Resource Load Balancing Research Articles

An admission control algorithm based on matching game and differentiated service in wireless mesh networks

With the widespread application and popularity of wireless mesh networks, related resource allocation, load balancing and network security issues have attracted the attention of many researchers and scholars. The admission control strategy can effectively solve related problems such as network load balancing and network resource allocation. At present, the majority of admission control related research is mainly aimed at a single factor such as user or access points. Little researches consider both of them at the same time. However, admission control strategies often require decision making based on multiple factors. This paper takes the overall benefit of the game participants as the optimization goal, establishes a reasonable and effective admission control model and completes the new user access control and network resource allocation problems. According to the network’s attribute and system resource allocation, this paper proposes an admission control model based on matching game and multi-attribute decision making. The algorithm proposed in this paper balances the interests of the network and user, reflects the superiority of the balanced decision of both parties, and guarantees the common interests of the network and the user.

Patterns for High Performance Multiscale Computing

We describe our Multiscale Computing Patterns software for High Performance Multiscale Computing. Following a short review of Multiscale Computing Patterns, this paper introduces the Multiscale Computing Patterns Software, which consists of description, optimisation and execution components. First, the description component translates the task graph, representing a multiscale simulation, to a particular type of multiscale computing pattern. Second, the optimisation component selects and applies algorithms to find the most suitable mapping between submodels and available HPC resources. Third, the execution component which a middleware layer maps submodels to the number and type of physical resources based on the suggestions emanating from the optimisation part together with infrastructure-specific metrics such as queueing time and resource availability. The main purpose of the Multiscale Computing Patterns software is to leverage the Multiscale Computing Patterns to simplify and automate the execution of complex multiscale simulations on high performance computers, and to provide both application-specific and pattern-specific performance optimisation. We test the performance and the resource usage for three multiscale models, which are expressed in terms of two Multiscale Computing Patterns. In doing so, we demonstrate how the software automates resource selection and load balancing, and delivers performance benefits from both the end-user and the HPC system level perspectives.

Load-balanced Resource Directory Architecture for Large-scale Internet of Things Local Networks

Load-balanced Resource Directory Architecture for Large-scale Internet of Things Local Networks

Review of the quality of service scheduling mechanisms in cloud

Cloud computing provides on demand access to a large pool of heterogeneous computational and storage resources to users over the internet. Optimal scheduling mechanisms are needed for the efficient management of these heterogeneous resources. The optimal scheduler can improve the Quality of Services (QoS) as well as maintaining efficiency and fairness among these tasks. In large scale distributed systems, the performance of these scheduling algorithms is crucial for better efficiency. Now the cloud customers are charged based upon the amount of resources they are consumed or held in reserve. Comparing these scheduling algorithms from different perspectives is needed for further improvement. This paper provides a comparative study about different resource allocation, load balancing and virtual machine consolidation algorithms in cloud computing. These algorithms have been evaluated in terms of their ability to provide QoS for the tasks and Service Level Agreement (SLA) guarantee amongst the jobs served. This study identifies current and future research directions in this area for QoS enabled cloud scheduling.

Resource Scheduling and Load Balancing Fusion Algorithm with Deep Learning Based on Cloud Computing

With the wide application of the cloud computing, the contradiction between high energy cost and low efficiency becomes increasingly prominent. In this article, to solve the problem of energy consumption, a resource scheduling and load balancing fusion algorithm with deep learning strategy is presented. Compared with the corresponding evolutionary algorithms, the proposed algorithm can enhance the diversity of the population, avoid the prematurity to some extent, and have a faster convergence speed. The experimental results show that the proposed algorithm has the most optimal ability of reducing energy consumption of data centers.

