List of Articles Load Balancing

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  1 - A Novel Approach for Cluster Self-Optimization Using Big Data Analytics
  Abbas Mirzaei Amir Rahimi
  10.7508/jist.2019.01.005
  One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided More

  One of the current challenges in providing high bitrate services in next generation mobile networks is limitation of available resources. The goal of proposing a self-optimization model is to maximize the network efficiency and increase the quality of services provided to femto-cell users, considering the limited resources in radio access networks. The basis for our proposed scheme is to introduce a self-optimization model based on neighbouring relations. Using this model, we can create the possibility of controlling resources and neighbouring parameters without the need of human manipulation and only based on the network’s intelligence. To increase the model efficiency, we applied the big data technique for analyzing data and increasing the accuracy of the decision-making process in a way that on the uplink, the sent data by users is to be analyzed in self-optimization engine. The experimental results show that despite the tremendous volume of the analyzed data – which is hundreds of times bigger than usual methods – it is possible to improve the KPIs, such as throughput, up to 30 percent by optimal resource allocation and reducing the signaling load. Also, the presence of feature extraction and parameter selection modules will reduce the response time of the self-optimization model up to 25 percent when the number of parameters is too high Moreover, numerical results indicate the superiority of using support vector machine (SVM) learning algorithm. It improves the accuracy level of decision making based on the rule-based expert system. Finally, uplink quality improvement and 15-percent increment of the coverage area under satisfied SINR conditions can be considered as outcome of the proposed scheme. Manuscript profile
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  2 - BSFS: A Bidirectional Search Algorithm for Flow Scheduling in Cloud Data Centers
  Hasibeh Naseri Sadoon Azizi Alireza Abdollahpouri
  10.7508/jist.2019.03.002
  To support high bisection bandwidth for communication intensive applications in the cloud computing environment, data center networks usually offer a wide variety of paths. However, optimal utilization of this facility has always been a critical challenge in a data cent More

  To support high bisection bandwidth for communication intensive applications in the cloud computing environment, data center networks usually offer a wide variety of paths. However, optimal utilization of this facility has always been a critical challenge in a data center design. Flow-based mechanisms usually suffer from collision between elephant flows; while, packet-based mechanisms encounter packet re-ordering phenomenon. Both of these challenges lead to severe performance degradation in a data center network. To address these problems, in this paper, we propose an efficient mechanism for the flow scheduling problem in cloud data center networks. The proposed mechanism, on one hand, makes decisions per flow, thus preventing the necessity for rearrangement of packets. On the other hand, thanks do SDN technology and utilizing bidirectional search algorithm, our proposed method is able to distribute elephant flows across the entire network smoothly and with a high speed. Simulation results confirm the outperformance of our proposed method with the comparison of state-of-the-art algorithms under different traffic patterns. In particular, compared to the second-best result, the proposed mechanism provides about 20% higher throughput for random traffic pattern. In addition, with regard to flow completion time, the percentage of improvement is 12% for random traffic pattern Manuscript profile
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  3 - A New BGP-based Load Distribution Approach in Geographically Distributed Data Centers
  A. Esmaeili B. Bakhshi
  Today, hosting services in geographically distributed data centers is very common among service provider companies, because of more efficiency of energy consumption, high availability of the system, and providing quality of service. Load distribution is the main issue i More

  Today, hosting services in geographically distributed data centers is very common among service provider companies, because of more efficiency of energy consumption, high availability of the system, and providing quality of service. Load distribution is the main issue in the geographical data centers. On the one hand, there are several architectures to distribute load between different clusters, e.g., central load balancer, DNS-based systems, and IGP based schemes; one the other hand, the optimum traffic load balancing between clusters is a very challengeable issue. The proposed solutions have different facilities to distribute incoming traffic; nevertheless, they are vulnerable in terms of propagation delay, centralized load balancer failure, and maintaining connections. In this paper, a new architecture based on BGP and Anycast routing protocols in SDN based data centers is proposed to distribute traffic loads between clusters. Simulation result shows improvement in comparison to the existing techniques. Manuscript profile
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  4 - A Prediction-Based Load Distribution Approach for Software-Defined Networks
  Hossein Mohammadi سیداکبر مصطفوی
  Software-defined networking is a new network architecture which separates the control layer from the data layer. In this approach, the responsibility of the control layer is delegated to the controller software to dynamically determine the behavior of the entire network More

