Static Load Balancing Algorithms Research Articles

A Review Load balancing algorithms in Fog Computing

With the rapid advance of the Internet of Things (IoT), technology has entered a new era. It is changing the way smart devices relate to such fields as healthcare, smart cities, and transport. However, such rapid expansion also challenges data processing, latency, and QoS. This paper aims to consider fog computing as a key solution for addressing these problems, with a special emphasis on the function of load balancing to improve the quality of service in IoT environments. In addition, we study the relationship between IoT devices and fog computing, highlighting why the latter acts as an intermediate layer that can not only reduce delays but also achieve efficient data processing by moving the computational resources closer to where they are needed. Its essence is to analyze various load balancing algorithms and their impact in fog computing environments on the performance of IoT applications. Static and dynamic load balancing strategies and algorithms have been tested in terms of their impact on throughput, energy efficiency, and overall system reliability. Ultimately, dynamic load balancing methods of this sort are better than static ones for managing load in fog computing scenarios since they are sensitive to changing workloads and changes in the system. The paper also discusses the state of the art of load balancing solutions, such as secure and sustainable techniques for Edge Data Centers (EDCs), It manages the allocation of resources for scheduling. We aim to provide a general overview of important recent developments in the literature while also pointing out limitation where improvements might be made. To this end, we set out to better understand and describe load balancing in fog computing and its importance for improving QoS. We thus hope that a better understanding of load balancing technologies can lead us towards more resilient and secure systems.

Profiling the BLAST bioinformatics application for load balancing on high-performance computing clusters

BackgroundThe Basic Local Alignment Search Tool (BLAST) is a suite of commonly used algorithms for identifying matches between biological sequences. The user supplies a database file and query file of sequences for BLAST to find identical sequences between the two. The typical millions of database and query sequences make BLAST computationally challenging but also well suited for parallelization on high-performance computing clusters. The efficacy of parallelization depends on the data partitioning, where the optimal data partitioning relies on an accurate performance model. In previous studies, a BLAST job was sped up by 27 times by partitioning the database and query among thousands of processor nodes. However, the optimality of the partitioning method was not studied. Unlike BLAST performance models proposed in the literature that usually have problem size and hardware configuration as the only variables, the execution time of a BLAST job is a function of database size, query size, and hardware capability. In this work, the nucleotide BLAST application BLASTN was profiled using three methods: shell-level profiling with the Unix “time” command, code-level profiling with the built-in “profiler” module, and system-level profiling with the Unix “gprof” program. The runtimes were measured for six node types, using six different database files and 15 query files, on a heterogeneous HPC cluster with 500+ nodes. The empirical measurement data were fitted with quadratic functions to develop performance models that were used to guide the data parallelization for BLASTN jobs.ResultsProfiling results showed that BLASTN contains more than 34,500 different functions, but a single function, RunMTBySplitDB, takes 99.12% of the total runtime. Among its 53 child functions, five core functions were identified to make up 92.12% of the overall BLASTN runtime. Based on the performance models, static load balancing algorithms can be applied to the BLASTN input data to minimize the runtime of the longest job on an HPC cluster. Four test cases being run on homogeneous and heterogeneous clusters were tested. Experiment results showed that the runtime can be reduced by 81% on a homogeneous cluster and by 20% on a heterogeneous cluster by re-distributing the workload.DiscussionOptimal data partitioning can improve BLASTN’s overall runtime 5.4-fold in comparison with dividing the database and query into the same number of fragments. The proposed methodology can be used in the other applications in the BLAST+ suite or any other application as long as source code is available.

A Proposed Load Balancing Algorithm Over Cloud Computing (Balanced Throttled)

Cloud computing refers to the services and applications that are accessible throughout the world from data centers. All services and applications are available online. Virtual machine migration is an important part of virtualization which is considered as essential part in cloud computing environment. Virtual Machine Migration means transferring a running Virtual Machine with all its applications and the operating system state as it is to target destination machine where it continues to run as if nothing happened. It makes balancing between servers. This improves the performance by redistributing the workload among available servers. There are many algorithms of load balancing classified into two types: static load balancing algorithms and dynamic load balancing algorithms. This paper presents the algorithm (Balanced Throttled Load Balancing Algorithm- BTLB). It compares the results of the BTLB with round robin algorithm, AMLB algorithm and throttled load balancing algorithm. The results of these four algorithms would be presented in this paper. The proposed algorithm shows the improvement in response time (75 µs). Cloud analyst simulator is used to evaluate the results. BTLB was developed and tested using Java.

