Energy Consumption Of Servers Research Articles

Dependency-aware online task offloading based on deep reinforcement learning for IoV

The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%.

Service placement strategies in mobile edge computing based on an improved genetic algorithm

In mobile edge computing (MEC), quality of service (QoS) is closely related to optimizing service placement strategies, which is crucial to providing efficient services that meet user needs. However, due to the mobility of users and the energy consumption limit of edge servers, the existing policies make it difficult to ensure the QoS level of users. In this paper, a novel genetic algorithm based on a simulated annealing algorithm is proposed to balance the QoS of users and the energy consumption of edge servers. Finally, the effectiveness of the algorithm is verified by experiments. The results show that the QoS value obtained by the proposed algorithm is closer to the maximum value, which has significant advantages in improving QoS value and resource utilization. In addition, in software development related to mobile edge computing, our algorithm helps improve the program’s running speed.

Learning-Based Cloud Server Configuration for Energy Minimization Under Reliability Constraint

Cloud computing has attracted wide attention from both academia and industry, since it can provide flexible and on-demand hardware and software resources as services. Energy consumption of cloud servers is the main concern of cloud service providers since reducing energy consumption can bring them a lower operation cost (and hence a higher profit) and alleviate carbon footprints to the environment. Typically, the common power management techniques for enhancing energy efficiency would make cloud servers more vulnerable to soft errors and hence adversely impact the quality of services. Thus, reliability cannot be ignored in the design of methodologies for improving the energy efficiency of cloud servers. In this article, we aim to minimize the energy consumption of cloud servers under the soft-error reliability constraint by configuring the size and speed of servers. Specifically, we first derive the expected reliability based energy consumption of cloud servers to formulate the reliability-constrained energy minimization problem. We then leverage the reinforcement learning technique to obtain an optimal server configuration solution that maximizes system energy efficiency while maintaining the system reliability constraint. Finally, we perform extensive simulation experiments to analyze the relationship between system energy consumption and server configuration under varying arrival rates and execution requirements of service requests. Comparative experiments are also performed to validate the efficacy of the proposed learning-based server configuration scheme. Results show that compared to a benchmark method, the energy saved by the proposed scheme can reach up to 31.5%.

Agent-based cloud simulation model for resource management

Driven by the successful service model and growing demand, cloud computing has evolved from a moderate-sized data center consisting of homogeneous resources to a heterogeneous hyper-scale computing ecosystem. This evolution has made the modern cloud environment increasingly complex. Large-scale empirical studies of essential concepts such as resource allocation, virtual machine migration, and operational cost reduction have typically been conducted using simulations. This paper presents an agent-based cloud simulation model for resource management. The focus is on how service placement strategies, service migration, and server consolidation affect the overall performance of homogeneous and heterogeneous clouds, in terms of energy consumption, resource utilization, and violation of service-level agreements. The main cloud elements are modeled as autonomous agents whose properties are encapsulated. The complex relationships between components are realized through asynchronous agent-to-agent interactions. Operating states and statistics are displayed in real time. In the evaluation, the efficiency of the simulator is studied empirically. The performance of various resource management algorithms is assessed using statistical methods, and the accuracy of server energy consumption models is examined. The results show that agent-based models can accurately reflect cloud status at a fine-grained level.

A Novel Approach for Energy-Efficient Container Migration by Using Gradient Descent Namib Beetle Optimization

Cloud services are increasingly available through containers due to their scalability, portability, and reliable deployment, particularly in microservices and smart vehicles. The scheduler component of cloud containers plays a crucial role in optimizing energy efficiency and minimizing costs due to the diversity of workloads and cloud resources. The growing demand for cloud services poses a challenge in terms of energy consumption. Optimizing energy consumption in servers is possible by utilizing live migration technology. This study aims to propose a hybrid model that facilitates the migration of containers from one server to another using Gradient Descent Namib Beetle Optimization (GNBO) algorithms, thereby reducing the energy consumption of cloud servers. The work is carried out through cloud simulation using Physical Machines (PM), Virtual Machines (VM), and Containers. Tasks are allocated to VMs in a round-robin manner. The Actor-Critic Neural Network (ACNN) is employed to predict the load of PMs, and overloading and underloading conditions are determined based on the load. The proposed GNBO hybrid optimization calculates the optimal solution considering predicted load, migration costs, resource utilization, energy consumption, and network bandwidth. This approach achieves a load of 0.177 MIPS, migration costs of 10.146 J, and optimizes energy consumption to 0.068 W.

