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

An expansion of services and unprecedented traffic growth is anticipated in future networks, aligned with the adoption of the long-awaited Fifth Generation (5G) of mobile communications. To support this demand, without exposing mobile operators to the pressure of CAPEX and OPEX, 5G uses new frequency bands, and adopts promising trends, including: densification, softwarization, and autonomous management. While the first technology is proposed to handle the traffic growth requirements, the softwarization and autonomous management are expected to play, in synergy, to ensure the desired trade-off between reducing the CAPEX and OPEX, while guaranteeing the quality of service (QoS). Softwarization is expected to transform the network design, from one size fits all, to more demand oriented adaptive resource allocation. In this work, we focus on this point, by discussing how these technologies act in synergy towards enabling RAN sharing. Particularly, we focus on how they fit into the issue of energy efficient Multi-Operator Resource Allocation (MO-RA). After a survey and classification of schemes leveraging this synergy for distinct resource allocation (RA) objectives, we present a detailed survey and qualitative classification of RA schemes with respect to energy efficiency. This work presents an innovative survey, since it concentrates on multiple operators, and the enabling of Mobile Virtual Network Operators (MVNOs), which will come into play with the complete virtualization of mobile networks. Based on the deep literature analysis of the different operations that can bring energy savings to MO-RA, we conclude the work with listing open challenges and future research directions.

Long‐Term Evolution–unlicensed (LTE‐U) has recently attracted worldwide interest to meet the explosion in cellular traffic data. By using carrier aggregation, licensed and unlicensed bands are integrated to enhance transmission capacity while maintaining reliable and predictable performance. As there may exist other conventional unlicensed band users, such as WiFi users, LTE‐U users have to share the same unlicensed bands with them. Thus, an optimized resource allocation scheme to ensure the fairness between LTE‐U users and conventional unlicensed band users is critical for the deployment of LTE‐U networks. In this paper, we investigate an energy efficient resource allocation problem in LTE‐U coexisting with other wireless networks, which aims at guaranteeing fairness among the users of different radio access networks. We formulate the problem as a multiobjective optimization problem and propose a semidistributed matching framework with a partial information‐based algorithm to solve it. We demonstrate our contributions with simulations in which various network densities and traffic load levels are considered.

Resource capacity plans and allocation policies have a significant impact on the performance of business projects. This is particularly true in situations where multiple projects compete concurrently for scarce resources. Project management tools have limited ability to analyze the impact of resource allocation policies in systems with variability. Simulation tools are designed for this type of analysis. This paper focuses on simulation analyses of the relation between changes to resource capacity, resource allocation policies, variability, and project performance. Scenarios are simulated for different combinations of changes to resource quantities, work schedule durations, allocation policies, and task duration variability. Each scenario's performance is measured based on total project cycle-times and costs. The results demonstrate how increasing the flexibility of resource allocation policies can increase the effectiveness of resource capacity and significantly reduce project cycle-times without increasing project costs.

Recently, cloud computing has been the most popular method among all other promising technologies that provides the service for many users at the same time. all users can reach and use the resource that is provided by the Virtual Machines (VMs). Hence users process, data must arrive to the resources to perform as a disturbed system, which provides dynamical scale services and virtualized resources. As a result of limitation of resources, the deadlock status may occur in such type of systems. In this paper, we have designed an algorithm that could increase the efficiency of deadlock avoidance. Our technique will use the attribute of execution time in the process to get better resource allocation in additional to the checking system to stay in safe state and free of deadlock.

To enjoy rich Internet services, contemporary mobile devices generate heavy loads of computation tasks, which cannot be suffered locally due to the limited computing and energy capacities of each device. Cloud radio access network (Cloud RNA) based heterogeneous structure is widely accepted as a promising way to make up devices' shortage and computation need. To determine whether the computation task should be migrated from the device to the Cloud RAN, traditional work usually aims to improve resource utilization efficiency and constrain the latency of computation task. However, less concerned on the energy efficiency problem for the device. Through maintaining higher energy efficiency, device can utilize the limited energy in a reasonable way. In this paper, we formulate an energy efficient resource allocation problem for the computation tasks in Cloud-RAN based heterogeneous Network (Cloud-RAN HetNet). Within this problem, two subproblems are proposed. The first is determining whether computation task on the device should be offloaded to the the Cloud RAN. The second is energy efficiency based resource allocation while considering latency limitation for the computation tasks. To solve the proposed problem, a computation location selection and resource allocation (CCSRA) method is put forward. Finally, we utilize Various simulation results show the property of the strategy.

