Energy-aware Resource Management Research Articles

An Energy-Aware Resource Management Strategy Based on Spark and YARN in Heterogeneous Environments

An Energy-Aware Resource Management Strategy Based on Spark and YARN in Heterogeneous Environments

Self‐attention convolutional neural network optimized with Remora Optimization Algorithm for energy aware resource management in Non‐orthogonal Multiple Access networks

SummaryNon‐orthogonal multiple access (NOMA) is an efficient technology for future wireless communication systems. The resource management on NOMA networks is flattering for improving energy efficiency (EE) of systems. Therefore, self‐attention convolutional neural network (SACNN) optimized with Remora Optimization Algorithm (ROA) for energy aware resource management in NOMA network (SACNN‐ROA‐EE) is proposed in this paper for solving user allocation, channel allocation, and power allocation problems. The Matching‐Coalition approach is considered to face the user allocation issue, and the SACNN is considered to face the channel allocation and power allocation problems. SACNN does not adopt any optimization methods to determine the optimal parameters in channel allocation and power allocation. Therefore, the ROA is applied to optimize the SACNN weight parameters. The proposed technique is activated in MATLAB, and its efficacy is analyzed with under performances metrics, such as minimum user rate, sum rate, energy, and spectral performance. The proposed SACNN‐ROA‐EE attains 24.35%, 27.84%, and 38.58% better sum rate and 27.45%, 43.28%, 30.56% better EE compared with existing methods, such as EE utilizing communication deep neural network optimized with power allocation optimization (PAO‐CDNN‐EE), EE under deep transfer deterministic policy gradient along deep deterministic policy gradient approach (DPPGA‐DTDPPG‐EE), and EE utilizing deep reinforcement learning with baseline algorithms (BA‐DRL‐EE), respectively.

PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud Computing

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios.

An energy efficient fog level resource management scheme for software defined cities

The proliferation of Internet of Things (IoT) and smart city services have paved the path for complex network architectures including edge and fog layer resources in order to meet such services’ Quality of Service (QoS). Such services should also be energy efficient for sustainable cities. Though energy efficiency has been widely studied over the past decade, yet most such solutions have focused on device or sensor level solutions or alternatively on cloud/datacenter level solutions. With the prominence of Software Defined Networks (SDNs) in recent times owing to its flexible and scalable network management features, SDN based fog level resource management has become an attractive alternative. This paper addresses fog level energy-aware resource management with the goal of minimizing energy consumption while QoS fulfillment by means of Virtual Machine (VM) consolidation through live migrations. Since any decent sized smart city would have millions of devices and large numbers of edge/fog instances, it would lead any solution mechanism to a NP hard problem. This paper proposes a novel set based Differential Evolutionary (S-DE) algorithm. Simulations have been carried out to test the efficacy of the proposed S-DE solution and the results demonstrate the efficiency of the proposed method.

VECMAN: A Framework for Energy-Aware Resource Management in Vehicular Edge Computing Systems

In Vehicular Edge Computing (VEC) systems, the computing resources of connected Electric Vehicles (EV) are used to fulfill the low-latency computation requirements of vehicles. However, local execution of heavy workloads may drain a considerable amount of energy in EVs. One promising way to improve the energy efficiency is to share and coordinate computing resources among connected EVs. However, the uncertainties in the future location of vehicles make it hard to decide which vehicles participate in resource sharing and how long they share their resources so that all participants benefit from resource sharing. In this paper, we propose VECMAN, a framework for energy-aware resource management in VEC systems composed of two algorithms: (i) a resource selector algorithm that determines the participating vehicles and the duration of resource sharing period; and (ii) an energy manager algorithm that manages computing resources of the participating vehicles with the aim of minimizing the computational energy consumption. We evaluate the proposed algorithms and show that they considerably reduce the vehicles’ computational energy consumption compared to the state-of-the-art baselines. Specifically, our algorithms achieve between 7 and 18 percent energy savings compared to a baseline that executes workload locally and an average of 13 percent energy savings compared to a baseline that offloads vehicles’ workloads to RSUs.

