Energy-efficient Resource Research Articles

Discrete Event Simulation Approach for Energy Efficient Resource Management in Paper & Pulp Industry

To improve energy efficiency of a Paper & Pulp industry, full capacity utilization of the processing equipment and diverse energy source utilities are promoted, while same time processing of diverse raw materials, meeting of strict processing time requirements, seamless material flow and obtaining required product quality are important concerns to be fulfilled. This is similar to a resources management problem while energy is considered as one of the measurable resources, wherein estimation of energy consumptions is crucial/complicated aspect. Discretized form of energy consumption (approximate energy consumed by unit amount of intermediate products at different intermediate processing stages) concept is utilized here with discrete event simulation for consumed energy estimation/analysis. The research deals with a real time working Paper & Pulp industry resource management problem, wherein overall energy consumption rate and production rate were analyzed for some commonly occurring situation of efficiency reduction of the processing station(s). Energy efficient solutions are evaluated with varying production flow (varying the percentage of raw material) and resources management options (turning on/off paper machines). Energy efficient solution which ensures minimum loss in production rate and minimizes disturbances in material flow is suggested for implementation.

Joint Optimization of Spectrum Sensing and Transmit Power in Energy Harvesting-Based Cognitive Radio Networks

Proliferation of mass market applications and all forms of smart devices have driven the explosive growth of wireless sensor data traffic in recent years. In spite of notable advancements in wireless sensor technologies, energy scarcity due to a limited battery capacity still remains a critical impediment to consumer electronics applications. Thus, the energy harvesting (EH) technology, utilizing extra energy collected from radio frequency (RF) signals, is regarded as a promising solution for addressing the battery problem. This paper presents, analyzes, and discusses a joint sensing and power allocation scheme for cognitive radio in conjunction with EH, in which a secondary transmitter can harvest energy from RF signals transmitted by a primary transmitter during spectrum sensing. We formulate a non-convex optimization problem to find the optimal sensing time and power allocation for maximizing energy efficiency, while satisfying the constraints on the amount of harvested energy and interference at primary receivers. Using nonlinear fractional programming, the original problem can be reformulated into a tractable convex one, and an energy efficient resource allocation algorithm is devised while taking into account RF EH. Simulation results are used to verify the optimality of the proposed scheme, where the secondary network can accomplish maximum energy efficiency.

Multi-objective optimization oriented policy for performance and energy efficient resource allocation in Cloud environment

Cloud computing is a hybrid paradigm which makes use of utility computing, high performance cluster computing and grid computing and it offers various benefits such as flexibility, expandability, little or almost no capital investment, disaster recovery, moveable work space and much more. However, due to constantly increasing number of data centers worldwide, the issue of energy consumption by these data centers has attracted attention of researchers. Resource allocation and resource utilization are the major criterion in which the problem of energy efficiency can be addressed. In this research, we aim to provide energy-efficient resource allocation using Multi-Objective Optimization (MOO) method. Further, We propose MOO-based virtual machine (VM) allocation policy and implement it in CloudSim environment. The results are compared with existing policies. The results depict that MOO-based policy leads to saving in energy due to efficient resource allocation, without compromising performance of data center operations.

Energy efficient constrained shortest path first-based joint resource allocation and route selection for multi-hop CRNs

Cognitive radio networks (CRNs) are expected to improve spectrum utilization efficiently by allowing secondary users (SUs) to opportunistically access the licensed spectrum of primary users (PUs). In CRNs, source and destination SUs may achieve information interaction in an ad hoc manner. In the case that no direct transmission link between the SU transmission pairs is available, multi-hop relay SUs can be applied to forward information for the source and destination SUs, resulting in multi-hop CRNs. In this paper, we consider a multi-hop CRN consisting of multiple PUs, SU transmission pairs and relay SUs. Stressing the importance of transmission hops and the tradeoff between data rate and power consumption, we propose an energy efficient constrained shortest path first (CSPF)-based joint resource allocation and route selection algorithm, which consists of two sub-algorithms, i.e., CSPF-based route selection sub-algorithm and energy efficient resource allocation sub-algorithm. More specifically, we first apply CSPF-based route selection sub-algorithm to obtain the shortest candidate routes (SCRs) between the SU pair under the transmission constraints. Then, an energy efficient resource allocation problem of the SCRs is formulated and solved by applying iterative algorithm and Lagrange dual method. Simulation results demonstrate the effectiveness of the proposed algorithm.