The Markov Concept of the Energy Efficiency Assessment of the Edge Computing Infrastructure Peripheral Server Functioning over Time

The article is devoted to the research of the peripheral server energy consumption managing process defined based on the threshold policy by manipulating the values of the characteristic parameters of the arithmetic-logical complex of the latter. The research object is formalized by a Markov queue model with a single-threshold control scheme for the intensity of accepted requests service. A characteristic feature of the life cycle of a peripheral server is the non-stationary mode of operation in terms of energy consumption, due to the need to periodically poll the controlled sensor network and process the received data in real-time. To take into account this circumstance, the intensities of transitions in the heterogeneous birth-and-death Markov process of the created model are interpreted as non-random periodic functions of time. The resulting mathematical apparatus is summarized in the metric that allows us to estimate the target peripheral server’s energy consumption both at a specific moment and for a censored time interval (this distinguishes the obtained result from existing analogs).

EWM: An entropy‐based framework for estimating energy consumption of edge servers

AbstractIn mobile edge computing (MEC), accurately predicting and monitoring the energy consumption of edge servers is a key challenge in achieving green computing. The importance of solving this problem is that it can help optimize the energy usage in data centers and thus reduce the carbon emission of MEC. To this end, we propose an innovative entropy‐based power modeling framework called entropy weighted model (EWM). The EWM framework weights and combines classical prediction models by analyzing the major components of a server and selecting appropriate parameters. We validate the performance of EWM using real server power and performance counter data and compare it with other classical prediction models by Friedman test. The results show that EWM outperforms other classical prediction models in all test datasets. This result validates the significant advantages of our EWM framework in solving the critical problem of edge server power prediction, and provides an effective tool for achieving data center energy optimization and promoting green computing, resulting in a highly general and accurate prediction model.

A Hybrid Many-Objective Optimization Algorithm for Task Offloading and Resource Allocation in Multi-Server Mobile Edge Computing Networks

Mobile edge computing (MEC) is an effective computing tool to cope with the explosive growth of data traffic. It plays a vital role in improving the quality of service for user task computing. However, the existing solutions rarely address all the significant factors that impact the quality of service. To challenge this problem, a trusted many-objective model is built by comprehensively considering the task time delay, server energy consumption, trust metrics between task and server, and user experience utility factors in multi-server MEC networks. We decompose the original problem into task offloading (TO) and resource allocation (RA) to address the model. Then a novel hybrid many-objective optimization algorithm based on cascading clustering and incremental learning is designed to optimize the TO decision solutions. A low-complexity heuristic method is adopted based on the optimal TO decision solutions to optimize the RA problem continuously. To verify the model's validity and the optimisation algorithm's superiority, five other advanced many-objective algorithms are used for comparison. The results show that our algorithm has more than half the number of the superior values for the benchmark problem. And the obtained model solution shows good performance on different indicators metrics for the decomposition problem.

Data center server workload and infrastructure control based on a joint RSM and CFD approach

The rapid expansion of data centers and cloud computing has exacerbated the issue of high energy consumption and localized overheating in servers. Consequently, the identification of effective cooling methods and resource allocation strategies for server rooms has become pivotal in enhancing overall airflow distribution and optimizing thermal performance within data centers. Time-efficient Computational Fluid Dynamics (CFD) tools offer an alternative to resource-intensive experimental measurements. However, it is difficult to monitor the information in real time and optimize the design of DC. In this study, an innovative application of Response Surface Methodology (RSM) is introduced to prediction thermal environment information and optimization performance of data center. In contrast to traditional single-factor optimization methods, this approach incorporates multiple factors, such as server workloads and air supply conditions, as optimization parameters. Furthermore, maximum server temperatures, temperature differences, and other metrics are defined as key performance indicators (KPIs). A predictive model was developed with the aim of offering comprehensive information about the thermal environment of the data center. Moreover, the study qualitatively analyzes the influence of each parameter on various indicators. Fitting equations are solved based on actual conditions to determine optimized configuration schemes in real time for the entire data center or specific scenarios. This approach effectively reduces cooling energy consumption and optimizes data center thermal management. The optimized configuration resulted in a significant reduction of approximately 30% in the maximum server temperature and approximately 20% in the temperature difference. Additionally, the optimization methodology established in this study facilitates the implementation of a real-time control mechanism for data center cooling systems, enabling energy demand and management optimization. The findings of this study offer valuable insights and generalized methods for enhancing the performance of air-cooled servers.

