Online peak-aware energy scheduling with untrusted advice

2021 ◽  
Vol 1 (1) ◽  
pp. 59-77
Author(s):  
Russell Lee ◽  
Jessica Maghakian ◽  
Mohammad Hajiesmaili ◽  
Jian Li ◽  
Ramesh Sitaraman ◽  
...  
Keyword(s):  
Competitive Ratio ◽  
Energy Demand ◽  
Large Scale ◽  
Performance Metrics ◽  
Randomized Algorithm ◽  
Deterministic Algorithm ◽  
Pareto Optimal ◽  
Energy Prices ◽  
Worst Case ◽  
Cost Of Energy

This paper studies the online energy scheduling problem in a hybrid model where the cost of energy is proportional to both the volume and peak usage, and where energy can be either locally generated or drawn from the grid. Inspired by recent advances in online algorithms with Machine Learned (ML) advice, we develop parameterized deterministic and randomized algorithms for this problem such that the level of reliance on the advice can be adjusted by a trust parameter. We then analyze the performance of the proposed algorithms using two performance metrics: robustness that measures the competitive ratio as a function of the trust parameter when the advice is inaccurate, and consistency for competitive ratio when the advice is accurate. Since the competitive ratio is analyzed in two different regimes, we further investigate the Pareto optimality of the proposed algorithms. Our results show that the proposed deterministic algorithm is Pareto-optimal, in the sense that no other online deterministic algorithms can dominate the robustness and consistency of our algorithm. Furthermore, we show that the proposed randomized algorithm dominates the Pareto-optimal deterministic algorithm. Our large-scale empirical evaluations using real traces of energy demand, energy prices, and renewable energy generations highlight that the proposed algorithms outperform worst-case optimized algorithms and fully data-driven algorithms.

Online Job Dispatching and Scheduling to Minimize Job Completion Time and to Meet Deadlines

2018 ◽  
Vol 18 (04) ◽  
pp. 1850012
Author(s):  
YUPENG LI
Keyword(s):  
Competitive Ratio ◽  
Completion Time ◽  
Online Algorithm ◽  
Performance Metrics ◽  
Case Analysis ◽  
Deterministic Algorithm ◽  
Worst Case Analysis ◽  
Worst Case ◽  
Job Dispatching ◽  
Shed Light

In this paper, we study the problem of job dispatching and scheduling, where each job consists of a set of tasks. Each task is processed by a set of machines simultaneously. We consider two important performance metrics, the average job completion time (JCT), and the number of deadline-aware jobs that meet their deadlines. The goal is to minimize the former and maximize the latter. We first propose OneJ to minimize the job completion time (JCT) when there is exactly one single job in the system. Then, we propose an online algorithm called MultiJ, taking OneJ as a subroutine, to minimize the average JCT, and prove it has a good competitive ratio. We then derive another online algorithm QuickJ to maximize the number of jobs that can meet their deadlines. We show that QuickJ is competitive via a worst case analysis. We also conjecture that the competitive ratio of QuickJ is likely to be the best one that any deterministic algorithm can achieve. We also shed light on several important merits of MultiJ and QuickJ, such as no severe coordination overhead, scalability, work conservation, and no job starvation.

ON THE LOWER BOUND OF THE COMPETITIVE RATIO FOR THE WEIGHTED ONLINE ROOMMATES PROBLEM

2010 ◽  
Vol 02 (02) ◽  
pp. 257-262
Author(s):  
SATYAJIT BANERJEE
Keyword(s):  
Lower Bound ◽  
Competitive Ratio ◽  
Deterministic Algorithm ◽  
Weighted Version ◽  
Worst Case ◽  
Competitive Algorithm

We show that the best possible worst case competitive ratio of any deterministic algorithm for weighted online roommates problem is arbitrarily close to 4. This proves that the 4-competitive algorithm proposed by Bernstein and Rajagopalan [3] for the weighted version of the online roommates problem actually attains the best possible competitive ratio.

