On scheduling transactions in a grid processing system considering load through Ant Colony Optimization

Packet switching remains one of the bottlenecks in building fast Internet routers. Load balancing and switch scheduling are two important algorithms in the effort to maximize the throughput and minimize the latency of these packet switches. A load balancing algorithm regulates the traffic to conform to the service rates while a switch scheduling algorithm allocates the service rates adaptive to the arrival patterns. Many existing load balancing and switch scheduling algorithms are very similar. We show that load balancing and switch scheduling systems are dual systems based on the linear queue dynamics approximation. This allows us to cast a load balancing problem as a scheduling problem, and vice versa. We further show an example of designing a new algorithm for load balancing using an existing scheduling algorithm based on the duality. The duality perspective also allows us to solve unknown problems. We find the entropy rate of the randomized bandwidth allocation system with linear queue dynamics based on the knowledge of the entropy rate of the randomized load balancing system. For the general case, we find both an upper and a lower bound on the entropy rate. The joint use of dual load balancing and switch scheduling algorithms leads to performance gains as we show using mean field analysis.

Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

We consider the problem of maximizing the weighted sum data rate in multi-cell and multi-carrier wireless data systems in the presence of interference. We present a scheme that jointly considers load balancing, user scheduling, and interference mitigation to improve the system performance. Our proposed scheme iteratively applies two processes. The first process solves the sub-problem of load balancing and user scheduling while fixing the power allocation of each BS (and thus fixing the interference). We prove that this sub-problem is NP-hard, and devise a 1/2-approximation algorithm to solve the problem. We also consider an extended model capturing finite queue size and propose a 1/2-approximation algorithm under this model. The second process solves the problem of interference mitigation assuming fixed load balancing and user scheduling. We develop a local-improvement based algorithm to solve this problem. Via simulations, we demonstrate that our joint scheme improves both average system throughput and fairness significantly. Compared to the scheme with fixed user-BS association and 1/3 frequency reuse, the lowest 10% cell-edge users obtain more than 60% performance improvement and 90%of users enjoy more than 30%throughput improvement.

In recent years, the attractiveness of Peer-to-Peer (P2P) networks has been grown rapidly due to the easiness of use. The P2P system is a decentralized relationship model in which every party has the analogous abilities and either party can start a relationship session. In these networks, due to the high number of users, the resource discovery process becomes one of the important parts of the P2P networks. But, in many previously proposed methods, there is a common problem that is called load balancing. If the balance of workload is inefficient, it reduces the resource utilization. Therefore, in this article, we propose the Inverted Ant Colony Optimization (IACO) algorithm, a variety of the basic Ant Colony Optimization (ACO) algorithm, to improve load balancing among the peers. In the proposed method, the effect of pheromone on the selected paths by ants is inverted. In this approach, ants start to traverse the graph from the requester peer and each ant chooses the best peer for moving. Then, requirements and pheromone amount are updated. Finally, we simulate the method and evaluate its performance in comparison to the ACO algorithm in different terms. The obtained results show that the performance of the IACO is better than the ACO algorithm in terms of load balancing, waiting time and resource utilization.

The cutting and sewing process is a traditional flow shop scheduling problem in the real world. This two-stage flexible flow shop is often commonly associated with manufacturing in the fashion and textiles industry. Many investigations have demonstrated that the ant colony optimization (ACO) algorithm is effective and efficient for solving scheduling problems. This work applies a novel effective ant colony optimization (EACO) algorithm to solve two-stage flexible flow shop scheduling problems and thereby minimize earliness, tardiness, and makespan. Computational results reveal that for both small and large problems, EACO is more effective and robust than both the particle swarm optimization (PSO) algorithm and the ACO algorithm. Importantly, this work demonstrates that EACO can solve complex scheduling problems in an acceptable period of time.

In this paper, the no-wait flow shop scheduling problem under makespan and flowtime criteria is addressed. The no-wait flowshop is a variant of the well-known flowshop scheduling problem where all processes follow the previous one without any interruption for operations of a job. Owing to the problem is known to be NP-hard for more than two machines, a hybrid meta-heuristic algorithm based on ant colony optimization (ACO) and simulated annealing (SA) algorithm is improved. First, at each step, due to the characteristic of ACO algorithm that include solution construction and pheromone trail updating, some different areas of search space are checked and best solution is selected. Then, to enhance the quality and diversity of the solution and finding best neighbor of this solution, a novel SA is presented. Moreover, a new principle is applied for global pheromone update based on the probability function like SA algorithm. The proposed approach solution is compared with several the state-of-the-art algorithms in the literature. The reported results show that the proposed algorithms are effective and the new approach for local search in ACO algorithm is efficient for solving the no-wait flow shop problem. Then, we employed another hybrid ACO algorithm based on hybridization of ACO with variable neighborhood search (VNS) and compare the results given by two proposed algorithms. These results show that our new hybrid provides better results than ACO-VNS algorithm.

