Fault Tolerance Load Balancing Research Articles

Optimized task scheduling approach with fault tolerant load balancing using multi-objective cat swarm optimization for multi-cloud environment

Multi-cloud environment enables an organization to access services from more than one cloud service providers the use of multiple cloud computing and it can be treated as single heterogeneous environment. It enables autonomy to run the tasks on private or public cloud based on business or technical requirements. In a multi-cloud platform, load balancing is an essential task to serve the requests from multiple users with different resources effectively. It helps to improve utilization of the cloud resources, throughput, reduce makespan and avoid overload at resources. Load balancing also facilitates the redirection of traffic to resources running in another cloud when a failure occurs in a cloud. Hence, it is more vital to have optimized load balancing methods in multi-cloud infrastructure in order to improve the system performance. This paper presents an optimized fault tolerant load balancing method using multi-objective cat swarm optimization algorithm called MCSOFLB and the results are then compared against other powerful optimization algorithms. The experimental results evidently show that the proposed algorithm ranks first on the whole. The MCSOFLB method produces an average improvement of 31 % makespan, 6 % resource utilization, 12 % cost, 6 % success rate and 32 % average throughput over other benchmark algorithms.

Fault tolerant and load balancing model for software defined networking controllers

Previous years have seen increased interest in a new network paradigm, Software defined networking (SDN). The basic idea behind this new concept consists of removing the smart parts of the connectivity components and moving them to a seul control point known as a controller. This centralized network view makes the network maintenance and management easier and facilitates the creation of new services. Despite many advantages of SDN, the concentration of network intelligence in a single controller raises serious challenges that impact SDN scalability, performance, and fault tolerance. One of the main problems in SDN is controller failure. In this article, we develop a fault tolerant model called fault-tolerant load balancing (FTLBC) for SDN controllers. To reduce the cascading failure problem, the proposed model requires the load of the failing controller to be shared among other controllers. In the case of a controller failure, The FTLBC model concentrates on distributing the load among the remaining controllers based on the load of the orphans' switches and the load of the remaining controllers.

A New Fault-Tolerant Algorithm Based on Replication and Preemptive Migration in Cloud Computing

Cloud computing is a promising paradigm that provides users higher computation advantages in terms of cost, flexibility, and availability. Nevertheless, with potentially thousands of connected machines, faults become more frequent. Consequently, fault-tolerant load balancing becomes necessary in order to optimize resources utilization while ensuring the reliability of the system. Common fault tolerance techniques in cloud computing have been proposed in the literature. However, they suffer from several shortcomings: some fault tolerance techniques use checkpoint-recovery which increases the average waiting time and thus the mean response time. While other models rely on task replication which reduces the cloud's efficiency in terms of resource utilization under variable loads. To address these deficiencies, an efficient and adaptive fault tolerant algorithm for load balancing is proposed. Based on the CloudSim simulator, some series of test-bed scenarios are considered to assess the behavior of the proposed algorithm.

A Distributed Fault Tolerant Algorithm for Load Balancing in Cloud Computing Environments

Cloud computing is a promising paradigm that provides users with higher computing benefits in terms of cost, availability and flexibility. Nevertheless, with potentially thousands of connected machines, faults become more frequent and may have an adverse effect on the application. Consequently, fault-tolerant load balancing becomes necessary in order to optimize resource utilization while ensuring the reliability of the system. Different approaches have been proposed in the literature for fault tolerance in cloud computing. However, they suffer from several shortcomings: some fault tolerance techniques use task replication which reduce the cloud’s efficiency in terms of resource utilization. While other models rely on checkpoint recovery to tolerate failures, resulting in an increase in the mean response time. To address these shortcomings, an efficient and adaptive fault tolerant algorithm for load balancing is proposed. Based on the CloudSim simulator, some series of test-bed scenarios are considered to assess the behavior of the proposed algorithm.

Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review

Nowadays, cloud computing is growing daily and has been developed as an effective and flexible paradigm in solving large-scale problems. It has been known as an Internet-based computing model in which computing and virtual resources, such as services, applications, storage, servers, and networks, are shared among numerous cloud users. Since the number of cloud users and their requests are increasing rapidly, the loads on the cloud systems may be underloaded or overloaded. These situations cause different problems, such as high response time and power consumption. To handle the mentioned problems and improve the performance of cloud servers, load balancing methods have a significant impact. Generally, a load balancing method aims to identify under-loaded and overloaded nodes and balance the load among them. In the recent decade, this problem has attracted a lot of interest among researchers, and several solutions have been proposed. Considering the important role of fault-tolerant in load balancing algorithms, there is a lack of an organized and in-depth study in this field yet. This gap prompted us to provide the current study aimed to collect and review the available papers in the field of fault tolerance load balancing methods in cloud computing. The existing algorithms are divided into two categories, namely, centralized and distributed, and reviewed based on vital qualitative parameters, such as scalability, makespan, reliability, resource utilization, throughput, and overhead. In this regard, other criteria such as the type of detected faults and adopted simulation tools are taken into account.

Auto-scalable and fault-tolerant load balancing mechanism for cloud computing based on the proof-of-work election

Auto-scalable and fault-tolerant load balancing mechanism for cloud computing based on the proof-of-work election

Fault Tolerance Based Load Balancing Approach for Web Resources in Cloud Environment

Cloud computing consists group of heterogeneous resources scattered around the world connected through the network. Since high performance computing is strongly interlinked with geographically distributed service to interact with each other in wide area network, Cloud computing makes the architecture consistent, low-cost, and well-suited with concurrent services. This paper presents a fault tolerance load balancing technique based on resource load and fault index value. The proposed technique works in two phases: resource selection and task execution. The resource selection phase selects the suitable resource for task execution. A resource with least resource load and fault index value is selected for task execution. Further task execution phase sets checkpoints at various intervals for saving the task state periodically. The checkpoints are set at various intervals based on resource fault index. When a task is executed on a resource, fault index value of selected resource is updated accordingly. This reduces the checkpoint overhead by avoiding unnecessary placements of checkpoints. The proposed model is validated on CloudSim and provides improved performance in terms of response time, makespan, throughput and checkpoint overhead in comparison to other state-of-the-art methods.

Fault tolerant load balancing in vehicular communication using distributed spanning tree

Fault tolerant load balancing in vehicular communication using distributed spanning tree

Design of a Fault Tolerant Load Balancing Scheduler for Computational

Grid is a collection of heterogeneous resources from a decentralized management. These resources are from large number of organizations and each resource with a local administration and scheduling policy are connected by wide area network. Due to these factors, strong variations prevail in the availability of grid resources and lead to increase in the probability of failures than in traditional parallel and distributed systems. An efficient fault tolerant scheduling approach is needed to schedule the jobs to the appropriate resources which can handle the failure nature of grid resources. Load balancing is required to improve the utilization of the resources and throughput. In this paper, a new scheduling approach is proposed that schedules the grid tasks by considering the failure rate of the resources in order to handle task failures. The proposed Fault tolerant Load Balancing (FTLB) algorithm increases the execution success rate and improves makespan. This algorithm also considers load of each resource while scheduling the jobs to increase resource utilization and throughput of the system. By considering user demand such as deadline for job completion, it improves user satisfaction. The analysis of the experimental results proves that the proposed FTLB algorithm has improved makespan, hit rate and average resource utilization than the existing algorithms.

Improving Fault-Tolerant Load Balancing Algorithms in Computational Grids

Fault tolerant scheduling of many jobs in an environment with millions of unpredictable nodes is not an easy issue. To the best of our knowledge, no work in the literature has proposed a solution that combines the merits of active and passive replication schemes of fault tolerance with the advantages of performance-driven load balancing so as to make the most of the strong points of each. While extensive fault tolerant scheduling and load balancing methods have been presented for the sequential jobs, none have taken into account fault-tolerant load balancing that minimizes the job make-span, provides efficient network and node utilization, achieves a Ill-balanced load and high system flexibility even during the resource failures. Hence, in this article, I present an Adaptive Scheduling Algorithm namely ASA that overcomes these problems. With thorough simulations, I conclude that ASA allocates any number of jobs to a million nodes with relatively low overhead and high flexibility. Experimental results show that the performance of ASA is better than those of its counterparts.

