Multiobjective Load Balancing for Multiband Downlink Cellular Networks: A Meta- Reinforcement Learning Approach

Cloud services are based on datacenter which provides resources on demand with higher capacity, lowest response time and improved resource utilization. The data center comprises of physical hosts which are effectively utilized in the form of Virtual Machines. The task scheduling problem is the mapping of tasks to suitable resources (VMs) as required and it is NP-hard problem. Further, the scheduling algorithms are followed by load balancing techniques for efficient utilization of VMs. In this paper a framework for Load balancing in Cloud Environment has been proposed and implemented for overflow and underflow VM identification. Two metaheuristics and one heuristic have been used in the proposed framework to achieve effective and efficient utilization of VMs in cloud environment. Further, the performance of the proposed framework has been analysed on the basis of makespan and cost metrics which are computed while executing scientific workflow tasks.

We study the problem of balancing the load on processors in an arbitrary network. If jobs arrive or depart during the process of load balancing, we have the dynamic load balancing problem; otherwise, we have the static load balancing problem. While static load balancing on a number of different networks has been well-studied, analytical work on dynamic load balancing is limited. The difficulty lies in modeling the arrivals and departures of jobs in a clean manner. In this paper, we introduce a new framework for dynamic load balancing in which the job traffic is modeled by an adversary. Our main result is that a simple local load balancing algorithm maintains the load of the network within a stable level against a powerful adversary. Our result holds for different models of traffic patterns and processor communication.

We describe the design of a flexible load balancing framework and runtime software system for supporting the development of adaptive applications on distributed-memory parallel computers. The runtime system supports a global namespace, transparent object migration, automatic message forwarding and routing, and automatic load balancing. These features can be used at the discretion of the application developer in order to simplify program development and to eliminate complex bookkeeping associated with mobile data objects. An evaluation of this system in the context of a three-dimensional tetrahedral advancing front parallel mesh generator shows that overall runtime improvements of 15 percent compared to common stop-and-repartition load balancing methods, 30 percent compared to explicit intrusive load balancing methods, and 42 percent compared to no load balancing are possible on large processor configurations. At the same time, the overheads attributable to the runtime system are a fraction of 1 percent of the total runtime. The parallel advancing front method is a coarse-grained and highly adaptive application and therefore exercises all of the features of the runtime system.

Distributed content-based publish/subscribe systems suffer from performance degradation and poor scalability caused by uneven load distributions typical in real-world applications. The reason for this shortcoming is the lack of a load balancing scheme. This article proposes a load balancing solution specifically tailored to the needs of content-based publish/subscribe systems that is distributed, dynamic, adaptive, transparent, and accommodates heterogeneity. The solution consists of three key contributions: a load balancing framework, a novel load estimation algorithm, and three offload strategies. A working prototype of our solution is built on an open-sourced content-based publish/subscribe system and evaluated on PlanetLab, a cluster testbed, and in simulations. Real-life experiment results show that the proposed load balancing solution is efficient with less than 0.2% overhead; effective in distributing and balancing load originating from a single server to all available servers in the network; and capable of preventing overloads to preserve system stability, availability, and quality of service.

Cloud computing is a commercial application of distributed system. With the rapid development of cloud computing, resource management problem has been widely studied and discussed. Most of the existing researches concern resource allocation by launching virtual machines, however, instant monitoring of the hosts, load balancing of the cloud platform is much more important for cloud providers. In this paper, a multi-Agent based distributed load balancing framework is proposed for cloud platform, it uses workload prediction technology and threshold buffering strategies to reduce the migration of VMs. Experiments show that our method can significantly reduce the workload data transmission and solve the frequent migration problem, which can effectively achieve load balancing, promote the utilization of the entire data center resources.

Edge computing provides crucial support to next-generation mobile services that demand high data rates, low latency, and high reliability. The development of high data rate communication has lead to the emergence of cell densification as a key enabler for 5G. Unfortunately, the cost of fiber for backhaul and fronthaul poses hindrance to wide-scale adoption of small cells. Free Space Optics (FSO) offers a cost-effective alternative, though its performance is vulnerable to adverse weather, particularly fog. Recent advances in adaptive optics and automatic steering/tracking have improved FSO reliability by reducing pointing errors and enabling dynamic link reconfiguration, which further supports intelligent traffic diversion and load balancing across edge servers. In this paper, we propose a multi-hop mesh optical access network that integrates fiber-based Passive Optical Networks (PONs) with steerable FSO links to provide reliable and scalable backhaul for dense Radio Access Networks (RANs). A joint optimization framework for dynamic routing and edge server load balancing is developed. Extensive simulations demonstrate that the proposed design ensures fairness, reduces the number of required FSO transceivers, and delivers data rates comparable to established benchmarks.

