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Author

Jagannathan Venkatesh

Other affiliations: University of California
Bio: Jagannathan Venkatesh is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Smart grid & Renewable energy. The author has an hindex of 8, co-authored 14 publications receiving 343 citations. Previous affiliations of Jagannathan Venkatesh include University of California.

Papers
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Proceedings ArticleDOI
23 Oct 2011
TL;DR: This paper designs an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production, which enables the number of jobs to be scaled to the expected energy availability, thus reducingThe number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy.
Abstract: As brown energy costs grow, renewable energy becomes more widely used. Previous work focused on using immediately available green energy to supplement the non-renewable, or brown energy at the cost of canceling and rescheduling jobs whenever the green energy availability is too low [16]. In this paper we design an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production. This enables us to scale the number of jobs to the expected energy availability, thus reducing the number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy.

151 citations

Journal ArticleDOI
TL;DR: This paper designs an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production, which enables the number of jobs to be scaled to the expected energy availability, thus reducingThe number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy.
Abstract: As brown energy costs grow, renewable energy becomes more widely used Previous work focused on using immediately available green energy to supplement the non-renewable, or brown energy at the cost of canceling and rescheduling jobs whenever the green energy availability is too low [16] In this paper we design an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production This enables us to scale the number of jobs to the expected energy availability, thus reducing the number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy

65 citations

Proceedings ArticleDOI
27 Jun 2013
TL;DR: HomeSim is proposed, a residential electrical energy simulation platform that enables investigating the impact of technologies such as renewable energy and different battery types, and simulating different scenarios including centralized vs. distributed in-home energy storage, intelligent appliance rescheduling, and outage management.
Abstract: Residential energy constitutes 38% of the total energy consumption in the United States [1]. Although a number of building simulators have been proposed, there are no residential electrical energy simulators capable of modeling complex scenarios and exploring the tradeoffs in home energy management. We propose HomeSim, a residential electrical energy simulation platform that enables investigating the impact of technologies such as renewable energy and different battery types. Additionally, HomeSim allows us to simulate different scenarios including centralized vs. distributed in-home energy storage, intelligent appliance rescheduling, and outage management. Using measured residential data, HomeSim quantifies different benefits for different technologies and scenarios, including up to 50% reduction in grid energy through a combination of distributed batteries and reschedulable appliances.

28 citations

Journal ArticleDOI
TL;DR: This work uses a simulator to demonstrate that by accurately provisioning green energy availability for longer time intervals, green energy prediction can improve overall energy efficiency.
Abstract: Many simulators are available to evaluate performance and power tradeoffs in datacenters. The authors use one such simulator to demonstrate that by accurately provisioning green energy availability for longer time intervals, green energy prediction can improve overall energy efficiency.

25 citations

Journal ArticleDOI
TL;DR: A proposed approach breaks these applications up into an equivalent set of functional units called context engines, whose I/O transformations are driven by general-purpose machine learning, whose computational redundancy and complexity are decreased with a minimal impact on accuracy.
Abstract: The Internet of Things envisions a web-connected infrastructure of sensing and actuation devices. However, the current state of the art presents another reality: monolithic end-to-end applications tightly coupled to a limited set of sensors and actuators. Growing such applications with new devices or behaviors, or extending the existing infrastructure with new applications, involves redesign and deployment. A proposed approach breaks these applications up into an equivalent set of functional units called context engines, whose I/O transformations are driven by general-purpose machine learning. This approach decreases computational redundancy and complexity with a minimal impact on accuracy. Researchers evaluated this approach's scalability--how the context engines' overhead grows as the input data and number of computational nodes increase. In a large-scale case study of residential smart-grid control, this approach provided better accuracy and scaling than the state-of-the-art single-stage approach.

22 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper proposes a dynamic energy-aware cloudlet-based mobile cloud computing model (DECM) focusing on solving the additional energy consumptions during the wireless communications by leveraging dynamic cloudlets (DCL)-based model.