The power of locality: Exploring the limits of randomness in distributed computing

2020 ◽  
Vol 62 (5-6) ◽  
pp. 271-278
Author(s):  
Yannic Maus
Keyword(s):  
Large Scale ◽  
Edge Coloring ◽  
Randomized Algorithm ◽  
Deterministic Algorithm ◽  
Vertex Coloring ◽  
Linear Programs ◽  
Theoretic Approach ◽  
Coloring Problem ◽  
Complete Problems ◽  
Vertex Coloring Problem

AbstractMany modern systems are built on top of large-scale networks like the Internet. This article provides an overview of a dissertation [29] that addresses the complexity of classic graph problems like the vertex coloring problem in such networks. It has been known for a long time that randomization helps significantly in solving many of these problems, whereas the best known deterministic algorithms have been exponentially slower. In the first part of the dissertation we use a complexity theoretic approach to show that several problems are complete in the following sense: An efficient deterministic algorithm for any complete problem would imply an efficient algorithm for all problems that can be solved efficiently with a randomized algorithm. Among the complete problems is a rudimentary looking graph coloring problem that can be solved by a randomized algorithm without any communication. In further parts of the dissertation we develop efficient distributed algorithms for several problems where the most important problems are distributed versions of integer linear programs, the vertex coloring problem and the edge coloring problem. We also prove a lower bound on the runtime of any deterministic algorithm that solves the vertex coloring problem in a weak variant of the standard model of the area.

scholarly journals Green Hydrogen Value Chain in the Sustainability for Port Operations: Case Study in the Region of Valparaiso, Chile

2021 ◽  
Vol 13 (24) ◽  
pp. 13681
Author(s):  
Yunesky Masip Macía ◽  
Pablo Rodríguez Machuca ◽  
Angel Alexander Rodríguez Soto ◽  
Roberto Carmona Campos
Keyword(s):  
Fuel Cell ◽  
Value Chain ◽  
Energy Demand ◽  
Large Scale ◽  
Electrical Energy ◽  
Diesel Generator ◽  
Cost Of Energy ◽  
Main Components ◽  
The Cost ◽  
Port Facility

The paper presents a complete value chain for the use of green hydrogen in a port facility. The main objective was to propose the sizing of the main components that make up green hydrogen to ensure the supply of 1 MWe in replacing the diesel generator. The energy demand required for the port was determined by establishing the leading small and large-scale conventional energy-consuming equipment. Hence, 60 kgH2 was required to ensure the power supply. The total electrical energy to produce all the hydrogen was generated from photovoltaic solar energy, considering three-generation scenarios (minimum, maximum and the annual average). In all cases, the energy supply in the electrolyzer was 3.08 MWe. In addition, the effect of generating in the port facility using a diesel generator and a fuel cell was compared. The cost of 1 kgH2 could be 4.09 times higher than the cost of 1 L of diesel, meaning that the output kWh of each system is economically similar. In addition, the value of electrical energy through a Power Purchase Agreement (PPA) was a maximum of 79.79 times the value of a liter of diesel. Finally, the Levelized Cost of Energy (LCOE) was calculated for two conditions in which the MWe was obtained from the fuel cell without and with the photovoltaic solar plant.

A Novel Static Path Planning Method for Mobile Anchor-Assisted Localization in Wireless Sensor Networks

2020 ◽  
Vol 10 ◽  
Author(s):  
Abdelhady M. Naguib ◽  
Shahzad Ali
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Path Planning ◽  
Sensor Node ◽  
Large Scale ◽  
Performance Metrics ◽  
Wireless Sensor ◽  
Anchor Node ◽  
Mobile Anchor ◽  
Planning Methods

Background: Many applications of Wireless Sensor Networks (WSNs) require awareness of sensor node’s location but not every sensor node can be equipped with a GPS receiver for localization, due to cost and energy constraints especially for large-scale networks. For localization, many algorithms have been proposed to enable a sensor node to be able to determine its location by utilizing a small number of special nodes called anchors that are equipped with GPS receivers. In recent years a promising method that significantly reduces the cost is to replace the set of statically deployed GPS anchors with one mobile anchor node equipped with a GPS unit that moves to cover the entire network. Objectives: This paper proposes a novel static path planning mechanism that enables a single anchor node to follow a predefined static path while periodically broadcasting its current location coordinates to the nearby sensors. This new path type is called SQUARE_SPIRAL and it is specifically designed to reduce the collinearity during localization. Results: Simulation results show that the performance of SQUARE_SPIRAL mechanism is better than other static path planning methods with respect to multiple performance metrics. Conclusion: This work includes an extensive comparative study of the existing static path planning methods then presents a comparison of the proposed mechanism with existing solutions by doing extensive simulations in NS-2.