  Software-defined networking is a new network architecture which separates the control layer from the data layer. In this approach, the responsibility of the control layer is delegated to the controller software to dynamically determine the behavior of the entire network. It results in a flexible network with centralized management in which network parameters can be well controlled. Due to the increasing number of users, the emergence of new technologies, the explosive growth of network traffic, meeting the requirements of quality of service and preventing underload or overload of resources, load balancing in software-based networks is of substantial importance. Load imbalance increases costs, reduces scalability, flexibility, efficiency, and delay in network service. So far, a number of solutions have been proposed to improve the performance and load balancing in the network, which take into account different criteria such as power consumption and server response time, but most of them do not prevent the system from entering the load imbalance mode and the risks of load imbalance. In this paper, a predictive load balancing method is proposed to prevent the system from entering the load imbalance mode using the Extreme Learning Machine (ELM) algorithm. The evaluation results of the proposed method show that in terms of controller processing delay, load balance and response time, it performs better than CDAA and PSOAP methods. Manuscript profile
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  5 - Load Balancing in Fog Nodes using Reinforcement Learning Algorithm
  niloofar tahmasebi pouya Mehdi-Agha  Sarram
  Fog computing is an emerging research field for providing cloud computing services to the edges of the network. Fog nodes process data stream and user requests in real-time. In order to optimize resource efficiency and response time, increase speed and performance, task More

  Fog computing is an emerging research field for providing cloud computing services to the edges of the network. Fog nodes process data stream and user requests in real-time. In order to optimize resource efficiency and response time, increase speed and performance, tasks must be evenly distributed among the fog nodes. Therefore, in this paper, a new method is proposed to improve the load balancing in the fog computing environment. In the proposed algorithm, when a task is sent to the fog node via mobile devices, the fog node using reinforcement learning decides to process that task itself, or assign it to one of the neighbor fog nodes or cloud for processing. The evaluation shows that the proposed algorithm, with proper distribution of tasks between nodes, has less delay to tasks processing than other compared methods. Manuscript profile
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  6 - A Two-Level Method Based on Dynamic Programming for Partitioning and Optimization of the Communication Cost in Distributed Quantum Circuits
  zohreh davarzani maryam zomorodi-moghadam M. Houshmand
  Nowadays, quantum computing has played a significant role in increasing the speed of algorithms. Due to the limitations in the manufacturing technologies of quantum computers, the design of a large-scale quantum computer faces many challenges. One solution to overcome t More

  Nowadays, quantum computing has played a significant role in increasing the speed of algorithms. Due to the limitations in the manufacturing technologies of quantum computers, the design of a large-scale quantum computer faces many challenges. One solution to overcome these challenges is the design of distributed quantum systems. In these systems, quantum computers are connected to each other through the teleportation protocol to transfer quantum information. Since quantum teleportation requires quantum resources, it is necessary to reduce the number of that. The purpose of this paper is to present a distributed quantum system considering the two goals of balanced distribution of qubits and minimizing the number of teleportation protocols in two levels. In the first level, by presenting a dynamic programming algorithm, an attempt has been made to distribute qubits in a balanced manner and reduce the number of connections between subsystems. According to the output partitioning obtained from the first level, in the second level and in the stage of implementation of global gates, when one of the qubits of this gate is teleported from the home to the desired destination, this qubit may be able to be used by a number of global gates, observing the precedence restrictions and as a result it reduces the number of teleportations. The obtained results show the better performance of the proposed algorithm. Manuscript profile
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  7 - Improved routing for load balancing in wireless sensor networks on the Internet of things, based on multiple ant colony algorithm
  Farhang Padidaran Moghaddam Hamid Maghsoudi
  An important issue in dynamic computer networks such as Internet networks, where the cost of connections varies continuously, is to create a traffic load balancing and increase the transmission speed of packets in the network, so that data packets are using paths with m More