453 citations

Proceedings ArticleDOI
10 Apr 2012
TL;DR: GreenHadoop is proposed, a MapReduce framework for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup) and can significantly increase green energy consumption and decrease electricity cost, compared to Hadoop.
Abstract: Interest has been growing in powering datacenters (at least partially) with renewable or "green" sources of energy, such as solar or wind. However, it is challenging to use these sources because, unlike the "brown" (carbon-intensive) energy drawn from the electrical grid, they are not always available. This means that energy demand and supply must be matched, if we are to take full advantage of the green energy to minimize brown energy consumption. In this paper, we investigate how to manage a datacenter's computational workload to match the green energy supply. In particular, we consider data-processing frameworks, in which many background computations can be delayed by a bounded amount of time. We propose GreenHadoop, a MapReduce framework for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup). GreenHadoop predicts the amount of solar energy that will be available in the near future, and schedules the MapReduce jobs to maximize the green energy consumption within the jobs' time bounds. If brown energy must be used to avoid time bound violations, GreenHadoop selects times when brown energy is cheap, while also managing the cost of peak brown power consumption. Our experimental results demonstrate that GreenHadoop can significantly increase green energy consumption and decrease electricity cost, compared to Hadoop.

330 citations

Proceedings ArticleDOI
Íñigo Goiri1, William Katsak1, Kien Le1, Thu D. Nguyen1, Ricardo Bianchini1 
16 Mar 2013
TL;DR: The tradeoffs involved in building green datacenters today and in the future are discussed, and GreenSwitch, a model-based approach for dynamically scheduling the workload and selecting the source of energy to use is described.
Abstract: Several companies have recently announced plans to build "green" datacenters, i.e. datacenters partially or completely powered by renewable energy. These datacenters will either generate their own renewable energy or draw it directly from an existing nearby plant. Besides reducing carbon footprints, renewable energy can potentially reduce energy costs, reduce peak power costs, or both. However, certain renewable fuels are intermittent, which requires approaches for tackling the energy supply variability. One approach is to use batteries and/or the electrical grid as a backup for the renewable energy. It may also be possible to adapt the workload to match the renewable energy supply. For highest benefits, green datacenter operators must intelligently manage their workloads and the sources of energy at their disposal.In this paper, we first discuss the tradeoffs involved in building green datacenters today and in the future. Second, we present Parasol, a prototype green datacenter that we have built as a research platform. Parasol comprises a small container, a set of solar panels, a battery bank, and a grid-tie. Third, we describe GreenSwitch, our model-based approach for dynamically scheduling the workload and selecting the source of energy to use. Our real experiments with Parasol, GreenSwitch, and MapReduce workloads demonstrate that intelligent workload and energy source management can produce significant cost reductions. Our results also isolate the cost implications of peak power management, storing energy on the grid, and the ability to delay the MapReduce jobs. Finally, our results demonstrate that careful workload and energy source management can minimize the negative impact of electrical grid outages.

264 citations

Book
01 Jan 1980

199 citations

Proceedings ArticleDOI
16 Jun 2014
TL;DR: This paper proposes that prediction-based pricing is an appealing market design, and shows that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue, and provides analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction- based pricing.
Abstract: Demand response is crucial for the incorporation of renewable energy into the grid. In this paper, we focus on a particularly promising industry for demand response: data centers. We use simulations to show that, not only are data centers large loads, but they can provide as much (or possibly more) flexibility as large-scale storage if given the proper incentives. However, due to the market power most data centers maintain, it is difficult to design programs that are efficient for data center demand response. To that end, we propose that prediction-based pricing is an appealing market design, and show that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue. However, prediction-based pricing may be inefficient when predictions are inaccurate, and so we provide analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction-based pricing. These bounds hold even when network constraints are considered, and highlight that prediction-based pricing is surprisingly robust to prediction error.

189 citations