Energy-aware resource management in Internet of vehicles using machine learning algorithms

Internet of Vehicles (IoV) presents a new generation of vehicular communications with limited computation offloading, energy and memory resources with 5G/6G technologies that have grown enormously and are being used in wide variety of Intelligent Transportation Systems (ITS). Due to the limited battery power in smart vehicles, the concept of energy consumption is one of the main and critical challenges of the IoV environments. Optimizing resource management strategies for improving the energy consumption using AI-based methods is one of important solutions in the IoV environments. There are various machine learning algorithms for selecting optimal solutions for energy-efficient resource management strategies. This paper presents the existing energy-aware resource management strategies for the IoV case studies, and performs a comparative analysis among their applied AI-based methods and machine learning algorithms. This analysis presents a technical and deeper understanding of the technical aspects of existing machine learning and AI-based algorithms that will be helpful in design of new hybrid AI approaches for optimizing resource management strategies with reducing their energy consumption.

A novel approach to energy-aware resource management: Toward green NOMA heterogeneous networks

The dense deployment of small cell networks is a key feature of next generation mobile networks aimed at providing the necessary capacity increase. In order to reach an acceptable performance in such ultra-dense networks, real-time resource management is of great importance. Therefore, self-optimization networking is proposed as the only viable solution to increase the networks’ utility. This paper proposed a self-optimizing model to enhance network performance and guarantee the users’ QoS requirements by considering limited resources and using effective user association, carrier scheduling and handover optimization algorithms. In order to maximize the network performance, we applied the smart backhauling technique in order to analyze the signaling to increase the validity of the decision making process. Based on the semantic information extracted from the access layer, the network decision-making center is able to adjust the network parameters and resource allocation effectively. The goal function is defined as maximizing the total energy efficiency by considering the transmission power, energy harvesting capability and the user QoS constraints so that the idle small cells are considered turned off temporarily to boost the power efficiency. Although the optimization problem is non-convex, a quadratic mixed-integer function is solved to obtain a global optimal solution. Since the actual implementation of the real-time algorithm has high computational complexity, two algorithms with different complexity levels are proposed. These algorithms use the carrier matching feature and optimal transmission power for problem-solving. The simulation results prove that, despite the increased computational complexity, effective resource allocation and optimal HO relations made the proposed approach capable to increase performance indices such as network throughput by up to 30%.

Energy aware resource control mechanism for improved performance in future green 6G networks

The increasing power consumption and the energy crisis owing to several billion of connected devices raises concern. The battery powered devices or mobile terminals consume a lot of power and led to huge energy overhead. Thus, the need is to design energy sustainable networks which aims for optimum utilization of power resources. 6G enabled networks offer enhanced network coverage along with green communication capabilities and energy-aware resource management. Cell-free Massive MIMO is a promising 6G technology which offers power optimization by allowing only a subset of access points (APs) to serve a particular user. In this paper, the performance of the proposed network is evaluated for different power control methods. The average mean square error (MSE), average signal-to-noise ratio (SNR) and spectral efficiency (SE) are obtained for different users,transmit powers and pilot lengths. An efficient resource management algorithm namely, MIPA-MCAS is proposed which offers Minimum Interference Power Allocation and Maximum Channel gain AP Selection. It is observed that the spectral efficiency obtained at 95% of all user locations improves by 3.39% with the proposed algorithm. In the end, a comparative analysis is carried out with two other algorithms namely, Round-Robin Power Allocation and Maximum Channel gain AP Selection (RRPA-MCAS) and Random Power Allocation and Maximum Channel gain AP Selection (RPA-MCAS) for the proposed and conventional system models.

Efficient Energy-Aware Resource Management Model (EEARMM) Based Dynamic VM Migration

In cloud environment, an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user’s demands. The proficient resource management and work allocation determines the accomplishment of the cloud infrastructure. However, it is very difficult to persuade the objectives of the Cloud Service Providers (CSPs) and end users in an impulsive cloud domain with random changes of workloads, huge resource availability and complicated service policies to handle them, With that note, this paper attempts to present an Efficient Energy-Aware Resource Management Model (EEARMM) that works in a decentralized manner. Moreover, the model involves in reducing the number of migrations by definite workload management for efficient resource utilization. That is, it makes an effort to reduce the amount of physical devices utilized for load balancing with certain resource and energy consumption management of every machine. The Estimation Model Algorithm (EMA) is given for determining the virtual machine migration. Further, VM-Selection Algorithm (SA) is also provided for choosing the appropriate VM to migrate for resource management. By the incorporation of these algorithms, overloading of VM instances can be avoided and energy efficiency can be improved considerably. The performance evaluation and comparative analysis, based on the dynamic workloads in different factors provides evidence to the efficiency, feasibility and scalability of the proposed model in cloud domain with high rate of resources and workload management.