Joint User Scheduling and Power Allocation Optimization for Energy-Efficient NOMA Systems With Imperfect CSI

Non-orthogonal multiple access (NOMA) exploits successive interference cancellation technique at the receivers to improve the spectral efficiency. By using this technique, multiple users can be multiplexed on the same subchannel to achieve high sum rate. Most previous research works on NOMA systems assume perfect channel state information (CSI). However, in this paper, we investigate energy efficiency improvement for a downlink NOMA single-cell network by considering imperfect CSI. The energy efficient resource scheduling problem is formulated as a non-convex optimization problem with the constraints of outage probability limit, the maximum power of the system, the minimum user data rate, and the maximum number of multiplexed users sharing the same subchannel. Different from previous works, the maximum number of multiplexed users can be greater than two, and the imperfect CSI is first studied for resource allocation in NOMA. To efficiently solve this problem, the probabilistic mixed problem is first transformed into a non-probabilistic problem. An iterative algorithm for user scheduling and power allocation is proposed to maximize the system energy efficiency. The optimal user scheduling based on exhaustive search serves as a system performance benchmark, but it has high computational complexity. To balance the system performance and the computational complexity, a new suboptimal user scheduling scheme is proposed to schedule users on different subchannels. Based on the user scheduling scheme, the optimal power allocation expression is derived by the Lagrange approach. By transforming the fractional-form problem into an equivalent subtractive-form optimization problem, an iterative power allocation algorithm is proposed to maximize the system energy efficiency. Simulation results demonstrate that the proposed user scheduling algorithm closely attains the optimal performance.

A Mobile Ad hoc Cloud Computing and Networking Infrastructure for Automated Video Surveillance System

Mobile automated video surveillance system involves application of real-time image and video processing algorithms which require a vast quantity of computing and storage resources. To support the execution of mobile automated video surveillance system, a mobile ad hoc cloud computing and networking infrastructure is proposed in which multiple mobile devices interconnected through a mobile ad hoc network are combined to create a virtual supercomputing node. An energy efficient resource allocation scheme has also been proposed for allocation of realtime automated video surveillance tasks. To enable communication between mobile devices, a Wi-Fi Direct based mobile ad hoc cloud networking infrastructure has been developed. More specifically, a routing layer has been developed to support communication between Wi-Fi Direct devices in a group and multi-hop communication between devices across the group. The proposed system has been implemented on a group of Wi-Fi Direct-enabled Samsung mobile devices.

Empirical modeling and simulation of an heterogeneous Cloud computing environment

Abstract Cloud computing offers users a convenient scalable and flexible scenario for developing, running, and testing different applications in a pay-as-you-go model. However, the increasing demand of computational resources have resulted in more power consumption. In order to address this issue, the use of energy efficient resources such as FPGAs within a Cloud computing environment is nowadays a hot research topic, driven both from industry and academy. The efficient use of this type of resources can lead to increase the profit obtained by Cloud providers by serving more client requests while fulfilling their requirements with the minimum set of resources. In this paper, we survey an architectural framework and algorithms for managing heterogeneous resources, focusing on FPGAs. Based on this architecture, a simulation tool for studying the impact on performance and energy consumption of scaling the number of FPGAs in the system is presented. This tool is based on statistical models of processing time and energy consumption. The scalability study demonstrates that increasing the number of FPGAs can improve energy consumption up to 40%, while also admitting up to 35% more requests into the system.

