network resources. Due to the flexibility in resource provisioning and configuration, VI is being increasingly adopted for hosting data storage services, development platforms and distributed applications. However, when migrating applications to a cloud, users are faced with a significant amount of providers, brokers and services offered. Above all, cloud computing technologies, although innovative, do not ease the migration of virtualized resources between distinct providers, inducing in vendor lock-in. A wide range of cloud providers, APIs, models and management tools further limit the development of VI migration solutions between providers, characterizing an open challenge. This work proposes a mechanism to perform VI migration among providers, based on an architecture agnostic to source and destination providers, as well as VI-hosted applications. In addition, a prototype based on Docker containers and OpenStack clouds was developed both to validate the proposed mechanism and to serve as a reference implementation.

Network configuration, performance, and congestion impact the Quality of Service (QoS) of Moodle Learning Management System (LMS). Moreover, LMSs composing modules have different QoS demands for each activity. Upon a network, they compete for resources with applications for social media, streaming, chat, cloud file synchronization, games, among others. Eventually, the network congestion can leave the LMS service unavailable for students and professors. Recently, the popularization of Software Defined Networks (SDN) revolutionized the network management. We propose SDN4Moodle, a toolset to increase the QoS of Moodle services. SDN4Moodle was implemented in conformity to Moodle's plugin guidelines, and it is controlled by internal Moodle events. Each action performed by a Moodle's user is dynamically translated into network forwarding rules, and switches configuration. Our experimental results indicates SDN4Moodle, an open-source system, efficiently achieves flows isolation and bandwidth control on shared networks.

The disparity between the bandwidth provided by modern processors and by the main memory led to the issue known as memory wall, in which application performance becomes completely bound by memory speed. Newer technologies are trying to increase memory bandwidth to address this issue, but the fact is that the effects of increasing bandwidth to application performance still lack exploration. This paper investigates these effects for scientific workflows focusing on the definition of a performance model and on the execution of experiments to validate the rationale for the model. The main contribution is based on two observations: memory bound applications benefit more from an increase to memory bandwidth, and the effects of improving bandwidth for a particular application gradually diminish as bandwidth is increased.

Network Functions Virtualization (NFV) is a key technology to reduce management costs as well as to improve scalability and elasticity of computer networks. Still, recent research efforts have been exposing additional management challenges. Concerning monitoring in particular, new types of entities and requirements are underexploited. To address these issues, we propose DReAM, a resource management architecture based on management by delegation and distributed monitoring, where each agent runs a diagnostic model to compute the network service state. In this paper, we describe DReAM's proposed architecture and its major components. We also discuss the feasibility of DReAM through experimental and analytical evaluations, where we observed application throughput, CPU utilization, communication overhead, scalability, and diagnosis complexity. We provide a trade-off analysis on the monitoring strategies in NFV scenarios. Our results indicate that a result-aware strategy is a better option when the monitored environment has more than 256 agents or when the diagnosis module induces at least 10% of CPU utilization.