Dynamic Cloud Task Scheduling Based on a Two-Stage Strategy

To maximize task scheduling performance and minimize nonreasonable task allocation in clouds, this paper proposes a method based on a two-stage strategy. At the first stage, a job classifier motivated by a Bayes classifier’s design principle is utilized to classify tasks based on historical scheduling data. A certain number of virtual machines (VMs) of different types are accordingly created. This can save time of creating VMs during task scheduling. At the second stage, tasks are matched with concrete VMs dynamically. Dynamic task scheduling algorithms are accordingly proposed. Experimental results show that they can effectively improve the cloud’s scheduling performance and achieve the load balancing of cloud resources in comparison with existing methods. Note to Practitioners — Task scheduling is one of the challenging problems in cloud computing, especially when deadline and cost are considered. As an important actuator, virtual machines (VMs) play a vital role for cloud task scheduling. To meet task deadlines, one needs to save the time of creating VMs, task waiting time, and executing time. To minimize the task execution cost, one needs to schedule tasks onto their most suitable VMs for execution. We propose a cloud task scheduling framework based on a two-stage strategy to do so. It precreates VMs according to historical scheduling data, therefore saving time for tasks to wait for creating VMs. It matches tasks with their most suitable VMs dynamically, therefore saving their execution cost. Under the premise of meeting task deadlines, it minimizes the waiting time of VMs to schedule tasks, thus minimizing the cost to be paid by users who utilize VMs. The readily deployable algorithms are designed and illustrated to improve cloud task scheduling and execution results in comparison with those using traditional methods.

Exponential gravitational search algorithm-based VM migration strategy for load balancing in cloud computing

With the advancement in the science and technology, cloud computing has become a recent trend in environment with immense requirement of infrastructure and resources. Load balancing of cloud computing environments is an important matter of concern. The migration of the overloaded virtual machines (VMs) to the underloaded VM with optimized resource utilization is the effective way of the load balancing. In this paper, a new VM migration algorithm for the load balancing in the cloud is proposed. The migration algorithm proposed (EGSA-VMM) is based on exponential gravitational search algorithm which is the integration of gravitational search algorithm and exponential weighted moving average theory. In our approach, the migration is done based on the migration cost and QoS. The experimentation of proposed EGSA-based VM migration algorithm is compared with ACO and GSA. The simulation of experiments shows that the proposed EGSA-VMM algorithm achieves load balancing and reasonable resource utilization, which outperforms existing migration strategies in terms of number of VM migrations and number of SLA violations.

Resource Optimization With Load Coupling in Multi-Cell NOMA

Optimizing non-orthogonal multiple access (NOMA) in multi-cell scenarios is much more challenging than the single-cell case because inter-cell interference must be considered. Most papers addressing NOMA consider a single cell. We take a significant step of analyzing NOMA in multi-cell scenarios. We explore the potential of NOMA networks in achieving optimal resource utilization with arbitrary topologies. Towards this goal, we investigate a broad class of problems consisting in optimizing power allocation and user pairing for any cost function that is monotonically increasing in time-frequency resource consumption. We propose an algorithm that achieves global optimality for this problem class. The basic idea is to prove that solving the joint optimization problem of power allocation, user pair selection, and time-frequency resource allocation amounts to solving a so-called iterated function without a closed form. We prove that the algorithm approaches optimality with fast convergence. Numerically, we evaluate and demonstrate the performance of NOMA for multi-cell scenarios in terms of resource efficiency and load balancing.

Elastic Resource Allocation for Multi-Granularity Multicasting Traffic in OFDM-Based Optical Networks

With the increasing requirements of the multicast services in the whole data traffic service, the optical multicast technology becomes a key technology supporting wide bandwidth and high speed multicasting communication. The transmission efficiency, capacity and robustness of optical multicast network can be further improved by introducing network coding technology into optical multicast networks. Meanwhile, facing to demand of emerging rate-variable multi-granularity multicast service, a multi-path transmission scheme based on network coding for routing and spectrum allocation (RSA) is proposed. It can not only allocate spectrum resources effectively and flexibly for various-rate multicast traffic, but also balance the network load, improve network throughput and reduce transmission blocking rate. In this paper, RSA problem is decomposed into two subproblems, namely routing allocation based on network coding and spectrum allocation based on maximum spectrum first (MSF) strategy, which are solved sequentially. Simulation experiments are carried out to analyze transmission performance with proposed RSA scheme. The simulation results show that the proposed RSA mechanism can allocate spectrum resources efficiently and flexibly for multi-granularity multicast traffic. Compared with RSA schemes based on shortest path tree (SPT) and minimal spanning tree (MST), the proposed RSA scheme is more efficient for spectrum resource utilization and load balancing, and spectrum resource is saved more than 20%.

A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment

Cloud computing is one of the rapidly growing environment in recent days where it interconnects the entire world in human’s day to day life activities. Resource allocation, scheduling and load balancing are the three important things which improve the quality of service in cloud computing. To do this and choose an optimum resource, optimum schedule and through this balancing the load can be obtained using ABC-SA method. The main contribution of this paper is to implement a hybrid optimization algorithm by integrating the functionality of simulated annealing (SA) into artificial bee colony (ABC) algorithm to do the efficient scheduling according to the task size, priority of the request and closest distance between client nodes to a server in the cloud environment. This ABC-SA based optimized scheduling approach has the capability of improving the efficiency in terms of searching optimum resource time where the dynamic and random searching behavior is obtained from SA. ABC-SA is implemented and experimented in the CloudSim tool and the results are verified. The performance of the proposed approach is evaluated by comparing the results with the existing system results.

