Joint optimization of power, electricity cost and delay in IP over WDM networks

In this paper, we investigate the joint optimization of power, electricity cost and propagation delay in IP over WDM networks employing renewable energy. We develop a mixed integer linear programming (MILP) model to jointly minimize the three parameters and compare its results to the results of optimizing these parameters individually. The models results show that the joint optimization maintains the power consumption and electricity cost savings obtained by the non-renewable powerminimized and the electricity cost-minimized models while hardly affecting the propagation delay. Compared to the delay-minimized model, the joint optimization model achieves power consumption and electricity cost savings of 73% and 74%, respectively under the non-bypass approach considering a unicasting traffic profile. The power and cost savings under an anycasting traffic profile increases to 82%

WDM/TDM over Passive Optical Networks with Cascaded-AWGRs for Data Centers

Data centers based on Passive Optical Networks (PONs) can provide high capacity, low cost, scalability, elasticity and high energy-efficiency. This paper introduces the use of WDM-TDM multiple access in a PON-based data center that offers multipath routing via two-tier cascaded Arrayed Waveguide Grating Routers (AWGRs) to improve the utilization of resources. A Mixed Integer Linear Programming (MILP) model is developed to optimize resource allocation while considering multipath routing. The results show that all-to-all connectivity is achieved in the architecture through the use of two different wavelength within different time slots for the communication between racks in the same or different cells, as well as with the OLT switches

Electricity Cost Aware Virtual Machine Placement Schemes in Distributed Cloud

In recent times, energy efficiency has become an important criterion considered by information and communication technology (ICT) infrastructure providers. This is largely motivated by economic and environmental reasons. By leveraging on technological advancements, it is possible to accomplish desired energy efficiencyimprovementsin the ICT industry. This paper leverages on the virtualization concept (behind the success of cloud computing) and time of usage concept (enabled by smart grid technology) to reduce non-renewable power consumption and total electricity cost of cloud and network infrastructure providers. A mixed integer linear programming (MILP) model was developed to study the impact of varying electricity prices across time zones on the placement of virtual machine (VM) clusters in distributed clouds connected via internet protocol over wavelength division multiplexing (IP over WDM) core network. Results showed that VMclusters are placed at nodes with the cheapest electricity for the three VM cluster placement schemes considered. These results are achieved at the expense of increased non-renewable power consumption and electricity cost in the IP over WDM core network

Network virtualization in next-generation cellular networks: a spectrum pooling approach

The hardship of expanding the cellular network market results from the tremendous high cost of mobile infrastructure, i.e. the capital expenditures (CAPEX) and the operational expenditures (OPEX). Spectrum Sharing is one of the proposed solution for the high-cost of scalability of cellular networks. However, most of the proposed spectrum pooling frameworks in the literature are mostly approached from a technical view besides there are no good cost models based on real datasets for quantifying the circumstances under which sharing the spectrum and network resources would be beneficial to mobile operators. In this thesis, by studying different sharing scenarios in a fiber-based backhaul mobile network, we assess the incentives for service providers (SPs) to share spectrum/infrastructure in different cellular market areas/economic areas (CMA/BEAs) with different population density, allocated bandwidth (BW), spectrum bid values and considering different network topologies. Moreover, we look at the technical problem of sharing the spectrum between two SPs sharing the same basestation (BS), yet they have different traffic demand as well as different QoS constraints. We design a resource allocation scheme to provision real-time (RT), non-real-time (NRT) as well as Ultra-reliable Low Latency Communications (URLLC) traffic in a single shared BS scenario such that SPs achieve isolation, fairness and enforce their QoS constraints. Finally, we exploit spectrum pooling to develop an approach for dynamically re-configuring the base stations that survive a disaster and are powered by a microgrid to form a multi-hop mesh network in order to provide local cellular service

Carbon-Intelligent Global Routing in Path-Aware Networks

The growing energy consumption of Information and Communication Technology (ICT) has raised concerns about its environmental impact. However, the carbon efficiency of data transmission over the Internet has so far received little attention. This carbon efficiency can be enhanced effectively by sending traffic over carbon-efficient inter-domain paths. However, challenges in estimating and disseminating carbon intensity of inter-domain paths have prevented carbon-aware path selection from becoming a reality. In this paper, we take advantage of path-aware network architectures to overcome these challenges. In particular, we design CIRo, a system for forecasting the carbon intensity of inter-domain paths and disseminating them across the Internet. We implement a proof of concept for CIRo on the codebase of the SCION path-aware Internet architecture and test it on the SCIONLab global research testbed. Further, we demonstrate the potential of CIRo for reducing the carbon footprint of endpoints and end domains through large-scale simulations. We show that CIRo can reduce the carbon intensity of communications by at least 47% for half of the domain pairs and the carbon footprint of Internet usage by at least 50% for 87% of end domains

Energy management in communication networks: a journey through modelling and optimization glasses

The widespread proliferation of Internet and wireless applications has produced a significant increase of ICT energy footprint. As a response, in the last five years, significant efforts have been undertaken to include energy-awareness into network management. Several green networking frameworks have been proposed by carefully managing the network routing and the power state of network devices. Even though approaches proposed differ based on network technologies and sleep modes of nodes and interfaces, they all aim at tailoring the active network resources to the varying traffic needs in order to minimize energy consumption. From a modeling point of view, this has several commonalities with classical network design and routing problems, even if with different objectives and in a dynamic context. With most researchers focused on addressing the complex and crucial technological aspects of green networking schemes, there has been so far little attention on understanding the modeling similarities and differences of proposed solutions. This paper fills the gap surveying the literature with optimization modeling glasses, following a tutorial approach that guides through the different components of the models with a unified symbolism. A detailed classification of the previous work based on the modeling issues included is also proposed

Energy Efficient Anycast Routing for Sliding Scheduled Lightpath Demands in Optical Grids

Optical grids have been thought as an answer to support large-scale data intensive applications. Data centers and Optical grids are largest and fastest growing consumers of electricity. Energy efficient routing schemes and traffic models can answer the problem of energy consumption. In Optical Grids, it is possible to select destination node from the set of possible destinations which is known as anycasting. We propose ILP formulations for flexible sliding scheduled traffic model, where setup and tear down times may vary within larger window frame. The problem of energy consumption is addressed by switching off ideal network components in low utilization periods. Our proposed novel formulation that exploits knowledge of demand holding times to optimally schedule demands achieved 7-13% reduction in energy consumption compared to previously best known model