Efficient Load Balancing Scheme Research Articles

Distributed Systems for Real-Time Computing in Cloud Environment: A Review of Low-Latency and Time Sensitive Applications

As a result of its many benefits, including cost-efficiency, speed, effectiveness, greater performance, and increased security, cloud computing has seen a boom in popularity in recent years. This trend has attracted both consumers and businesses. Being able to process and provide data or services in a quick and effective manner while adhering to low latency and time limits is the hallmark of an efficient distributed system that is designed particularly for real-time computing in cloud environments. It is essential to place a high priority on low latency and time sensitivity while developing and putting into action a distributed system for real-time computing in a cloud environment. In order to fulfil the particular requirements of the application or service, consideration must be given to a number of different aspects. In particular, the topic of load balancing will be discussed in this paper. It is possible to ensure a more effective distribution of workload and reduce latency by using load balancers, which distribute incoming traffic over many servers or instances. The throttled algorithm is believed to be the most efficient load balancing strategy for reducing service delivery delay in cloud computing. This research investigates a hybrid method known as Equally Spread Current Execution (ESCE), which is known for its combination with the throttled algorithm.

A novel two-phase energy efficient load balancing scheme for efficient data collection for energy harvesting WSNs using mobile sink

A novel two-phase energy efficient load balancing scheme for efficient data collection for energy harvesting WSNs using mobile sink

An Efficient Load-Balancing Scheme for UAVs in 5G Infrastructure

Deploying caches at the macro base station (MBS), unmanned aerial vehicle (UAV), and mobile user caches can effectively reduce the retransmission of duplicate content in the 5G cellular wireless hotspot network. As the storage capacity of MBS is much higher than UAVs and other hotspot cache nodes, the MBS advertises its vacant storage space so that the participating nodes can rent it. In this article, we proposed an efficient load-balancing scheme by using the Stackelberg equilibrium game model. The proposed scheme sets a unit price ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\xi$</tex-math></inline-formula> ) based on constraints to avoid data traffic uncertainty caused by participation nodes and rent vacant space of MBS. Furthermore, we proposed an efficient scheme for the placement and delivery of hotspot content by using Knapsack and Zipf. Moreover, ensuring the device-to-device link support also minimizes transportation costs. The results validate that considering the above-mentioned techniques significantly improves the overall hotspot network performance.

Latency and Energy-Aware Load Balancing in Cloud Data Centers: A Bargaining Game Based Approach

With the rapid surge in cloud services, cloud load balancing has become a paramount research issue. The major part of a cloud computing systems operational costs is attributed to energy consumption. Therefore, to provide better QoS, considering the energy minimization factor in load balancing is important. This paper addresses the latency and energy-aware load balancing problem in a cloud computing system. In this paper, two fundamental performance criteriaresponse time and energyfor the load balancing problem are considered. To solve this problem, first, the load balancing problem is formulated as an optimization problem. Then it is modeled as a cooperative game so that the solution of the game, called the Nash bargaining solution (NBS), can simultaneously optimize both criteria. The existence and computation of NBS is analyzed theoretically, and an efficient algorithm, called Latency and Energy aWare load balancIng Scheme (LEWIS), is proposed to compute the NBS. Further, to assess the efficacy of LEWIS, it is compared with three other approaches, i.e., Coop RT, Coop EN, and NCG on problem instances of various settings. The experimental results show that LEWIS not only provides less response time while consuming less energy, but also gauntness fairness to the end-user

Modern Load Balancing Techniques and Their Effects on Cloud Computing

Cloud computing is a new and advanced viewpoint for large-scale parallel and distributed computing systems. Cloud computing is growing quickly, and users are demanding more services and better results, so cloud-computing load balancing has become a very thought-provoking and important research area. Load on the cloud is growing extremely with the expansion of new applications. Load balancing is a major area of the cloud computing environment, which guarantees that all connected devices or processors simultaneously perform the same amount of work. Hence, an efficient load-balancing scheme is needed to improve the performance of cloud computing. Different researchers in the past years have proposed several load-balancing algorithms. This paper examines the important necessities and concerns for designing and implementing a suitable load balancer for cloud environments. In addition, we constitute an entire survey of recently proposed cloud load balancing solutions; finally, we propose evaluating these solutions based on suitable metrics and discuss their advantages and disadvantages.