Resource Management for Pervasive-Edge-Computing-Assisted Wireless VR Streaming in Industrial Internet of Things

Wireless virtual reality (VR) is increasingly used in industrial Internet of Things (IIoTs). However, ultra-high viewport rendering demands and excessive terminal energy consumption restrict the application of wireless VR. Pervasive edge computing emerges as a promising method for wireless VR. In this article, we propose an energy-aware resource management scheme for wireless-VR-supported IIoTs. To reduce the energy consumption of VR equipments (VEs) while ensuring a smooth immersive VR experience, we formulate the viewport rendering offloading, computing, and spectrum resource allocation to be a joint optimization problem, considering content correlation between VEs, fluctuating channel conditions, and VR quality of experience. By applying dual approximation, the original problem is transformed to be a Markov decision process and an reinforcement learning (RL)-based online learning algorithm is designed to find the optimal policy. To improve the learning efficiency, the quantum parallelism is integrated into the RL to overcome “curse of dimensionality”. In the simulations, the convergence rate and the performance in terms of energy consumption and stalling rate are evaluated. Simulation results demonstrate the effectiveness of the proposed scheme.

Energy-Aware Resource Management for Federated Learning in Multi-Access Edge Computing Systems

In Federated Learning (FL), a global statistical model is developed by encouraging mobile users to perform the model training on their local data and aggregating the output local model parameters in an iterative manner. However, due to limited energy and computation capability at the mobile devices, the performance of the model training is always at stake to meet the objective of local energy minimization. In this regard, Multi-access Edge Computing (MEC)-enabled FL addresses the tradeoff between the model performance and the energy consumption of the mobile devices by allowing users to offload a portion of their local dataset to an edge server for the model training. Since the edge server has high computation capability, the time consumption of the model training at the edge server is insignificant. However, the time consumption for dataset offloading from mobile users to the edge server has a significant impact on the total time consumed to complete a single round of FL process. Thus, resource management in MEC-enabled FL is challenging, where the objective is to reduce the total time consumption while saving the energy consumption of the mobile devices. In this article, we formulate an energy-aware resource management for MEC-enabled FL in which the model training loss and the total time consumption are jointly minimized, while considering the energy limitation of mobile devices. In addition, we recast the formulated problem as a Generalized Nash Equilibrium Problem (GNEP) to capture the coupling constraints between the radio resource management and dataset offloading. To that end, we analyze the impact of the dataset offloading and computing resource allocation on the model training loss, time, and the energy consumption. Finally, we present the convergence analysis of the proposed solution, and evaluate its performance against the traditional FL approach. Simulation results demonstrate the efficacy of our proposed solution approach.

A novel energy-aware resource management technique using joint VM and container consolidation approach for green computing in cloud data centers

Cloud computing is being rapidly adopted for managing IT services as a notable solution due to diverse beneficiaries such as automatically optimized resource management as well as modern service delivery models. The container as a service has been recently introduced by cloud providers as a new service apart from traditional cloud services. Containers enable applications to run and deploy on isolated virtual space, and the operating system kernel is shared among them. Also, containerization has some attributes such as scalability, highly portable properties, and lightweight, for those reasons, it is applied for running isolated applications. Reducing energy consumption, as well as their CO2 emissions, are great deals for cloud providers. In this direction, consolidation is recommended as a vital energy-aware approach in cloud data centers. Previously, independent virtual machine migration or container migration was proposed in the literature for green computing in cloud data centers. However, this paper proposes a new cloud resource management procedure based on a multi-criteria decision-making method that takes advantage of a joint virtual machine and container migration approach concurrently. The results of simulations using ContainerCloudsim simulator validates the applicability of the proposed approach which shows notable reductions in energy consumption, SLA violation, and number of migrations in comparison with the state-of-the-art algorithms.

