Load balance optimization with replication degree customization

In cloud computing, a lot of challenges like the server failures, loss of confidentiality, improper workloads, etc. are still bounding the efficiency of cloud systems in real-world scenarios. For this reason, many research works are being performed to overcome the shortcoming of existing systems. Among them, load balancing seems to be the most critical issue that worsen the performance of the cloud sector, and hence there necessitates the optimal load balancing with optimal task scheduling. With the intention of accomplishing optimal load balancing by effectual task deployment, this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic algorithms. Usually, handling of tasks in cloud system is an NP-hard problem and moreover, nonpreemptive independent tasks are crucial in cloud computing. This paper goes with the introduction of a new optimal load balancing model by considering three major objectives: minimum makespan, priority, and load balancing, respectively. Moreover, a new single-objective function is also defined that incorporates all the three objectives mentioned above. Furthermore, the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insisted WOA (FM-WOA) is introduced. This FM-WOA is the conceptual amalgamation of standard Whale Optimization Algorithm (WOA) and Firefly (FF) algorithm. Finally, the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan, task completion (priority), and degree of imbalance as well.

Optimization of load balancing using fuzzy Q-Learning for next generation wireless networks

Software Defined Networking (SDN) has emerged as a new paradigm that offers the programmability required to dynamically configure and control a network. In this paper, we present a path assignment method based on load balancing(PA-LB) for a centralized controller to calculate optimal end-to-end virtual paths over the underlying network infrastructure, considering multiple requests and load balance simultaneously. In the paper, theoretical analysis shows that the path selection is NP-hard, and strongly polynomial time inapproximable under the assumption P not equal to NP. We also give a constant-factor polynomial time approximation algorithm for this problem under some bounded parameters. Extensive simulation results, over a wide range of underlying network topologies and input parameters, demonstrate that the proposed method outperforms traditional shortest path first (SPF) approaches not only in paths mapping number, but also in network load balance. Under the condition of limited resources, such as bandwidth, the proposed method has a better performance with the increase of the number of nodes. Moreover, optimal load balance can be kept in the network, in order to avoid network congestion and minimize the average packet transmission latency.

This article deals with randomized allocation processes placing sequentially n balls into n bins. We consider multiple-choice algorithms that choose d locations (bins) for each ball at random, inspect the content of these locations, and then place the ball into one of them, for example, in a location with minimum number of balls. The goal is to achieve a good load balancing. This objective is measured in terms of the maximum load, that is, the maximum number of balls in the same bin.Multiple-choice algorithms have been studied extensively in the past. Previous analyses typically assume that the d locations for each ball are drawn uniformly and independently from the set of all bins. We investigate whether a nonuniform or dependent selection of the d locations of a ball may lead to a better load balancing. Three types of selection, resulting in three classes of algorithms, are distinguished: (1) uniform and independent, (2) nonuniform and independent, and (3) nonuniform and dependent.Our first result shows that the well-studied uniform greedy algorithm (class 1) does not obtain the smallest possible maximum load. In particular, we introduce a nonuniform algorithm (class 2) that obtains a better load balancing. Surprisingly, this algorithm uses an unfair tie-breaking mechanism, called Always-Go-Left, resulting in an asymmetric assignment of the balls to the bins. Our second result is a lower bound showing that a dependent allocation (class 3) cannot yield significant further improvement.Our upper and lower bounds on the maximum load are tight up to additive constants, proving that the Always-Go-Left algorithm achieves an almost optimal load balancing among all sequential multiple-choice algorithm. Furthermore, we show that the results for the Always-Go-Left algorithm can be generalized to allocation processes with more balls than bins and even to infinite processes in which balls are inserted and deleted by an oblivious adversary.

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. For providing optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. Also, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. Finally, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

At the end of 2016, the state of Oregon had an installed wind generating power capacity of over 3 Gigawatts. Wind power, while clean and renewable, requires constant load balancing due to variable weather conditions. These conditions must be met with state of the art forecasting methods in order to maximize load balancing efficiency. New techniques in Numerical Weather Prediction (NWP) have been developed to better optimize load balancing. Visualizations of these new NWP simulations show how winds behave on the leeward side of Mt. Hood. These winds generate a wake that affects Oregon wind farms; causing swings in power that require dynamic load balancing.