Dynamic energy efficient load balancing strategy for computational grid

AbstractComputational grid is a pool of computational resources that is used by various organizations for storage purposes and execution of a large number of tasks. Load balancing and energy consumption are two core performance parameters in the computational grid. Since, performance of a computational grid is highly dependable to variations in load and energy consumption, therefore, it is important to pay attention to these aspects in grid. Therefore, we proposed an energy‐aware dynamic load balancing strategy for tasks scheduling in grid referred to as dynamic energy efficient load balancing strategy (DEEL) for computational grid. DEEL combined energy efficiency and load balancing paradigms in order to improve the utilization and reduce the energy consumption of resources. Simulations were performed by using GridSim. Simulation results were compared with published strategy IEGDC with respect to performance indices: finished gridlets, unfinished gridlets, resubmitted times, and response time. The simulation results were evaluated through performance metrics that is, (a) cost, (b) energy consumption, and (c) energy saved. As per simulation results, DEEL outperformed the published method IEGDC by reducing the execution time of finished gridlets up to 28%, minimizing the execution and resource cost up to 35%, and saving the energy consumption up to 38%.

A Hybrid Meta-Heuristic for Optimal Load Balancing in Cloud Computing

Nowadays, a trending technology that provides a virtualized computer resources based on the internet is named as cloud computing, these clouds performance mostly depends on the various factors among the load balancing. The allocation of the dynamic workload in between the cloud systems and equally shares the resources so that no database server is overloaded or under loaded is technically referred to as load balancing (LB). Therefore, in cloud an active load balancing scheme can perhaps enhance the reliability, services and the utilization of resources as well. In this manuscript, the benefits are integrated for Harries Hawks Optimization and Pigeon inspired Optimization Algorithm to create efficient load balancing scheme, which ensures the optimal resources utilizations with tasks response time. The proposed approach is implemented in JAVA Net beans IDE incorporated in the cloudsim framework that is analyzed based on different number of task in order to assess the performance. However, the simulation outcomes demonstrate that the proposed Hawks Optimization and Pigeon inspired Optimization algorithm based load balancing scheme is significantly balance the load optimally amid the Virtual Machines within a shorter period of time than the existing algorithms. The efficiency of the proposed method is 97% compared to the other existing methods. The computational time, cost, throughput analysis, make span, latency, execution time are determined and gets analysed, compared with the Harries Hawks Optimization, Spider Monkey Algorithm, Ant Colony Optimization and Honey Bee Optimization.

Ernie: Scalable Load-Balanced Multicast Source Routing for Cloud Data Centers

Nowadays, most applications hosted on public cloud data centers (DCs) disseminate data from a single source to a group of receivers for service deployment, data replication, software upgrade, etc. For such one-to-many data communication paradigm, multicast routing is the natural choice as it reduces network traffic and improves application throughput. Unfortunately, recent approaches adopting IP multicast routing suffer from scalability and load balancing issues, and do not scale well with the number of supported multicast groups when used for cloud DC networks. Furthermore, IP multicast does not exploit the topological properties of DCs, such as the presence of multiple parallel paths between end hosts. Despite the recent efforts aimed at addressing these challenges, there is still a need for multicast routing protocol designs that are both scalable and load-balancing aware. This paper proposes Ernie, a scalable load-balanced multicast source routing for large-scale DCs. At its heart, Ernie further exploits DC network structural properties and switch programmability capabilities to encode and organize multicast group information inside packets in a way that minimizes downstream header sizes significantly, thereby reducing overall network traffic. Additionally, Ernie introduces an efficient load balancing strategy, where multicast traffic is adequately distributed at downstream layers. To study the effectiveness of Ernie, we extensively evaluate Ernie’s scalability behavior (i.e., switch memory, packet size overheads, and CPU overheads), and load balancing ability through a mix of simulation and analysis of its performances. For example, experiments of large-scale DCs with 27k+ servers show that Ernie requires a downstream header sizes that are 10× smaller than those needed under state-of-the-art schemes while keeping end-host overheads at low levels. Our simulation results also indicate that at highly congested links, Ernie can achieve a better multicast load balancing than other existing schemes.