This paper presents a methodology to solve the manufacturing cell creation and the production scheduling problems for designing virtual cellular manufacturing systems (VCMSs). The objective is to minimize the total materials and components travelling distance incurred. The methodology consists of (i) a mathematical model that describes the characteristics of a VCMS and includes constraints such as delivery due dates of products, maximum capacities of resources, critical tools limitation, and (ii) an ant colony optimization (ACO) algorithm for manufacturing cell formation and production scheduling. Since the proposed ACO algorithm does not indicate a feasible schedule explicitly, two simple heuristics are developed to assign workstations to the operations of the jobs, and to construct the final schedule. To demonstrate the effectiveness of the proposed methodology, both the ACO algorithm and the genetic algorithm are applied to design manufacturing cells for a company in China producing internal combustion engine components. Comparison of the results obtained with the results supplied by the company on the existing manufacturing system show that both the ACO algorithm and the genetic algorithm together with the virtual cellular manufacturing concept perform better than the current manufacturing practice in terms of average workstation utilization, product completion time and system throughput. Also, the results of a set of randomly generated numerical experiments show that the proposed ACO algorithm generates excellent final solutions in a much shorter computation time when compared with the genetic algorithm. Therefore, the mathematical model and the ACO algorithm proposed in this paper form a simple, but effective and efficient methodology to solve the manufacturing cell creation and production scheduling problems for designing VCMSs.

The fundamental problems in dynamic load balancing and job scheduling in parallel and distributed computers involve moving load between processors. In this paper, we consider a new model for load movement in synchronous parallel and distributed machines. In each step of our model, each processor can transfer load to at most one neighbor; also, any amount of load can be moved along a communication link between two processors in one step. This is a reasonable model for load movement in significant classes of dynamic load balancing problems.We derive efficient algorithms for a number of task reallocation problems under our model of load movement. These include dynamic load balancing on processor networks, adaptive mesh re-partitioning such as those in finite element methods, and progressive job migration under dynamic generation and consumption of load.To obtain the above-mentioned results, we introduce and solve the abstract problem of Incremental Weight Migration (IWM) on arbitrary graphs. Our main result is a simple, randomized, algorithm for this problem which provably results in asymptotically optimal convergence towards the state where weights on the nodes of the graph are all equal. This algorithm utilizes an appropriate random set of edges forming a matching. Our algorithm for the IWM problem is used in deriving efficient algorithms for all the problems mentioned above.Our results are very general. The algorithms we derive are local, and hence, scalable. They work for arbitrary load distributions and for networks of arbitrary topology which can possibly undergo link failures. Of independent interest is our proof technique which we use to lower bound the convergence of our algorithms in terms of the eigenstructure of the underlying graph.Finally, we present preliminary experimental results analyzing issues in load balancing related to our algorithms.

Deep learning is an artificial intelligence technique that has been used for various tasks. Deep learning performance is determined by its hyperparameter, architecture, and training (connection weight and bias). Finding the right combination of these aspects is very challenging. Convolution neural networks (CNN) is a deep learning method that is commonly used for image classification. It has many hyperparameters; therefore, tuning its hyperparameter is difficult. In this research, a metaheuristic approach is proposed to optimize the hyperparameter of convolution neural networks. Three metaheuristic methods are used in this research: ant colony optimization (ACO), genetic algorithm (GA), and Harmony Search (HS). The metaheuristics methods are used to find the best combination of 8 hyperparameters with 8 options each which creates 1.6. 107 of solution space. The solution space is too large to explore using manual tuning. The Metaheuristics method will bring benefits in terms of finding solutions in the search space more effectively and efficiently. The performance of the metaheuristic methods is evaluated using MNIST datasets. The experiment results show that the accuracy of ACO, GA and HS are 99,7%, 97.7% and 89,9% respectively. The computational times for the ACO, GA and HS algorithms are 27.9 s, 22.3 s, and 56.4 s, respectively. It shows that ACO performs the best among the three algorithms in terms of accuracy, however, its computational time is slightly longer than GA. The results of the experiment reveal that the metaheuristic approach is promising for the hyperparameter tuning of CNN. Future research can be directed toward solving larger problems or improving the metaheuristics operator to improve its performance.

