Task Offloading with Multi-cluster Collaboration for Computing and Network Convergence

Aiming at the virtual network embedding problem under dynamic service request, Service Request Aware-based Dynamic Virtual Network Embedding (SRAD) algorithm is proposed. We first present virtual network embedding model, and then define virtual network construction cost according to the dynamic characteristic of service request. Our goal is to find the optimal reconfiguration policies by awaring service request type and changing trends, which can minimize the overall construction cost using substrate resource. Experimental results show that the proposed algorithm satisfies the service request, achieves higher success ratio and gains higher revenue/cost ratio for substrate network comparing with the existing algorithms.

The tremendous advancement in distributed computing and Internet of Things (IoT) applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud computing. Thus, suitable and effective applications could be performed to satisfy the applications’ latency requirement. Resource allocation techniques are essential aspects of fog networks which prevent unbalanced load distribution. Effective resource management techniques can improve the quality of service metrics. Due to the limited and heterogeneous resources available within the fog infrastructure, the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT network. There has been limited research on resource management strategies in fog networks in recent years, and a limited systematic review has been done to compile these studies. This article focuses on current developments in resource allocation strategies for fog-IoT networks. A systematic review of resource allocation techniques with the key objective of enhancing QoS is provided. Steps involved in conducting this systematic literature review include developing research goals, accessing studies, categorizing and critically analysing the studies. The resource management approaches engaged in this article are load balancing and task offloading techniques. For the load balancing approach, a brief survey of recent work done according to their sub-categories, including stochastic, probabilistic/statistic, graph theory and hybrid techniques is provided whereas for task offloading, the survey is performed according to the destination of task offloading. Efficient load balancing and task-offloading approaches contribute significantly to resource management, and tremendous effort has been put into this critical topic. Thus, this survey presents an overview of these extents and a comparative analysis. Finally, the study discusses ongoing research issues and potential future directions for developing effective management resource allocation techniques.

Any micro service-based programme, including alumni management software, must include load balancing. The effective use of resources is made possible by load balancing, which also ensures that application performance is unaffected by unexpected increases in requests or user usage. In an alumni management application, load balancing can be achieved with a combination of techniques such as round-robin scheduling, weighted load balancing, and dynamic scheduling. These techniques help to ensure that the application remains up and running, even during peak usage times. Additionally, load balancing can be used to ensure users get the best possible experience when using the application. By distributing traffic among multiple back-end services, users can expect faster response times and more reliable performance.Load balancing in a micro services-based alumni management application is a way of distributing incoming web traffic across multiple back end servers in order to efficiently manage traffic, reduce latency, and improve overall performance. Load balancing works by distributing incoming requests across a cluster of servers, and can be done either statically or dynamically. Static load balancing assigns each request to a specific server, while dynamic load balancing uses algorithms to decide which server should handle each request. In the alumni management application, load balancing is important for ensuring that requests are handled efficiently and that the system is not overwhelmed by too many requests. This is especially important when multiple users are accessing the application simultaneously. By distributing requests across multiple servers, the application can remain responsive and reliable even under heavy load.

Task offloading is crucial for the efficient management of vehicular wireless networks, requiring access to real-time information on network status and available computational resources. However, because vehicles are mobile network nodes with variable speeds and limited computational resources for near-real-time task offloading, task offloading schemes may incur high latency, rendering this goal practically infeasible. To address this issue, this paper presents a trajectory-prediction-based pre-offloading decision (TPPD) algorithm that analyzes historical vehicle trajectories to predict future coordinates, thereby enabling the allocation of computational resources in advance. We first use a Long Short-Term Memory (LSTM) network to predict vehicle trajectories with error correction. Then, based on the task requirements and predicted trajectories, a dynamic resource allocation algorithm using a Double Deep Q-Network (DDQN) is designed to enable the multi-access edge server to minimize task processing delay while ensuring effective utilization of available computational resources. Numerical results verify the effectiveness of the proposed approach, demonstrating that, compared to traditional real-time task offloading strategies, the proposed TPPD algorithm significantly reduces task processing delay while improving resource utilization.