Fault Tolerant Scheduling for Parallel Loops on Shared Memory Systems

While multicore/multiprocessor systems achieve significant speedup for many applications by exploiting loop level parallelism, they also suffer from increased reliability problems as a result of ever scaling device size. This paper addresses the reliability of loop dominated applications, aiming to execute parallel loops efficiently in the presence of various types of hardware faults. In this paper, we present a fault tolerant work-stealing scheme which makes parallel loop execution resilient to hardware faults. A lightweight buffer-commit mechanism is applied in the proposed scheme to ensure the correctness of the re-execution of loop iterations. In addition, we split large failing chunks of loop iterations at runtime to improve load balancing, and a worker thread is discarded when faults occur frequently on it. We evaluated our techniques on a multi-socket multicore system, using a set of loop dominated benchmarks. The proposed scheme achieves the minimum overhead of supporting fault tolerance and optimal load balancing.

Fault Tolerant ACO using Checkpoint in Grid Computing

This paper proposed an algorithm for fault tolerant distributed computation in a grid by the means of meta-heuristic Ant Colony Optimization (ACO) technique and Check-Pointing. Load Balancing is the process of distributing the workload among the nodes in a grid. The load can be CPU cycles, memory capacity or network load. Due to the emerging computing methodology of grid computing over the heterogeneous environment there is a need of hybrid fault tolerant load balancing technique which takes into account the grid scenario including computer heterogeneity, network bandwidth and communication delay. General Terms Algorithms Used: ACO Algorithm for Load Balancing, Check-Point Setter Algorithm and Fault Index Update Algorithm as fault tolerance mechanisms.

Economical and Fault-Tolerant Load Balancing in Distributed Stream Processing Systems

We present an economical and fault-tolerant load balancing strategy (EFTLBS) based on an operator replication mechanism and a load shedding method, that fully utilizes the network resources to realize continuous and highly-available data stream processing without dynamic operator migration over wide area networks. In this paper, we first design an economical operator distribution (EOD) plan based on a bin-packing model under the constraints of each stream bandwidth as well as each server's CPU capacity. Next, we devise super-operator (SO) that load balances multi-degree operator replicas. Moreover, for improving the fault-tolerance of the system, we color the SOs based on a coloring bin-packing (CBP) model that assigns peer operator replicas to different servers. To minimize the effects of input rate bursts upon the system, we take advantage of a load shedding method while keeping the QoS guarantees made by the system based on the SO scheme and the CBP model. Finally, we substantiate the utility of our work through experiments on ns-3.

A hybrid policy for fault tolerant load balancing in grid computing environments

Due to the emergence of grid computing over the Internet, there is a need for a hybrid load balancing algorithm which takes into account the various characteristics of the grid computing environment. Hence, this research proposes a fault tolerant hybrid load balancing strategy namely AlgHybrid_LB, which takes into account grid architecture, computer heterogeneity, communication delay, network bandwidth, resource availability, resource unpredictability and job characteristics. AlgHybrid_LB juxtaposes the strong points of neighbor-based and cluster based load balancing algorithms. Our main objective is to arrive at job assignments that could achieve minimum response time and optimal computing node utilization. Major achievements include low complexity of proposed approach and drastic reduction of number of additional communications induced due to load balancing. A simulation of the proposed approach using Grid Simulation Toolkit (GridSim) is conducted. Experimental results show that the proposed algorithm performs very well in a large grid environment.

Issues in the Design of Adaptive Middleware Load Balancing

Load balancing middleware is used extensively to improve scalability and overall system throughput in distributed systems. Many load balancing middleware services are simplistic, however, since they are geared only for specic use-cases and environments. These limitations make it hard to use the same load balancing service for anything other than the distributed application it was designed for originally. This lack of generality forces continuous re-development of application-specic load balancing services. Not only does re-development increase deployment costs of distributed applications, but it also increases the potential of producing non-optimal load balancing implementations since proven load balancing service optimizations cannot be reused directly.This paper presents a set of load balancing service features that address many existing middleware load balancing service inadequacies, such as lack of server-side transparency, centralized load balancing, sole support for stateless replication, fixed load monitoring granularities, lack of fault tolerant load balancing, non-extensible load balancing algorithms, and simplistic replica management. All the capabilities described in this paper are currently under development for the next generation of middleware-based load balancing service distributed with our CORBA-compliant ORB (TAO).