The ant colony optimization (ACO) algorithm is a meta-heuristic optimization method used to solve challenging optimization problems. Notably, the pheromone model of ACO impacts algorithmic performance. Hence, this paper presents an ACO algorithm with three types of pheromones for solving the component assignment problem of the linear consecutive-k-out-of-n:F system. This configuration can be used to represent a real system in which consecutive failed components cause system failures. Moreover, the component assignment problem seeks a component arrangement in which system reliability is maximized. The proposed algorithm is incorporated with either adjacence-, position-, or k-interval-wise pheromones that are compared using a numerical experiment. The results indicate that the ACO algorithm with the position-wise pheromone performs well within the scope of the experiment.

Problem statement: Ant Colony Optimization (ACO) provides a meta-heuristic optimization tool and collective intelligence model to several applications such as routing and load balancing. A lot of work found in the literature on using ACO in load balancing. However, as far as our knowledge, there was no work relating load balancing in distributed systems with ACO. Approach: In this study, a proposed ACO algorithm for load balancing in distributed systems will be presented. This algorithm is fully distributed in which information is dynamically updated at each ant movement. Multiple colonies paradigm will be adopted such that each node will send a colored colony throughout the network. Results: In this study, colored ant colonies are used to prevent ants of the same nest from following the same route and hence enforcing them to be distributed all over the nodes in the system and each ant acts like a mobile agent that carries newly updated load balancing information to the next visited node. Conclusion: Finally, the performance of the proposed ACO algorithm is compared with the work-stealing approach for load balancing in distributed systems.

With the rapid development of processing and storage technologies and the success of the Internet, computing resources have become cheaper, more powerful and more ubiquitously available than ever before. This technological trend has enabled the realization of a new computing model, called cloud computing. In cloud, scheduling is an important application. In cloud environments, load balancing task scheduling is an important problem that directly affects resource utilization. Undoubtedly, load balancing scheduling is a serious aspect that should be considered because of its significant impact on both the back end and the front end of the cloud research industry. Good resource utilization is achieved whenever an effective load balance is achieved in the cloud. But, load balancing in cloud computing is an NP-hard optimization problem. In order to accomplish this problem, a novel load balancing task scheduling algorithm in cloud using Adaptive Dragonfly algorithm (ADA) is proposed. The ADA is a combination of dragonfly algorithm and firefly algorithm. Moreover, to attain the better performance, multi-objective function is developed based on three parameters namely, completion time, processing costs and load. Finally, the performance of proposed methodology is evaluated in terms of different metrics namely, execution cost and execution time. The experimental results demonstrate that a proposed approach accomplishes better load balancing result compared to other approaches.

Optimal load balancing scheduler for MPTCP-based bandwidth aggregation in heterogeneous wireless environments

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

When provided as a service, Cloud Computing (CC) makes it possible to access dynamically scalable and regularly virtualized resources through the Internet. It is hoped that this research will contribute to the disciplines of load balancing and task scheduling by understanding the concept of load balancing in cloud computing through the use of a simulator known as "CloudSim." In this research, it was found that, one of the most significant challenges in cloud computing is load balancing. This prevents scenarios in which some nodes are heavily loaded while others are idle or performing limited work. Load balancing (LB) spreads the dynamic local workload evenly among all the nodes in the cloud. The most important decision to make is the sort of program to run on the available system in order to maximise resource utilisation. Scheduling is the process of allocating a job to the resources that can complete it in the Cloud Computing environment.A heuristic is a strategy that may be used to solve issues rapidly. It is vital to have efficient and effective scheduling procedures in place in order to make full use of the large array of cloud computing possibilities. As a consequence of this investigation, a novel scheduling algorithm is introduced that is derived from two old approaches, Min-min and Max-min, and is designed to capitalise on their advantages while eliminating their disadvantages. The parameters are then evaluated by the CloudSim simulator. There are high-level techniques that function as the driving force behind a problem-specific heuristic known as Meta-Heuristics. A New Meta-Heuristic methodology has been developed by investigating and analysing previously developed processes. The primary purpose is to avoid the drawbacks of iterative improvement, particularly descents, by allowing the local search to escape local optima while doing local searches. One technique to accomplish this is to provide more "intelligent" possibilities to the local search engine rather than random initial responses as a way to assist it. The proposed meta-heuristic model is evaluated using CloudSim and the advantages of two existing techniques, Particle Swarm Optimisation and Genetic Algorithm with the results of several simulations.