Future Energy Efficient Data Centers With Disaggregated Servers

The popularity of the Internet and the demand for 24/7 services uptime is driving system performance and reliability requirements to levels that today's data centers can no longer support. This paper examines the traditional monolithic conventional server (CS) design and compares it to a new design paradigm: the disaggregated server (DS) data center design. The DS design arranges data centers resources in physical pools, such as processing, memory, and IO module pools, rather than packing each subset of such resources into a single server box. In this paper, we study energy efficient resource provisioning and virtual machine (VM) allocation in DS-based data centers compared to CS-based data centers. First, we present our new design for the photonic DS-based data center architecture, supplemented with a complete description of the architectural components. Second, we develop a mixed integer linear programming (MILP) model to optimize VM allocation for the DS-based data center, including the data center communication fabric power consumption. Our results indicate that, in DS data centers, the optimum allocation of pooled resources and their communication power yields up to 42% average savings in total power consumption when compared with the CS approach. Due to the MILP high computational complexity, we developed an energy efficient resource provisioning heuristic for DS with communication fabric (EERP-DSCF), based on the MILP model insights, with comparable power efficiency to the MILP model. With EERP-DSCF, we can extend the number of served VMs, where the MILP model scalability for a large number of VMs is challenging. Furthermore, we assess the energy efficiency of the DS design under stringent conditions by increasing the CPU to memory traffic and by including high noncommunication power consumption to determine the conditions at which the DS and CS designs become comparable in power consumption. Finally, we present a complete analysis of the communication patterns in our new DS design and some recommendations for design and implementation challenges.

A micro-distributed ESS-based smart LED streetlight system for intelligent demand management of the micro grid

Currently, many researchers worldwide are focusing on creating a novel service model by integrating IoT (Internet of Things) technologies with ICT (Information and Communications Technologies) to increase energy efficiency by reducing power consumption and greenhouse gas emissions. This paper examines the problems related to the current integrated ESS (Energy Storage System) in a smart grid: high installation costs and low management efficiency. To solve these problems, this paper studies the development of a micro-distributed ESS in an intelligent LED (Light Emitting Diode) streetlight system, and its low-cost installation and high management efficiency in a micro grid. Through the application of this system, the initial installation costs can be reduced by using micro-distributed ESS and IoT-based intelligent energy management, facilitating power monitoring of the streetlights and energy efficient demand resource management in the micro grid. This provides a more intelligent and efficient environment, such as in a smart-city.

An effective task scheduling algorithm based on dynamic energy management and efficient resource utilization in green cloud computing environment

With the expanding of its scale and the energy cost factors being ignored in green cloud computing, the problem of high energy cost and low efficiency is exposed. Based on the concepts and principles of load balancing, a novel energy-efficient load balancing global optimization algorithm, called resource-aware load balancing clonal algorithm for task scheduling, is proposed to deal with the problem of energy consumption in green cloud computing. Firstly, the problem is formulated as a combinatorial optimization problem that aims to optimize both energy consumption and load balancing. Then, the resource-aware scheduling algorithm is proposed based on load balancing strategy and clonal selection principle. Finally, simulation studies show that the proposed algorithm can effectively reduce energy consumption in green cloud computing, and its exploration and exploitation abilities can be enhanced and well balanced.

Technologies of low-potential heat utilization on thermal power plants

In this article there are given results of possible efficiency enhancement on thermal power plants with low-potential heat utilization. Intensive usage and restricted stocks of organic fuel creates demand of methods reducing its consumption. Thus, on thermal power plants one of important tasks is increasing of primary energy resources efficiency usage. In the research different thermal schemes based on thermal pumps, thermotransformers and installations using detander-generating aggregates were designed and thermal efficiency of these schemes was determined. Schemes calculation was executed using «Thermoflow» and «Gate Cycle» software products. It is shown, that implementation of various low-potential heat utilization technologies at thermal power plants leads to fuel economy. There are presented financial and economic performance indicators for the researched schemes.

Energy-Efficient Optimal Power Allocation in Integrated Wireless Sensor and Cognitive Satellite Terrestrial Networks.

This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach’s method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint.