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

Traditional cloud computing faces issues like high bandwidth pressure, service delay, and energy consumption, while simple edge computing struggles with complex tasks due to limited resources. Thus, a collaborative edge cloud data processing and adaptive optimization method for intelligent perception systems is proposed. A three-layer model (terminal, edge cluster, cloud center) formalizes tasks, communication, and computing resources for dynamic optimization. A collaborative data processing flow is designed, with lightweight terminal preprocessing based on priority and an intelligent edge caching mechanism to reduce redundant data transmission. Task offloading and resource allocation are framed as reinforcement learning tasks, using the PPO algorithm for intelligent decision-making. Experiments show average processing latencies of 150.2-260.1 ms under varying loads, achieving high energy efficiency (98.5 FLOPs/J, Floating-point Operations per Joule) and object detection accuracy (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$97.2 \% \text{mAP}$</tex>, mean Average Precision). The method outperforms others in latency and balances energy efficiency and accuracy, efficiently utilizing resources amid dynamic tasks and uncertainties.

The convergent communication network will play an important role as a single platform to unify heterogeneous networks and integrate emerging technologies and existing legacy networks. Although there have been proposed many feasible solutions, they could not become convergent frameworks since they mainly focused on converting functions between various protocols and interfaces in edge networks, and handling functions for multiple services in core networks, e.g., the Multi-protocol Label Switching (MPLS) technique. Software-defined networking (SDN), on the other hand, is expected to be the ideal future for the convergent network since it can provide a controllable, dynamic, and cost-effective network. However, SDN has an original structural vulnerability behind a lot of advantages, which is the centralized control plane. As the brains of the network, a controller manages the whole network, which is attractive to attackers. In this context, we proposes a novel solution called adaptive suspicious prevention (ASP) mechanism to protect the controller from the Denial of Service (DoS) attacks that could incapacitate an SDN. The ASP is integrated with OpenFlow protocol to detect and prevent DoS attacks effectively. Our comprehensive experimental results show that the ASP enhances the resilience of an SDN network against DoS attacks by up to 38%.

Now-a-days, cloud computing has become a cornerstone of modern technology, driving innovation, efficiency and accessibility across various industries and application. Distributed computing solves the difficulty of using distributed autonomous machines and communicating with each other over a network. Cloud computing provides clients with a range of services and capabilities that enhances productivity, accessibility and scalability while reducing the need for extensive hardware and infrastructure investments. When interest in distributed computing rises, it implies that more individuals, businesses, and organizations are exploring, adopting, and implementing distributed computing solutions. This surge in interest leads to increase in data traffic. There are two solutions for the issue of increase in data traffic, one is to server optimization (or) server performance enhancement (upgrade single server to a high performance server) but upgraded server may exceed its capacity (overload).Another one is multi servers. Ulti-server configurations are common in scenarios where the demands of an application or service exceed the capabilities of a single server, and the distribution of tasks across multiple servers is necessary for optimal performance and reliability. When there are multiple servers, there is an issue i.e. Load Adjusting. [1] Load balancing is one of the critical issues in cloud computing. In a cloud environment, where resources are often dynamically allocated and distributed, load balancing plays a central role in managing workloads efficiently. Load balancing in cloud computing is a technique used to distribute computing workloads and network traffic across multiple servers or resources within a cloud environment. The primary goal of load balancing is to optimize resource utilization, prevent individual servers from becoming overloaded, and ensure that the overall system can handle varying levels of demand efficiently. Here we also discussed about the hybrid of Cat and Mouse Optimization and Grey Wolf Optimization algorithms. This paper refers to Cloud Computing, Load balancing techniques, Load balancing algorithms.