A Survey on Load Balancing Algorithms

Cloud computing is referred to as biggest technology of today’s environment that provide access to distributed resources on the basis of pay-per-use. Everyone try to use cloud to reduce the cost and maintenance of infrastructure due to which lots of load is increasing day by day. Therefore, there is need to balance that load since resources of cloud are limited but usage is increasing at every moment. This paper discuss how the resources are allocated and how the tasks are scheduled among those resources. Task scheduling mainly focuses on enhancing the utilization of resources and hence reduction in response time. There are various static and dynamic load balancing algorithms to balance the load, this paper discusses comparative study of these algorithms.

Cloud Computing based on the Load Balancing Algorithm

Emerging cloud computing technology is a big step in virtual computing. Cloud computing provides services to clients through the internet. Cloud computing enables easy access to resources distributed all over the world. Increase in the number of the population has further increased the challenge. The main challenge of cloud computing technology is to achieve efficient load balancing. Load balancing is a process of assigning load to available resources in such a way that it avoids overloading of resources. If load balancing is performed efficiently, it improves QoS metric including cost, throughput, response time, resource utilization and performance. Efficient load balancing techniques also provide better user satisfaction. Various load balancing algorithms are used in different scenarios for ensuring the same. In the current research, we will study different algorithms for load balancing and benefits and limitations caused to the system due to the algorithms. In this paper, we will compare static and dynamic load balancing algorithms for various measures of efficiency. These will be useful for future research in the concerned field.

LOAD BALANCING ALGORITHMS IN CLOUD COMPUTING ENVIRONMENT

Cloud computing is a radical technology which provides computing as-a-service instead of providing computing as a product. It provisions computing resources on demand by paying for fraction of time we are using them. Concept of cloud reduces overall cost and management efforts through efficient utilization of various resources. Cloud computing has developed too much but still it has various research challenges like security, reliability, availability, load balancing, power management and data traffic analysis etc. which need researchers immediate attention. Load balancing is one of the hot research areas nowadays. It is the process of uniformly distributing workload on geographically distributed data centers. Load balancing algorithms provide the mechanism of adjusting load according to the application and node capacity. This paper focuses on load balancing, dynamic and static load balancing algorithms and future work in this direction.

SECURE: DYNAMIC DISTRIBUTED LOAD BALANCING TECHNIQUE IN CLOUD COMPUTING

The increase in demands of information services, cloud computing proves itself to enhance scaling, agility, availability and flexibility. But cloud computing has very critical issues as load balancing, security and fault tolerance. As number of users is increasing day by day, the crucial task of cloud computing is to adjust loads of memory, CPU and network to fulfill demands of all clients. For that a number of static and dynamic load balancing algorithms are proposed. Here we are proposing a load balancing algorithm based on clusters in which cluster are formed on geographical bases. All cloud servers arranged in distributed manner. Each Cloud server have a queue length (for ex. 100) of jobs allocated to it. A server can serve up to 100 requests. As 101 request came cluster apply its load balancing algorithm. Along with Dynamic distributed load balancing algorithm we have proposed a security algorithm to secure data transmission between client and Cloud service provider. According to this security algorithm client initiates a key generation process to generate encryption and decryption key pairs that will be valid for a particular message. Likewise, a number of key pairs generated. Client can use any key pair for any data encryption/decryption which will be stored on Cloud. Also Client have to keep a record of Key Pairs with Password/PIN. This algorithm performs better in terms of Throughput, Overhead, Fault Tolerance, Resource Utilization, Response Time, and Scalability.

Efficient Static Load Balancing Algorithm in Cloud Computing

Cloud Computing is one of the emerging necessities in the IT world and has shown immense growth over the years. The applications can be run remotely using other people’s servers. This is done with a simple user interface or an application format. In general, Cloud is simply the Internet, which in turn is a network of remote servers. “Payment only for what you are using” is the bottom line for computing in cloud environment. The Cloud is a pool of heterogeneous resources. The storage dealt with cloud computing has opened the gates to Load Balancing. The prime motive of this paper is to make efficient use of the existing static load balancing algorithms. This is implemented by the addition of Max-Min Algorithm and Weighted Round Robin Algorithm, the efficient and the successful division of the workload, thereby multiplying the scalability and the performance of computing, subtracting the response time.