Internet, since the beginning of its work, has undergone many changes one of the latest changes is how the internet cloud computing. New technologies cloud computing offers because of features all kinds of facilities to the users as a service. Each evolution, change and novel concept in the world of technologies has its own problems and complications. Accordingly, benefiting from cloud computing is no exception to this rule and it has challenged researchers and proponents in this research domain. Indeed, some major challenges in cloud computing are: load balancing, safety, reliability, ownership, data backup, data portability and supporting several platforms. One challenge for such matters in the field of cloud computing is load balancing optimization in the cloud. The so-called cloud computing, including virtualization, distributed computing, networking, software and Web services. With respect to the ever increasing significance of load balancing in cloud computing the researchers in this paper intended to improve load balancing by using a novel method. The related studies were reviewed, evaluated and compared with each other. The efficiency of the proposed method was analyzed and compared with those of other studies. The results of the present study revealed that the proposed method is better than other dynamic virtual machine (VM) consolidation algorithms in terms of reducing SLA (service levels agreement) violation and the amount of transmitted data volume transmission has to present a better performance than other methods.

Energy efficiency of wireless networks is becoming more and more important because of the steadily rising energy cost and environmental concerns. In a real network with unequal traffic load distributions among cells, by offloading some edge users in a heavily-loaded cell to neighboring lightly-loaded cell, load balance, aims to utilize the radio resource more efficiently and consequently can improve the end-user performance. In this paper an energy efficient load balancing algorithm is proposed. In the proposed algorithm, the target cell in load balance is selected based on the prediction of the possible performance. Furthermore, an energy efficiency scaling factor is introduced into the criteria when selecting the target cell in load balance to achieve the joint optimization of both load balance and energy saving. Simulation results show that the proposed algorithm can reduce the power consumption in the network and at the same time improve the cell throughput.

In parallel I/O systems, fast response to disk access and load balancing are two important performance objectives pursued by end users and applications. These performances are largely determined by the data allocation strategies, or file assignment algorithms. However, most existing algorithms can only obtain one of the performance objectives, including the well-known algorithms like Greedy, Sort Partition (SP) and Hybrid Partition (HP). New algorithms that can achieve both objectives are very necessary for parallel I/O systems. In this paper, we have proposed two new allocation algorithms for file assignment in parallel I/O systems: an offline Balanced Allocation with Sort (BAS) algorithm and an online Balanced Allocation with Sort for Batch (BASB) algorithm. Both algorithms aim to achieve the optimal mean response time and load balancing at the same time. The experiment results have shown that the BAS algorithm can get the optimal performance on response time among all compared algorithms and better performance on load balancing than SP. For online BASB algorithm, it can achieve the optimal performances on both response time and load balancing among all algorithms for comparison.

It is difficult to manage traditional networks due to network growth, an increase in user numbers, and the emergence of new technologies like big data and cloud computing. Consequently, it is required to alter the current network design. SDN is a well-liked architecture because it offers customizable control and centralized management in data centers. To achieve scalability and dependability, it was necessary to propose the geographical dispersal of a logically centralized control plane due to the huge scale of networks. Software-defined networking (SDN) load-balancing optimization has been studied for a long time. Many approaches to the load-balancing conundrum have been put forth by researchers, but very few have taken the impact of transmission delay between controllers and switches under heavy network load into account. In this article we elaborate the various techniques of load balancing in SDN those working on different areas like multi-controller, switch migration, multi-agent, strategy, time sharing, prediction, reinforcement learning etc. We discuss the methods used along with the advantages and disadvantages of various costs, service quality, and network performance metrics.

