How asymmetry helps load balancing

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.

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.

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.

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

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.

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.

Nowadays, web servers have to respond to the huge amount of requests; therefore, balancing the load between server replicas becomes vital and critical. Conventional load balancers have been dedicated hardware that is susceptible being a single point of failure, vendor-dependent and not flexible. In this work, we propose a flexible and programmable scheduling algorithm that ponders different type of requests, various server processing capacity based upon their CPU, memory usage, I/O rate, and a number of connections. This algorithm uses optimization algorithm, considering not only load of servers but also network congestion. The proposed algorithm includes four main phases. The first two phases have the responsibility of creating sub-topology that causes to increase performance and scalability of the proposed algorithm by reduction of overall solution space. In the third phase, the joint optimal network and server-side load balancing approach are performed, and in the last phase, the post-processing is performed in order to select the server, creates a path, and injects appropriate flow table entries on the OpenFlow switches. The evaluation results show that the dynamic load balancing algorithm significantly decreases response-time whereas increases throughput, in comparison with the existing load balancing algorithms.

The paper concentrates on the Fog Computing (FC) application to a Smart Grid (SG), that comprises of a Distribution Generation System recognized as a Microgrid (MG). FC acts as an additional layer of computation and communication. It decreases the load on the Cloud and provides same facilities as Cloud. The main concern in FC environment is Load Balancing. Fog contains many software and hardware resources and handling these will play a significant role in completing a client’s request. Today, from different regions of the world clients are requesting for the numerous services in a continuous frequency. The Fog manages the load by assigning the Virtual Machines (VMs) to clients’ requests. In this regard, the techniques that should be employed to stabilize the load on the Fog should be very effective in assigning the VM to user requests. In the proposed work, for load balancing we have used four different load balancing algorithms: Round Robin (RR), Throttled, Particle Swarm Optimization (PSO) and Active VM Load Balancing Algorithm (AVMLB). Further, the Cloud Analyst simulator is used to analyze and compare the performances of the algorithms.

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 this paper, we present a hybrid intelligent simulation system for optimization of mesh routers in Wireless Mesh Networks (WMNs) called WMN-PSOHCDGA. We implemented six mesh router replacement methods: CM, RIWM, LDIWM, LDVM, RDVM and FC-RDVM and consider Two Islands distribution of mesh clients. We carry out a comparison study of these router replacement methods for small and middle scale WMNs. We assessed the performance by computer simulations. The simulation results show that six methods have a good performance for connectivity and coverage metrics, for both small and middle scale WMNs. However, they have different behavior for load balancing. For small scale WMNs, the load balancing of LDIWM, RIWM and FC-RDVM is better than CM, LDVM and RDVM. While, comparing LDIWM, RIWM and FC-RDVM, the LDIWM has better load balancing. We found that the load balancing for small scale WMNs is not good, because there is a concentration of mesh routers in some areas. For middle scale WMNs, the CM, LDIWM, LDVM and RDVM have not a good load balancing. While, the RIWM and FC-RDVM have better performance. Comparing RIWM and FC-RDVM, we found that the load balancing of FC-RDVM is better than RIWM.

Load balancing arises as a fundamental problem, underlying the dimensioning and operation of many computing and communication systems, such as job routing in data center clusters, multipath communication, Big Data and queueing systems. In essence, the decision-making agent maps each arriving job to one of the possibly heterogeneous servers while aiming at an optimization goal such as load balancing, low average delay or low loss rate. One main difficulty in finding optimal load balancing policies here is that the agent only partially observes the impact of its decisions, e.g., through the delayed acknowledgements of the served jobs. In this paper, we provide a partially observable (PO) model that captures the load balancing decisions in parallel buffered systems under limited information of delayed acknowledgements. We present a simulation model for this PO system to find a load balancing policy in real-time using a scalable Monte Carlo tree search algorithm. We numerically show that the resulting policy outperforms other limited information load balancing strategies such as variants of Join-the-Most-Observations and has comparable performance to full information strategies like: Join-the-Shortest-Queue, Join-the-Shortest-Queue(d) and Shortest-Expected-Delay. Finally, we show that our approach can optimise the real-time parallel processing by using network data provided by Kaggle.

DSMLB: Dynamic switch-migration based load balancing for software-defined IoT network

The load balance optimization is an important research topic in a replication system, which will affect the performance a lot. Especially in a capacity restricted system, the load balance will be adversely affected by the strategy to replicate the object. In this paper, an algorithm which will calculate the different replication degree based on the popularity and the size of each object to improve the load balance of the system is proposed. The simulation of a cache system shows that the algorithm could achieve around 50% performance increase compare with the identical replication degree approach as the system is much more balanced and could process much more requests in a certain time period.