Xiaodao Chen

Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home

High quality demand side management has become indispensable in the smart grid infrastructure for enhanced energy reduction and system control. In this paper, a new demand side management technique, namely, a new energy efficient scheduling algorithm, is proposed to arrange the household appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. The proposed algorithm takes into account the uncertainties in household appliance operation time and intermittent renewable generation. Moreover, it considers the variable frequency drive and capacity-limited energy storage. Our technique first uses the linear programming to efficiently compute a deterministic scheduling solution without considering uncertainties. To handle the uncertainties in household appliance operation time and energy consumption, a stochastic scheduling technique, which involves an energy consumption adaptation variable , is used to model the stochastic energy consumption patterns for various household appliances. To handle the intermittent behavior of the energy generated from the renewable resources, the offline static operation schedule is adapted to the runtime dynamic scheduling considering variations in renewable energy. The simulation results demonstrate the effectiveness of our approach. Compared to a traditional scheduling scheme which models typical household appliance operations in the traditional home scenario, the proposed deterministic linear programming based scheduling scheme achieves up to 45% monetary expense reduction, and the proposed stochastic design scheme achieves up to 41% monetary expense reduction. Compared to a worst case design where an appliance is assumed to consume the maximum amount of energy, the proposed stochastic design which considers the stochastic energy consumption patterns achieves up to 24% monetary expense reduction without violating the target trip rate of 0.5%. Furthermore, the proposed energy consumption scheduling algorithm can always generate the scheduling solution within 10 seconds, which is fast enough for household appliance applications.

Parallel Processing of Dynamic Continuous Queries over Streaming Data Flows

More and more real-time applications need to handle dynamic continuous queries over streaming data of high density. Conventional data and query indexing approaches generally do not apply for excessive costs in either maintenance or space. Aiming at these problems, this study first proposes a new indexing structure by fusing an adaptive cell and KDB-tree, namely CKDB-tree. A cell-tree indexing approach has been developed on the basis of the CKDB-tree that supports dynamic continuous queries. The approach significantly reduces the space costs and scales well with the increasing data size. Towards providing a scalable solution to filtering massive steaming data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The CKDB-tree-based approach has been extended to operate on both the CPU (host) and the GPU (device). The GPGPU-aided approach performs query indexing on the host while perform streaming data filtering on the device in a massively parallel manner. The two heterogeneous tasks execute in parallel and the latency of streaming data transfer between the host and the device is hidden. The experimental results indicate that (1) CKDB-tree can reduce the space cost comparing to the cell-based indexing structure by 60 percent on average, (2) the approach upon the CKDB-tree outperforms the traditional counterparts upon the KDB-tree by 66, 75 and 79 percent in average for uniform, skewed and hyper-skewed data in terms of update costs, and (3) the GPGPU-aided approach greatly improves the approach upon the CKDB-tree with the support of only a single Kepler GPU, and it provides real-time filtering of streaming data with 2.5M data tuples per second. The massively parallel computing technology exhibits great potentials in streaming data monitoring.

Modeling and simulation for natural disaster contingency planning driven by high-resolution remote sensing images

Natural disasters occur unexpectedly and usually result in huge losses of life and property. How to effectively make contingency plans is an intriguing question constantly faced by governments and experts. Human rescue operations are the most critical issue in contingency planning. A natural disaster scenario is, in general, highly complicated and dynamic. Modeling and simulation technologies have been gaining considerable momentum in investigating natural disaster scenarios to enable contingency planning. However, existing MS and (2) the absence of methods and platforms to describe the collective behaviors of people in disaster situations. Considering these problems, an M&S framework for human rescue operations in a typical natural disaster, i.e., a landslide, has been developed in this study. The framework consists of three modules: (1) remote sensing information extraction, (2) landslide simulation, and (3)crowd simulation. The crowd simulation module is driven by the real/virtual data provided by the former modules. A number of simulations (using the Zhouqu landslide as an example) have been performed to study human relief operations spontaneously and under manipulation, with the effect of contingency plans highlighted. The experimental results demonstrate that (1) the simulation framework is an effective tool for contingency planning, and (2) real data can make the simulation outputs more meaningful. We enable evaluation of contingency plans using modelling and simulation technology.We model crowd behaviors under natural disasters.We develop a DDDAS simulation framework with the support of high resolution remote sensing information.