  An important issue in dynamic computer networks such as Internet networks, where the cost of connections varies continuously, is to create a traffic load balancing and increase the transmission speed of packets in the network, so that data packets are using paths with minimal congestion, as a result, one of the main approaches to solve routing problems and load balancing algorithms is based on ant - based algorithms using a novel approach based on optimization of multiple ant colony optimization, the purpose of this research is to present an appropriate routing algorithm in order to shorten and improve the path due to end - to - end delay parameters, packet loss rate, bandwidth and energy consumption rate, to reach a sense of data on the Internet systems. this method has been implemented in MATLAB software and shows the results of the improvement experiments in the mentioned parameters. Manuscript profile
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  8 - WSTMOS: A Method For Optimizing Throughput, Energy, And Latency In Cloud Workflow Scheduling
  Arash Ghorbannia Delavar Reza Akraminejad sahar mozafari
  Application of cloud computing in different datacenters around the world has led to generation of more co2 gas. In addition, energy and throughput are the two most important issues in this field. This paper has presented an energy and throughput-aware algorithm for sche More

  Application of cloud computing in different datacenters around the world has led to generation of more co2 gas. In addition, energy and throughput are the two most important issues in this field. This paper has presented an energy and throughput-aware algorithm for scheduling of compressed-instance workflows in things-internet by cluster processing in cloud. A method is presented for scheduling cloud workflows with aim of optimizing energy, throughput, and latency. In the proposed method, time and energy consumption has been improved in comparison to previous methods by creating distance parameters, clustering inputs, and considering real execution time. In WSTMOS method by considering special parameters and real execution time, we managed to reach the optimized objective function. Moreover, in the proposed method parameter of time distance of tasks to virtual machines for reduction of number of migration in virtual machines was applied. In WSTMOS method by organizing the workflow inputs to low, medium and heavy groups and also by distributing appropriate load on more suitable servers for processors threshold, we accomplished to optimize energy and cost. Energy consumption was reduced by 4.8 percent while the cost was cut down by 4.4 percent using this method in comparison to studied method. Finally, average delay time, power and workload are optimized in comparison to previous methods. Manuscript profile
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  9 - Improving the load balancing in Cloud computing using a rapid SFL algorithm (R-SFLA)
  Kiomars Salimi Mahdi Mollamotalebi
  Nowadays, Cloud computing has many applications due to various services. On the other hand, due to rapid growth, resource constraints and final costs, Cloud computing faces with several challenges such as load balancing. The purpose of load balancing is management of th More

  Nowadays, Cloud computing has many applications due to various services. On the other hand, due to rapid growth, resource constraints and final costs, Cloud computing faces with several challenges such as load balancing. The purpose of load balancing is management of the load distribution among the processing nodes in order to have the best usage of resources while having minimum response time for the users’ requests. Several methods for load balancing in Cloud computing have been proposed in the literature. The shuffled frog leaping algorithm for load balancing is a dynamic, evolutionary, and inspired by nature. This paper proposed a modified rapid shuffled frog leaping algorithm (R-SFLA) that converge the defective evolution of frogs rapidly. In order to evaluate the performance of R-SFLA, it is compared to Shuffled Frog Leaping Algorithm (SFLA) and Augmented Shuffled Frog Leaping Algorithm (ASFLA) by the overall execution cost, Makespan, response time, and degree of imbalance. The simulation is performed in CloudSim, and the results obtained from the experiments indicated that the proposed algorithm acts more efficient compared to other methods based on the above mentioned factors. Manuscript profile