We consider the load balancing problem in large-scale heterogeneous systems with multiple dispatchers. We introduce a general framework called Local-Estimation-Driven (LED). Under this framework, each dispatcher keeps local (possibly outdated) estimates of the queue lengths for all the servers, and the dispatching decision is made purely based on these local estimates. The local estimates are updated via infrequent communications between dispatchers and servers. We derive sufficient conditions for LED policies to achieve throughput optimality and delay optimality in heavy-traffic, respectively. These conditions directly imply delay optimality for many previous local-memory based policies in heavy traffic. Moreover, the results enable us to design new delay optimal policies for heterogeneous systems with multiple dispatchers. Finally, the heavy-traffic delay optimality of the LED framework also sheds light on a recent open question on how to design optimal load balancing schemes using delayed information.

Multi-access edge computing (MEC) has emerged as a promising technology to facilitate efficient vehicular applications, such as autonomous driving, path planning and navigation. By offloading tasks from vehicles to MEC servers (MECSs), the MEC system can facilitate computation-intensive applications with hard latency constraints in vehicles with limited computing resources. However, owing to the mobility of vehicles, the vehicles are not evenly distributed across the MEC system. Therefore, some MECSs are heavily congested, whereas others are lightly loaded. If a task is offloaded to a congested MECS, it can be blocked or have high latency. Moreover, service interruption would occur because of the high mobility and limited coverage of the MECS. In this paper, we assume that the task can be divided into a set of subtasks and computed by multiple MECSs in parallel. Therefore, we propose a method of task migration with partitioning. To balance loads, the MEC system migrates the set of subtasks of tasks in an overloaded MECS to one or more underloaded MECSs according to the load difference. Simulations have indicated that, compared with conventional methods, the proposed method can increase the satisfaction of quality-of-service requirements, such as low latency, service reliability, and MEC system throughput by optimizing load balancing and task partitioning.

As a novel technology, Internet of Vehicles (IoV) is employed to gather real-time traffic information for drivers from sensors and video surveillance devices with image processing, circumstances analysis and events recognition. In spite of multiple advantages of IoV, preprocessing the huge data may demand abundant computation resources for video surveillance devices. Migrating tasks to remote servers for performing is efficient to solve this problem, but it needs high network bandwidth, which causes traffic congestion and delay. Edge computing has capability to enhance processing performance, which complements video surveillance device and addresses numerous shortcomings. Nevertheless, edge computing for video surveillance remains a challenge to achieve low-latency and load balance through limited amount of edge servers. To handle this challenge, an Edge computing-enabled Resource Provisioning Method (ERPM) for Video Surveillance in IoV is proposed in this paper. Technically, SPEA2 (improving the Strength Pare to Evolutionary Algorithm) is picked to solve the multi-objective optimization problem aiming at minimizing the time consumption and optimizing load balance. Finally, experimental simulation for Evolution algorithm demonstrate the appropriation and efficiency of ERPM.

Efficient load balancing is essential for achieving optimal performance and scalability in cloud-based applications. This paper explores various strategies for optimizing load balancing to effectively distribute incoming traffic across multiple servers or resources. By dynamically adjusting load distribution based on server loads, network conditions, and other relevant metrics, cloud applications can better handle varying workloads and prevent overload on any single component.

Software-defined networking (SDN) is a paradigm that provides flexibility and programmability to computer networks. By introducing SDN nodes in a legacy IP network topology, network operators can benefit on higher control over the infrastructure. However, this migration is not a fast or straightforward process. Furthermore, to provide an adequate quality of service in hybrid IP/SDN networks, the coordination of both IP and SDN paradigm is fundamental. In this paper, this coordination is used to solve two optimization problems that are typically solved separately: (i) traffic load balancing and (ii) power consumption minimization. Each of these problems has opposing objectives, and thus, their joint consideration implies striking a balance between them. Therefore, this paper proposes the Hybrid Spreading Load Algorithm (HSLA) heuristic that jointly faces the problems of balancing traffic by minimizing link utilization and network’s power consumption in a hybrid IP/SDN network. HSLA is evaluated over differently sized topologies using different methods to select which nodes are migrated from IP to SDN. These evaluations reveal that alternative approaches that only address one of the objectives are outperformed by HSLA.