An advanced dynamic feedback and random dispatching load-balancing algorithm for GMLC in 3G

Nginx is a commonly used and free open-source web server that is used as a reverse proxy server, load balancer and HTTP cache. It consumes less memory and can handle more clients with less number of processes. Nginx provides users with five predefined load balancing algorithms. However, most of these algorithms are static and some of the load balancing rules are inefficient. In order to make the load of a cluster more stable under high concurrent requests, we developed a Dynamic Load Balancing (DLB) algorithm that uses Nginx as a network security control panel to provide load balancing for a cluster of backend servers. The DLB algorithm is based on the weighted round robin module of Nginx, Logistic Regression and Maximum Likelihood Estimation (MLE) algorithm. It handles the situation of high concurrent requests and reduces the probability of omitted or under-reported incident and status. We also propose a Hybrid Load Balance Method (HLBM) that incorporates the DLB algorithm and evolutionary computing to further improve the performance. We have conducted limited experiment by using dynamic load balancing algorithm. We will complete to develop the Hybrid Load Balance method and conduct experiments for HLBM.

CFDA parallel Method for Adaptive Unstructured Grids with Optimum Static Grid Repartitioning

POLCA is a material control system designed for make-to-order or engineer-to-order companies. These firms have to cope with a high variety of customised products, and strong pressure to provide short throughput times. POLCA constrains the amount of work in progress on the shop floor in order to achieve a short average shop floor throughput time. Earlier work has shown that the POLCA system has the capability to reduce both the average shop floor throughput time and the average total throughput time, but it is only effective if the POLCA system has been appropriately designed. The design of the POLCA system is therefore being investigated and discussed in this paper. We give an extensive literature review and give attention to the authorisation mechanisms of POLCA, the design of control loops, the route-specific capacity signals (POLCA cards), and specific facilities needed to use the POLCA system in practice. Finally, we report on the design of a POLCA system in practice, the first complete implementation of POLCA in The Netherlands, and present the quick scan that was used in the design of the POLCA system for this SME.

An algorithm for load balancing in multiprocessor systems

The detection of early-warning signals of performance deterioration can help technical support teams in taking swift remedial actions, thus ensuring rigor in production support operations of large scale software systems. Performance anomalies or deterioration, if left unattended, often result in system slowness and unavailability. In this paper, we presents a simple, intuitive and low-overhead technique for recognizing the early warning signs in near real time before they impact the system The technique is based on the inverse relationship which exists between throughput and average response time in a closed system. Because of this relationship, a significant increase in the average system response time causes an abrupt fall in system throughput. To identify such occurrences automatically, Individuals and Moving Range (XmR) control charts are used. We also provide a case study from a real-world production system, in which the technique has been successfully used. The use of this technique has reduced the occurrence of performance related incidents significantly in our daily operations. The technique is tool agnostic and can also be easily implemented in popular system monitoring tools by building custom extensions.

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.

Cloud Computing can be described as any where any time storage and it is able to provide on demand services like server, storage, software etc. to the users over internet. Cloud consists of multiple virtual machines which includes several facilities like computation and storage. The foremost aim of cloud computing is to provide systematic and well regulated access to distant and geographical resources. The efficient discharge of task scheduling computing is one of the primary factors. This paper deals with the subject of dynamic load balancing for task scheduling in cloud environment. Satisfactory results can be obtained by implementing the proposed algorithm by considering distribution of tasks on the basis of types (nature), processing efficiency and load efficiency factor. In this paper, algorithm has been implemented by taking into account cloud VM groups, configuration and nature of tasks which determines that resources have been utilized efficiently. The result has been compared and shows better results in terms of average turnaround time, average response time, average waiting time and cost efficiency ratio than existing load balancing algorithms [1, 2].

Reduction of the execution time of a job through equitable distribution of work load among the processors in a distributed system is the goal of load balancing. Performance of static and dynamic load balancing algorithms for the extended hypercube, is discussed. Threshold algorithms are very well-known algorithms for dynamic load balancing in distributed systems. An extension of the threshold algorithm, called the multilevel threshold algorithm, has been proposed. The hierarchical interconnection network of the extended hypercube is suitable for implementing the proposed algorithm. The new algorithm has been implemented on a transputer-based system and the performance of the algorithm for an extended hypercube is compared with those for mesh and binary hypercube networks.

The throughput time within a cluster tool can be approximated as T+lt, where T is the fixed throughput time of the cluster, l is the lot size, and t is the average incremental throughput time resulting from a lot size increase of one wafer. Simulations of different cluster tools are used to illustrate and validate the model. The fixed throughput time (T) consists of both an external component associated with loadlock operations such as loading and pumping, and an internal component resulting from dynamic effects such as congestion in the cluster tool. The expressions for the incremental throughput time (t) includes the wafer handling time, and may include the module processing time. This model predicts that the maximum throughput rate of the cluster tool is 1/t, and that this throughput rate can only be achieved if multiple lots can simultaneously access the cluster tool. The model also predicts that this number of lots must increase, for example, as lot size decreases, the number of identical modules on a cluster increases, or lot loading time increases. This model was applied to a CVD cluster tool at Intel Corporation. The model predicted an increase in the cluster's throughput rate of roughly 10% over current operating practice, if one lot could be loaded while a different lot was being processed. The model was verified using a simulator and then on the cluster tool itself.

