Energy Optimization Algorithm Research Articles

Time and Energy Optimization Algorithms for the Static Scheduling of Multiple Workflows in Heterogeneous Computing System

The heterogeneous computing system (HCS), which is used to deal with complex and enormous business or scientific workflows, is playing a very important role as cloud computing rapidly develops. For multiple workflows computing in HCS, one of challenging issues is how to make a reasonable tradeoff between the schedule length and energy consumption. In this paper, we focus on a workflow that can be represented by a directed acyclic graph (DAG). We propose the corresponding algorithms which cooperate with dynamic voltage and frequency scaling (DVFS) technique to address the aforementioned concern and evaluate the algorithms in terms of randomly generated DAGs, real application DAGs and their hybrids under DVFS-enabled HCS. From the experimental results, we draw the conclusion that interleaving workflows lead to a better average tradeoff when scheduling multiple workflows in HCS.

Critical Features of Fragment Libraries for Protein Structure Prediction.

The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction.

A hybrid Big Bang-Big Crunch algorithm for energy optimization on heterogeneous distributed system

A hybrid algorithm is presented for global optimization of the energy consumption rather than makespan of the system. The cost profile is optimized by extending the execution time of the tasks on dynamic voltage scalable processing elements in embedded environment with meeting the execution constraints. The modified Big Bang Big Crunch (BBBC) method improves the scheduling efficiency of the tasks by simulating one of the theories of the evolution of the universe. BBBC algorithm generates disorder data points inspired by energy dissipation procedure of the Big Bang phase, and moves those data points to a single representative data point inspired by a center of mass cost approach in the Big Crunch phase. The Logistic and Sinusoidal chaotic maps are investigated and utilized to improve the movement step of the BBBC algorithm. The proposed hybrid algorithm is tested on several benchmark data sets and its performance is compared with those of Ant Colony Optimization and HEFT strategies. The simulation experiment shows that the presented evolutionary optimization algorithm is robust and suitable for energy saving.

Structure relaxation via long trajectories made stable.

Molecular dynamics (MD) is appearing in increasing applications in materials science, nanotechnologies, condensed matter physics, computational physics, biochemistry, and biophysics. Finding mechanically static equilibrium configurations of molecular systems is one of the most practical tasks in MD. Most existing potential energy optimization algorithms do not permit searching equilibrium configurations through longer MD trajectories. We introduce a simple method of utilizing a microcanonical (NVE) ensemble to obtain static equilibriums of molecular systems, that is significantly faster than the standard implementations of quick-min (QM) and fast inertial relaxation engine (FIRE) optimization algorithms. The new method is based on the capability of NVE to convert potential energy to kinetic energy. The surprising efficiency of the method is illustrated using an indentation test on monolayer graphene and, in particular, the versatility of the method is illustrated using relaxation of a polystyrene chain through longer MD trajectories and large deformation. The capability of the new method in finding more stable equilibrium configurations than common optimization algorithms is demonstrated in relaxation of a pressured lubricating oil layer and a warped monolayer graphene cantilever.

Energy-optimal algorithms for computing aggregative functions in random networks

We investigate a family of algorithms minimizing energetic effort in random networks computing aggregative functions. In contrast to previously considered models, our results minimize maximal energetic effort over all stations instead of the average usage of energy. Such approach seems to be much more suitable for some kinds of networks, in particular ad hoc radio networks, wherein we need all stations functioning and replacing batteries after the deployment is not feasible. We analyze also the latency of proposed energy-optimal algorithms. We model a network by placing randomly and independently $n$ points in a $d$-dimensional cube of side-length $n^{1/d}$. We place an edge between vertices that interact with each other. We analyze properties of the resulting graphs in order to obtain estimates on energetic effort and latency of proposed algorithms.

Sunlight to hydrogen conversion: Design optimization and energy management of concentrated photovoltaic (CPV-Hydrogen) system using micro genetic algorithm

Owing to the intermittent solar irradiance from cloud cover in the diurnal period and unavailability at night time, the practical design of a solar system requires energy backup storage for an uninterrupted supply or for off-grid operation. However, for highly efficient CPV (concentrated photovoltaic) system, the literature is lacking for energy management and optimization algorithm and tool for standalone operation. In this paper, a system with CPV and electrolyser is presented where beam irradiance of sunlight is harnessed to convert the instantaneously generated electricity into useful Hydrogen/Oxygen gas, where they can be stored and re-used for downstream applications such as the fuel cells, etc. The multi-variable design and multi-objective optimization strategies are proposed and presented for a standalone operation of the CPV-Hydrogen system as well as their system performances, particularly electrical rating of CPV based upon the real weather data of Singapore.

Energy Consumption Optimization for Mobile Robots Motion Using Predictive Control

Operation of mobile robots in off-road environment requires the attention to the torque saturation problem that occurs in the wheels DC motors while climbing hills. In the present work, off-road conditions are utilized to benefit while avoiding torque saturation. Energy optimization algorithm using predictive control is implemented on a two-DC motor-driven wheels mobile robot while crossing a ditch. The predictive control algorithm is simulated and compared with the PID control and the open-loop control. Predictive control showed more capability to avoid torque saturation and noticeable reduction in the energy consumption. Furthermore, using the wheels motors armature current instead of the supply voltage as control variable in the predictive control showed more efficient speed control. Simulation results showed that in case of known ditch dimensions ahead of time, the developed algorithm is feasible. Experimental examination of the developed energy optimization algorithm is presented. The experimental results showed a good agreement with the simulation results. The effects of the road slope and the prediction horizon length on the consumed energy are evaluated. The analytical study showed that the energy consumption is reduced by increasing the prediction horizon until it reaches a limit at which no more energy reduction is obtained. This limit is proportional to the width of the ditch in front of the mobile robot. Curve fitting is applied to the obtained results to address further the effect of the parameters on the energy consumption.

Energy Efficient and Optimal Cloud Storage Algorithms for Temporal Query Processing in Cloud Databases

The storage and retrieval of time dependent data in cloud storage provides challenges with respect to energy, processing time and security. In order to provide effective query performance and security, it is necessary to propose new algorithms for optimal and secure storage and retrieval algorithms for providing optimal performance in cloud databases. In addition, the temporal nature of cloud data necessitates the use of rules to satisfy temporal constraints for providing optimal performance in database services. In this paper, we propose new temporal storage and query processing algorithms for effective storage of temporal data in the cloud using Temporal-B-tree which is a new data structure proposed in this paper which effectively checks the temporal constraints. The proposed algorithm performs optimization by reducing Input Output operations, speed, time, energy and at the same time enhances the security. The main advantage of these proposed algorithms for storage and retrieval are that they reduce the retrieval time and also the storage cost. The experimental results which are obtained from this work show that these two new algorithms provide storage efficiency, retrieval efficiency, energy efficiency and increased security.

An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network.

Wireless Sensor Network (WSN) is a network which formed with a maximum number of sensor nodes which are positioned in an application environment to monitor the physical entities in a target area, for example, temperature monitoring environment, water level, monitoring pressure, and health care, and various military applications. Mostly sensor nodes are equipped with self-supported battery power through which they can perform adequate operations and communication among neighboring nodes. Maximizing the lifetime of the Wireless Sensor networks, energy conservation measures are essential for improving the performance of WSNs. This paper proposes an Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for Wireless Sensor Network in which clustering and clustering head selection are done by using Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in WSN. The performance metrics are evaluated and results are compared with competitive clustering algorithm to validate the reduction in energy consumption.

Low complexity energy optimization algorithm for massive MIMO systems

We consider the problem of nearly optimal energy efficiency in massive (Multiinput Multi-output) MIMO systems. Considering the correlated channel in practice, we derive the ergodic expression with zero-forcing precoding and analyze the simplified antennas selection method. Aiming at optimizing the energy efficiency, the closed form expressions of the nearly optimal number of transmit antennas and transmit power are given under the circuit consumption model. The joint solution of the number of transmit antennas and transmit power was replaced to only solve transmit power. Based on the expression only related with transmit power, we give an energy efficiency optimization algorithm. The simulation results show that the proposed algorithm can achieve nearly optimal energy efficiency with fast convergence speed.

Power Consumption Optimization of a Building Using Multiobjective Particle Swarm Optimization

This paper discusses the development of a building energy optimization algorithm by using multiobjective Particle Swarm Optimization for a building. Particle Swarm Optimization is a well known algorithm that is proven to be effective in many complex optimization problems. Multiobjective PSO is developed by utilizing non-dominated sorting algorithm in tandem with majority-based selection algorithm. The optimizer is written by using MATLAB alongside its GUI interface. Results are then analyzed by using the Binh and Korn benchmark test and natural distance performance metrics. From the results, the optimizer is capable to minimize up to 42 percent of energy consumption and lowering the electricity bills up to 43 percent, while still maintaining comfort at more than 95 percent as well. With this, building owner can save energy with a low-cost and simple solution.

Research on Resource Allocation Algorithm Based on Energy Optimization and Immune Algorithm in Cloud Computing Environment

This paper will explore the key factors affecting the resource allocation performance; reveal the essential relati- onship among CPU dynamic power consumption, resources computing capacity, resources supply voltage strategy, the amount of resources, supply voltage, and the frequency of CPU; and solve the huge energy consumption problem and the poor performance of load balancing in the process of resource allocation. The issues include: design and analysis of the key technology of resource allocation in cloud computing environment; combined with time span, load balancing and energy optimization, design of multi-objective optimization model for resource allocation and heuristic resource alloca- tion algorithm. This research has important significance for further study on resource allocation and energy optimization in cloud computing environment.

Crystal Energy Optimization Algorithm

Nature has always been a muse for those who dream in art or science. As it goes, optimization algorithms inspired by nature have been widely used to solve various scientific and engineering problems because of their intelligence and simplicity. As a novel nature‐inspired algorithm, the crystal energy optimizer (CEO) is proposed in this article. The proposed CEO is motivated by the following general observation on lake freezing in nature: the dynamics of crystals have possession of parallelism, openness, local interactivity, and self‐organization. It stimulates us to extend a crystal dynamic model in physics to a generalized crystal energy optimizer for traveling salesman problems, so as to exploit the advantages of crystal dynamic system and to realize the aforementioned purposes. The proposed CEO has these advantages: (1) it has the ability to perform large‐scale distributed parallel optimization; (2) it can converge and avoid local optimum; and (3) it is flexible and easy to adapt to a wide range of optimization problems.

Optimizing the NoC Slack Through Voltage and Frequency Scaling in Hard Real-Time Embedded Systems

Hard real-time embedded systems impose a strict latency requirement on interconnection subsystems. In the case of network-on-chip (NoC), this means each packet of a traffic stream has to be delivered within a time interval. In addition, with the increasing complexity of NoC, it consumes a significant portion of total chip power, which boosts the power footprint of such chips. In this paper, we propose a methodology to minimize the energy consumption of NoC without violating the prespecified latency deadlines of real-time applications. First, we develop a formal approach based on network calculus to obtain the worst-case delay bound of all packets, from which we derive a safe estimate of the number of cycles that a packet can be further delayed in the network without violating its deadline—the worst-case slack. With this information, we then develop an optimization algorithm that trades the slacks for lower NoC energy. Our algorithm recognizes the distribution of slacks for different traffic streams, and assigns different voltages and frequencies to different routers to achieve NoC energy-efficiency, while meeting the deadlines for all packets. Furthermore, we design a feedback-control strategy to enable dynamic frequency and voltage scaling on the network routers in conjunction with the energy optimization algorithm. It can flexibly improve the energy-efficiency of the overall network in response to sporadic traffic patterns at runtime.