A Minimum-Aware Container Live Migration Algorithm in the Cloud Environment

Load imbalance is a problem faced by the distributed cloud computing platform. It often requires the information collaboration by each server in the cluster to carry out the container migration. Most of the algorithms which aim to reduce the downtime do not consider migration cost of the containers and perform some unnecessary migration. In this paper, with the aim to reduce the unnecessary migration of containers, an optimal minimum migration algorithm (OMNM) is proposed. By fitting the growth rate of Docker containers in the source server, the model can estimate the growth trend of each Docker container and determine which container needs to be migrated. While ensuring the load balancing of the cluster, the number of the migration is reduced, and the utilization ratio of the resource is improved. Experimental results show that the algorithm is effective to reduce the total number of live migration of Docker containers and reduce the workload of migration. Finally, it achieves the load balancing of cloud resources.

Performance Analysis of Voice and Video Conference over DiffServ and MPLS

Multiprotocol Label Switching (MPLS) and Differentiated Services (DiffServ) play a vital role for minimizing the congestion by efficient load balancing and management of the network resources used to support Quality of Service (QoS). The lower network delay and efficient forwarding mechanism enhancing the speed of packet transfer make it more suitable for implementing real-time applications such as Voice and Video Conferencing. This paper evaluates the performance measures such as Packet Delay Variation (PDV) and end-to-end delay when applying some case studies comprising numbers of routers serving different number of clients. Both (PDV) and (End-to-End Delay) are determined using combination of Open Shortest Path First (OSPF), DiffServ and MPLS protocols. The results may lead to the measures of selection of the appropriate protocol.

Resource Accounting of Shared IT Resources in Multi-Tenant Clouds

In today's IT platforms, the capability to accurately account overall resource usage among applications is crucial for variety of management actions (e.g., capacity planning, dynamic resource reallocation and/or load balancing). However, in the environments where small number of shared services cater to a large number of distinct entities' requests, resource accounting becomes significantly challenging. First, the overall resource consumption at the shared service is the aggregate of the resource consumption for multiple remote entities whose identities are not visible to the shared service. Second, even if such information becomes available, common monitoring tools (e.g., top, iostat) are unable to deliver accurate break-down of resource consumption since sharing occurs at sub-instance level (i.e., service instances are not exclusive). We study inherent challenges of performing resource accounting of shared resource. We compare two nonintrusive approaches having different balance between local monitoring and collective inference - (i) LR that uses easily-available tools which provide aggregate measurement and applying well-known linear regression as inference, and (ii) Rameter that puts more emphasis on gathering fine-grained per-thread information from within the hypervisor and applying light inference on the data. Evaluation shows that Rameter offers less than 1% error in accounting whereas LR's error fluctuates between 5-150%.

Distributed User Association with Resource Partitioning in Heterogeneous Cellular Networks

To fully utilize the resources of heterogeneous cellular networks (HCNs), an effective approach that offloads users to the underloaded small cells from overloaded macrocells is widely advocated. However, this operation often leads to a bad result that the offloaded users achieve lower signal-to-interference-plus-noise-ratio (SINR) than these users in macrocells. Thus, some appropriate interference avoidance techniques should be adopted to partially alleviate the SINR degradation. For this, we consider the resource (frequency) partitioning that turns off some fraction of such resources in a macrocell. Naturally, an optimal offloading strategy should be closely coupled with resource partitioning, and in turn the optimal partition decides the offloading performance. In this paper, we propose a distributed association strategy with joint offloading and resource partitioning for HCNs. We reveal that load balancing, by itself, is insufficient, and additional resource partitioning is required to improve system performance. Meanwhile, we also show that, compared with the best power association and range extension association, the proposed scheme provides better association performance.