Energy-Efficient Resource Allocation for Backscatter-Assisted Wireless Powered MEC

In this paper, we study the energy-efficient resource allocation for a backscatter-assisted wireless powered mobile edge computing (MEC) network. Considering the binary offloading mode and the limited computation capacity of the MEC server, we propose to minimize the MEC server's energy consumption by jointly optimizing the binary offloading decision of each IoT node, the computing frequencies and time at the MEC server and IoT nodes, the backscatter time and reflection coefficients of IoT nodes, as well as the MEC server's transmit power and time. It is formulated into a non-convex mixed integer programming problem. To solve this problem, we first obtain the optimal computing time and frequency for the MEC server and the optimal computing time of each IoT node based on the proof by contradiction and the monotonicity of the objective function, then we decouple the resulting simplified problem into two subproblems and transform each subproblem into a convex one. On this basis, we propose a low-complexity iterative algorithm to solve the simplified problem by solving the two convex subproblems alternately. Simulation results verify the fast convergence of the proposed iterative algorithm and its superiority over the baseline schemes in terms of the MEC server's energy consumption.

ECDX: Energy consumption prediction model based on distance correlation and XGBoost for edge data center

The rapid development of artificial intelligence (AI) and edge computing technology has promoted the rapid growth of the number of data centers, but also caused large energy consumption. How to accurately predict the energy consumption of servers is crucial to optimize data centers. Thus, a real-time server energy consumption prediction model that integrates distance correlation and extreme gradient boosting (XGBoost) named ECDX is proposed. First, the distance correlation coefficient method is used to filter essential feature parameters and remove redundant features. Second, the cross-validation method is employed to optimize the hyperparameters. Thereafter, a data center server energy consumption prediction model is constructed using the XGBoost algorithm. Numerous experiments have been conducted to compare the performance of the ECDX model with that of benchmark models. The results show that ECDX can adapt to changes in workload, the prediction accuracy is significantly improved, and the average relative error is reduced by 4.698%.

Adaptive Resource Allocation in Cloud Data Centers using Actor-Critical Deep Reinforcement Learning for Optimized Load Balancing

This paper proposes a deep reinforcement learning-based actor-critic method for efficient resource allocation in cloud computing. The proposed method uses an actor network to generate the allocation strategy and a critic network to evaluate the quality of the allocation. The actor and critic networks are trained using a deep reinforcement learning algorithm to optimize the allocation strategy. The proposed method is evaluated using a simulation-based experimental study, and the results show that it outperforms several existing allocation methods in terms of resource utilization, energy efficiency and overall cost. Some algorithms for managing workloads or virtual machines have been developed in previous works in an effort to reduce energy consumption; however, these solutions often fail to take into account the high dynamic nature of server states and are not implemented at a sufficiently enough scale. In order to guarantee the QoS of workloads while simultaneously lowering the computational energy consumption of physical servers, this study proposes the Actor Critic based Compute-Intensive Workload Allocation Scheme (AC-CIWAS). AC-CIWAS captures the dynamic feature of server states in a continuous manner, and considers the influence of different workloads on energy consumption, to accomplish logical task allocation. In order to determine how best to allocate workloads in terms of energy efficiency, AC-CIWAS uses a Deep Reinforcement Learning (DRL)-based Actor Critic (AC) algorithm to calculate the projected cumulative return over time. Through simulation, we see that the proposed AC-CIWAS can reduce the workload of the job scheduled with QoS assurance by around 20% decrease compared to existing baseline allocation methods. The report also covers the ways in which the proposed technology could be used in cloud computing and offers suggestions for future study.

Energy-Aware Service Allocation: A Crow Search-Based Approach

The allocation of services from the cloud to edge servers in the Industrial Internet of Things (IIoT) emerges as a promising solution for providing time-critical services by shifting computation near sensor nodes while improving the energy cost of the cloud servers and energy consumption of sensor nodes. However, allocation of services faces challenges due to– a) limited computation, storage, and energy constraint of edge servers, b) increasing brown energy consumption of cloud servers, and c) compatibility of communication protocols used by edge servers. In this work, we propose crow search-based two schemes, EAllocS and EAllocM, for allocating unsplittable and splittable services to edge servers. In contrast to existing literature, we consider energy consumption, delay, utility score of edge servers, compatibility of the communication protocols, and energy cost of the cloud servers. Extensive simulation results show that EAllocS reduces energy consumption, delay, and maximum load by 56.7%, 49.9%, and 41.4% compared to the benchmarks. EAllocM reduces energy consumption, delay, and maximum load by 58.5%, 24.8%, and 38%, respectively. The results show that proposed schemes save more energy by reducing the energy consumption of processing services. EAllocS and EAllocM also improve total device allocation cost compared to the benchmark schemes.