Reliability-Driven Energy-Efficient Task Scheduling for Multiprocessor Real-Time Systems

This paper proposes a reliability-driven task scheduling scheme for multiprocessor real-time embedded systems that optimizes system energy consumption under stochastic fault occurrences. The task scheduling problem is formulated as an integer linear program where a novel fault adaptation variable is introduced to model the uncertainties of fault occurrences. The proposed scheme, which considers both the dynamic power and the leakage power, is able to handle the scheduling of independent tasks and tasks with precedence constraints, and is capable of scheduling tasks with varying deadlines. Experimental results have demonstrated that the proposed reliability-driven parallel scheduling scheme achieves energy savings of more than 15% when compared to the approach of designing for the corner case of fault occurrences.

A Dynamic Programming Algorithm for Leveraging Probabilistic Detection of Energy Theft in Smart Home

In the modern smart home and community, smart meters have been massively deployed for the replacement of traditional analog meters. Although it significantly reduces the cost of data collection as the meter readings are wireless transmitted, a smart meter is not tamper-resistant. As a consequence, the smart grid infrastructure is under threat of energy theft, by means of attacking a smart meter so that it undercounts the electricity usage. Deployment of feeder remote terminal unit (FRTU) helps narrow the search zone of energy theft in smart home and community. However, due to budgetary limit, utility companies can only afford to insert the minimum number of FRTUs. This imposes a signifcant challenge to deploy the minimum number of FRTUs while each smart meter is still effectively monitored. To the best of our knowledge, the only work addressing this problem is [1], which uses stochastic optimization methods. Their algorithm is not very practical as it cannot handle large distribution networks because of the scalability issue. Due to the inherent heuristic and non-deterministic nature, there is no guarantee on the solution quality as well. Thus, the high performance energy theft detection is still needed for this energy theft problem. In order to resolve this challenge, we propose a new dynamic programming algorithm that inserts the minimum number of FRTUs satisfying the detection rate constraint. It evaluates every candidate solution in a bottom-up fashion using an innovative pruning technique. As a deterministic polynomial time algorithm, it is able to handle large distribution networks. In contrast to [1] which can only handle small system, our technique can perform FRTU insertion for a large scale power system. Our experimental results demonstrate that the average number of FRTUs required is only 26% of the number of smart meters in the community. Compared with the previous work, the number of FRTUs is reduced by 18.8% while the solution quality in terms of anomaly coverage index metric is still improved.

Design Automation for Interwell Connectivity Estimation in Petroleum Cyber-Physical Systems

In a petroleum cyber-physical system (CPS), interwell connectivity estimation is critical for improving petroleum production. An accurately estimated connectivity topology facilitates reduction in the production cost and improvement in the waterflood management. This paper presents the first study focused on computer-aided design for a petroleum CPS. A new CPS framework is developed to estimate the petroleum well connectivities. Such a framework explores an innovative water/oil index integrated with the advanced cross-entropy optimization. It is applied to a real industrial petroleum field with massive petroleum CPS data. The experimental results demonstrate that our automated estimations well match the expensive tracer-based true observations. This demonstrates that our framework is highly promising.

An Interconnect Reliability-Driven Routing Technique for Electromigration Failure Avoidance