An Efficient Load Balancing Scheme of Energy Gauge Nodes to Maximize the Lifespan of Constraint Oriented Networks

Resource limited networks have various applications in our daily life. However, a challenging issue associated with these networks is a uniform load balancing strategy to prolong their lifespan. In literature, various schemes try to improve the scalability and reliability of the networks, but majority of these approaches assume homogeneous networks. Moreover, most of the technique uses distance, residual energy and hop count values to balance the energy consumption of participating nodes and prolong the network lifetime. Therefore, an energy efficient load balancing scheme for heterogeneous wireless sensor networks (WSNs) need to be developed. In this article, an energy gauge node (EGN) based communication infrastructure is presented to develop a uniform load balancing strategy for resource-limited networks. EGN measures the residual energy of the participating nodes i.e., C i ∈ Network. Moreover, EGN nodes advertise hop selection information in the network which is used by ordinary nodes to update their routing tables. Likewise, ordinary nodes use this information to uni-cast its collected data to the destination. EGN nodes work on built-in configuration to categorize their neighboring nodes such as powerful, normal and critical energy categories. EGN uses the strength of packet reply (SPR) and round trip time (RTT) values to measure the neighboring node's residual energy (E r ) and those node(s) which have a maximum E r values are advertised as reliable paths for communication. Furthermore, EGN transmits a route request (RREQ) in the network and receives route reply (RREP) from every node reside in its closed proximity which is used to compute the E r energy values of the neighboring node(s). If E r value of a neighboring node is less than the defined category threshold value then this node is advertised as non-available for communication as a relaying node. The simulation results show that our proposed scheme surpasses the existing schemes in terms of lifespan of individual nodes, throughput, packet loss ratio (PLR), latency, communication costs and computation costs, etc,. Moreover, our proposed scheme prolongs the lifespan of WSNs and as well as an individual node against exiting schemes in the operational environment.

An Efficient Smart Weighted and Neighborhood-enabled Load Balancing Scheme for Constraint Oriented Networks

In wireless sensor networks (WNSs), uniform load or traffic distribution strategy is one of the main challenging issues, which is tightly coupled with the resource-limited networks. To address this problem, various mechanisms have been developed and presented in the literature. However, these approaches were either application specific that is designed for a specific application area such as smart building or overlay complex. Therefore, a simplified and energy efficient load-balancing scheme is always needed for the resource-limited networks. In this paper, an efficient and neighborhood-enabled load-balancing scheme is presented to resolve the aforementioned issues specifically with available resources. For this purpose, the proposed scheme bounds every member node to collect various information about neighboring nodes i.e., those nodes resides in its communication range. Moreover, if residual energy Er of sensor node is less than the defined threshold value then it shares this information with neighboring nodes. In the proposed neighborhood-enabled load balancing scheme, every sensor node Ci prefers to route packets through the optimal paths particularly those paths where probability of critical nodes is negligible i.e., path where critical node(s) are not deployed. Simulation results showed that the proposed neighborhood-enabled load-balancing scheme is better than existing approaches in terms of network lifetime (both individual node and whole WSNs), throughput, and average packet delivery ratio and end-to-end delay performance metrics.

Load Balancing Scheme in Hybrid WiGig/LiFi Network

Recently, Light fidelity (LiFi) is proposed as a high-speed wireless communication technology. A LiFi access point provides the service in an area of a few square meters known as LiFi attocells. Therefore, by utilizing frequency reuse, LiFi networks provide a high spatial spectral efficiency. Unfortunately, beside to uplink and mobility problems, the LiFi networks suffer more difficulties with increasing of the number of mobile devices. As a solution, hybrid LiFi and radio frequency (RF) networks are proposed. In this article, a hybrid network, that combines LiFi with RF Wireless Gigabit Alliance (WiGig) networks, is proposed. The WiGig access points will provide gigabit-per-second data rates as a result of the availability of large bandwidth at the millimeter wave (mmWave) frequency ranges. Such hybrid networks need an efficient load balancing (LB) scheme. In this article, two modified versions of the separate optimization algorithm (SOA) are proposed; Assign WiGig First SOA (AWFS) algorithm and Consecutive Assign WiGig First SOA (CAWFS) algorithm. The simulation results show that the two proposed algorithms offer better achievable data rates and outage probability performances compared with the SOA algorithm.

Fast Parallel Algorithms for Counting and Listing Triangles in Big Graphs

Big graphs (networks) arising in numerous application areas pose significant challengesfor graph analysts as these graphs grow to billions of nodes and edges and are prohibitively large to fit in the main memory. Finding the number of triangles in a graph is an important problem in the mining and analysis of graphs. In this article, we present two efficient MPI-based distributed memory parallel algorithms for counting triangles in big graphs. The first algorithm employs overlapping partitioning and efficient load balancing schemes to provide a very fast parallel algorithm. The algorithm scales well to networks with billions of nodes and can compute the exact number of triangles in a network with 10 billion edges in 16 minutes. The second algorithm divides the network into non-overlapping partitions leading to a space-efficient algorithm. Our results on both artificial and real-world networks demonstrate a significant space saving with this algorithm. We also present a novel approach that reduces communication cost drastically leading the algorithm to both a space- and runtime-efficient algorithm. Further, we demonstrate how our algorithms can be used to list all triangles in a graph and compute clustering coefficients of nodes. Our algorithm can also be adapted to a parallel approximation algorithm using an edge sparsification method.

