Packet flow analysis in IP networks via abstract interpretation

Static analysis (aka offline analysis) of a model of an IP network is useful for understanding, debugging, and verifying packet flow properties of the network. There have been static analysis approaches proposed in the literature for networks based on model checking as well as graph reachability. Abstract interpretation is a method that has typically been applied to static analysis of programs. We propose a new, abstract-interpretation based approach for analysis of networks. We formalize our approach, mention its correctness guarantee, and demonstrate its flexibility in addressing multiple network-analysis problems that have been previously solved via tailor-made approaches. Finally, we investigate an application of our analysis to a novel problem -- inferring a high-level policy for the network -- which has been addressed in the past only in the restricted single-router setting.Comment: 8 page

Consumer Segmentation and Knowledge Extraction from Smart Meter and Survey Data

Many electricity suppliers around the world are deploying smart meters to gather fine-grained spatiotemporal consumption data and to effectively manage the collective demand of their consumer base. In this paper, we introduce a structured framework and a discriminative index that can be used to segment the consumption data along multiple contextual dimensions such as locations, communities, seasons, weather patterns, holidays, etc. The generated segments can enable various higher-level applications such as usagespecific tariff structures, theft detection, consumer-specific demand response programs, etc. Our framework is also able to track consumers' behavioral changes, evaluate different temporal aggregations, and identify main characteristics which define a cluster

iDR: Consumer and Grid Friendly Demand Response System

Peak demand is a major challenge for power utilities across the world. Demand Response (DR) is considered to be effective in addressing peak demand by altering consumption of end consumers, so as to match supply capability. However, an efficient DR system needs to respect end consumer convenience and understand their propensity of participating in a particular DR event, while altering the consumer demand. Understanding such preferences is non-trivial due to the large-scale and variability of consumers and the infrastructure changes required for collecting essential (smart meter and/or appliance specific) data. In this paper, we propose an inclusive DR system, iDR, that helps an electricity provider to design an effective demand response event by analyzing its consumers’ house-level consumption (smart meter) data and external context (weather conditions, seasonality etc.) data. iDR combines analytics and optimization to determine optimal power consumption schedules that satisfy an electricity provider’s DR objectives - such as reduction in peak load - while minimizing the inconvenience caused to consumers associated with alteration in their consumption patterns. iDR uses a novel context-specific approach for determining end consumer baseline consumptions and user convenience models. Using these consumer specific models and past DR experience, iDR optimization engine identifies -(i) when to execute a DR event, (ii) who are the consumers to be targeted for the DR, and (iii) what signals to be sent. Some of iDR’s capabilities are demonstrated using real-world house-level as well as appliance-level data

DRSim: A Cyber Physical Simulator for Demand Response Systems

Demand Response (DR) is a mechanism in which electricity consumers alter their demand in response to power grid’s supply and economic conditions. DR programs have the potential to improve resource efficiency, sustainability, grid reliability and economic viability by providing tighter alignment between demand and supply. However, implementing DR program is a non-trivial task as it requires good knowledge of electricity consumption preferences, economic models and contextual factors. Developing such knowledge through real world studies can be expensive and be time consuming. As a result, utility companies have been finding it complicated to analyze potential viability and return on investments of DR programs for various ‘what-if’ scenarios. To address this problem, we present DRSim – a cyber-physical simulator that allows utility companies to study demand side participation aspects of communities with various practical scenarios. DRSim is based on the principles of agent-oriented modeling of users’ behavior and context. It is able to model the emergent behavior of a community based on real data traces that contain partial information about the environment. DRSim is a highly extensible framework to accommodate new data sources, new analytical functionalities and evolve its modeling power. Feasibility experiments show the modeling and analysis potential of DRSim in practical settings

Packet flow analysis in IP networks using data-flow analysis

Static analysis (aka offline analysis) of a model of an IP network is useful for understanding, debugging, and verifying packet flow properties of the network. Data-flow analysis is a method that has typically been applied to static analysis of programs. We propose a new, data-flow based approach for static analysis of packet flows in networks. We also investigate an application of our analysis to the problem of inferring a high-level policy from the network, which has been addressed in the past only for a single router

Deep conservation in urban india and its implications for the design of conservation technologies

Rapid depletion of fossil fuels and water resources has become an international problem. Urban residential households are among the primary consumers of resources and are deeply affected by resource shortages. Despite the global nature of these problems, most of the solutions being developed to address these issues are based on studies done in the developed world. We present a study of energy, water and fuel conservation practices in urban India. Our study highlights a culture of deep conservation and the results raise questions about the viability of typical solutions such as home energy monitors. We identify new opportunities for design such as point-of-use feedback technologies, modular solutions, distributed energy storage, harnessing by-products and automated load shifting