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This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

This work presents an estimation of the global electricity usage that can be ascribed to Communication Technology (CT) between 2010 and 2030. The scope is three scenarios for use and production of consumer devices, communication networks and data centers. Three different scenarios, best, expected, and worst, are set up, which include annual numbers of sold devices, data traffic and electricity intensities/efficiencies. The most significant trend, regardless of scenario, is that the proportion of use-stage electricity by consumer devices will decrease and will be transferred to the networks and data centers. Still, it seems like wireless access networks will not be the main driver for electricity use. The analysis shows that for the worst-case scenario, CT could use as much as 51% of global electricity in 2030. This will happen if not enough improvement in electricity efficiency of wireless access networks and fixed access networks/data centers is possible. However, until 2030, globally-generated renewable electricity is likely to exceed the electricity demand of all networks and data centers. Nevertheless, the present investigation suggests, for the worst-case scenario, that CT electricity usage could contribute up to 23% of the globally released greenhouse gas emissions in 2030.

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On Global Electricity Usage of Communication Technology: Trends to 2030

Computation offloading services provide required computing resources for vehicles with computation-intensive tasks. Past computation offloading research mainly focused on mobile edge computing (MEC) or cloud computing, separately. This paper presents a collaborative approach based on MEC and cloud computing that offloads services to automobiles in vehicular networks. A cloud-MEC collaborative computation offloading problem is formulated through jointly optimizing computation offloading decision and computation resource allocation. Since the problem is non-convex and NP-hard, we propose a collaborative computation offloading and resource allocation optimization (CCORAO) scheme, and design a distributed computation offloading and resource allocation algorithm for CCORAO scheme that achieves the optimal solution. The simulation results show that the proposed algorithm can effectively improve the system utility and computation time, especially for the scenario where the MEC servers fail to meet demands due to insufficient computation resources.

Computation Offloading and Resource Allocation For Cloud Assisted Mobile Edge Computing in Vehicular Networks

Smart grid has modernized the way electricity is generated, transported, distributed, and consumed by integrating advanced sensing, communications, and control in the day-to-day operation of the grid. Electricity is a core utility for the functioning of society and for the services provided by information and communication technologies (ICTs). Several concepts of the smart grid, such as dynamic pricing, distributed generation, and demand management, have significantly impacted the operation of ICT services, in particular, communication networks and data centers. Ongoing energy-efficiency and operational expenditures reduction efforts in communication networks and data centers have gained another dimension with those smart grid concepts. In this paper, we provide a comprehensive survey on the smart grid-driven approaches in energy-efficient communications and data centers, and the interaction between smart grid and information and communication infrastructures. Although the studies on smart grid, energy-efficient communications, and green data centers have been separately surveyed in previous studies, to this end, research that falls in the intersection of those fields has not been properly classified and surveyed yet. We start our survey by providing background information on the smart grid and continue with surveying smart grid-driven approaches in energy-efficient communication systems, followed by energy, cost and emission minimizing approaches in data centers, and the corresponding cloud network infrastructure. We discuss the open issues in smart grid-driven approaches in ICTs and point some important research directions such as the distributed renewable energy generation capability-coupled communication infrastructures, optimum energy-efficient network design for the smart grid environment, the impact of green communication techniques on the reliability and latency requirements of smart grid data, workload consolidation with smart grid-awareness, and many more.

Energy-Efficient Information and Communication Infrastructures in the Smart Grid: A Survey on Interactions and Open Issues

Most of the previous research on data centers power consumption has focused on understanding how to minimize the power consumption inside the data center. It is, however, also important to investigate the power consumption associated with transporting data between data centers and end users. In this paper, we consider three problems. First, through linear programming (LP) models and through simulations we determine the optimal location of a data center or multiple data centers in an IP over wavelength-division-multiplexing network so as to minimize the network's power consumption. Here, we consider the impact of network topology, traffic profile, upload and download rates, number of data centers and the impact of power minimization on delay. Second, we study how to replicate content that has different popularity to minimize power consumption through the use of an LP model. Here, we consider five classes (but the models are general) of content that have different levels of popularity and consider multiple data centers. The optimization attempts to identify where to store a data object that has a given popularity such that the network's power consumption is minimized. We have also developed a novel routing algorithm, energy-delay optimal routing, to minimize the power consumption of the network under replication while maintaining QoS. Third, we investigate through LP the problem of whether to locate data centers next to renewable energy or to transmit renewable energy to data centers in a given network topology under different traffic conditions and taking into account the network components' power consumption. Given a number of wind Farms whose locations are known together with the electrical power transmission losses, we identify the optimal location of data centers such that the network's power consumption is minimized and consider a network where the nodes that are not connected to wind farms have access to solar power. The results show that by identifying the optimum data center locations, combining the multi-hop bypass heuristic with renewable energy and the replication scheme, power consumption savings of up to 73% can be achieved.