Energy-Efficient Resource Utilization for Heterogeneous Embedded Computing Systems

In this paper, the joint optimization problem with energy efficiency and effective resource utilization is investigated for heterogeneous and distributed multi-core embedded systems. The system model is considered to be fully a heterogeneous model, that is, all nodes have different maximum speeds and power consumption levels from the perspective of hardware while they can employ different scheduling strategies from the perspective of applications. Since the concerned problem by nature is a multi-constrained and multi-variable optimization problem in which a closed-form solution cannot be obtained, our aim is to propose a power allocation and load balancing strategy based on Lagrange theory. Furthermore, when the problem cannot be fully solved by Lagrange approach, a data fitting method is employed to obtain core speed first, and then load balancing schedule is solved by Lagrange method. Several numerical examples are given to show the effectiveness of the proposed method and to demonstrate the impact of each factor to the present optimization system. Finally, simulation and practical evaluations show that the theoretical results are consistent with the practical results. To the best of our knowledge, this is the first work that combines load balancing, energy efficiency, hardware heterogeneity and application heterogeneity in heterogeneous and distributed embedded systems.

Energy Efficient Resource Allocation for Virtual Services Based on Heterogeneous Shared Hosting Platforms in Cloud Computing

Abstract This paper is an extended and updated version, presented at the INDIA 2017 conference. Optimal resource provisioning for virtual services in the Cloud computing is one of the concerns nowadays. For cloud computing service providers, reducing the number of physical machines providing resources for virtual services in cloud computing is one of the efficient ways to reduce the amount of energy consumption, which in turn enhances the performance of data centers. Multi-dimensional resource provisioning on a Heterogeneous Shared Hosting Platforms for virtual services is known as a NP-hard problem. Therefore, it is necessary to apply the metaheuristic algorithms for estimating the outcome of the problem. In this study, we propose the resource allocation problem for reducing the energy consumption. ECRA-SA algorithms were designed to solve it and were evaluated through CloudSim simulation tool compared with FFD algorithm. The experimental results show that the proposed ECRA-SA algorithm yields a better performance than FFD algorithm.

Energy-efficient anycast scheduling and resource allocation in optical grids

In recent years, optical grid networks has been used as an ideal infrastructure to support high-performance computing environment, data intensive applications and interconnection of data centers. Due to rapid increase in the high-bandwidth applications, the power consumption of communications equipment for such networks has been increasing steadily over the past decade. Therefore, energy efficient routing schemes and traffic models can be developed to reduce the energy consumption. In many applications it is possible to select the destination node from a set of possible destinations, which have the required computing/storage resources. This is known as anycasting compared to unicasting where there is only one destination for each communication. In this paper we adopt the sliding scheduled traffic model, where setup and tear down times may vary within larger window frame. We propose a novel problem that exploits knowledge of demand holding times using anycasting model. We show how the flexibility of anycast routing can lead to additional energy saving. The problem was formulated as an integer linear program to optimally schedule demands (in time) and route them in order to minimize overall network energy consumption. The problem of energy consumption is addressed by switching off idle network components in low utilization periods. We analyze the performance of the proposed approach for anycast and unicast routing models. Our simulation results demonstrate that the proposed approach can lead to significant reductions in energy consumption, compared to traditional routing schemes.

Energy efficient resource allocation in timesharing multiuser systems with hybrid energy harvesting transmitter

An energy efficient resource allocation scheme in timesharing multiuser system with a hybrid energy harvesting transmitter is studied in this paper. Specially, the operation energy of system is supplied by constant energy and energy harvesting, which harvests energy from external environment. Our goal is to maximize the energy efficiency of timesharing multiuser systems by considering jointly allocation of transmission time and power control in an off-line manner. The original nonconvex objective function is transformed into convex optimization problem via the fractional programming approach. Then, we solve the convex problem by Lagrange dual decomposition method. Simulation results show that the proposed energy efficient resource allocation scheme has a better performance than the scheme which decomposes optimization problem into two parts (power allocation, time allocation) to solve iteratively.

Energy efficient power allocation for co-located antenna systems with D2D communication

This paper investigates the energy efficient resource allocation when adding dedicated device-to-device (D2D) communication in the co-located antenna systems (CAS). We consider the user equipments (UEs) are changing over time and the appropriate potential UEs to form D2D pairs are varying with time. Our optimization objective is to maximize the system’s energy efficiency (EE) with the constraints of the maximum transmit power of UEs and D2D pairs in different total power consumption models. Firstly, an algorithm is developed to choose appropriate potential UEs to form D2D pairs in this paper. Then we exploit fractional programming method to obtain optimal energy efficient power allocation solutions. Simulation results are provided to demonstrate the effectiveness of the developed D2D pairs choosing algorithm and the power allocation algorithm.