Approximation Algorithms for Submodular Data Summarization with a Knapsack Constraint

2021 ◽  
Vol 5 (1) ◽  
pp. 1-31
Author(s):  
Kai Han ◽  
Shuang Cui ◽  
Tianshuai Zhu ◽  
Enpei Zhang ◽  
Benwei Wu ◽  
...  
Keyword(s):  
Approximation Algorithms ◽  
Fundamental Problem ◽  
Randomized Algorithm ◽  
Deterministic Algorithm ◽  
Submodular Function ◽  
Approximation Ratio ◽  
Performance Bounds ◽  
Data Summarization ◽  
Submodular Optimization ◽  
Knapsack Constraint

Data summarization, i.e., selecting representative subsets of manageable size out of massive data, is often modeled as a submodular optimization problem. Although there exist extensive algorithms for submodular optimization, many of them incur large computational overheads and hence are not suitable for mining big data. In this work, we consider the fundamental problem of (non-monotone) submodular function maximization with a knapsack constraint, and propose simple yet effective and efficient algorithms for it. Specifically, we propose a deterministic algorithm with approximation ratio 6 and a randomized algorithm with approximation ratio 4, and show that both of them can be accelerated to achieve nearly linear running time at the cost of weakening the approximation ratio by an additive factor of ε. We then consider a more restrictive setting without full access to the whole dataset, and propose streaming algorithms with approximation ratios of 8+ε and 6+ε that make one pass and two passes over the data stream, respectively. As a by-product, we also propose a two-pass streaming algorithm with an approximation ratio of 2+ε when the considered submodular function is monotone. To the best of our knowledge, our algorithms achieve the best performance bounds compared to the state-of-the-art approximation algorithms with efficient implementation for the same problem. Finally, we evaluate our algorithms in two concrete submodular data summarization applications for revenue maximization in social networks and image summarization, and the empirical results show that our algorithms outperform the existing ones in terms of both effectiveness and efficiency.

scholarly journals Wireless Sensor Networks for Smart Cities: Network Design, Implementation and Performance Evaluation

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 218
Author(s):  
Ala’ Khalifeh ◽  
Khalid A. Darabkh ◽  
Ahmad M. Khasawneh ◽  
Issa Alqaisieh ◽  
Mohammad Salameh ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Experimental Evaluation ◽  
Large Scale ◽  
Performance Metrics ◽  
Smart Cities ◽  
Field Tests ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Real Field

The advent of various wireless technologies has paved the way for the realization of new infrastructures and applications for smart cities. Wireless Sensor Networks (WSNs) are one of the most important among these technologies. WSNs are widely used in various applications in our daily lives. Due to their cost effectiveness and rapid deployment, WSNs can be used for securing smart cities by providing remote monitoring and sensing for many critical scenarios including hostile environments, battlefields, or areas subject to natural disasters such as earthquakes, volcano eruptions, and floods or to large-scale accidents such as nuclear plants explosions or chemical plumes. The purpose of this paper is to propose a new framework where WSNs are adopted for remote sensing and monitoring in smart city applications. We propose using Unmanned Aerial Vehicles to act as a data mule to offload the sensor nodes and transfer the monitoring data securely to the remote control center for further analysis and decision making. Furthermore, the paper provides insight about implementation challenges in the realization of the proposed framework. In addition, the paper provides an experimental evaluation of the proposed design in outdoor environments, in the presence of different types of obstacles, common to typical outdoor fields. The experimental evaluation revealed several inconsistencies between the performance metrics advertised in the hardware-specific data-sheets. In particular, we found mismatches between the advertised coverage distance and signal strength with our experimental measurements. Therefore, it is crucial that network designers and developers conduct field tests and device performance assessment before designing and implementing the WSN for application in a real field setting.

scholarly journals Saving energy in residential buildings: the role of energy pricing

2021 ◽  
Vol 167 (1-2) ◽  
Author(s):  
Jens Ewald ◽  
Thomas Sterner ◽  
Eoin Ó Broin ◽  
Érika Mata
Keyword(s):  
Energy Demand ◽  
Income Elasticity ◽  
Residential Buildings ◽  
Policy Framework ◽  
Energy Prices ◽  
Residential Energy ◽  
Residential Energy Demand ◽  
Long Run ◽  
Zero Carbon