Cloud computing has transformed IT infrastructure by offering scalable, pay-as-you-go resources. However, efficiently allocating cloud resources under dynamic workloads while minimizing operational costs and maintaining service-level agreements (SLAs) remains a critical challenge. This paper presents a hybrid AI-driven framework for cost-efficient resource allocation in cloud environments. The proposed model integrates Long Short-Term Memory (LSTM) networks for workload forecasting, reinforcement learning (RL) for dynamic decision-making, heuristic scheduling (inspired by ACO and GA) for optimal task assignments, and economic pricing using the ERA model. A real-world data set from Google Cluster Trace is used to validate the framework. Simulation results show a forecasted CPU utilization of 10.79% with an estimated cost of ₹1.08 and one SLA violation, demonstrating the potential of hybrid AI models for real-time and cost-aware resource provisioning.

WITHDRAWN: Experimental performance analysis of load balancing of tasks using honey bee inspired algorithm for resource allocation in cloud environment

The synergistic convergence of Cloud Computing and the Internet of Things (IoT) has unlocked unprecedented potential for smart applications. However, this integration presents a critical challenge: the massive data volume from IoT devices leads to substantial energy consumption in cloud data centers, resulting in high operational costs and environmental impact. Consequently, energy-efficient resource allocation has emerged as a paramount research objective. This survey provides a comprehensive review of state-of-the-art architectures, algorithms, and techniques designed to enhance energy efficiency in cloud-centric IoT ecosystems. It begins by outlining the fundamental architecture of IoT-cloud systems and identifying the primary sources of energy drain across computational, storage, and network resources. The paper then presents a systematic taxonomy and analysis of key strategies, including dynamic resource provisioning, virtual machine (VM) consolidation, and network optimization. A dedicated section examines data management strategies—encompassing data reduction, distributed storage, and efficient retrieval—which are crucial for mitigating the data-intensive burden of IoT workloads. Furthermore, the paper compares the performance of various intelligent algorithms against traditional methods, highlighting trends where they consistently outperform in metrics like storage efficiency and query latency. Finally, it discusses persistent open challenges and future research directions, such as the integration of machine learning for predictive allocation, the role of edge computing, and the need for standardized evaluation metrics. This survey serves as a foundational resource for researchers and practitioners aiming to advance the sustainability and scalability of cloud-based IoT services.

Cloud systems are inefficient in processing delay-sensitive applications due to the WAN latency associated. To augment the processing of cloud services and provide delay-free computation, fog computing is used. The delay sensitivity of the tasks and heterogeneity of the fog-cloud hybrid architecture calls for efficient resource allocation policies. The decision making must be precise and also multiple criteria must be considered while deciding which resources to allocate. In this article, we propose two variants of analytic hierarchy process (AHP)-based resource allocation policies for fog-cloud hybrid systems. The proposed resource allocation policies consider network load, in addition, to the compute load during decision making. The overall aim of the resource allocation policies is to reduce the delay incurred by each task. The allocation policies differ in the way they assign weights to each criterion of optimization. One of the resource allocation policies uses predetermined weights for compute and network while the second method finds the weights dynamically from the overall data. The experimental results show that the proposed approach outperforms existing resource allocation approaches thereby showing the usefulness of AHP-based optimization in fog-cloud hybrid systems.

Cloud computing become a new technology in enterprises and markets. It allows us to access applications anywhere at any time. Companies rent the resources from cloud to reduce the cost. Further they can access the application based on pay as you go manner. The important issue in resource allocation is optimization of resources being allocated. The objective of resource allocation is to scale down the expenditure and increase revenue of cloud service provider. The main challenge in resource allocation is to meet the service level agreement of both customer and cloud service provider. In this paper various resource allocation mechanisms and the optimization techniques are discussed. In addition to that we also taught about a range of challenges involved and classification of applications based on Qos metrics.

SummaryDue to the high performance of cloud computing‐based microservices, a wide range of industries and fields rely on them. In a containerized cloud, traditional resource management strategies are typically used to allocate and migrate virtual machines. A major problem for cloud service providers is resource allocation for containers, which directly affects system performance and resource consumption. In this paper, we propose a joint optimization of load balancing and resource allocation in the cloud using an optimal container management strategy. We aim to enhance scheduling efficiency and reduce costs by improving the container's schedule requested digitally by users. An improved backtracking search optimization (IBSO) algorithm is used to allocate resources between end‐users/IoT devices and the cloud under the consideration of service‐level agreements. Mechanic quantum recurrent neural networks (MQ‐RNNs) are designed to allocate, consolidate, and migrate containers in cloud environments. The various simulation measures used to validate the proposed strategy are energy consumption, number of active servers, number of interruptions, total cost, runtime, and statistical measures.