Dynamic Load Balancing (DLB) is sine qua non in modern distributed systems to ensure the efficient utilization of computing resources therein. This paper proposes a novel framework for hybrid dynamic load balancing. Its framework uses a Genetic Algorithms (GA) based supernode selection approach within. The GA-based approach is useful in choosing optimally loaded nodes as the supernodes directly from data set, thereby essentially improving the speed of load balancing process. Applying the proposed GA-based approach, this work analyzes the performance of hybrid DLB algorithm under different system states such as lightly loaded, moderately loaded, and highly loaded. The performance is measured with respect to three parameters: average response time, average round trip time, and average completion time of the users. Further, it also evaluates the performance of hybrid algorithm utilizing OnLine Transaction Processing (OLTP) benchmark and Sparse Matrix Vector Multiplication (SPMV) benchmark applications to analyze its adaptability to I/O-intensive, memory-intensive, or/and CPU-intensive applications. The experimental results show that the hybrid algorithm significantly improves the performance under different system states and under a wide range of workloads compared to traditional decentralized algorithm.

Traditional load-balancing techniques in distributed stream processing systems face significant challenges when handling fluctuating workloads, often resulting in data skew, inefficient partitioning, and delayed responses to workload shifts. This article presents a comprehensive framework for adaptive load balancing that integrates dynamic key partitioning, operator migration, and feedback-driven scaling mechanisms. The proposed solution incorporates artificial intelligence techniques to predict workload patterns in real-time, enabling preemptive resource redistribution that prevents bottlenecks before they occur. The framework extends to event-driven scaling within serverless architectures, where computational resources automatically adjust based on streaming data intensity. By combining reactive mechanisms with predictive capabilities, the system achieves optimal resource utilization while maintaining high throughput. Experimental evaluations demonstrate significant improvements in processing efficiency and resource allocation compared to conventional approaches. The integration of these techniques contributes to the development of self-optimizing stream processing frameworks that minimize operational overhead while maximizing system performance in dynamic environments.

With the rapid development of Internet Technology, the traditional database system can no longer meet the demands of massive data access. Cluster technology has been widely used in the internet applications. This dissertation started from analyzing some widely used technologies in clusters. Aiming at the specific context, this dissertation designed a reusable database load balancing framework which uses an adaption of consistency hashing algorithm as the strategy of load balancing.

For the cloud computing based on software-defined networks (SDNs), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer service response time due to the capacity-limited Internet. To solve this problem, a novel service orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and service response delay. In SODA, the network is divided into three layers. 1) Data centers layer (DCL). Data centers (DCs) release software with a specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce service response delay. 2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along the same routing path is higher to reduce redundancy data. 3) Vehicle network layer (VNL). Mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation is conducted. The results illustrate that the proposed scheme has better performance compared with the traditional scheme.

Effective scheduling in dynamic disassembly job shops requires balancing makespan minimization and energy reduction while handling uncertainty and variability. Traditional approaches often fail to address the complexities of dynamic environments, including stochastic processing times and fluctuating workforce availability. The Pareto front (Pf) diversity poses a persistent challenge in multi-objective optimization, while static reward formulations hinder responsiveness to dynamic priorities and evolving deadlines. We propose a multi-objective reinforcement learning (MORL) framework to address the challenges of optimizing makespan and TEC in dynamic disassembly job shop scheduling problems (DDJSSP). The proposed MORL-DDJSSP framework incorporates a dynamic scheduling environment accounting for random variations in processing times, workforce availability, and task prioritization based on deadlines. We design a reward function to capture the relationship between task urgency and operational speed, allowing RL agents to balance multiple performance objectives adaptively. Furthermore, we apply a decomposition approach to adjust objective weights and evaluate policies, optimizing multiple objectives efficiently. Experimental evaluations using standard benchmark datasets demonstrate promising performances of the MORL-DDJSSP framework compared with other MORL algorithms, particularly in terms of hypervolume and sparsity metrics.