An Efficient Dynamic Load Balancing Scheme Based on Nash Bargaining in SDN

Static multi-controller deployment architecture cannot adapt to the drastic changes of network traffic, which will lead to a load imbalance between controllers, resulting in a high packet loss rate, high latency, and other network performance degradation problems. In this paper, an efficient dynamic load balancing scheme based on Nash bargaining is proposed for a distributed software-defined network. Firstly, considering the connectivity of network nodes, the switch migration problem is transformed into a network mapping relationship reconstruction problem. Then, we establish the Nash bargaining game model to fairly optimize the two contradictory goals of migration cost and load balance. Finally, the model is solved by an improved firefly algorithm, and the optimal network mapping state is obtained. The experimental results show that this scheme can optimize the migration cost and load balance at the same time. Compared with the existing research schemes, the migration process of the switch is optimized, and, while effectively balancing the load of the control plane, the migration cost is reduced by 14.5%.

An Improved Efficient Dynamic Load Balancing Scheme Under Heterogeneous Networks in Hybrid Cloud Environment

In the rapid development of computer network technology. The cloud computing is a novel technology had become a highly demanded service due to several new challenges to all organizations the advantages of high computing power, cost of services, scalability, accessibility and availability. However, Cloud computing supports virtual machines system is more complex while dispatching variety of tasks to server’s applications simultaneously. That dispatching tasks to the servers is a challenge since there has a large number of applications in the heterogeneous cloud environment servers, all application services need to cooperate with each other in the cloud computing environment network. The huge number of tasks, an appropriate and effective scheduling algorithm is to allocate these tasks to appropriate servers within the minimum completion time, and to achieve the load balancing of performance workload of the cloud system. In this paper, we present a novel improved efficient dynamic load balancing scheme to organizing the virtualized resources algorithm, called Improved Efficient Scheme (IES) algorithm in the cloud computing network. The main concept of the IES algorithm is to allocate the tasks to server host by comparing all value of makespan time of the server nodes between each task. Basically, the IES algorithm can obtain better task completion time than previous works and can achieve dynamic load balancing in cloud computing environment.

MiniForest: Distributed and Dynamic Multicasting in Datacenter Networks

The emerging cloud applications require group communications. For these applications, multicast is a better choice than unicast, because it can significantly improve the performance by eliminating the duplicated packets generated by servers. However, existing multicast schemes for datacenters are either based on IP multicast or centralized scheduling. IP multicast is inefficient for datacenters as it cannot take full advantage of the multipath property. And centralized schemes suffer from single-point failure and scalability problems. To solve these problems, we propose MiniForest, a distributed multicast framework for large-scale datacenter networks. It consists of new routing algorithms and a dynamic group management mechanism. A new address mapping solution is then designed for compatibility to existing upper-layer applications. Based on the mapping solution, we propose an efficient load balancing strategy, with which a minimal forest is constructed for all multicast trees. To study the performance of the new multicast scheme in theory, we further provide an analytical model for Clos-based datacenter networks and analyze the overloading behaviors from a new perspective. We show that the distributed scheme can be used in any size of datacenters. It has much lower complexity and better performance than centralized schemes.

An efficient load balancing and performance optimization scheme for constraint oriented networks

A constraint oriented wireless sensor network (WSN) faces a challenging task of handling different issues i.e., routing mechanism, coverage of the underlined sensing fields for as long as possible, energy efficiency, reliable transmission of packets, congestion controls etc. Load balancing or routing protocols, which are specifically designed for the resource limited sensor networks, resolve some of these issues. However, most of these mechanisms were either applications specific or overlay complex. Moreover, these techniques underestimated an important aspect of WSNs i.e., sensor nodes criticality or vulnerability factor that is defined as the importance of a node from the network connectivity perspective. In this paper, an efficient load balancing and performance optimization scheme (LBS) is presented to address these issues such as throughput, long term connectivity, end to end delay and energy efficiency in resource limited WSNs. The proposed LBS technique conflates a sensor node criticality factor, residual energy and hop-count to form an optimized load balancing strategy. Initially, the proposed LBS routes most of the load or traffic on the shortest paths by assigning a highest weight-age factor, 80%, to the hop count metric and lower weight-age to both criticality and residual energy metrics i.e., 20%. Load balancing strategy is revised if one or more nodes deplete 50% of their on-board batteries. Simulation results verify that the proposed LBS scheme outperforms the existing field proven approaches i.e., shortest path, vulnerability aware routing, energy based spreading and multiple path based load balancing schemes.

