Dario Vieira

A fair QoS-aware dynamic LTE scheduler for machine-to-machine communication

The long term evolution (LTE) standard plays an important role in the development of internet of things (IoT) and machine-to-machine (M2M) communication because of its high data rates, low latency, high flexibility and low cost. However, improvements are needed in the network to support the uplink-heavy traffic generated by M2M communication and also to ensure the diversity of service requirements of this communication and to control the performance reduction in the classical mobile services of human-to-human (H2H) communication. In this paper, we present a new LTE uplink scheduler that dynamically adjusts to the level of congestion of the network based on the current traffic information of each device to support the M2M traffic. Furthermore, the main goals of our approach are (i) satisfy the quality of service (QoS) requirements, (ii) ensure fair allocation of resources and (iii) control the impact of H2H traffic performance. The simulation results demonstrated that the proposed scheduling has good performance according to the three objectives aforementioned.

A dynamic LTE uplink packet scheduler for Machine-to-Machine communication

The Long Term Evolution (LTE) standard plays an important role in the development of Machine-to-Machine (M2M) communication. However, the M2M communication has several different characteristics regarding to Human-to-Human (H2H) communication. Therefore, the shortage of radio resources, satisfaction of the Quality of Service (QoS) requirements and the reduction of the H2H traffic performance are important issues to be addressed when introducing the M2M communication in the network. In this article, we present a scheduler that may dynamically adjust to the level of congestion of the network based on the current traffic information of each device. The main goals of our approach are (i) satisfy the QoS requirements (ii) ensure fair allocation of resources and (iii) control the impact of H2H traffic performance. The simulation results demonstrated that the proposed scheduling has good performance according to the three objectives aforementioned.

A mechanism for uplink packet scheduler in LTE network in the context of machine-to-machine communication

This paper proposes a mechanism for uplink packet scheduler in LTE network in the context of Machine-to-Machine (M2M) communication. The proposed approach uses the current and past information of the system to satisfy the Quality of Service (QoS) requirements, to ensure fairness in resource allocation and to control the congestion caused by M2M devices. We carried out some network simulations by using a NS-3 simulator so as to show the effectiveness of the proposed approach. The results indicate that our solution can reduce the impact of M2M communication on Human-to-Human (H2H) communication and avoid the problem of starvation, when compared to related approaches.

Dynamic RACH distribution for M2M massive access in LTE-A

Machine-to-machine communication (M2M) is a core element for the Internet of Things (IoT). The Long-Term Evolution-Advanced (LTE-A) is expected to become a potential access network for M2M devices. However, M2M communication poses some challenges to LTE-A, with highlight to the congestion and overload problems in the Radio Access Network (RAN) during the Random Access Channel (RACH) procedure. In this paper, we propose two solutions to mitigate the impact of M2M communication in the context of LTE-A network. More specifically, we model how the random access resources should be allocated to the different types of devices as a bankruptcy problem. In addition, we propose solutions for the bankruptcy problem using a game theory and an axiomatic strategy. The simulation results show that our approaches present improvements in terms of energy efficiency, impact control of M2M over H2H accesses and define priority among the different classes of devices.

Comparative performance study of LTE uplink schedulers for M2M communication

The Long Term Evolution (LTE) standard is one of the most promising technologies of wireless network for the development of Machine-to-Machine (M2M) communication because of its high data rates, low latency, high flexibility and low cost. However, improvements are needed in the network to support the uplink-heavy traffic generated by M2M communication and also to ensure the diversity of service requirements of this communication. The uplink packet scheduler is included in the necessary improvements. In this article, we evaluate the performance of the main schedulers for M2M communication regarding the aspects of service requirements, fairness, energy efficiency and impact on the system performance of current LTE traffic.

PacificClouds: A Flexible MicroServices based Architecture for Interoperability in Multi-Cloud Environments.

