Jagannathan Venkatesh

Utilizing green energy prediction to schedule mixed batch and service jobs in data centers

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.

HomeSim: Comprehensive, smart, residential electrical energy simulation and scheduling

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.

Using Datacenter Simulation to Evaluate Green Energy Integration

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.

Context-Aware Mobile Power Management Using Fuzzy Inference as a Service

As smartphones become ubiquitous, their energy consumption remains one of the most important issues. Mobile devices operate in a dynamically changing context, and their embedded sensors can be used to extract the relevant context needed for resource optimization. In this paper, we present a context-aware power management system implemented as a widely-applicable middleware application. Fuzzy inference is used to represent a high-level description of context, which is provided as a service. We test our approach using actual periodic and streaming applications on a mobile phone. Our results show energy reduction of 13-50% for periodic applications, and 18-36% for streaming applications.

Distributed control of a swarm of buildings connected to a smart grid: demo abstract

Energy-efficient control mechanisms are necessary to manage the ever increasing energy demand. Recently several tools for building energy consumption control have been proposed for small (e.g. homes) [8] and large (e.g. offices) buildings [3][6][1]. The mechanism each tool uses is different, e.g. HVAC control [3] and appliance rescheduling [8], but they share the goal of improving consumption of the buildings with respect to a given cost function. Some examples of cost functions are reduced energy consumption, reduced electricity bill, lower peak power, and increased ancillary service participation. The tools however do not capture the impacts of their control actions on the grid. These actions can lead to supply/demand imbalance and voltage/frequency deviation and thus, threaten grid stability. Utilities can take protective actions against those who cause instability by increasing electricity price or even momentarily disconnecting them from the grid. The effects of these protective actions can be so severe that the savings obtained by building management tools might disappear.

A Modular Approach to Context-Aware IoT Applications

The Internet of Things (IoT) refers to an environment of ubiquitous sensing and actuation, where devices are connected to a distributed backend infrastructure. It offers the opportunity to access a large amount of input data, and process it into contextual information about different system entities for reasoning and actuation. State-of-the-art IoT applications are generally black-box, end-to-end application-specific implementations, and cannot keep up with timely resolution of all this live, continually updated, heterogeneous data. In this work, we propose a modular approach to these context-aware applications, breaking down monolithic applications into an equivalent set of functional units, or context engines. By exploiting the characteristics of context-aware applications, context engines can reduce compute redundancy and computational complexity. In conjunction with formal data specifications, or ontologies, we can replace application-specific implementations with a composition of context engines that use common statistical learning to generate output, thus improving context reuse. We implement interconnected context-aware applications using our approach, extracting both user activity and location context from wearable sensors. We compare our infrastructure to single-stage monolithic implementations, demonstrating a reduction in application latency by up to 65% and execution overhead by up to 50% with only a 3% reduction in accuracy.

Modular and Personalized Smart Health Application Design in a Smart City Environment

The Internet of Things (IoT) envisions to create a smart, connected city that is composed of ubiquitous environmental and user sensing along with distributed, low-capacity computing. This provides ample information regarding the citizens in various smart environments. We can leverage this people-centric information, provided by the smart city infrastructure, to improve “smart health” applications: user data from connected wearable devices can be accompanied with ubiquitous environmental sensing and versatile actuation. The state-of-the-art in smart health applications is black-box, end-to-end implementations which are neither intended for use with heterogeneous data nor adaptable to a changing set of sensing and actuation. In this paper, we apply our modular approach for IoT applications—the context engine—to smart health problems, enabling the ability to grow with available data, use general-purpose machine learning, and reduce compute redundancy and complexity. For smart health, this improves response times for critical situations, more efficient identification of health-related conditions and subsequent actuation in a smart city environment. We demonstrate the potential with three sets of interconnected context-aware applications, extracting health-related people-centric context, such as user presence, user activity, air quality, and location from IoT sensors.

Renewable Energy Prediction for Improved Utilization and Efficiency in Datacenters and Backbone Networks

Datacenters are one of the important global energy consumers and carbon producers. However, their tight service level requirements prevent easy integration with highly variable renewable energy sources. Short-term green energy prediction can mitigate this variability. In this work, we first explore the existing short-term solar and wind energy prediction methods, and then leverage prediction to allocate and migrate workloads across geographically distributed datacenters to reduce brown energy consumption costs. Unlike previous works, we also study the impact of wide area networks (WAN) on datacenters, and investigate the use of green energy prediction to power WANs. Finally, we present two different studies connecting datacenters and WANs: the case where datacenter operators own and manage their WAN and the case where datacenters lease networks from WAN providers. The results show that prediction enables up to 90 % green energy utilization, a 3\(\times \) improvement over the existing methods. The cost minimization algorithm reduces expenses by up to 16 % and increases performance by 27 % when migrating workloads across datacenters. Furthermore, the savings increase up to 30 % compared with no migration when servers are made energy-proportional. Finally, in the case of leasing the WAN, energy proportionality in routers can increase the profit of network providers by 1.6\(\times \).

Lifetime-dependent battery usage optimization for grid-connected residential systems

Batteries are an important element for residences that are in grid-connected systems with energy procurement. They provide storage for local generation and a buffer against the inconsistent output from renewables such as rooftop solar. In addition, they can independently provide a medium for buying and selling retail energy. The growing deployment of reverse power-operation systems provides residences with the ability to buy and sell energy at the retail time-of-use rate. While the nonlinear models of chemical batteries have been extensively studied, they have not been applied to strategies for residential battery use. In this work, we develop a formulation for battery usage based on more realistic battery models, optimizing the benefit of discharging the battery. We design the scheme for the actual use of batteries in an energy-trading environment, considering the total cost of ownership and return on investment. Finally, we simulate the system in different geographic locations using the actual time-of-use pricing for each, and demonstrating return on investment in as few as 5 years.

A comprehensive approach to reduce the energy cost of network of datacenters

Several studies have proposed job migration over the wide area network (WAN) to reduce the energy of networks of datacenters by taking advantage of different electricity prices and load demands. Each study focuses on only a small subset of network parameters and thus their results may have large errors. For example, datacenters usually have long-term power contracts instead of paying market prices. However, previous work neglects these contracts, thus overestimating the energy savings by 2.3x. We present a comprehensive approach to minimize the energy cost of networks of datacenters by modeling performance of the workloads, power contracts, local renewable energy sources, different routing options for WAN and future router technologies. Our method can reduce the energy cost of datacenters by up to 28%, while reducing the error in the energy cost estimation by 2.6x.