Showing papers by "David Irwin published in 2010"

Private memoirs of a smart meter

Abstract: Household smart meters that measure power consumption in real-time at fine granularities are the foundation of a future smart electricity grid. However, the widespread deployment of smart meters has serious privacy implications since they inadvertently leak detailed information about household activities. In this paper, we show that even without a priori knowledge of household activities or prior training, it is possible to extract complex usage patterns from smart meter data using off-the-shelf statistical methods. Our analysis uses two months of data from three homes, which we instrumented to log aggregate household power consumption every second. With the data from our small-scale deployment, we demonstrate the potential for power consumption patterns to reveal a range of information, such as how many people are in the home, sleeping routines, eating routines, etc. We then sketch out the design of a privacy-enhancing smart meter architecture that allows an electric utility to achieve its net metering goals without compromising the privacy of its customers.

Automated negotiation with decommitment for dynamic resource allocation in cloud computing

Abstract: We consider the problem of allocating networked resources in dynamic environment, such as cloud computing platforms, where providers strategically price resources to maximize their utility. Resource allocation in these environments, where both providers and consumers are selfish agents, presents numerous challenges since the number of consumers and their resource demand is highly dynamic. While numerous auction-based approaches have been proposed in the literature, this paper explores an alternative approach where providers and consumers automatically negotiate resource leasing contracts. Since resource demand and supply can be dynamic and uncertain, we propose a distributed negotiation mechanism where agents negotiate over both a contract price and a decommitment penalty, which allows agents to decommit from contracts at a cost. We compare our approach experimentally, using representative scenarios and workloads, to both combinatorial auctions and the fixed-price model used by Amazon's Elastic Compute Cloud, and show that the negotiation model achieves a higher social welfare.

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

Abstract: To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its energy demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a system's ability to satisfy demand by improving its predictions. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a wind or solar energy harvesting prediction, and quantify its accuracy. We evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a sensor testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts in both wind- and solar-powered sensor systems increases each system's ability to satisfy its demands compared with existing prediction strategies.

Networked cloud orchestration: A GENI perspective

Abstract: This paper describes the experience of developing a system for creation of distributed linked configurations of heterogeneous resources (slices) in GENI. Our work leverages a number of unique architectural solutions (distributed architecture, declarative resource specifications, unique approach to slice instantiation) which is applicable to a wider set of problems related to autonomic co-scheduling and provisioning of heterogeneous networked resources. We discuss the architecture, the resource description mechanisms and some of the algorithms used to enable our system. We conclude with an analysis of a real experiment at allocating resources from multiple providers across a very wide geographic area (spanning Massachusetts, Illinois and North Carolina) to create a single private Layer 2 network connecting virtual machines on the campus of Duke University to a sensor testbed at University of Massachusetts, Amherst.

Resource management in data-intensive clouds: Opportunities and challenges

Abstract: Today's cloud computing platforms have seen much success in running compute-bound applications with time-varying or one-time needs. In this position paper, we will argue that the cloud paradigm is also well suited for handling data-intensive applications, characterized by the processing and storage of data produced by high-bandwidth sensors or streaming applications. The data rates and the processing demands vary over time for many such applications, making the on-demand cloud paradigm a good match for their needs. However, today's cloud platforms need to evolve to meet the storage, communication, and processing demands of data-intensive applications. We present an ongoing GENI project to connect high-bandwidth radar sensor networks with computational and storage resources in the cloud and use this example to highlight the opportunities and challenges in designing end-to-end data-intensive cloud systems.

Towards a Virtualized Sensing Environment

Abstract: While deploying a sensor network is necessary for proof-of-concept experimentation, it is a time-consuming and tedious task that dramatically slows innovation Treating sensor networks as shared testbeds and integrating them into a federated testbed infrastructure, such as FIRE, GENI, AKARI, or CNGI, enables a broad user community to benefit from time-consuming deployment exercises In this paper, we outline the challenges with integrating sensor networks into federated testbeds in the context of ViSE, a sensor network testbed we have integrated with GENI, and describe our initial deployment experiences ViSE differs from typical embedded sensor networks in its focus on high-bandwidth steerable sensors