AbstractA zero-carbon society requires dramatic change everywhere including in buildings, a large and politically sensitive sector. Technical possibilities exist but implementation is slow. Policies include many hard-to-evaluate regulations and may suffer from rebound mechanisms. We use dynamic econometric analysis of European macro data for the period 1990–2018 to systematically examine the importance of changes in energy prices and income on residential energy demand. We find a long-run price elasticity of −0.5. The total long-run income elasticity is around 0.9, but if we control for the increase in income that goes towards larger homes and other factors, the income elasticity is 0.2. These findings have practical implications for climate policy and the EU buildings and energy policy framework.

scholarly journals Reducing environmental impacts from garments through best practice garment use and care, using the example of a Merino wool sweater

2021 ◽  
Author(s):  
Stephen G. Wiedemann ◽  
Leo Biggs ◽  
Quan V. Nguyen ◽  
Simon J. Clarke ◽  
Kirsi Laitala ◽  
...  
Keyword(s):  
Environmental Impacts ◽  
Energy Demand ◽  
Best Practice ◽  
Western Europe ◽  
Impact Category ◽  
Fashion Industry ◽  
Worst Case ◽  
Multiple Users ◽  
Washing Machines ◽  
The Impact

Abstract Purpose Garment production and use generate substantial environmental impacts, and the care and use are key determinants of cradle-to-grave impacts. The present study investigated the potential to reduce environmental impacts by applying best practices for garment care combined with increased garment use. A wool sweater is used as an example because wool garments have particular attributes that favour reduced environmental impacts in the use phase. Methods A cradle-to-grave life cycle assessment (LCA) was used to compare six plausible best and worst-case practice scenarios for use and care of a wool sweater, relative to current practices. These focussed on options available to consumers to reduce impacts, including reduced washing frequency, use of more efficient washing machines, reduced use of machine clothing dryers, garment reuse by multiple users, and increasing number of garment wears before disposal. A sixth scenario combined all options. Worst practices took the worst plausible alternative for each option investigated. Impacts were reported per wear in Western Europe for climate change, fossil energy demand, water stress and freshwater consumption. Results and discussion Washing less frequently reduced impacts by between 4 and 20%, while using more efficient washing machines at capacity reduced impacts by 1 to 6%, depending on the impact category. Reduced use of machine dryer reduced impacts by < 5% across all indicators. Reusing garments by multiple users increased life span and reduced impacts by 25–28% across all indicators. Increasing wears from 109 to 400 per garment lifespan had the largest effect, decreasing impacts by 60% to 68% depending on the impact category. Best practice care, where garment use was maximised and care practices focussed on the minimum practical requirements, resulted in a ~ 75% reduction in impacts across all indicators. Unsurprisingly, worst-case scenarios increased impacts dramatically: using the garment once before disposal increased GHG impacts over 100 times. Conclusions Wool sweaters have potential for long life and low environmental impact in use, but there are substantial differences between the best, current and worst-case scenarios. Detailed information about garment care and lifespans is needed to understand and reduce environmental impacts. Opportunities exist for consumers to rapidly and dramatically reduce these impacts. The fashion industry can facilitate this through garment design and marketing that promotes and enables long wear life and minimal care.

scholarly journals Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture

2021 ◽  
Vol 11 (8) ◽  
pp. 3623
Author(s):  
Omar Said ◽  
Amr Tolba
Keyword(s):  
Packet Loss ◽  
Large Scale ◽  
Performance Metrics ◽  
Medical System ◽  
Healthcare Data ◽  
High Altitude Platforms ◽  
Wide Range ◽  
Simulation Results ◽  
Healthcare Architecture ◽  
The Internet Of Things

Employment of the Internet of Things (IoT) technology in the healthcare field can contribute to recruiting heterogeneous medical devices and creating smart cooperation between them. This cooperation leads to an increase in the efficiency of the entire medical system, thus accelerating the diagnosis and curing of patients, in general, and rescuing critical cases in particular. In this paper, a large-scale IoT-enabled healthcare architecture is proposed. To achieve a wide range of communication between healthcare devices, not only are Internet coverage tools utilized but also satellites and high-altitude platforms (HAPs). In addition, the clustering idea is applied in the proposed architecture to facilitate its management. Moreover, healthcare data are prioritized into several levels of importance. Finally, NS3 is used to measure the performance of the proposed IoT-enabled healthcare architecture. The performance metrics are delay, energy consumption, packet loss, coverage tool usage, throughput, percentage of served users, and percentage of each exchanged data type. The simulation results demonstrate that the proposed IoT-enabled healthcare architecture outperforms the traditional healthcare architecture.

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