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

Energy-aware simulation with DVFS

International audienceIn recent years, research has been conducted in the area of large systems models, especially distributed systems, to analyze and understand their behavior. Simulators are now commonly used in this area and are becoming more complex. Most of them provide frameworks for simulating application scheduling in various Grid infrastructures, others are specifically developed for modeling networks, but only a few of them simulate energy-efficient algorithms. This article describes which tools need to be implemented in a simulator in order to support energy-aware experimentation. The emphasis is on DVFS simulation, from its implementation in the simulator CloudSim to the whole methodology adopted to validate its functioning. In addition, a scientific application is used as a use case in both experiments and simulations, where the close relationship between DVFS efficiency and hardware architecture is highlighted. A second use case using Cloud applications represented by DAGs, which is also a new functionality of CloudSim, demonstrates that the DVFS efficiency also depends on the intrinsic middleware behavior

Evolutionary approaches to signal decomposition in an application service management system

The increased demand for autonomous control in enterprise information systems has generated interest on efficient global search methods for multivariate datasets in order to search for original elements in time-series patterns, and build causal models of systems interactions, utilization dependencies, and performance characteristics. In this context, activity signals deconvolution is a necessary step to achieve effective adaptive control in Application Service Management. The paper investigates the potential of population-based metaheuristic algorithms, particularly variants of particle swarm, genetic algorithms and differential evolution methods, for activity signals deconvolution when the application performance model is unknown a priori. In our approach, the Application Service Management System is treated as a black- or grey-box, and the activity signals deconvolution is formulated as a search problem, decomposing time-series that outline relations between action signals and utilization-execution time of resources. Experiments are conducted using a queue-based computing system model as a test-bed under different load conditions and search configurations. Special attention was put on high-dimensional scenarios, testing effectiveness for large-scale multivariate data analyses that can obtain a near-optimal signal decomposition solution in a short time. The experimental results reveal benefits, qualities and drawbacks of the various metaheuristic strategies selected for a given signal deconvolution problem, and confirm the potential of evolutionary-type search to effectively explore the search space even in high-dimensional cases. The approach and the algorithms investigated can be useful in support of human administrators, or in enhancing the effectiveness of feature extraction schemes that feed decision blocks of autonomous controllers

Non-Intrusive Presence Detection and Position Tracking for Multiple People Using Low-Resolution Thermal Sensors

This paper presents a framework to accurately and non-intrusively detect the number of people in an environment and track their positions. Different from most of the previous studies, our system setup uses only ambient thermal sensors with low-resolution, using no multimedia resources or wearable sensors. This preserves user privacy in the environment, and requires no active participation by the users, causing no discomfort. We first develop multiple methods to estimate the number of people in the environment. Our experiments demonstrate that algorithm selection is very important, but with careful selection, we can obtain up to 100% accuracy when detecting user presence. In addition, we prove that sensor placement plays a crucial role in the system performance, where placing the sensor on the room ceiling yields to the best results. After accurately finding the number of people in the environment, we perform position tracking on the collected ambient data, which are thermal images of the space where there are multiple people. We consider position tracking as static activity detection, where the user’s position does not change while performing activities, such as sitting, standing, etc. We perform efficient pre-processing on the data, including normalization and resizing, and then feed the data into well-known machine learning methods. We tested the efficiency of our framework (including the hardware and software setup) by detecting four static activities. Our results show that we can achieved up to 97.5% accuracy when detecting these static activities, with up to 100% class-wise precision and recall rates. Our framework can be very beneficial to several applications such as health-care, surveillance, and home automation, without causing any discomfort or privacy issues for the users

Building an Energy-Efficient Ad-Hoc Network for Wildlife Observation

This study evaluated the design of an energy-efficient ad-hoc network used for wildlife observations, particularly in order to understand the social relationships in an animal group, where the distance between individuals, i.e., proximity, can be used to measure a relationship. Our proposed network consists of a full mesh topology and contains nodes that communicate via Bluetooth Low Energy (BLE) in advertisement mode. The initial hardware configuration and software algorithm duty cycles the BLE communication to on and off states using a parameter called the BLE active triggering interval. The algorithm is improved by placing the BLE subsystem and CPU in deep sleep when there are no BLE or CPU tasks to process. This improves the power performance by up to 94.48%. To scale up power optimization and track the trade-off between power and throughput, we created a simulator that modeled our network with dynamic wireless sensor nodes. The simulator verified the base case hardware results. It also showed a median power performance increase of 97.79% in comparison to the base case, yet throughput decreased by 66.65%. The highest power performance increased by 98.89% when a wireless sensor node was configured with a BLE active triggering interval of 50 s and its CPU was set to 14 MHz; however, the simulator showed a throughput drop of 79.97%. Depending on the application, a design may tolerate the decline in throughput to achieve higher power performance

Energy and Cost Efficient Data Centers

Data centers need efficient energy management mechanisms to reduce their consumption, energy costs and the resulting negative grid and environmental effects. Many of the state of the art mechanisms come with performance overhead, which may lead to service level agreement violations and reduce the quality of service. This thesis proposes novel methods that meet quality of service targets while decreasing energy costs and peak power of data centers. We leverage short term prediction of green energy as a part of our novel data center job scheduler to significantly increase the green energy efficiency and job throughput. We extend this analysis to distributed data centers connected with a backbone network. As a part of this work, we devise a green energy aware routing algorithm for the network, thus reducing its carbon footprint. Consumption during peak periods is an important issue for data centers due to its high cost. Peak shaving allows data centers to increase their computational capacity without exceeding a given power budget. We leverage battery-based solutions because they incur no performance overhead. We first show that when using an idealized battery model, peak shaving benefits can be overestimated by 3.35x. We then present a distributed control mechanism for a more realistic battery system that achieves 10x lower communication overhead than the centralized solution. We also demonstrate a new battery placement architecture that outperforms existing designs with better peak shaving and battery lifetime, and doubles the savings. Data centers are also good candidates for providing ancillary services in the power markets due to their large power consumption and flexibility. This thesis develops a framework that explores the feasibility of data center participation in these markets, focusing specifically on regulation services. We use a battery- based design to not only help by providing ancillary services, but to also limit peak power costs without any workload performance degradatio

Static and Dynamic Activity Detection with Ambient Sensors in Smart Spaces

Convergence of Machine Learning, Internet of Things, and computationally powerful single-board computers has boosted research and implementation of smart spaces. Smart spaces make predictions based on historical data to enhance user experience. In this paper, we present a low-cost, low-energy smart space implementation to detect static and dynamic human activities that require simple motions. We use low-resolution (4 × 16) and non-intrusive thermal sensors to collect data. We train six machine learning algorithms, namely logistic regression, naive Bayes, support vector machine, decision tree, random forest and artificial neural network (vanilla feed-forward) on the dataset collected in our lab. Our experiments reveal a very high static activity detection rate with all algorithms, where the feed-forward neural network method gives the best accuracy of 99.96%. We also show how data collection methods and sensor placement plays an important role in the resulting accuracy of different machine learning algorithms. To detect dynamic activities in real time, we use cross-correlation and connected components of thermal images. Our smart space implementation, with its real-time properties, can be used in various domains and applications, such as conference room automation, elderly health-care, etc