A Novel QoS Framework Based on Admission Control and Self-Adaptive Bandwidth Reconfiguration

This paper proposes a novel end-to-end QoS framework, called Self-Adaptive bandwidth Reconfiguration QoS framework (SAR). SAR provides end-to-end QoS guarantees on a per-flow basis through admission control and end-to-end bandwidth reservation. In order to adapt to short and long time traffic load changing, SAR performs dynamic bandwidth reconfiguration. Due to a new organization of the network physical lines, SAR allows for a better utilization of the links’ capacity and a smaller number of rejected flows, increasing the network’s availability

Serverless Computing: An Investigation of Deployment Environments for Web APIs

Cloud vendors offer a variety of serverless technologies promising high availability and dynamic scaling while reducing operational and maintenance costs. One such technology, serverless computing, or function-as-a-service (FaaS), is advertised as a good candidate for web applications, data-processing, or backend services, where you only pay for usage. Unlike virtual machines (VMs), they come with automatic resource provisioning and allocation, providing elastic and automatic scaling. We present the results from our investigation of a specific serverless candidate, Web Application Programming Interface or Web API, deployed on virtual machines and as function(s)-as-a-service. We contrast these deployments by varying the number of concurrent users for measuring response times and costs. We found no significant response time differences between deployments when VMs are configured for the expected load, and test scenarios are within the FaaS hardware limitations. Higher numbers of concurrent users or unexpected user growths are effortlessly handled by FaaS, whereas additional labor must be invested in VMs for equivalent results. We identified that despite the advantages serverless computing brings, there is no clear choice between serverless or virtual machines for a Web API application because one needs to carefully measure costs and factor-in all components that are included with FaaS

Theoretical perspectives on end-to-end QoS frameworks over heterogeneous networks

Providing Quality of Service in heterogeneous networks is a major challenge. Guarantying end-to-end delivery of services over heterogeneous networks, where more than one management entity exists is one of the main concerns of the current Internet, in terms of Quality of Service. Due to the complex, underdeveloped business relations among ISPs and a low level of trust, new QoS frameworks are being designed to guarantee accessibility, availability and network performance. This paper is an effort to briefly discuss some of the developments in the QoS research area for heterogeneous networks

An Extendable Software Architecture for Mitigating ARP Spoofing-Based Attacks in SDN Data Plane Layer

Software-defined networking (SDN) is an emerging network architecture that brings benefits in network function virtualization, performance, and scalability. However, the scalability feature also increases the number of possible vulnerabilities through multiple entry points in the network. Address Resolution Protocol (ARP) spoofing-based attacks are widely encountered and allow an attacker to assume the identity of a different computer, facilitating other attacks, such as Man in the Middle (MitM). In the SDN context, most solutions employ a controller to detect and mitigate attacks. However, interacting with the control plane involves asynchronous network communication, which causes delayed responses to an attack. The current work avoids these delays by being implemented solely in the data plane through extendable and customizable software architecture. Therefore, faster response times improve network reliability by automatically blocking attackers. As attacks can be generated with a variety of tools and in networks experiencing different traffic patterns, the current solution is created to allow flexibility and extensibility, which can be adapted depending on the running environment. Experiments were run performing ARP spoofing-based attacks using KaliLinux, Mininet, and OpenVSwitch. The presented results are based on traffic pattern analysis offering greater customization capabilities and insight compared to similar work in this area

On developing a QoS framework with self-adaptive bandwidth reconfiguration

Computer networks transport simultaneously several flows, fact that makes necessary a multiplexing mechanism. Transport procedures affect the traffic flows; reason for which the traffic has to be characterized and quality of service (QoS) requirements need to be established. Traffic types and their QoS requirements impose the implementation of QoS methods and architectures. Several frameworks have been developed in order to improve the network services but each one has several disadvantages. This paper presents the architecture, design and implementation of a new end-to-end QoS framework with self-adaptive bandwidth reconfiguration

Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs