Energy-aware Resource Allocation Research Articles

A secured energy aware resource allocation and task scheduling based on improved cuckoo search algorithm and deep reinforcement learning for e-healthcare applications

Healthcare industries have begun using modern technologies and tools like cloud computing these days. When cloud computing and assisted technologies are used in the healthcare domain the storage cost improves along with the quality and time availability. The present work makes use of cryptography based architecture for improving the overall security aspects. However, the tasks of the user take a longer duration for the execution to complete when the needed resources are unavailable on the main server. ICSA (Improved Cuckoo Search Algorithm) with DRL (Deep Reinforcement Learning) to solve this issue. The main goal of this work is to increase overall security and reduce execution times of jobs. The objective also includes using underutilized resources. Scheduling jobs, Binary In-order Traversal Trees with weights are used. Subsequently, DRL algorithm has been used to decrease the space complexity by splitting the individual resources. There will be idle resources in a state space that are used in allocation of tasks. The scheduled tasks will then do a search on the resources based on the ICSA algorithm. The server will then distribute the resources to the action area after an optimal resource has been chosen and assigned to the job. DRL for resource allocation so that the inactive resource usage is minimized and the execution time is reduced. Finally, MTAC-IBEA (multi tenant authentication control with improved blowfish encryption algorithm) for decrypt health data. The outcomes of simulation demonstrate that the suggested method offers optimum work schedules, resource allocations, and security in e-healthcare systems.

Radial basis function neural network-based algorithm unfolding for energy-aware resource allocation in wireless networks

Radial basis function neural network-based algorithm unfolding for energy-aware resource allocation in wireless networks

An energy aware resource allocation based on combination of CNN and GRU for virtual machine selection

An energy aware resource allocation based on combination of CNN and GRU for virtual machine selection

Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments

Offloading Internet of Things (IoT) tasks to the cloud for further processing might not always lead to an optimal execution time, particularly in situations such as resource contention, under-provisioning, over-provisioning, and fragmentation. In addition, dynamically optimizing the number of Virtual Machines (VMs) for resource scheduling in order to meet application requirements remains a major research challenge. Further, existing resource scheduling algorithms focus primarily on minimizing operational costs while maximizing resource sharing and utilization. Considering energy utilization as part of the resource allocation and scheduling process as an optimization objective for maintaining load balancing has often been neglected. To address these challenges and more, we propose a cooperative energy-aware resource allocation and scheduling strategy based on a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. We used the Grid Workloads Archive dataset to evaluate our proposed TOPSIS-based Resource Allocation (TOPREAL) approach. Experimental results with respect to the allocation of VM resources when considering processing a large segment of tasks indicate that TOPREAL outperforms existing algorithms in terms of energy savings, with an average improvement of 40.25%, while maintaining an average improvement of 16.21% when it comes to execution time. Results also demonstrate that our method can save an average of 78.06 processing hours and 63,215 kJ of energy when compared to existing scheduling algorithms. These results demonstrate the effectiveness of our proposed model and the viability of using multi-criteria decision-making techniques such as TOPSIS to solve the resource allocation and scheduling problem in edge environments.

EARA-PSOCAE: an energy aware resource allocation and particle swarm optimisation based cryptographic algorithm in e-healthcare cloud environment

EARA-PSOCAE: an energy aware resource allocation and particle swarm optimisation based cryptographic algorithm in e-healthcare cloud environment

EARA-PSOCAE: an energy aware resource allocation and particle swarm optimisation based cryptographic algorithm in e-healthcare cloud environment

EARA-PSOCAE: an energy aware resource allocation and particle swarm optimisation based cryptographic algorithm in e-healthcare cloud environment

QoS and Energy-aware Resource Allocation in Cloud Computing Data Centers using Particle Swarm Optimization Algorithm and Fuzzy Logic System

QoS and Energy-aware Resource Allocation in Cloud Computing Data Centers using Particle Swarm Optimization Algorithm and Fuzzy Logic System

An energy-aware resource allocation method for avionics systems based on improved ant colony optimization algorithm

With the growing number of resources and the expansion of the scale of avionics systems, the problem of energy consumption has become increasingly prominent. Even though high energy consumption increases energy costs and greenhouse gas emissions, it has a significant effect on the reliability and availability of a system. To efficiently reduce energy consumption and improve the performance of avionics systems, an energy-aware resource allocation method based on an improved ant colony optimization algorithm is proposed. First, a mathematical model of resource allocation is established, which reflects the relationship between task requirements and resource energy consumption. Then, an improved ant colony optimization algorithm is proposed to allocate heterogeneous resources, which contributes to minimizing the makespan and achieving low energy consumption. Finally, experimental results show that our proposed algorithm outperforms other existing algorithms. In particular, it reduces makespan by at least 5.48% and reduces energy consumption by at least 5.89%.

Cost-Effective and Energy-Aware Resource Allocation in Cloud Data Centers

Cloud computing supports the fast expansion of data and computer centers; therefore, energy and load balancing are vital concerns. The growing popularity of cloud computing has raised power usage and network costs. Frequent calls for computational resources may cause system instability; further, load balancing in the host requires migrating virtual machines (VM) from overloaded to underloaded hosts, which affects energy usage. The proposed cost-efficient whale optimization algorithm for virtual machine (CEWOAVM) technique helps to more effectively place migrating virtual machines. CEWOAVM optimizes system resources such as CPU, storage, and memory. This study proposes energy-aware virtual machine migration with the use of the WOA algorithm for dynamic, cost-effective cloud data centers in order to solve this problem. The experimental results showed that the proposed algorithm saved 18.6%, 27.08%, and 36.3% energy when compared with the PSOCM, RAPSO-VMP, and DTH-MF algorithms, respectively. It also showed 12.68%, 18.7%, and 27.9% improvements for the number of virtual machine migrations and 14.4%, 17.8%, and 23.8% reduction in SLA violation, respectively.

A new fuzzy-based method for energy-aware resource allocation in vehicular cloud computing using a nature-inspired algorithm

Vehicular cloud computing is a hopeful solution to utilize underused vehicle resources such as processing energy, storage space, Internet connection, etc. These resources can be shared among vehicles or rented by landlords for multiple purposes, such as meeting the hardware needs of automotive network services and applications. It is possible to meet the growing need for resources in the automotive network. Although this plan seems possible, its implementation has some problems. Several scholars have concentrated on architectural design to solve various difficulties and provide users with trustworthy service. This paper presents a fuzzy-based method to allocate resources in vehicular cloud computing using a nature-inspired (cuckoo search algorithm). The suggested algorithm is compared to some state-of-the-art algorithms. The outcomes illustrated that the recommended method outperforms other algorithms in terms of execution time, delay, and makespan.

Multiple linear regression-based energy-aware resource allocation in the Fog computing environment

Fog computing is a promising computing paradigm for time-sensitive Internet of Things (IoT) applications. It helps to process data close to the users, in order to deliver faster processing outcomes than the Cloud; it also helps to reduce network traffic. The computation environment in the Fog computing is highly dynamic and most of the Fog devices are battery powered hence the chances of application failure is high which leads to delaying the application outcome. On the other hand, if we rerun the application in other devices after the failure it will not comply with time-sensitiveness. To solve this problem, we need to run applications in an energy-efficient manner which is a challenging task due to the dynamic nature of Fog computing environment. It is required to schedule application in such a way that the application should not fail due to the unavailability of energy. In this paper, we propose a multiple linear, regression-based resource allocation mechanism to run applications in an energy-aware manner in the Fog computing environment to minimise failures due to energy constraint. Prior works lack of energy-aware application execution considering dynamism of Fog environment. Hence, we propose A multiple linear regression-based approach which can achieve such objectives. We present a sustainable energy-aware framework and algorithm which execute applications in Fog environment in an energy-aware manner. The trade-off between energy-efficient allocation and application execution time has been investigated and shown to have a minimum negative impact on the system for energy-aware allocation. We compared our proposed method with existing approaches. Our proposed approach minimises the delay and processing by 20%, and 17% compared with the existing one. Furthermore, SLA violation decrease by 57% for the proposed energy-aware allocation.

Minimization of Energy-Efficient Outage Probability in AF-Relayed PLC

Energy-aware resource allocation to achieve desired throughput over cooperative power line communications (PLCs) has gained growing interest from past few years. In this article, we design an energy-efficient amplify-and-forward relay-assisted PLC, where outage probability based on energy-efficiency is minimized by jointly optimizing the relay location and power allocation (PA) in two scenarios: 1) absence and 2) presence of the end-to-end direct link. To reduce complexity, using a novel and nontrivial approach, we obtain a closed-form expression of the outage probabilities for both the scenarios. Further, an equivalent problem is formulated to make the optimization problem more tractable to obtain its solution. In the absence of direct link, a closed-form solution of the problem is obtained by the optimization schemes where underlying variables are optimized individually and jointly. While, in the presence of the direct link, a closed-form solution is obtained using a nontrivial tight approximation algorithm. Via numerical results, the outage expressions are validated, and various insights on optimization of PA and relay location are obtained. Finally, with respect to energy-efficiency, it is shown that the joint optimization gives an outage improvement of around 55% and 37% in the absence and presence of the direct link, respectively.

Managing Heterogeneous and Time-Sensitive IoT Applications through Collaborative and Energy-Aware Resource Allocation

In the Internet of Things (IoT) environment, the computing resources available in the cloud are often unable to meet the latency constraints of time critical applications due to the large distance between the cloud and data sources (IoT devices). The adoption of edge computing can help the cloud deliver services that meet time critical application requirements. However, it is challenging to meet the IoT application demands while using the resources smartly to reduce energy consumption at the edge of the network. In this context, we propose a fully distributed resource allocation algorithm for the IoT-edge-cloud environment, which (i) increases the infrastructure resource usage by promoting the collaboration between edge nodes, (ii) supports the heterogeneity and generic requirements of applications, and (iii) reduces the application latency and increases the energy efficiency of the edge. We compare our algorithm with a non-collaborative vertical offloading and with a horizontal approach based on edge collaboration. Results of simulations showed that the proposed algorithm is able to reduce 49.95% of the IoT application request end-to-end latency, increase 95.35% of the edge node utilization, and enhance the energy efficiency in terms of the edge node power consumption by 92.63% in comparison to the best performances of vertical and collaboration approaches.

ENERDGE: Distributed Energy-Aware Resource Allocation at the Edge.

Mobile applications are progressively becoming more sophisticated and complex, increasing their computational requirements. Traditional offloading approaches that use exclusively the Cloud infrastructure are now deemed unsuitable due to the inherent associated delay. Edge Computing can address most of the Cloud limitations at the cost of limited available resources. This bottleneck necessitates an efficient allocation of offloaded tasks from the mobile devices to the Edge. In this paper, we consider a task offloading setting with applications of different characteristics and requirements, and propose an optimal resource allocation framework leveraging the amalgamation of the edge resources. To balance the trade-off between retaining low total energy consumption, respecting end-to-end delay requirements and load balancing at the Edge, we additionally introduce a Markov Random Field based mechanism for the distribution of the excess workload. The proposed approach investigates a realistic scenario, including different categories of mobile applications, edge devices with different computational capabilities, and dynamic wireless conditions modeled by the dynamic behavior and mobility of the users. The framework is complemented with a prediction mechanism that facilitates the orchestration of the physical resources. The efficiency of the proposed scheme is evaluated via modeling and simulation and is shown to outperform a well-known task offloading solution, as well as a more recent one.

A comprehensive review and open issues on energy aware resource allocation in cloud

A comprehensive review and open issues on energy aware resource allocation in cloud

A comprehensive review and open issues on energy aware resource allocation in cloud

A comprehensive review and open issues on energy aware resource allocation in cloud

Multiple Linear Regression-Based Energy-Aware Resource Allocation in the Fog Computing Environment

Fog computing is a promising computing paradigm for time-sensitive Internet of Things (IoT) applications. It helps to process data close to the users, in order to deliver faster processing outcomes than the Cloud; it also helps to reduce network traffic. The computation environment in the Fog computing is highly dynamic and most of the Fog devices are battery powered hence the chances of application failure is high which leads to delaying the application outcome. On the other hand, if we rerun the application in other devices after the failure it will not comply with time-sensitiveness. To solve this problem, we need to run applications in an energy-efficient manner which is a challenging task due to the dynamic nature of Fog computing environment. It is required to schedule application in such a way that the application should not fail due to the unavailability of energy. In this paper, we propose a multiple linear, regression-based resource allocation mechanism to run applications in an energy-aware manner in the Fog computing environment to minimise failures due to energy constraint. Prior works lack of energy-aware application execution considering dynamism of Fog environment. Hence, we propose A multiple linear regression-based approach which can achieve such objectives. We present a sustainable energy-aware framework and algorithm which execute applications in Fog environment in an energy-aware manner. The trade-off between energy-efficient allocation and application execution time has been investigated and shown to have a minimum negative impact on the system for energy-aware allocation. We compared our proposed method with existing approaches. Our proposed approach minimises the delay and processing by 20%, and 17% compared with the existing one. Furthermore, SLA violation decrease by 57% for the proposed energy-aware allocation.

Energy and Quality Aware Multi-Objective Resource Allocation Algorithm in Cloud

Cloud gaming has become the new service provisioning prototype that hosts the video games in the cloud and broadcasts the interactive game streaming to the players through the Internet. Here, the cloud must use massive resources for video representation and its streaming when several simultaneous players reach a particular point. Alternatively, various players may have separate necessities on Quality-of Experience, like low delay, high-video quality, etc. The challenging task is providing better service by the fixed cloud resource. Hence, there is a necessity for an energy-aware multi-resource allocation in the cloud. This paper devises a Fractional Rider-Harmony search algorithm (Fractional Rider-HSA) for resource allocation in the cloud. The Fractional Rider-HSA combines fractional calculus, Rider Optimization algorithm (ROA), and HSA. Moreover, the fitness function, like mean opinion score (MOS), gaming experience loss, fairness, energy consumption, and network parameters, is considered to determine the optimal resource allocation. The proposed model produces the maximal MOS of 0.8961, maximal gaming experience loss (QE) of 0.998, maximal fairness of 0.9991, the minimum energy consumption of 0.3109, and minimal delay 0.2266, respectively.

Survival Multipath Energy-Aware Resource Allocation in SDM-EONs During Fluctuating Traffic

Energy consumption efficiency during resource allocation is crucial for large scale networks. The evolution of space division multiplexing elastic optical networks (SDM-EONs) has presented tremendous spatial dimensions over single-mode networks to manage transmission of largetraffic volumes, during peak load of the fluctuating traffic. However, due to large capacitytransmission rates, the resource allocation challenges have led to incremental energy consumption. This is due to the impact of crosstalk (XT), which occurs between active adjacent cores, during core switching, and increasing service disruptions caused by rampant link failures. To address the above issues, this article proposes a distance-adaptive energy-aware resource allocation (DERA) algorithm using a survival multipath scheme. The DERA algorithm implements the concept of the survival multipath scheme with Spectrum Compactness (SC) to minimize dynamic XT effects in SDM-EONs, during resource allocation. The objective is to improve spectral efficiency to save energy consumption, when the load of the fluctuating traffic peaks. During Routing, Modulation, Spectrum, and Core Allocation (RMSCA), the DERA algorithm can efficiently allocate resources in a survival multipath scheme. The performance of the DERA-RMSCA is compared with the two Multipath Protection (MPP) schemes, PRIME-RMSCA, and CMDE-RMSCA. The results demonstrate that DERA-RMSCA can achieve a better performance compared with PRIME-RMSCA, and CMDE-RMSCA.

Energy aware resource allocation and complexity reduction approach for cognitive radio networks using game theory

Nowadays, the demand for mobile wireless communication systems has increased drastically due to its significant use for various real-time applications. This increased demand for communication causes heavy utilization of the radio spectrum to improve ubiquitous computing services. However, systems providing high-speed communication fail to achieve the desired performance due to unsystematic spectrum utilization and resources. The problem addressed by Cognitive Radio Network (CRN) architecture has attracted research and industrial community to enhance the real-time communication systems. Although CRN based real-time communication systems suffer from resource allocation, spectrum sensing, and power consumption issues. In this paper, we introduce a novel approach for resource allocation and sharing based on cooperative game theory, and cooperative node selection ensures maximized payoff. The proposed method optimizes the overhead, energy consumption, and resource utilization. Further, energy consumption and resource allocation issues transformed into an optimization problem. A backtracking search algorithm is applied to reduce the computation complexity and to find the optimal solution for resource utilization. The simulation result obtained achieves better performance compared to the existing energy-aware scheduling approach in CRN.