Cloud computing is emerging as an increasingly popular computing paradigm, allowing dynamic scaling of resources available to users as needed. This requires a highly accurate demand prediction and a resource allocation methodology. The existing methodologies for dynamic resource allocation do not provide effective performance isolation between the VM and Artificial Demand Analysis machines since it gets affected by interferences. To overcome these issues, this paper proposes a conceptual model and an effective algorithm to achieve dynamic resource allocation by migrating tasks or requests in VMs. At first, task demands from the multiple users go to the feature extraction process. In feature extraction, features of the user's tasks and cloud server are extracted. Next both features are reduced by using Modified PCA algorithm to reduce the dynamic resource allocation processing time. Finally, both the features are combined and resource allocation is performed using Hybrid Particle Swarm Optimization and Modified Genetic Algorithm (HPSO-MGA). Then the optimized task has been scheduled to particular VM for allocating the resources. The experimental result of the proposed resource allocation methodology indicates better performance when compared with the existing methods Firefly and Krill herd Load Balancing (LB). For 100 VMs the reliability of HPSO-MGA is 0.87 but the exiting krill herd LB and IDSA gives 0.78 and 0.85, which is lower than the proposed one.

In this paper, resource allocation for energy efficient communication in multi-cell orthogonal frequency division multiple access (OFDMA) downlink networks with cooperative base stations (BSs) is studied. The considered problem is formulated as a non-convex optimization problem which takes into account the circuit power consumption, the limited backhaul capacity, and the minimum required data rate for joint BS zero-forcing beamforming (ZFBF) transmission. By exploiting the properties of fractional programming, the considered non-convex optimization problem in fractional form is transformed into an equivalent optimization problem in subtractive form, which enables the derivation of an efficient iterative resource allocation algorithm. For each iteration, the optimal power allocation solution is derived with a low complexity suboptimal subcarrier allocation policy for maximization of the energy efficiency of data transmission (bit/Joule delivered to the users). Simulation results illustrate that the proposed iterative resource allocation algorithm converges in a small number of iterations, and unveil the trade-off between energy efficiency and network capacity.

Cloud computing is one among the emerging platforms in business, IT enterprise and mobile computing applications. Resources like Software, CPU, Memory and I/O devices etc. are utilized and charged as per the usage, instead of buying it. A Proper and efficient resource allocation in this dynamic cloud environment becomes the challenging task due to drastic increment in cloud usage. Various promising technologies have been developed to improve the efficiency of resource allocation process. But still there is some incompetency in terms of task scheduling and power consumption, when the system gets overloaded. So an energy efficient task scheduling algorithm is required to improve the efficiency of resource allocation process. In this paper an improved task scheduling and an optimal power minimization approach is proposed for efficient dynamic resource allocation process. Using prediction mechanism and dynamic resource table updating algorithm, efficiency of resource allocation in terms of task completion and response time is achieved. This framework brings an efficient result in terms of power reduction since it reduces the power consumption in data centers. The proposed approach gives accurate values for updating resource table. An efficient resource allocation is achieved by an improved task scheduling technique and reduced power consumption approach. The Simulation result gives 8% better results when comparing to other existing methods.

Unmanned aerial vehicles (UAVs) have been conceived as an available solution to substitute terrestrial base stations (TBSs) to provide downloading services for user equipment (i.e. mobile devices) that have difficulty communicating directly with TBSs. However, the mobility of user equipment (UE) and the random nature of the number of UE will cause several challenges including 1) the hardness of determining optimal user association and UAV resource (i.e., bandwidth and transmit power) allocation decision, 2) the burden of network function maintenance owing to the necessity of shutting down the entire system. Therefore, in this article, joint user association and resource allocation are designed for a software-defined network (SDN)-adopted leaderless softwarized UAV network, where each UAV is regarded as a flying SDN controller to enhance the control ability of the considered network. The purpose is to maximize energy efficiency (EE) with satisfying the quality of service (QoS). To this end, a joint method based on hierarchical agglomerative clustering (HAGC) and multi-agent deep deterministic policy gradient (MADDPG) is proposed. Specifically, the HAGC approach is utilized to determine the optimal MDs association with UAVs. Afterward, MADDPG approach is leveraged to obtain the best policy for resource allocation, aiming to achieve the maximum EE. Finally, the effectiveness of the proposed method is confirmed by the evaluation results.