A hybrid dynamic programming-rule based algorithm for real-time energy optimization of plug-in hybrid electric bus

The optimization of the control strategy of a plug-in hybrid electric bus (PHEB) for the repeatedly driven bus route is a key technique to improve the fuel economy. The widely used rule-based (RB) control strategy is lacking in the global optimization property, while the global optimization algorithms have an unacceptable computation complexity for real-time application. Therefore, a novel hybrid dynamic programming-rule based (DPRB) algorithm is brought forward to solve the global energy optimization problem in a real-time controller of PHEB. Firstly, a control grid is built up for a given typical city bus route, according to the station locations and discrete levels of battery state of charge (SOC). Moreover, the decision variables for the energy optimization at each point of the control grid might be deduced from an off-line dynamic programming (DP) with the historical running information of the driving cycle. Meanwhile, the genetic algorithm (GA) is adopted to replace the quantization process of DP permissible control set to reduce the computation burden. Secondly, with the optimized decision variables as control parameters according to the position and battery SOC of a PHEB, a RB control is used as an implementable controller for the energy management. Simulation results demonstrate that the proposed DPRB might distribute electric energy more reasonably throughout the bus route, compared with the optimized RB. The proposed hybrid algorithm might give a practicable solution, which is a tradeoff between the applicability of RB and the global optimization property of DP.

Prediction-based manufacturing center self-adaptive demand side energy optimization in cyber physical systems

Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufacturing center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

Optimized Synthesis of Art Patterns and Layered Textures

Line drawings and digital arts appear everywhere, from simple icons and logos to cartoons, maps, and illustrations. We define art patterns as the subset of line drawings and digital arts that are comprised of repeated elements. There exist textures that share characteristics with art patterns. Examples of such textures include piled discrete elements with curved contours. Inspired by recent success of exemplar-based texture synthesis, in this paper, we focus on synthesizing art patterns and textures with curvilinear features from exemplars, which we cast as a global optimization problem. Our energy function for this problem measures both the appearance similarity of color patterns and shape similarity of curvilinear features between an input exemplar and a synthesized image. We develop an overall expectation-maximization-style algorithm for minimizing this energy function. The shape similarity part of the energy is minimized through an innovative application of the level set method. We further generalize our energy function and optimization algorithm to multilayer pattern and texture synthesis. Our generalized optimization can effectively handle multiple layers and synthesize valid instances of interaction.

Reliability-Aware Energy Optimization Algorithm for Network-on-Chip with Voltage-Scalable Links

A reliability-aware energy optimization algorithm is proposed for Network-on-Chip (NoC) with voltage scalable links. Considering the effect of reduced voltage on fault rates, this approac h achieves energy/reliability trade-off when performing routing path allocation and links voltage assignment statically. A novel energy-effi ciency gradient driven heuristic is proposed to assign the voltages for the links during global design space exploration of routing pa th allocation. Experimental results show that the presented method can efficiently guarantee the communication reliability and bandwidth c onstraints, without significant loss of energy savings.

A Distributed Algorithm for Energy Optimization in Hydraulic Networks

An industrial case study in the form of a large-scale hydraulic network underlying a district heating system is considered. A distributed control is developed that minimizes the aggregated electrical energy consumption of the pumps in the network without violating the control demands. The algorithm is distributed in the sense that all calculations are implemented where the necessary information is available, including both parameters and measurements. A communication network between the pumps is implemented for global optimization. The local implementation of the algorithm means that the system becomes a Plug & Play control system as most commissioning can be done during the manufacture of the pumps. Only information on the graph-structure of the hydraulic network is needed during installation.

An Analysis of Energy Optimization algorithm in Wireless Sensor Network

Wireless sensor networks consist of small nodes with sensing, computation, and wireless communications capabilities. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy awareness is an essential design issue. Routing protocols in WSNs might differ depending on the application and network architecture. In this article we present a survey of state- of-the-art routing techniques in WSNs. We first outline the design challenges for routing protocols in WSNs followed by a comprehensive survey of routing techniques. Overall, the routing techniques are classified into three categories based on the underlying network structure: flit, hierarchical, and location-based routing. Furthermore, these protocols can be classified into multipath-based, query-based, negotiation-based, QoS- based, and coherent-based depending on the protocol operation. We study the design trade-offs between energy and communication overhead savings in every routing paradigm. We also highlight the advantages and performance issues of each routing technique. The article concludes with possible future research areas. Keyword: Quality of service, QOS-based protocol, Coherent based protocol, Routing protocol and Wireless Sensor Network