RETRACTED: Energy-aware resource management for uplink non-orthogonal multiple access: Multi-agent deep reinforcement learning

Abstract Non-orthogonal multiple access (NOMA) is one of the promising technologies to meet the huge access demand and the high data rate requirements of the next generation networks. In this paper, we investigate the joint subchannel assignment and power allocation problem in an uplink multi-user NOMA system to maximize the energy efficiency (EE) while ensuring the quality-of-service (QoS) of all users. Different from conventional model-based resource allocation methods, we propose two deep reinforcement learning (DRL) based frameworks to solve this non-convex and dynamic optimization problem, referred to as discrete DRL based resource allocation (DDRA) framework and continuous DRL based resource allocation (CDRA) framework. Specifically, for the DDRA framework, we use a deep Q network (DQN) to output the optimum subchannel assignment policy, and design a distributed and discretized multi-DQN based network to allocate the corresponding transmit power of all users. For the CDRA framework, we design a joint DQN and deep deterministic policy gradient (DDPG) based network to generate the optimal subchannel assignment and power allocation policy. The entire resource allocation policies of these two frameworks are adjusted by updating the weights of their neural networks according to feedback of the system. Numerical results show that the proposed DRL-based resource allocation frameworks can significantly improve the EE of the whole NOMA system compared with other approaches. The proposed DRL based frameworks can provide good performance in various moving speed scenarios through adjusting learning parameters.

Energy-Aware Resource Management in Heterogeneous Cellular Networks With Hybrid Energy Sources

In this paper, we focus on reducing the on-grid energy consumption in heterogeneous radio access networks (HetNets) supplied with hybrid power sources (grid and renewables). The energy efficiency problem is analyzed over both short- and long-timescales by means of reactive and proactive management strategies. For short-timescale case, a renewable-energy (RE) aware user equipment-base station association is proposed and analyzed for the cases when no storage infrastructure is available. For long-timescale case, a traffic flow method is proposed for load balancing in RE base stations (BSs), which is combined with a model predictive controller (MPC) to include forecast capabilities of the RE source behavior in order to better exploit a Green HetNet with storage support. The mechanisms are evaluated with data of solar measurements from the region of Valle de Aburra, Medellin, Colombia and wind estimations from the Moscow region, Russian Federation. Results show how the green association proposal can reduce on-grid energy consumption in a HetNet by up to 34%, while is able to exceed the savings obtained by other methods, including the best-signal level policy by up to 15%, additionally providing high network efficiency and low computational complexity. For the long-timescale case, MPC attainable savings can be up to 22% with respect to the on-grid only Macro-BS approach. Finally, an analysis of our proposals in a common scenario is included, which highlights the relevance of storage management, although emphasizing the importance of combining reactive and proactive methods in a common framework to exploit the best of each approach.

Semantic Framework for Energy-Aware Resource Management of IoT in Business Processes

Recently, IoT adoption has increased in several domains. IoT devices are multi-modal and heterogeneous due to their varied properties, standards, and manufactures. This leads to interoperability issues, which can be solved using semantic technologies. Likewise, these devices participate in numerous cross-organizational business processes (BPs). Being resource-constrained, they must be managed in an energy-aware manner to avoid BP failures. However, due to lack of a common ontology and formalization of energy-related concepts impedes their optimal management in BPs. To bridge this gap, the authors capitalize on existing semantic models such as FIESTA-IoT and IoT-BPO. They propose the following: (i) formalization of IoT concepts in BPs related to energy, their properties and constraints, and (ii) resolving resource conflicts based on strategies. The feasibility of this framework is illustrated by evaluating the semantic model for its coverage of concepts from IoT-A reference model, along with proof of concept tools that allows ontology-based support for process modeling.