Research on identification of server energy consumption characteristics via dirichlet max-margin factor analysis similarity preservation model

Growing server energy consumption is a significant environmental issue, and mitigating it is a key technological challenge. Application-level energy minimization strategies depend on accurate modeling of energy consumption during an application’s execution. This paper presents a theoretical and experimental study of the dpMMSPFA model in the field of server energy consumption identification. The dpMMSPFA for classification of hidden spaces uses latent variable support vector machines (LVSVM) to learn discriminative subspaces with maximal marginal constraints. The factor analysis (FA) model, the similarity preservation (SP) item, the Dirichlet process mixture (DPM) model, and the maximal marginal classifier are jointly learned beneath a unified Bayesian architecture to advance classification of predictive power. The parameters of the proposed model can be inferred by the simple and efficient Gibbs sampling in terms of the conditional conjugate property. Empirical results on various datasets demonstrate that 1) max-margin joint learning can significantly improve the prediction performance of the model implemented by feature extraction and classification separately and meanwhile retain the generative ability; 2) dpMMSPFA is superior to MMFA when employing SP item and Dirichlet process mixture as prior knowledge; 3) the classification of dpMMSPFA model can often achieve better results on benchmark and measured energy server consumption datasets; 4) and the recognition rate can reach as high as 95.79% at 10 components, far better than other models on measured energy server consumption datasets.

An energy-efficient quorum-based locking protocol by omitting meaningless methods on object replicas

In current information systems, a huge number of IoT (Internet of Things) devices are interconnected with various kinds of networks like WiFi and 5G networks. A large volume of data is gathered into servers from a huge number of IoT devices and is manipulated to provide application services. Gathered data is encapsulated along with methods to manipulate the data as an object like a database system. In object-based systems, each application is composed of multiple objects. In addition, each object is replicated on multiple physical servers in order to increase availability, reliability, and performance of an application service. On the other hand, replicas of each object is required to be mutually consistent in presence of multiple transactions. Here, a larger amount of electric energy and computation resources are consumed in physical servers than non-replication approaches to serialize conflicting transactions on multiple replicas. Many algorithms to synchronize conflicting transactions are so far proposed like 2PL (Two-Phase Locking) and TO (Timestamp Ordering). However, the electric energy consumption is not considered. In this paper, an EEQBL-OMM (Energy-Efficient Quorum-Based Locking with Omitting Meaningless Method) protocol is newly proposed to reduce not only the average execution time of each transaction but also the total electric energy consumption of servers by omitting the execution of meaningless methods on replicas of each object. Evaluation results show the total electric energy consumption of servers, the average execution time of each transaction, and the number of aborted instances of transactions in the EEQBL-OMM protocol can be on average reduced to 79%, 62%, and 80% of the ECLBQS (Energy Consumption Laxity-Based Quorum Selection) protocol which is proposed in our previous studies in a homogeneous set of servers, respectively. In addition, the evaluation results show the total electric energy consumption of servers, the average execution time of each transaction, and the number of aborted instances of transactions in the EEQBL-OMM protocol can be on average reduced to 73%, 50%, and 67% of the ECLBQS protocol in a heterogeneous set of servers, respectively. The evaluation results also show at most 48% and 51% of the total number of methods can be omitted as meaningless methods in a homogeneous set and heterogeneous set of servers, respectively, in the EEQBL-OMM protocol.

Energy Aware Genetic Algorithm for Independent Task Scheduling in Heterogeneous Multi-Cloud Environment

Cloud datacentres contain a vast number of processors. The rapid expansion of cloud computing is resulting in massive energy usage and carbon emissions which has reported a substantial increase day by day. Consequently, the cloud service providers are looking for eco-friendly solutions. The energy consumption can be evaluated with an energy model, which identifies that, server energy consumption scales linearly with resource (cloud) utilization. This research provides an alternate solution to task scheduling problem which designs an optimized task schedule to minimize the makespan and energy consumptions in cloud datacenters. The proposed method is based on the principle of Genetic Algorithm (GA). In the context of task-scheduling using GA, chromosomal representation is considered as a schedule of set of independent tasks mapped with available cloud or machine in the proposed methodology. A fitness function is taken to optimize the overall execution time or makespan. Energy consumption is evaluated based on minimum makespan value. The proposed technique also tested upon synthesized and benchmark dataset which outperforms the conventional cloud task scheduling algorithms like Min-Min, Max-Min, and suffrage heuristics in heterogeneous multi-cloud system.