Optimization Techniques for Resource Provisioning and Load Balancing in Cloud Environment: A Review

Optimization Techniques for Resource Provisioning and Load Balancing in Cloud Environment: A Review

Resource Scheduling Method of Meteorological Satellite Ground System Based on Load Balancing

Meteorological satellite data is large, and the corresponding data tasks are complex and diverse. Satellite ground application system makes the entire ground system resources applications some bottlenecks due to carrying more tasks. That how to improve the resource utilization of the whole system has become a key problem in how to allocate the resources of the satellite ground application system rationally. In this paper, a resource scheduling method for meteorological satellite terrestrial application system based on load balancing is proposed. Firstly, the resources are defined quantitatively. Then, the resource requirements are formally described by matrix. Finally, the reasonable scheduling of resources is realized by load balancing. The proposed method can realize the load balance of the system resources under the premise of guaranteeing the efficiency of task execution.

Energy Management in LTE Networks

Wireless cellular networks have seen dramatic growth in number of mobile users. As a result, data requirements, and hence the base-station power consumption has increased significantly. It in turn adds to the operational expenditures and also causes global warming. The base station power consumption in long-term evolution (LTE) has, therefore, become a major challenge for vendors to stay green and profitable in competitive cellular industry. It necessitates novel methods to devise energy efficient communication in LTE. Importance of the topic has attracted huge research interests worldwide. Energy saving (ES) approaches proposed in the literature can be broadly classified in categories of energy efficient resource allocation, load balancing, carrier aggregation, and bandwidth expansion. Each of these methods has its own pros and cons leading to a tradeoff between ES and other performance metrics resulting into open research questions. This paper discusses various ES techniques for the LTE systems and critically analyses their usability through a comprehensive comparative study.

An Optimal Load Balanced Resource Allocation Scheme for Heterogeneous Wireless Networks based on Big Data Technology

An important issue in heterogeneous wireless networks is how to optimally utilize various radio resources. While many methods have been proposed for managing radio resources in each network, these methods are not suitable for heterogeneous wireless networks. In this study, a new management method is proposed which provides acceptable service quality and roaming rate, reduces the cost of the service, and utilizes big data technology for its operation. In our proposed scheme, by considering various parameters such as the type of the service, the information related to the location of the user, the movement direction of the user, the cost of the service, and a number of other statistical measures, the most suitable technology for radio access will be selected. It is expected that besides improving the decision making accuracy in selecting the radio access technology and the balanced distribution of network resources, the proposed method provides lower roaming and lower probability of stopping roaming requests entering the network. By considering the various service classes and various quality of service requirements regarding delay, vibration and so on, this can be useful in optimal implementation of heterogeneous wireless networks.

Quick Evidence Synopsis

Wireless Sensor Network (WSN) provides a significant contribution in the emerging fields such as ambient intelligence and ubiquitous computing. In WSN, optimization and load balancing of network resources are critical concern to provide the intelligence for long duration. Since clustering the sensor nodes can significantly enhance overall system scalability and energy efficiency this paper presents a distributed cluster head scheduling (DCHS) algorithm to achieve the network longevity in WSN. The major novelty of this work is that the network is divided into primary and secondary tiers based on received signal strength indication of sensor nodes from the base station. The proposed DCHS supports for two tier WSN architecture and gives suggestion to elect the cluster head nodes and gateway nodes for both primary and secondary tiers. The DCHS mechanism satisfies an ideal distribution of the cluster head among the sensor nodes and avoids frequent selection of cluster head, based on Received Signal Strength Indication (RSSI) and residual energy level of the sensor nodes. Since the RSSI is the key parameter for this paper, the practical experiment was conducted to measure RSSI value by using MSP430F149 processor and CC2500 transceiver. The measured RSSI values were given input to the event based simulator to test the DCHS mechanism. The real time experimental study validated the proposed scheme for various scenarios.

A Multi-Objective Approach for VNE Problems using multiple ILP formulations

Network Virtualization is a key technology for the Future Internet, allowing the deployment of multiple independent virtual networks that use resources of the same basic infrastructure. An important challenge in the dynamic provision of virtual networks resides in the optimal allocation of physical resources (nodes and links) to requirements of virtual networks. This problem is known as Virtual Network Embedding (VNE).For the resolution of this problem, previous research has focused on designing algorithms based on the optimization of a single objective. On the contrary, in this work we present a multi-objective algorithm, called VNE-MO-ILP, for solving dynamic VNE problem, which calculates an approximation of the Pareto Front considering simultaneously resource utilization and load balancing. Experimental results show evidences that the proposed algorithm is better or at least comparable to a state-of-the-art algorithm. Two performance metrics were simultaneously evaluated: (i) Virtual Network Request Acceptance Ratio and (ii) Revenue/Cost Relation.The size of test networks used in the experiments shows that the proposed algorithm scales well in execution times, for networks of 84 nodes.