A Load Balancing Analysis of Cloud Base Application with different Service Broker Policies

provisioning and resource optimization are the key issues in cloud computing. To balance the load in across virtual machine load balancing algorithms are classified into two categories i.e. static, dynamic. For homogeneous and stable environment we prefer static load balancing algorithms. For heterogeneous, dynamic environment we prefer dynamic load balancing algorithms. Load balancing may take place in the public, private or hybrid cloud. In this paper, we focus on a load balancing policy i.e. Closest data Center with different no of virtual machines. The evaluation metrics is the response time and data center processing time. Cloud Environment is simulated for the scenario of Internet banking of an international bank in simulation toolkit CloudAnalyst. Using these two evaluation metrics we identify that for real deployment of such customers application what should be a threshold value of key parameters which are supported by the Cluster of users across the Globe. KeywordsCloudlet, Clouds, DVFS, VM, CPU;

A dynamic load-balancing scheme for the parcel hub-scheduling problem

In this paper, a dynamic load-balancing algorithm (DLBA) was proposed to solve the parcel hub-scheduling problem (PHSP), which is a combinatorial optimization problem commonly found at a parcel consolidation terminal in the parcel delivery industry (PDI). The problem consists of processing a large number of inbound trailers at a much smaller number of unload docks. The parcels in the inbound trailers must be unloaded, sorted and transferred to the load docks, and loaded onto the outbound trailers. Because the transfer operation is labor intensive and the PDI operates in a time-sensitive environment, the unloading, sorting, transferring, and loading of the parcels must be done in such a way as to minimize the timespan of the transfer operation. A DLBA that considered the state of the system was proposed and evaluated to schedule the inbound trailers to the unload docks. The performance of the algorithm was compared to the performance of an efficient static load-balancing algorithm (SLBA) and to an existing lower bound. An experimental analysis shows that the DLBA offers solutions that are 8.6% better than the solutions offered by the SLBA for large-size problems and that the average computational time is only 1.34 min for the DLBA compared to 15.04 min for the SLBA.

Development, Analysis, and Verification of a Parallel Hybrid Dataflow Computer Architectural Framework and Associated Load-Balancing Strategies and Algorithms via Parallel Simulation

This paper first presents the requirements for and the architectural framework of a type of real-time parallel dataflow computer architecture, specifically, a Hybrid Data/Command Driven Architecture (HDCA). Application of such an architecture is discussed. Goals, strategies, and algorithms for both static load-balancing and dynamic resource allocation on an HDCA system are presented. A parallel simulation of an HDCA system employing the presented static load-balancing and dynamic resource allocation algorithms is developed and presented. The parallel simulation was executed on a parallel computer system for increased fidelity. Analysis of simulation results for two example applications of the architecture allowed verification of design goals and correct performance of the HDCA architectural framework and associated static load-balancing and dynamic resource allocation strategies and algorithms.

Load-Balanced Join Processing in Shared-Nothing Systems

In a shared-nothing parallel database system, a join operation is split into a set of tasks that are allocated to the nodes in the system to be executed concurrently and independently. While parallel processing could greatly reduce the completion time of a join operation, the system performance may degrade because of load imbalance across the nodes caused by data skewness in the relations. Load-balanced join processing uses various techniques to evenly distribute the load among nodes in a system and hence improves the overall system performance. In this paper, the basic issues in designing load-balanced parallel join algorithms are identified. From the solutions to those issues, a large set of load-balanced join algorithms can be constructed. Performance of four representative algorithms-two dynamic load-balancing algorithms proposed in this paper and two static load-balancing algorithms adapted from similar algorithms in the literature-is studied and compared with that of a parallel join algorithm that does not balance the join load. The results of our study clearly show the benefits of load-balancing. This study also demonstrates that the dynamic load-balancing techniques proposed in this paper not only are feasible but also provide good system performance.

Load balancing algorithms for an extended hypercube

Reduction of the execution time of a job through equitable distribution of work load among the processors in a distributed system is the goal of load balancing. Performance of static and dynamic load balancing algorithms for the extended hypercube, is discussed. Threshold algorithms are very well-known algorithms for dynamic load balancing in distributed systems. An extension of the threshold algorithm, called the multilevel threshold algorithm, has been proposed. The hierarchical interconnection network of the extended hypercube is suitable for implementing the proposed algorithm. The new algorithm has been implemented on a transputer-based system and the performance of the algorithm for an extended hypercube is compared with those for mesh and binary hypercube networks.