In this study, a heterogeneous distributed computing environment (Grid) is employed as a computing platform to perform some computationally intensive tasks. In order to increase the efficiency of the system (utilization and average response time), a dynamic task scheduling algorithm is proposed to balance the load among the nodes of the system. The technique is composed of two main phases. Firstly, the heterogeneous distributed system is embedded onto a structure similar to B+tree and then the algorithm of load-balancing is executed on the virtual structure. The optimum degree of the tree is identified in order to achieve optimal performance. The experimental and simulation results show the benefit of using the technique and additionally they show that it is relatively easy to identify the crossover points (thresholds) for which the system should be load balanced.

We investigate optimal power and performance management for heterogeneous and arbitrary cloud servers in a data center. In particular, we study the problems of power-constrained performance optimization and performance-constrained power optimization in a data center with multiple heterogeneous and arbitrary servers. These problems are essential to find optimal server speeds, such that: 1) the average task response time is minimized, and that the total power consumption does not exceed certain power constraint or 2) the total power consumption is minimized, and that the average task response time does not exceed certain performance constraint. Each server is treated as a G/G/1 queuing system, whose task interarrival times and task execution requirements can have arbitrary probability distributions. Furthermore, these servers are entirely heterogeneous in terms of task interarrival time, task execution requirement, and power consumption models. The main contributions of this paper are summarized as follows: 1) we formulate the average task response time as well as the total power consumption in a data center with multiple heterogeneous and arbitrary servers as the functions of server speeds; 2) we define our optimization problems by finding optimal server speeds, since the server speeds determine both the average task response time and total power consumption; 3) we develop algorithms to find the optimal solutions and demonstrate numerical data; and 4) we also develop several closed-form heuristic solutions and compare their quality with that of the optimal solution. Our approach provides an analytical way of studying the power-performance tradeoff at the data center level.

In the current approach of automotive electronic system design, the multicore processors have prevailed to achieve high computing performance at low thermal dissipation. Multicore processors offer functional parallelism that helps in meeting the safety critical requirements of vehicles. The number of Electronic Control Units (ECUs) in high-end cars could be reduced by conglomerating more functions into a multicore ECU. AUTOSAR stack has been designed to support the applications developed for multicore ECUs. The real challenges lie in adapting new design methods while developing sophisticated applications with multicore constraints. It is imperative to utilize the most of multicore computational capability towards enhancing the overall performance of ECUs. In this context, the scheduling of the real-time multitasking software components by the operating system is one of the challenging issues to be addressed. Here, the state-of-the-art scheduling algorithm is reviewed and its merits and limitations are identified. A hybrid scheduler has been proposed, tested and compared with the state-of-the-art algorithm that offers better performance in terms of CPU utilization, average response time and deadline missing rate both in normal and high load conditions.

The emergence of semantic association networks has injected a new impetus for the development of online English teaching and provided a new model reference for the design of online education platforms. In this paper, the research and design of an online interactive teaching platform for college English draws on the algorithmic advantages of the semantic associative network model and utilizes the self-operation of the semantic associative network to realize the functions of autonomous addition, deletion, modification, and checking. The text semantic similarity is predicted by word embedding model, convolutional neural network, and other algorithms so as to better achieve the integration of teaching resources, connecting English knowledge and highlighting the teaching focus in the online teaching process of college English. Dynamic load balancing algorithms are used to solve the problems of short-term surges in the number of visits and the concentration of call requests, and the optimization of load balancing algorithms is further realized through genetic algorithms to finally complete the design of the online teaching interactive platform. Comparison experiments concluded that the semantic association network proposed in this paper could hold a more stable repair effect when cleaning inconsistent data in the dataset, highlighting the effectiveness of the semantic association network model in this paper. The online interactive teaching platform designed in this paper also performs well in the performance test, with only a 0.01% abnormality rate in the concurrency performance test, and the load balancing ability test also achieves the expected effect.

In this paper, an engineering interpretation of small world effects in networks is given. After briefly summarizing the main features of small world networks, an attempt is made to investigate the relevance that small world effects can have in communication networks. The aim is that of investigating the relationship between small world behaviour and some of the characteristic parameters associated to the overall performance of the network. It is shown that the performance of a communication network is indeed affected by the presence or absence of small world effects. Namely, the link between these effects and the average throughput and delivery time on the network is established and assessed through numerical simulations.

Developing a new scheduling algorithm and conducting the performance analysis to recognize its effect in practice can be a laborious task. CPU scheduling is crucial in achieving the operating system's (OS) design goals. There exists a variety of scheduling algorithms in the field and in this paper, a performance comparison of different existing scheduling algorithms by simulating the same bundle of tasks is carried out. A variety of algorithms under batch OS and time-sharing OS are considered. Upon the analysis, a novel task scheduling algorithm incorporating the merits of existing algorithms is proposed for a single CPU system. The performance of various algorithms is compared with the proposed algorithm for parameters viz., throughput, CPU utilization, average turnaround time, waiting time, and response time. Extensive simulation analysis for the various bundle of tasks is conducted and the proposed algorithm is found to outperform the other algorithms in terms of guaranteed reduced average response time. Thus, an efficient CPU scheduler is proposed to accommodate varying workloads at run-time making the best use of the CPU in a particular execution scenario.