EDCSuS: Sustainable Edge Data Centers as a Service in SDN-Enabled Vehicular Environment

Cloud computing has emerged as one of the popular technologies which provide on-demand services to the end users. Such services are hosted by massive geo-distributed data centers (DCs). Nowadays, connected vehicles in a smart city can also avail cloud services through Internet using cellular technologies. But, the advent of 5G technology has posed challenges for DCs such as-low latency and higher data rate requirements. To handle these challenges, edge-DCs (EDCs) can be deployed across a smart city to provide low latency services to the connected vehicles. In lieu of this, in this paper, EDCSuS: Sustainable EDC as a service framework in software defined vehicular environment is proposed. In EDCSuS, first, a software defined controller handles the incoming requests and suggest an optimal flow path. Second, a multi-leader multi-follower Stackelberg game is presented for resource allocation. Third, to improve the resource utilization, a cooperative resource sharing scheme is designed, thereby minimizing the energy consumption of servers in the EDCs. Lastly, a caching scheme is presented to avert excessive energy consumption for retracing the lost link due to vehicular mobility. The efficacy of the proposed scheme has been evaluated using extensive simulations with respect to various parameters. The results obtained prove the effectiveness of EDCSuS.

ECMS: An Edge Intelligent Energy Efficient Model in Mobile Edge Computing

With the increasing popularity of mobile edge computing (MEC) for processing intensive and delay sensitive IoT applications, the problem of high energy consumption of MEC has become a significant concern. Energy consumption prediction and monitoring of edge servers are crucial for reducing MEC’s carbon footprint in accordance with green computing and sustainable development. However, predicting energy consumption of edge servers is a nontrivial problem due to the fluctuation and variation of different loads. To address this problem, we propose ECMS, a new edge intelligent energy modeling approach that jointly adopts Elman Neural Network (ENN) and feature selection to optimize the consumption of energy on edge servers. ECMS considers 29 parameters relevant to edge server energy consumption and uses the ENN to develop an energy consumption model. Unlike other energy consumption models, ECMS can successfully deal with load fluctuation and various sorts of tasks, such as CPU-intensive, online transaction-intensive, and I/O-intensive. We have validated ECMS through extensive experiments and compared its performance in terms of accuracy and training time to several baseline approaches. The experimental results show the superiority of ECMS to the baseline models. We believe that the proposed model can be used by the MEC resource providers to forecast and optimize energy use.

Deadline Aware Energy-Efficient Task Scheduling Model for a Virtualized Server

The demand of cloud-based services is increasing day by day. To fulfill the seamless computing demand of consumers, a large number of servers are installed in the datacenters. These servers consume high energy. Reducing the energy consumption of servers with the same performance of service is a vital challenge. Energy-efficient task scheduling is one of the method to reduce energy consumption while preserving the task constraints. This paper presents a deadline and energy-aware scheduling (DEAS) model for a virtualized server to achieve energy efficiency while executing deadline conscious tasks. These tasks are independent in nature and arrives dynamically. The presented DEAS model follows the heuristic approach where the first instance guarantee ratio (GR) is maximized to raise the energy efficiency of the server per unit of work performed. Task slack time is utilized in an innovative way to improve the GR of the server. In this way, the average energy consumption (energy consumption per task) of the server per unit of work done is minimized. The energy efficiency of the server in its idle state is further achieved by applying core-level granularity of dynamic voltage and frequency scaling (DVFS) technology. The DEAS model is evaluated through extensive simulation experiments using the CloudSim simulator. Results show that DEAS model performs better than existing models on the account of considered performance metrics, i.e., GR, total energy consumption, energy consumption per task, and resource utilization.

Nonintrusive Load Management Based on Distributed Edge and Secure Key Agreement

With the advancement of national policies and the rise of Internet of things (IoT) technology, smart meters, smart home appliances, and other energy monitoring systems continue to appear, but due to the fixed application scenarios, it is difficult to apply to different equipment monitoring. At the same time, the limited computing resources of sensing devices make it difficult to guarantee the security in the transmission process. In order to help users better understand the energy consumption of different devices in different scenarios, we designed a nonintrusive load management based on distributed edge and secure key agreement, which uses narrowband Internet of things (NB‐IoT) for transmission and uses edge devices to forward node data to provide real‐time power monitoring for users. At the same time, we measured the changes of server power under different behaviors to prepare for further analysis of the relationship between server operating state and energy consumption.