A Joint Power Efficient Server and Network Consolidation approach for virtualized data centers

Cloud computing and virtualization are enabling technologies for designing energy-aware resource management mechanisms in virtualized data centers. Indeed, one of the main challenges of big data centers is to decrease the power consumption, both to cut costs and to reduce the environmental impact. To this extent, Virtual Machine (VM) consolidation is often used to smartly reallocate the VMs with the objective of reducing the power consumption, by exploiting the VM live migration. The consolidation problem consists in finding the set of migrations that allow to keep turned on the minimum number of servers needed to host all the VMs. However, most of the proposed consolidation approaches do not consider the network related consumption, which represents about 10–20% of the total energy consumed by IT equipment in real data centers. This paper proposes a novel joint server and network consolidation model that takes into account the power efficiency of both the switches forwarding the traffic and the servers hosting the VMs. It powers down switch ports and routes traffic along the most energy efficient path towards the least energy consuming server under QoS constraints. Since the model is complex, a fast Simulated Annealing based Resource Consolidation algorithm (SARC) is proposed. Our numerical results demonstrate that our approach is able to save on average 50% of the network related power consumption compared to a network unaware consolidation.

Energy Aware Resource Management of Cloud Data Centers

Cloud Computing, the long-held dream of computing as a utility, has the potential to transform a large part of the IT industry, making software even more attractive as a service and shaping the way IT hardware is designed and purchased. Virtualization technology forms a key concept for new cloud computing architectures. The data centers are used to provide cloud services burdening a significant cost due to high energy consumption. Data centers are provisioned to accommodate peak demand rather than average demand and cloud applications consume much more electrical energy than they need. Thus, it necessitates that cloud computing solutions not only minimize operational costs, but also reduce the power consumption. In this paper, we investigate load balancing and power saving methods in virtualized cloud infrastructures. Imbalanced distribution of workloads across resources can lead to performance degradation and much electrical power consumption in such data centers. We present an architectural framework and principles for energy-efficient cloud computing environments. Resource provisioning and allocation algorithms, named Load-Power-aware, are proposed in this architecture. The algorithm employs a heuristic to dynamically improve the energy efficiency in data center, while guarantees the Quality of Service (QoS). The efficiency of the proposed approach is evaluated by using the most common cloud computing simulation toolkit, CloudSim. The performance modeling and simulation results are depicted the proposed approach significantly improves the energy efficiency in a given dynamic scenario, while a small amount of service level agreements (SLA) is missed.

Intelligent Resource Management in Blockchain-Based Cloud Datacenters

Nowadays, more and more companies migrate business from their own servers to the cloud. With the influx of computational requests, datacenters consume tremendous energy every day, attracting great attention in the energy efficiency dilemma. In this paper, we investigate the energy-aware resource management problem in cloud datacenters, where green energy with unpredictable capacity is connected. Via proposing a robust blockchain-based decentralized resource management framework, we save the energy consumed by the request scheduler. Moreover, we propose a reinforcement learning method embedded in a smart contract to further minimize the energy cost. Because the reinforcement learning method is informed from the historical knowledge, it relies on no request arrival and energy supply. Experimental results on Google cluster traces and real-world electricity price show that our approach is able to reduce the datacenters cost significantly compared with other benchmark algorithms.

Energy Aware Resource Management and Job Scheduling in Cloud Datacenter

Energy Aware Resource Management and Job Scheduling in Cloud Datacenter

Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms

In this paper, we address the problem of reducing Cloud datacenter high energy consumption with minimal Service Level Agreement (SLA) violation. Although there are many energy-aware resource management solutions for Cloud datacenters, existing approaches focus on minimizing energy consumption while ignoring the SLA violation at the time of virtual machine (VM) deployment. Also, they do not consider the types of application running in the VMs and thus may not really reduce energy consumption with minimal SLA violation under a variety of workloads. In this paper, we propose two novel adaptive energy-aware algorithms for maximizing energy efficiency and minimizing SLA violation rate in Cloud datacenters. Unlike the existing approaches, the proposed energy-aware algorithms take into account the application types as well as the CPU and memory resources during the deployment of VMs. To study the efficacy of the proposed approaches, we performed extensive experimental analysis using real-world workload, which comes from more than a thousand PlanetLab VMs. The experimental results show that, compared with the existing energy-saving techniques, the proposed approaches can effectively decrease the energy consumption in Cloud datacenters while maintaining low SLA violation.