Parallelization comparison and optimization of a scale-bridging framework to model Cottrell atmospheres

Low carbon steels undergo strain aging when heat treated, which causes an increased yield strength that can be observed macroscopically. Such strengthening mechanism is driven by atomistic scale processes, i.e., solute segregation of carbon (C) or nitrogen interstitial atoms. Due to its low solubility, alloying elements can diffuse to defects (e.g., dislocations) and form the so-called Cottrell atmospheres. Consequently, the mobility of defects is strongly reduced because of the interaction with solutes, and higher stresses are needed to unpin them from the Cottrell atmosphere. As C segregation and atomistic motion take place at separate timescales, Classical Molecular Dynamics (MD) and Metropolis Monte Carlo (MC) are coupled in a unified framework to capture collective effects with underlying slow dynamics. The number of degrees of freedom and the need for large computational resources in this simulation requires the choice of an optimal parallelization technique for the MC part of such multi-scale simulations using an unbiased sampling of the configuration space. In the present work, two different parallel approaches for the MC routine applied to the simulation of Cottrell atmospheres are implemented and compared: (i) a manager-worker speculative scheme and (ii) a distributed manager-worker over a cell-based domain decomposition approach augmented by an efficient load balancing scheme. The parallel performance of different Fe-C containing defects with several millions of atoms is analyzed, and also the possible optimization of the efficiency of the MC solute segregation process is evaluated regarding energy minimization.

Reprint of “LBBSRT: An efficient SDN load balancing scheme based on server response time”

The response time is the most important factor determining user experiences in the service provision model involving server clusters. However, traditional server cluster load balancing scheme are limited by the hardware conditions, and cannot completely exploit the server response times for load balancing. In order to effectively resolve the traditional load balancing schemes, we propose a load balancing scheme based on server response times by using the advantage of SDN flexibility, named LBBSRT. Using the real-time response time of each server measured by the controller for load balancing, we process user requests by obtaining an evenly balanced server loads. Simulation experiments show that our scheme exhibits a better load balancing effect and process requests with a minimum average server response times. In addition, our scheme is easy to implement, and exhibits good scalability and low cost characteristics.

Cloud Load Balancing: A Perspective Study

Cloud computing has boomed its horizon with large pace as commercial infrastructure in the IT industries meeting the vast requirements of computing resources. There are several issues such as load balancing, virtual machine migration, automated service provisioning, algorithm complexity, etc., demanding to be resolved. Each of these issues needs load balancing to be resolved. This aims for distributing the unwanted dynamic workload between the nodes residing in cloud and this desires of every computing resource must be assigned on proficient and reasonable ground. Load balancing has become crucial for efficient performance in distributed environments. Cloud computing is an emerging technology demanding more services and better results. Thus load balancing for the cloud is very interesting and important research area. Many algorithms are proposed to provide efficient techniques for assigning the client's requests to available cloud nodes. This paper studies cloud computing along with research challenges in load balancing. Load balancing has been a major issue for cloud computing environment. Efficient load balancing scheme ensures efficient resource utilization by providing the resources to cloud on-demand of users’ basis. By implementing appropriate scheduling criteria load balancing may prioritize users. The aim of this study is to peep in various load balancing algorithms to address its challenges in variety of cloud environment. This study provides a perspective view of the latest approaches in load balancing that will certainly help the future researchers in this field

Hybrid load balancing approach for cloud environment

Cloud computing is the result of growth in networking and computer technologies in last few years. It focuses on providing scalable and reliable infrastructure for implementation of internet-based services. Its future depends on effective installation of infrastructure, efficient utilisation of resources and dynamic transformation of load. This will help in achieving better fault tolerance ratio and higher customer satisfaction. To distribute the workload dynamically across multiple nodes so that no server is either underutilised or overwhelmed, there is a need of efficient and effective load balancing strategy which should change itself dynamically according to environment and task. In this paper, authors have proposed a hybrid load balancing approach named JIMC by combining best features of some of the existing approaches. JIMC approach has been tested using cloud analyst simulator on various scenarios. It has been observed that JIMC approach is efficient in comparison to existing approaches and also prevent the system failure by giving warning of systems overloading in advance.