On the possible geological and geophysical risks and prospects of the Chernobyl Exclusion Zone

Scientific and engineering center of radiohydrogeogeological polygon studies of the National Academy of Sciences of Ukraine.Evidence and mechanisms of the potential influence of local depression (micro-geodynamic) zones on the NPP safety were considered. The depression forms were revealed within the Chernobyl NPP site, including location of the new safe confinement. Nature of zones and character of their influence is determined by fault structures and conjugated deep degassing processes, which cannot be detected and studied by conventional methods of engineering-exploration works during NPP siting. The assumption was made that significant and still unexplored risks for nuclear facilities are associated with probable escape of deep hydrogen through the bottom of depressions along sub-bottom channels. The approaches were proposed to studying the system of “depression with its sub-bottom channel” within the sites of Chernobyl NPP and other nuclear power plants. The prospects of searching for hydrogen accumulations as efficient energy resource were considered.

Environmental assessment of the Peruvian industrial hake fishery with LCA

The Peruvian hake (Merluccius gayi peruanus) stock has been in a delicate state in the last decades due to overexploitation combined with adverse climatic events. The stock is showing certain signs of recovery since 2012. This work analyses the environmental impacts of current fleet operations and its likely trend. The fleet was divided into coherent segments, per holding capacity and engine power. The validity of both segmentations, as well as the presence of an effect of economies of scale driving fuel use intensity (FUI), was tested. Life cycle assessment was used to calculate environmental impacts, per individual sampled vessel and per segment, complemented with indicators of energy efficiency and biotic resource depletion. The fleet is highly fuel-efficient (120 kg fuel per tonne fish) when compared with other reported values, despite a large overcapacity that increases the impact of the construction and maintenance phases. Significant inter-annual FUI variations were observed (80.0 kg t−1 in 2008 to 210.3 kg t−1 in 2006), but no clear trend. Neither significant differences in FUI among fleet segments nor a clear effect of economies of scale were found (but FUI analysis was based on a small sample of 32 values for nine vessels, two of which had data for a single year). Only the largest vessels, featuring 242 m3 holding capacity and 850 hp engine power, were found to have lower FUI than any of the other vessels, but no statistical test could be applied to validate this difference. Differences in environmental impacts of individual vessels are mostly dominated by their relative FUI. Fuel use and, to a lower extent, maintenance are the main sources of environmental impacts. The most contributing impacts to ReCiPe single score are climate change, human toxicity and fossil depletion. The fishery’s impacts on the biotic natural resource were orders of magnitude higher than many other global hake stocks, due to overexploitation. The environmental impacts of the national hake fleet are relatively low during the study period, despite an overcapacity of the fleet. With the perspective of expanding its operations and obtaining better yields on the eventuality that the stock fully recovers, these impacts should decrease. More research based on additional FUI data is necessary to effectively compare the performance of these vessels with larger ones (featuring >180 m3 and >500 hp, of which nine existed in 2016) before possibly recommending their preferential use.

Efficient energy resource scheduling for sustainable diversified farming

Diversified farming has an important role to play in the economic activity of any country. It provides raw materials to numerous industries along with meeting the food demands of the people. Continuous and cost effective supply of electricity is one of the main challenges in the diversified farming sector. Integration of renewable energy sources at the farm level and/or the village level coupled with the efficient management of these resources can alleviate the problem of continuous and cost effective supply of electricity to the diversified farming sector. In this research, efficient energy resource scheduling in diversified farming has been proposed to manage the energy resources efficiently. The proposed scheme can perform real time energy forecasting of renewable resources and controllable loads for making proper short term scheduling to minimize the total operating cost. A branch and bound algorithm is used for binary integer linear programming problems for optimization. This helps to minimize energy consumption from utilities. It maximizes the profit of farmers and minimizes the cost of energy. Efficient energy resource scheduling in diversified farming helps to achieve a trade-off between the operation cost, peak load, and farmer welfare.