As VLSI technology enters the nanoscale regime, design reliability is becoming increasingly important. A major design reliability concern arises from electromigration which refers to the transport of material caused by ion movement in interconnects. Since the lifetime of an interconnect drastically depends on the current flowing through it, the electromigration problem aggravates with increasingly growing thinner wires. Further, the current-density-induced interconnect thermal issue becomes much more severe with larger current. To mitigate the electromigration and the current-density-induced thermal effects, interconnect current density needs to be reduced. Assigning wires to thick metals increases wire volume, and thus, reduces the current density. However, overstretching thick-metal assignment may hurt routability. Thus, it is highly desirable to minimize the thick-metal usage, or total wire cost, subject to the reliability constraint. In this paper, the minimum cost reliability-driven routing, which consists of Steiner tree construction and layer assignment, is considered. The problem is proven to be NP-hard and a highly effective iterative rounding-based integer linear programming algorithm is proposed. In addition, a unified routing technique is proposed to directly handle multiple current levels, which is critical in analog VLSI design. Further, the new algorithm is extended to handle blockage. Our experiments on 450 nets demonstrate that the new algorithm significantly outperforms the state-of-the-art work [CHECK END OF SENTENCE] with up to 14.7 percent wire reduction. In addition, the new algorithm can save 11.4 percent wires over a heuristic algorithm for handling multiple currents.

Hierarchical Cross-Entropy Optimization for Fast On-Chip Decap Budgeting

Decoupling capacitor (decap) has been widely used to effectively reduce dynamic power supply noise. Traditional decap budgeting algorithms usually explore the sensitivity-based nonlinear optimizations or conjugate gradient (CG) methods, which can be prohibitively expensive for large-scale decap budgeting problems and cannot be easily parallelized. In this paper, we propose a hierarchical cross-entropy based optimization technique which is more efficient and parallel-friendly. Cross-entropy (CE) is an advanced optimization framework which explores the power of rare event probability theory and importance sampling. To achieve the high efficiency, a sensitivity-guided cross-entropy (SCE) algorithm is introduced which integrates CE with a partitioning-based sampling strategy to effectively reduce the solution space in solving the large-scale decap budgeting problems. Compared to improved CG method and conventional CE method, SCE with Latin hypercube sampling method (SCE-LHS) can provide 2× speedups, while achieving up to 25% improvement on power supply noise. To further improve decap optimization solution quality, SCE with sequential importance sampling (SCE-SIS) method is also studied and implemented. Compared to SCE-LHS, in similar runtime, SCE-SIS can lead to 16.8% further reduction on the total power supply noise.

Thermal-aware correlated two-level scheduling of real-time tasks with reduced processor energy on heterogeneous MPSoCs

Abstract With the exponential increase in power density and the relentless scaling of transistors in VLSI circuits over the past decades, modern high-performance processors fall into a predicament of high energy consumption and elevated chip temperature. Such increased energy consumption and chip temperature could induce significant economic, ecological, and technical problems. Thus, energy-efficient task scheduling with thermal consideration has become a pressing research issue in sustainable computing systems, especially for battery-powered real-time embedded systems with limited cooling techniques. This paper tackles the above challenge through scheduling tasks leveraging correlated optimizations at two different scales. Precisely, a two-level thermal-aware energy-efficient scheduling algorithm for real-time tasks on DVFS-enabled heterogeneous MPSoC systems is developed considering the constraints of task deadlines, task precedences, and chip peak temperature limit. At the processor level, a multi-processor model supporting dynamic voltage/frequency scaling is transformed to a virtual multi-processor model supporting only one fixed frequency level. At the core level, real-time tasks are assigned to individual cores of the virtual processor under the constraints of task precedence and peak temperature limit. Through nicely interleaving optimizations at both levels, high quality task scheduling solutions can be computed efficiently. Extensive simulations of synthetic real-time tasks and real-life benchmarks are performed to validate the proposed algorithm. Experimental results demonstrate the effectiveness of the proposed algorithm as compared to the benchmarking schemes.

ADAPTIVE FAULT-TOLERANT TASK SCHEDULING FOR REAL-TIME ENERGY HARVESTING SYSTEMS

Fault tolerance and energy have become important design issues in multiprocessor system-on-chips (SoCs) with the technology scaling and the proliferation of battery-powered multiprocessor SoCs. This paper proposed an energy-efficient fault tolerance task allocation scheme for multiprocessor SoCs in real-time energy harvesting systems. The proposed fault-tolerance scheme is based on the principle of the primiary/backup task scheduling, and can tolerate at most one single transient fault. Extensive simulated experiment shows that the proposed scheme can save up to 30% energy consumption and reduce the miss ratio to about 8% in the presence of faults.