1,720,983 research outputs found
ePEM: An End-to-End Proactive Secure Connectivity Manager for 6G Orchestrator Solutions
No full textThe End-to-End Proactive Security Connectivity Manager (ePEM) system is introduced as a cutting-edge connectivity manager designed to orchestrate and secure end-to-end services within the Holistic, Omnipresent, Resilient Services for future 6G wireless and computing Ecosystems (HORSE) security infrastructure for future Sixth-Generation (6G) networks. It plays a pivotal role in managing Network Function Virtualization (NFV) and applicative services, providing observability and data simplification to improve decision-making and security management. ePEM employs meta-actions for security contingency planning and maintains a comprehensive database of the network's logical topology. Built on a modular architecture, it supports various Network Function (xNF) and ecosystems, using blueprint profiles to standardise the operations for network elements
Simple continuous optimal regions of the space of data
No full textThe definition of efficient programs for both maintenance and optimization is a struggling task in many industrial sectors. In this context, data analysis can significantly improve the state-of-the-art techniques, employed, for instance, to determine if a particular component or product is showing an anomalous behavior with respect to a defined nominal state. In fact, through the analysis of data collected on field, it is possible to detect optimal operating regions and to detect anomalies in advance. In this context, we propose a multi-purpose algorithm for unsupervised or semi-supervised learning in order to determine a simple continuous region of points. This region can be adopted in order to describe a component or a product nominal behavior and can be used in order to detect anomalies which are outside it. Such a region can be defined adopting a finite ensemble of thresholds, whose value can be physically interpreted. In order to show the effectiveness of our approach, the proposed method has been tested in an Anomaly Detection problem concerning Predictive Maintenance, exploiting data coming from a naval vessel, characterized by a combined diesel-electric and gas propulsion plant
Traffic merging for energy-efficient datacenter networks
No full textNumerous studies have shown that datacenter networks typically see loads of between 5% - 25% but the energy draw of these networks is equal to operating them at maximum load. In this paper, we propose a novel way to make these networks more energy proportional - that is, the energy draw scales with the network load. We propose the idea of traffic aggregation, in which low traffic from N links is combined together to create H < N streams of high traffic. These streams are fed to H switch interfaces which run at maximum rate while the remaining interfaces are switched to the lowest possible one. We show that this merging can be accomplished with minimal latency and energy costs (less than 0.1W) while simultaneously allowing us a deterministic way of switching link rates between maximum and minimum. Using simulations based on previously developed traffic models, we show that 49% energy savings are obtained for 5% of the load while we get an energy savings of 22% for a 50% load. Hence, forasmuch as the packet losses are statistically insignificant, the results show that energy-proportional datacenter networks are indeed possible. © 2012 Society for Modeling & Sim
Theoretical and technological limitations of power scaling in network devices
No full textThe largest part of routers and switches, today deployed in production networks, has very limited energy saving capabilities, and substantially requires the same amount of energy both when working at full speed or when being idle. In order to dynamically adapt such energy requirements to the real device work load, current approaches foster the introduction of low power idle and power scaling primitives in entire devices, internal components and network interfaces. Starting from these considerations, we focus on power scaling, and we propose an analysis of the theoretical and technological limitations in adopting such kind of mechanisms. Thus, our contribution is twofold. On one hand, we performed several tests to identify the technological limitations in a software router based on off-the-shelf hardware, which already includes such capabilities. The results achieved show that the power scaling allows a linear trade-off between consumption and network performance, but the time to switch between two power states may cause a non negligible service interruption. On the other hand, regarding the theoretical limitations, we consider the trade-off between the benefit in dynamically adapting the power states within short time-scales and the overhead needed to choose and select the new power state. ©2010 IEEE
Green Network Technologies and the Art of Trading-off
No full textIn this contribution, we focus on energy-aware
devices able to reduce their energy requirements by adapting
their performance. We propose an analytical model to
accurately represent the impact of green network technologies
(i.e., low power idle and adaptive rate) on network- and
energy-aware performance indexes. The model has been
validated with experimental results, performed by using
energy-aware software routers and real-world traffic traces.
The achieved results demonstrate how the proposed model can
effectively represent energy- and network-aware performance
indexes. Moreover, also an optimization procedure based on
the model has been proposed and experimentally evaluated.