According to the Shannon-Nyquist theory, the number of samples required to reconstruct a signal is proportional to its bandwidth. Recently, it has been shown that acceptable reconstructions are possible from a reduced number of random samples, a process known as compressive sampling. Taking advantage of this realization has radical impact on power consumption and communication bandwidth, crucial in applications based on small/mobile/unattended platforms such as UAVs and distributed sensor networks. Although the benefits of these compression techniques are self-evident, the reconstruction process requires the solution of nonlinear signal processing algorithms, which limit applicability in portable and real-time systems. In particular, (1) the power consumption associated with the difficult computations offsets the power savings afforded by compressive sampling, and (2) limited computational power prevents these algorithms to maintain pace with the data-capturing sensors, resulting in undesirable data loss. FPGA based computers offer low power consumption and high computational capacity, providing a solution to both problems simultaneously. In this paper, we present an architecture that implements the algorithms central to compressive sampling in an FPGA environment. We start by studying the computational profile of the convex optimization algorithms used in compressive sampling. Then we present the design of a pixel pipeline suitable for FPGA implementation, able to compute these algorithms.

Mobile edge computing (MEC) is a promising computing paradigm enabling mobile devices to offload computation-intensive tasks to nearby edge servers for fast processing. In this paper, we investigate the computing task offloading in small-cell MEC systems. Considering the unevenly distributed mobile users, it is critical to balance the computing load among edge servers to better utilize the computing resources. To this end, we formulate a joint task offloading control and load balancing problem to minimize the average computational cost of users. The formulated problem is a mixed-integer nonlinear optimization problem and is intractable with system scale. To solve the problem in real time, we propose a reinforcement learning-based grouping and task offloading control (RLGTC) scheme. Specifically, we first decompose the problem into two sub-problems with the Tammer method, i.e., the task offloading control (ToC) and server grouping (SeG) sub-problems. Then, we devise two algorithms based on the Kalman Filter technique and reinforcement learning with Dueling Double DQN to solve them, respectively. Extensive data-driven experiments demonstrate the effectiveness of the RLGTC scheme in achieving load balancing and reducing UEs' computational costs compared to the state-of-the-art benchmarks.

With the development of economics and the globalization of trade, the Multimedia Conference System is now widely used and the number of its users increases exponentially. But a single server can't handle the high concurrent user requests effectively. In this paper, we improved the Multimedia Conference System with load balance technology. With the load balance technology, the robustness and availability of the system can be enhanced significantly. Keywords-load balance, cluster, Nginx, Multimedia Conference System I. INTRODUCTION With the explosive popularity of the Internet and the World Wide Web, most popular web sites confronted with a problem that the server overloaded caused by the increasing number of concurrent accesses. In order to process the user requests timely, increase the network throughput and improve the quality of service, it's necessary to upgrade the hardware and software of the sever. But the the hardware is expensive and non-scalability. In this case, server cluster system appears. The sever cluster system refers to a server group which is composed of more than one homogeneous or heterogeneous severs and can provides services that are transparent for the external users. And it becomes a key point that how to achieve a reasonable distribution of load between multiple servers and avoid appearing one server with full load while other servers with little load. In this situation, the load balance technology was born. In recent years, cluster technology and load balance technology got fully developed and they have significant effect on solving the problem of overloading. As a web service, the Multimedia Conference System also has to face the problem that the overload of the server and uneven distribution of resources when there are too many users access the system. Using cluster and load balance technology, the requests the system received can be evenly distributed on each server, thus we can reduce the response time of the requests and improve the resource utilization. The Multimedia Conference System proposed in this paper contains Client Layer, Load Balancer Layer, Server Cluster Layer and Media Server Layer. The Client Layer receives the requests of the user including creating a conference and joining an existing conference. The Load Balancer Layer redirect the user's request to one of the application servers in the cluster according to the load balance algorithm and the different parameters from the Client Layer. The application servers in Server Cluster Layer call the Media Server resources and return them to the Client Layer. This implements a basic process of the system. The Load Balancer Layer contains four modules including User Authentication, Decision-Making, Redirecting and Log Parsing. With the Load Balancer Layer and its load balance algorithm, the user's request can be distributed on different servers and those who want to join the same conference can be allocated to the same application server. Thus we can minimize the response time of the request, improve the resource utilization and also ensure the conference can be held correctly. The rest of this paper is organized as follows: we describe related work in section II. Section III presents the architecture of the Multimedia Conference System. In section IV, we display the implementation of the Load Balancer Layer in detail. Some experiments of key features of load balancer in section V. We conclude in section VI.

Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC. Due to the limited storage resources of edge servers, it is a significant issue to develop a dynamical service caching strategy according to the actual variable user demands in task offloading. Therefore, this paper investigates the collaborative task offloading problem assisted by a dynamical caching strategy in MEC. Furthermore, a two-level computing strategy called joint task offloading and service caching (JTOSC) is proposed to solve the optimized problem. The outer layer in JTOSC iteratively updates the service caching decisions based on the Gibbs sampling. The inner layer in JTOSC adopts the fairness-aware allocation algorithm and the offloading revenue preference-based bilateral matching algorithm to get a great computing resource allocation and task offloading scheme. The simulation results indicate that the proposed strategy outperforms the other four comparison strategies in terms of maximum offloading delay, service cache hit rate, and edge load balance.

In wireless sensor networks, organizing nodes into clusters, finding routing paths and maintaining the clusters are three critical factors that significantly impact the network lifetime. In this paper, using a chaotic genetic algorithm, a clustering routing protocol combined with these three features called CRCGA is proposed to improve the network energy efficiency and load balancing. In CRCGA, the chaotic genetic algorithm is used to select the best cluster heads (CHs) and to find the optimal routing paths by coding them into a single chromosome simultaneously. Chaotic genetic operators based on a novel fitness function considering minimum energy consumption and load balancing along with new determination conditions make the algorithm converge quickly. Besides, an adaptive round time considering energy and load balancing is presented to maintain the clusters so as to further reduce energy consumption. Simulation results indicate that CRCGA is better than LEACH, GECR, OMPFM and GADA-LEACH in terms of convergence speed, energy efficiency, load balancing, network throughput and lifetime.

Joint optimization of phase shift and task offloading for RIS-assisted multi-access edge computing in beyond 6G communication

Load balancing is the foremost confront in a cloud environment. Load balancing is assisted to disseminate dynamic workload across many nodes to guarantee that not a single node gets overloaded. In the existing work Iterative Proximal Algorithm is introduced for the load balancing. This current work concentrates on request migration criteria between multiple servers for load balancing. It varies from common load balancing crisis, assume that it is under a disseminated, competitive environment, and is non-cooperative. For every server, its projected response time is taken to be a dis-utility function and its value is reduced. But load balancing with dependent and independent servers is a confronting errand. In order to resolve this challenge, Meta-heuristic scheme is carried out based on firefly algorithm to balance load on multiple servers. The main contribution of this research work is to perform the load balancing to improve the computational efficiency of the task submitted by the users. The anticipated multi-server load balancing is carried out based on dependent and independent tasks. The jobs comprise of various interdependent errands in which independent tasks might be processed in multiple cores of the VM or multiple VMs. The errands come through the server’s run-time in arbitrary time intervals for different loads. This crisis is resolved by using variation inequality (VI) theory and confirming that there prevails Nash equilibrium resolution set for the devised game. After this, a Nash equilibrium resolution for multi-server load balancing is calculated by using an Iterative Proximal algorithm (IP) is anticipated with Meta heuristic Firefly Optimization Algorithm. Convergence of IPA algorithm is analyzed so that it gets converged to Nash equilibrium. At last, many numerical computations are performed to confirm theoretical analysis.