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.

High-Performance Computing (HPC) is essential to support the advance in multiple research and industrial fields. Despite the recent growth in processing and networking power, the HPC Data Centers (DCs) are finite, and should be carefully managed to host multiple jobs. The scheduling of tasks (composing a job) is a crucial and complex task, once the reflexes of the scheduler’s decisions are perceptible both for users (e.g., slowdown) and for infrastructure administrators (e.g., use of resources and queue length). In fact, the process of scheduling workflows atop a DC can be modeled as a graph mapping problem. While an undirected graph is used to represent the DC, a Directed Acyclic Graph (DAG) is used to express the tasks dependencies . Each vertex and edge from both graphs can have weights associated with them, denoting the residual capacities for DC resources, as well as computing and networking demands for workflows. Motivated by the combinatorial explosion of the aforementioned scheduling problem, the integration of Machine Learning (ML) for generating or improving scheduling policies is a reality, however the proposals in the specialized literature opt, mostly, for using simplified models to reduce the search space or are trained to specific scenarios, which leads to policies that eventually fall short of real DCs expectations. Given this challenge, this work applies Actor–Critic (AC) Reinforcement Learning (RL) to schedule DAG-based workflows. Instead of proposing a new policy, the AC RL is used to select the appropriated scheduling policy from a pool of consolidated algorithms, guided by the DAGs workload and DC usage. The AC RL-based scheduler analyzes the DAGs queue and the DC status to define which algorithms are better suited to improve the overall performance indicators in each scenario instance. The simulation protocol comprises multiple analysis with distinct workload configurations, number of jobs, queue ordering polices and strategies to select the target DC servers. The results demonstrated that the AC RL selects the scheduling policy which fits the current workload and DC status.

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.

The 5G Quality-of-Service (QoS) objectives con-tributed to the Heterogeneous Cellular Network (HCN) evolution, dictating that applications can rely on low-latency and high-bandwidth networks. However, concurrent requests of large amount of multimedia data generate a burden on the backhaul and fronthaul networks due to redundant retransmissions and pose challenges for achieving the QoS objectives. Although mobile network operators can place content closer to the HCN edge to improve the overall QoS indicators, there are still challenges to design a cache policy aware of limited storage capacity, different content popularity, device mobility, and network congestion. This work innovates by introducing a cooperative policy to join caches placement and routing users' requests atop an HCN. By combining networking and cache QoS requirements, the policy balances the fronthaul network load and dynamically maps the caches to HCN resources. We formulated the cache policy through linear programming and in-depth evaluated its performance using extensive simulation scenarios. The results indicate that the proposed network-aware policy decreases the network latency, even when subject to changes in content popularity distribution and total HCN storage capacity.

The collective operations are considered critical for improving the performance of exascale-ready and highperformance computing applications. On this paper we focus on the Message-Passing Interface (MPI) Allgather many to many collective, which is amongst the most called and time-consuming operations. Each MPI algorithm for this call suffers from different operational and performance limitations, that might include only working for restricted cases, requiring linear amounts of communication steps with the growth in number of processes, memory copies and shifts to assure correct data organization, and non-local data exchange patterns, most of which negatively contribute to the total operation time. All these characteristics create an environment where there is no algorithm which is the best for all cases and this consequently implies that careful choices of alternatives must be made to execute the call. Considering such aspects, we propose the Stripe Parallel Binomial Trees (Sparbit) algorithm, which has optimal latency and bandwidth time costs with no usage restrictions. It also maintains a much more local communication pattern that minimizes the delays due to long range exchanges, allowing the extraction of more performance from current systems when compared with asymptotically equivalent alternatives. On its best scenario, Sparbit surpassed the traditional MPI algorithms on 46.43% of test cases with mean (median) improvements of 34.7% (26.16%) and highest reaching 84.16%.

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.

Virtualization of computing and communication infrastructures were disseminated as possible solutions for networks evolution and deployment of new services on cloud data centers. Although promising, their effective application faces obstacles, mainly caused by rigidity on the management of communication resources. Currently, the Software-Defined Networks (SDN) paradigm has been popularizing customization and flexibility in network management due to separation of control and data planes. However, benefits introduced by SDN are not trivially applied to Virtual Infrastructures (VIs) provisioning on SDN-based cloud providers. An allocation mechanism needs joint information of control and data planes in order to deliver Quality-of-Service (QoS)-aware mappings while achieving provider objectives. In this work, we formulate the online VI allocation on SDN-based cloud data centers as a Mixed Integer Program (MIP). Following, integer constraints are relaxed to obtain a linear program, and rounding techniques are applied. The mechanism performs VI allocation considering latency, bandwidth, and virtual machine requirements. The results indicate that the VIs mean internal latency can be reduced while simultaneously enforcing other QoS constraints.

The Cloud Computing wave consolidates the on-demand provisioning of configurable virtual machines. Recent projects have proposed the extension of the original IaaS paradigm to provide dynamic virtual networks to interconnect virtual IT resources, composing Virtual Infrastructures (VIs). In this new scenario, users with different objectives and expectations can rent dynamically provisioned virtual infrastructures to execute their applications during a given time slot. VIs can be allocated anywhere on top of a distributed and virtualized substrate. This decoupling from the geographical location introduces concerns such as a latency increase in network communications (user's perspective), and the fragmentation of physical resources (Infrastructure Provider's - InP - perspective). This context motivates efforts to investigate and deploy new models and tools which consider the geographical location of virtual infrastructures. Our work concentrates on the allocation of VIs guided by both the user's and the InP's constraints. We propose a formulation of the allocation problem considering the user's expectations as well as the physical-substrate provider's goals. Our initial experiments demonstrate that it is possible to improve the quality of the virtual-infrastructure allocation (user perspective) while simultaneously decreasing the physical substrate's fragmentation and the substrate's cost.

Through the recent emergence of joint resource and network virtualization, dynamic composition and provisioning of time-limited and isolated virtual infrastructures is now possible. One other benefit of infrastructure virtualization is the capability of transparent reliability provisioning (reliability becomes a service provided by the infrastructure). In this context, we discuss the motivations and gains of introducing customizable reliability of virtual infrastructures when executing large-scale distributed applications, and present a framework to specify, allocate and deploy virtualized infrastructure with reliability capabilities. An approach to efficiently specify and control the reliability at runtime is proposed. We illustrate these ideas by analyzing the introduction of reliability at the virtual-infrastructure level on a real application. Experimental results, obtained with an actual medical-imaging application running in virtual infrastructures provisioned in the experimental large-scale Grid'5000 platform, show the benefits of the virtualization of reliability.

With the expansion and the convergence of computing and communication, the dynamic provisioning of customized processing and networking infrastructures as well as resource virtualization are appealing concepts and technologies. Therefore, new models and tools are needed to allow users to create, trust and exploit such on-demand virtual infrastructures within wide area distributed environments. This paper proposes to combine network and system virtualization with cryptographic identification and SPKI/HIP principles to help the user communities to build and share their own resource reservoirs. These ideas are implemented in the HIPerNet framework enabling the creation and the management of customized confined execution environments in a large scale context. Based on the example of biomedical applications, the paper focuses on the security model of the HIPerNet system and develops the key aspects of our distributed security approach. Then the paper discusses and illustrates how HIPerNet solutions fulfill the security requirements of applications through different scenarios.