The procedure aims at dynamically adapting the energy-aware
device configuration to minimize energy consumption, while
coping with incoming traffic volumes and meeting network
performance constraints
Power scaling in network devices
No full textThe largest part of routers and switches, today deployed in production networks, has very limited energy saving capabilities, and substantially requires the same amount of energy both when working at full speed or when being idle. In order to dynamically adapt such energy requirements to the real device work load, current approaches foster the introduction of low power idle and power scaling primitives in entire devices, internal components and network interfaces. Starting from these considerations, we propose an analysis of the theoretical and technological limitations in adopting such kind of mechanisms. The results achieved show that the power scaling allows a linear trade-off between consumption and network performance, but the time to switch between two power states may cause a non negligible service interruption
A Game for Energy-Aware Allocation of Virtualized Network Functions
Full text linkNetwork Functions Virtualization (NFV) is a network architecture concept where network functionality is virtualized and separated into multiple building blocks that may connect or be chained together to implement the required services. The main advantages consist of an increase in network flexibility and scalability. Indeed, each part of the service chain can be allocated and reallocated at runtime depending on demand. In this paper, we present and evaluate an energy-aware Game-Theory-based solution for resource allocation of Virtualized Network Functions (VNFs) within NFV environments. We consider each VNF as a player of the problem that competes for the physical network node capacity pool, seeking the minimization of individual cost functions. The physical network nodes dynamically adjust their processing capacity according to the incoming workload, by means of an Adaptive Rate (AR) strategy that aims at minimizing the product of energy consumption and processing delay. On the basis of the result of the nodes’ AR strategy, the VNFs’ resource sharing costs assume a polynomial form in the workflows, which admits a unique Nash Equilibrium (NE). We examine the effect of different (unconstrained and constrained) forms of the nodes’ optimization problem on the equilibrium and compare the power consumption and delay achieved with energy-aware and non-energy-aware strategy profiles
Improving Efficiency of Edge Computing Infrastructures through Orchestration Models †
No full textEdge computing is an effective paradigm for proximity in computation, but must inexorably face mobility issues and traffic fluctuations. While software orchestration may provide effective service handover between different edge infrastructures, seamless operation with negligible service disruption necessarily requires pre-provisioning and the need to leave some network functions idle for most of the time, which eventually results in large energy waste and poor efficiency. Existing consolidation algorithms are largely ineffective in these conditions because they lack context, i.e., the knowledge of which resources are effectively used and which ones are just provisioned for other purposes (i.e., redundancy, resilience, scaling, migration). Though the concept is rather straightforward, its feasibility in real environments must be demonstrated. Motivated by the lack of energy-efficiency mechanisms in cloud management software, we have developed a set of extensions to OpenStack for power management and Quality of Service, explicitly targeting the introduction of more context for applications. In this paper, we briefly describe the overall architecture and evaluate its efficiency and effectiveness. We analyze performance metrics and their relationship with power consumption, hence extending the analysis to specific aspects that cannot be investigated by software simulations. We also show how the usage of context information can greatly improve the effectiveness of workload consolidation in terms of energy saving
Applying traffic merging to datacenter networks
No full textThe problem of reducing energy usage in datacenter networks is an important one. However, we would like to achieve this goal without compromising throughput and loss characteristics of these networks. Studies have shown that data-center networks typically see loads of between 5% - 25% but the energy draw of these networks is equal to operating them at maximum load. To this end we examine the problem of reducing the energy consumption of datacenter networks by merging traffic. The key idea is that low traffic from N links is merged together to create K ≤ N streams of high traffic. These streams are fed to K switch interfaces which run at maximum rate while the remaining interfaces are switched to the lowest possible rate. We show that this merging can be accomplished with minimal latency and energy costs (less than 0.1W total) while simultaneously allowing us a deterministic way of switching link rates between maximum and minimum. We examine the idea of traffic merging using three different datacenter networks flattened butterfly, mesh and hypercube networks. In addition to analysis, we simulate these networks and utilizing previously developed traffic models we show that 49% energy savings are obtained for 5% per-link load while we get 20% savings for a 50% load for the flattened butterfly and somewhat lower savings are obtained for the other two networks. The packet losses are statistically insignificant and the maximum latency increase is less than 3μs. The results show that energy-proportional datacenter networks are indeed possibl
Trading off Power Consumption and Delay in the Execution of Network Functions by Dynamic Activation of Processing Units
No full textBeside increasing flexibility and programmability, the current network “softwarization” trend is believed to be beneficial also in respect of energy efficiency, owing to the consolidation of resources made possible by virtualized networking components. However, the widespread use of generalpurpose hardware may jeopardize energy saving, unless proper control strategies are put in operation. In this context, the paper addresses a “smart sleeping” control problem, where computing resources in multi-core processors executing network functions are modelled as multi-server queues, and the number of active processing units (either physical or virtual) can be dynamically adjusted by parametric control over a time scale compatible with the long-term dynamics of the traffic flows that require processing. We show that, on average, up to 25 % of processing capacity of a network node can be turned off in the presence of bursty traffic with low load without significantly affecting packet latenc
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