Internet Download Manager (IDM) is one of the popular applications used by users to download various types of files from the internet. Load balancing is a technique used to increase the efficiency of using network resources by distributing data traffic evenly across several connection lines. This research aims to determine the extent to which IDM influences the effectiveness of load balancing as well as the influence of using Internet Download Manager (IDM) in the context of load balancing using the Policy-based Load Balancing (PCC), Equal Cost Multi-Path (ECMP), and Next-Hop Target methods. (NTH) on the MikroTik RB751 device. In this research, we used the PPDiOO method for systematic data collection and Quality of Services (QoS) analysis as a parameter for network data collection techniques. The research subject is the system or user who uses the device, while the research object is the impact of using IDM on load balancing performance. The main independent variable is the use of IDM, while the dependent variable is load balancing performance with different methods. Our findings show that using IDM consistently provides significant improvements in network performance compared to not using IDM.

• The problem of alignment to multiple values can be transformed into a linear program. • Our method for incentivising aligned behaviours works with up to 8 values. • The order of the values of a system drastically affects the value-aligned behaviour. This paper addresses the problem of ensuring that autonomous learning agents align with multiple moral values. Specifically, we present the theoretical principles and algorithmic tools necessary for creating an environment where we ensure that the agent learns a behaviour aligned with multiple moral values while striving to achieve its individual objective. To address this value alignment problem, we adopt the Multi-Objective Reinforcement Learning framework and propose a novel algorithm that combines techniques from Multi-Objective Reinforcement Learning and Linear Programming. In addition, we illustrate our value alignment process with an example involving an autonomous vehicle. Here, we demonstrate that the agent learns to behave in alignment with the ethical values of safety, achievement, and comfort, with achievement representing the agent’s individual objective. Such ethical behaviour differs depending on the ordering between values. We also use a synthetic multi-objective environment to evaluate the computational costs of guaranteeing ethical learning as the number of values increases.

The field of autonomous driving has seen increasing proposed use of machine learning methodologies. However, there are still challenges in applying such methods since autonomous driving involves complex and dynamic interactions with the environment. Supervised learning algorithms such as imitation learning can work in environments represented in the training data set, however, it is impractical or cost-prohibitive to collect data for all possible environments. Reinforcement learning methods can train the agent through trial and error, but it may still fail in new environments. To overcome these shortcomings, we thus propose a meta reinforcement learning (MRL) method to improve the agent's generalization capabilities to new environments. The method is applied to automated lane-changing maneuvers at different traffic congestion levels. Specifically, we train the model at light to moderate traffic conditions under a reinforcement learning framework, and then test it at heavy traffic conditions that are never encountered during training. For performance evaluation, we use both collision rate and success rate of the lane-change maneuvers to quantify the safety and effectiveness of the proposed model. A pretrained model is established as a benchmark, which uses the same network structure and training tasks as our proposed model for fair comparison. Simulation results show that the proposed method achieves an overall success rate up to 20% higher than the benchmark model when it is generalized to the new environment of heavy traffic density. The collision rate is also reduced by up to 18% compared with the benchmark model. Furthermore, the proposed model shows more stable and efficient adaptation capabilities and can achieve 100% successful rate and 0% collision rate with only a few steps of gradient update adaptation.

Modern Energy Harvesting Wireless Sensor Nodes (EHWSNs) need to intelligently allocate their limited and unreliable energy budget among multiple tasks to ensure long-term uninterrupted operation. Traditional solutions are ill-equipped to deal with multiple objectives and execute a posteriori tradeoffs. We propose a general Multi-objective Reinforcement Learning (MORL) framework for Energy Neutral Operation (ENO) of EHWSNs. Our proposed framework consists of a novel Multi-objective Markov Decision Process (MOMDP) formulation and two novel MORL algorithms. Using our framework, EHWSNs can learn policies to maximize multiple task-objectives and perform dynamic runtime tradeoffs. The high computation and learning costs, usually associated with powerful MORL algorithms, can be avoided by using our comparatively less resource-intensive MORL algorithms. We evaluate our framework on a general single-task and dual-task EHWSN system model through simulations and show that our MORL algorithms can successfully tradeoff between multiple objectives at runtime.