Green IP Over WDM Networks With Data Centers

Network virtualization is widely considered to be one of the main paradigms for the future Internet architecture as it provides a number of advantages including scalability, on demand allocation of network resources, and the promise of efficient use of network resources. In this paper, we propose an energy efficient virtual network embedding (EEVNE) approach for cloud computing networks, where power savings are introduced by consolidating resources in the network and data centers. We model our approach in an IP over WDM network using mixed integer linear programming (MILP). The performance of the EEVNE approach is compared with two approaches from the literature: the bandwidth cost approach (CostVNE) and the energy aware approach (VNE-EA). The CostVNE approach optimizes the use of available bandwidth, while the VNE-EA approach minimizes the power consumption by reducing the number of activated nodes and links without taking into account the granular power consumption of the data centers and the different network devices. The results show that the EEVNE model achieves a maximum power saving of 60% (average 20%) compared to the CostVNE model under an energy inefficient data center power profile. We develop a heuristic, real-time energy optimized VNE (REOViNE), with power savings approaching those of the EEVNE model. We also compare the different approaches adopting an energy efficient data center power profile. Furthermore, we study the impact of delay and node location constraints on the energy efficiency of virtual network embedding. We also show how VNE can impact the design of optimally located data centers for minimal power consumption in cloud networks. Finally, we examine the power savings and spectral efficiency benefits that VNE offers in optical orthogonal division multiplexing networks.

Energy Efficient Virtual Network Embedding for Cloud Networks

With network expansion, the energy consumption and CO2 emissions associated with networks are increasing rapidly. In this paper we propose an approach for energy minimization in IP over WDM networks and furthermore propose the use of renewable energy to further reduce the CO2 emissions at a given energy consumption level. We develop a Linear Programming (LP) model for energy minimization in the network when renewable energy is used and propose a novel heuristic for improving renewable energy utilization. Compared with routing in the electronic layer, routing in the optical layer coupled with renewable energy nodes significantly reduces the CO2 emission of the IP over WDM network considered by 47% to 52%, and the new heuristic introduced hardly affects the QoS. In order to identify the impact of the number and the location of nodes that employ renewable energy on the non-renewable energy consumption of whole network, we also constructed another LP model. The results show that the nodes at the center of the network have more impact than other nodes if they use renewable energy sources. We have also investigated the additional energy savings that can be gained through Adaptive Link Rate (ALR) techniques where different load dependent energy consumption profiles are considered. Our optimized REO-hop routing algorithm with renewable energy and ALR results in a maximum energy saving of 85% (average of 65%) compared to a current network design where all nodes are statically dimensioned for the maximum traffic in terms of IP ports and optical layer and hence consume power accordingly. Furthermore, when all the nodes have access to typical levels of renewable power we show that the associated reduction in non-renewable energy consumption reduces the network's CO2 emissions by 97% peak, 78% average.

IP Over WDM Networks Employing Renewable Energy Sources

The energy consumption of information and communications technology networks is increasing rapidly as a result of the Internet expansion in reach and capacity. In this paper, we investigate energy-efficient physical topologies for backbone IP over wavelength-division multiplexing (WDM) networks. We develop a mixed integer linear programming model to optimize the physical topology of IP over WDM networks with the objective of minimizing the network total power consumption. We consider the National Science Foundation network topology and compare its energy consumption with the energy consumption of optimized physical topologies under different IP over WDM approaches and nodal degree constraints. We study the physical topology optimization under a symmetric full-mesh connectivity traffic matrix and an asymmetric traffic demand, where data centers create a hot node scenario in the network. We also investigate the power savings obtained by deploying topologies that eliminate the need for IP routers, including a full-mesh topology and a star topology. Simulation results show that the full-mesh and star topologies result in significant power savings of 95% and 92%, respectively. Furthermore, the optimization of the physical topology is investigated considering the presence of renewable energy sources in the network. The results show that optimizing the physical topology increases the utilization of the renewable energy sources.

On the Energy Efficiency of Physical Topology Design for IP Over WDM Networks

In this paper, we introduce a framework for designing energy efficient cloud computing services over non-bypass IP/WDM core networks. We investigate network related factors including the centralization versus distribution of clouds and the impact of demand, content popularity and access frequency on the clouds placement, and cloud capability factors including the number of servers, switches and routers and amount of storage required in each cloud. We study the optimization of three cloud services: cloud content delivery, storage as a service (StaaS), and virtual machines (VMS) placement for processing applications. First, we develop a mixed integer linear programming (MILP) model to optimize cloud content delivery services. Our results indicate that replicating content into multiple clouds based on content popularity yields 43% total saving in power consumption compared to power un-aware centralized content delivery. Based on the model insights, we develop an energy efficient cloud content delivery heuristic, DEER-CD, with comparable power efficiency to the MILP results. Second, we extend the content delivery model to optimize StaaS applications. The results show that migrating content according to its access frequency yields up to 48% network power savings compared to serving content from a single central location. Third, we optimize the placement of VMs to minimize the total power consumption. Our results show that slicing the VMs into smaller VMs and placing them in proximity to their users saves 25% of the total power compared to a single virtualized cloud scenario. We also develop a heuristic for real time VM placement (DEER-VM) that achieves comparable power savings.

Taisir E. H. El-Gorashi

Papers

Green IP Over WDM Networks With Data Centers

Energy Efficient Virtual Network Embedding for Cloud Networks

IP Over WDM Networks Employing Renewable Energy Sources

On the Energy Efficiency of Physical Topology Design for IP Over WDM Networks

Distributed Energy Efficient Clouds Over Core Networks