Cloud Computing has become a popular IT service delivery model in recent years. While the cloud brings several benefits, there are still some challenges that need to be overcome to apply the cloud model in certain scenarios. One such problem is the so-called vendor lock-in since different cloud providers offer peculiar and often incompatible services, which results in the automatic migration impossibility of the application between cloud providers. This issue becomes even more problematic when thinking of future applications composed of services or components hosted by different cloud providers in a multi-cloud environment. Dealing with vendor lock-in in multiple clouds requires addressing two important challenges: interoperability and portability. Some solutions have been proposed to deal with both problems, but most of them fail to provide flexibility. Therefore, we propose PacificClouds, a novel architecture based on microservices for addressing interoperability in a multi-cloud environment. PacificClouds differs from previous works by providing greater flexibility due to the microservices architectural pattern. In this article, we also propose a definition of microservices and a comparative analysis of the works related to PacificClouds. Finally, we show the main challenges of PacificClouds, and we point out the future directions.

Dynamic Selecting Approach for Multi-cloud Providers

Since multi-cloud can be used both to mitigate vendor lock-in and take advantages of cloud computing, an application can be deployed to multiple cloud providers that best meet the user and application needs. For this, it is necessary to select the cloud providers that will host an application. The selection process is a complex task due to the fact that each application part has its constraints, but mainly due to the existence of many cloud providers’, each of them with its specific characteristics. In this article, we propose a cloud providers selection process to host applications based on microservices, in which each microservice must be hosted by the provider that best meet the user and microservice requirements. We use applications based on microservices because they can be independently deployed and scalable. In addition, we use Simple Additive Weighting method for ranking the candidate cloud providers then we select candidate providers amongst them to host each microservice by mapping the selection process to multi-choice knapsack problem. Besides that, the microservices are analyzed individually, except for the cost constraint. The selection process examines the cost constraint by observing all application microservices. Our approach still differs from others described in the literature because it selects multiple cloud providers to host the application microservices, which one for each application microservice. In this article, we also evaluated the proposed method through experiments and the result shows the viability of our approach. Finally, we point out future directions for the cloud providers selection process.

An Adaptive Metaheuristic for Unconstrained Multimodal Numerical Optimization

The purpose of this paper is to show an adaptive metaheuristic based on GA, DE, and PSO. The choice of which one will be used is made based on a probability that is uniform at the beginning of the execution, and it is updated as the algorithm evolves. That algorithm producing better results tend to present higher probabilities of being selected. The metaheuristic has been tested in four multimodal benchmark functions for 1000, 2000, and 3000 iterations, managing to reach better results than the canonical GA, DE, and PSO. A comparison between our adaptive metaheuristic and an adaptive GA has shown that our approach presents better outcomes, which was proved by a t-test, as well.

Evolutionary solutions for resources management in multiple clouds: State-of-the-art and future directions

Abstract Even though multiple clouds tackle the problem of vendor lock-in, portability and interoperability are still a challenge from the perspective of both user and provider. Resources management, which is an NP-Hard problem according to the literature, must be addressed to obtain both portability and interoperability. Several optimization techniques have been used to deal with this issue and, among them, evolutionary computation has received considerable attention due to its flexibility and efficiency. In such, to the best of our knowledge, our paper is the first in the literature to investigate the multiple clouds resources management from the user perspective, which presents and discusses works that propose solutions using evolutionary computation. To analyze those works, it is mandatory to know all demands of the users and applications; consequently, it is necessary to identify what a resource is and how it can be managed in multiple clouds. We also present demands of the users for an application management in the multiple clouds approach. Therefore, we propose a definition and a classification of resources in multiple clouds and three taxonomies related to the multiple clouds resources management. Finally, we point out some trends and future directions.

A mechanism to control the congestion in machine-to-machine communication in LTE-A networks

Machine-to-machine communication (M2M) is a core elements in the Internet of Things (IoT) vision. Due to the large number of devices expected, the Long-term Evolution-Advanced (LTA-A) networks may present congestion and overload problems. In this paper, we present two approaches to mitigate the impact of M2M communication in LTE-A. We model such overloaded scenario as a bankruptcy problem and apply two strategies to define how resources should be allocated. The simulation results show that our approaches present improvements in terms of energy efficiency, impact control of M2M over Human-to-human (H2H) and define priority among different classes of device.