The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

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The Environmental Protection Agency

SDN/NFV architectures for edge-cloud oriented IoT: A systematic review

Bandwidth-aware energy efficient flow scheduling with SDN in data center networks

The explosive growth in the volume of data storing and complexity of data processing drive data center networks (DCNs) to become incrementally scalable and cost-efficient while to maintain high network capacity and fault tolerance. To address these challenges, this paper proposes a new structure, called Totoro , which is defined recursively and hierarchically: dual-port servers and commodity switches are used to make Totoro affordable; a bunch of servers are connected to an intra-switch to form a basic partition; to construct a high-level structure, a half of the backup ports of servers in the low-level structures are connected by inter-switches in order to incrementally build a larger partition. Totoro is incrementally scalable since expanding the structure does not require any rewiring or routing alteration. We further design a distributed and fault-tolerant routing protocol to handle multiple types of failures. Experimental results demonstrate that Totoro is able to satisfy the demands of fault tolerance and high throughput. Furthermore, architecture analysis indicates that Totoro balances between performance and costs in terms of robustness, structural properties, bandwidth, economic costs and power consumption.

An Incrementally Scalable and Cost-Efficient Interconnection Structure for Data Centers

Electrical power demands have increased significantly over the last years due to rapid increase in air conditioning units and home appliances per domestic unit, particularly in Iraq. Having an uninterrupted power supply is essential for the continuity of power-generated home services and industrial platforms. Currently, in Iraq, electrical power interruption has become a big concern to the utility suppliers. Despite successive attempts to put an end to this dilemma, the issue still prevails. One of the main factors in power outages in local zones is persistent faults in distribution transformers (DTs). DT is considered one of the main elements in the electrical network that is essential for the reliability of the grid supply. Due to the internal lack of monitoring system and periodic maintenance, DT is relentlessly subject to faults due to high overhead utilization. Therefore, in order to enhance the grid reliability, transformer health check, and maintenance practices, we propose a remote condition Internet of Things monitoring and fault prediction system that is based on a customized software-defined networking (SDN) technology. This approach is a transition to smart grid implementation by fusing the power grid with efficient and real-time wireless communication architecture. The SDN implementation is considered in two phases: one is a controller installed per local zone and the other is the main controller that is installed between zones and connected to the core network. The core network consists of redundant links to recover from any future fails. Furthermore, we propose a prediction system based on an artificial neural network algorithm, called distribution transformer fault prediction, that is installed in the management plane for periodic prediction based on real-time sensor traffic to our proposed cloud. Moreover, we propose a communication protocol in the local zone called local SDN-sense. The SDN-sense ensures a reliable communication and local node selection to relay DT sensor data to the main controller. Our proposed architecture showcases an efficient approach to handle future interruption and faults in power grid using cost-effective and reliable infrastructure that can predict and provide real-time health monitoring indices for the Iraqi grid network with minimal power interruptions. After extensive testing, the prediction accuracy was about 96.1%.

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A Smart Optimization of Fault Diagnosis in Electrical Grid Using Distributed Software-Defined IoT System

Today’s giant data centers are power hungry. Data center energy saving not only helps control the operational cost, but also benefits the sustainable growth of cloud services. Due to the adoption of much more switches in modern data centers as well as the mature server-side power management techniques, energy saving for the data center network is becoming increasingly important. Most previous works on saving data center network energy focus on aggregating flows to as few switches as possible. However, in this paper we argue that this method may not work for network-limited flows, the throughputs of which are elastic based on the competing flows. To save the network energy consumed by this kind of elastic flows, we propose a flow scheduling approach called Willow , which takes both the number of switches involved and their active working durations into consideration. We formulate this problem by programming and design a greedy approximate algorithm to schedule flows in an online manner. Simulations based on MapReduce traces show that Willow can save up to 60 percent network energy compared with ECMP scheduling in typical settings, and outperforms other classical heuristic algorithms such as simulated annealing and particle swarm optimization. Testbed Experiments demonstrate that this kind of dynamic energy-efficient flow scheduling causes negligible impact on upper-layer applications.

Willow: Saving Data Center Network Energy for Network-Limited Flows

Towards full virtualization of SDN infrastructure

Power saving in the network infrastructure has received great attention in recent years. Power-aware traffic management is proposed in many works, in which a subset of routers/links are preferentially used to carry traffic while other links are activated only when traffic load is high. However, it remains challenging how to minimize the overhead to both ISPs and applications, which is important to the successful deployment of power-aware traffic management in a real network. This paper presents PALS, a new Power-Aware Link State routing based traffic management protocol. Compared with previous solutions, PALS remarkably reduces the overheads to ISPs and applications by the following innovations. First, PALS minimizes the forwarding table expansion due to dynamic power-aware routing, by using destination based routing instead of pairwise routing (e.g., MPLS). Second, PALS limits packet reordering for applications, by never splitting traffic between an IE (ingress-egress) router pair to multiple paths. Third, PALS significantly reduces the computation complexity of the power-aware routing algorithm, by running a simple path selection algorithm at each ingress router with the knowledge of local traffic information as well as global link utilization, which are much easier to obtain than global traffic matrix required by the state-of-the-art solutions (e.g., Zhang , IEEE ICNP 2010). Extensive simulations and testbed experiments show that, although bearing the simplicities to minimize the overhead, PALS saves satisfactory network power, with quick response to traffic variance and negligible impact on the packet delivery performance for applications.

PALS: Saving Network Power With Low Overhead to ISPs and Applications

Data center operators are accepting software defined networking (SDN) to manage their networks, but it remains challenging how to provide desirable virtual SDN services to tenants in a public cloud. We design SVirt, which enables highly flexible virtual SDN in a multi-tenant cloud by a substrate-agnostic SDN virtualization architecture. By redesigning the physical switch's processing pipeline with a "late-binding key extractor", SVirt supports virtual SDN switches with different processing pipelines simultaneously on a physical switch. In the control plane, SVirt enables "many-to-one" and "one-to-many" mapping when allocating the physical resource for a virtual network, which embraces arbitrary topology and TCAM resource demanded by a virtual network. In the data plane, SVirt explicitly carries the forwarding context information in the packets, overcoming the "context-loss problem" in a virtual SDN network. We develop a NetFPGA prototype of SVirt switch. Evaluations based on event-driven simulations and prototype-based experiments demonstrate that, compared with traditional approaches, SVirt significantly enhances the cloud's capability to accept various virtual SDN requests and improves the network's throughput.

Yirong Yu

Papers

Willow: Saving Data Center Network Energy for Network-Limited Flows

Towards full virtualization of SDN infrastructure

PALS: Saving Network Power With Low Overhead to ISPs and Applications

SVirt: A Substrate-agnostic SDN Virtualization Architecture for Multi-tenant Cloud