Meta-heuristic based framework for workflow load balancing in cloud environment

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

An emerging method of digital computing known as "cloud computing" has recently gained immense popularity. While there are many benefits to using the cloud over the internet, there are also serious challenges that must be addressed in order to boost the efficiency of this method. Challenges to cloud computing via the internet include load balancing, work scheduling, fault tolerance, and several security concerns. The effectiveness of the cloud may be enhanced by fixing a number of problems, one of the most pressing being load balancing. To prevent any one node from being too overburdened or underused, a system employs a technique known as load balancing. In order to increase utilization and minimize overall task execution time, load balancing algorithms are developed to distribute work fairly across available resources. In this article, a review and comparison of different load-balancing algorithms is provided. This paper provides a basis of the application of different load-balancing techniques, which utilize the approach of soft computing within the cloud computing environment.

Cloud computing is the means of accessing a shared pool of configurable computing resources (including hardware, software, networks, servers, storage applications and services) that can be rapidly provided, used and released with minimal effort on the part of users or service providers. But it has some of the main concerns like load management and fault tolerance. In this paper we are discussing load balancing approach in cloud computing. Load balancing is helped to distribute the workload across multiple nodes to ensure that no single node is overloaded. It helps in proper utilization of resources .It also improves the performance of the system This paper focuses on the load balancing algorithm which distributes the incoming jobs among VMs optimally in cloud data centers. In this paper, we have reviewed several existing load balancing mechanisms and we have tried to address the problems associated with them.

Cloud computing as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud environment comprises of abundant nodes with distributed computing resources in different geographic locations. The main aim is to maintain all the data centres to be available and also to distribute the load uniformly to all the data centres. Load balancing with switching mechanism contains two different algorithms in which, one algorithm is used to distribute the jobs in idle state divisions and another, token-based load balancing (TLB) algorithm is used to distribute the jobs in the normal state divisions. The basic demand of each user is to complete requested jobs in the short time, so the public cloud need a mechanism, which can complete the jobs of all users with reasonable response time. The proposed work chooses different strategies for different load conditions. Whenever the server is overloaded, the TLB algorithm migrates the job to the available data centre (DC) either within a cloud division or to the data centre in other cloud division.

With heavy demand for cloud technology, it is important to balance the cloud load to deliver seamless Quality of Services to the different cloud users. To address such issues, a new hybridized technique Artificial Neural Network based Load Balancing (ANN-LB) is introduced to calculate an optimized Virtual Machine (VM) load in cloud systems. The Particle Swarm Optimization (PSO) technique is used to perform task scheduling. The performance of the proposed ANN-LB approach has been analyzed with the existing CM-eFCFS, Round Robin, MaxMin, and MinMin algorithms based on MakeSpan, Average Resource Utilization, and Transmission Time. Calculated values and plotted graphs illustrate that the presented work is efficient and effective for load balancing. Hybridization of ANN and iK-mean methods obtains a proper load balancing among VMs and results have been remarkable.

Cloud computing is an emerging technological paradigm, which provides computing resources as utility. Like other day-to-day utilities, cloud computing follows pay-as-you-use model, where users are charged according to the usage without regard to where the services are hosted or how they are delivered. Today, majority of companies follow an IT infrastructure-driven business model. With the growing demand, rise in customer base and market place competitions, companies prefer focusing on respective business policies and services they offer, rather than IT management overheads. Therefore, there is a high probability that the future of present day business model may shift to clouds where non-IT companies no longer have to procure, manage, and maintain IT resources. They will host applications and data to the servers, which are deployed by cloud providers, possibly in geographically dispersed locations. However, security is a major challenge before outsourcing any IT needs of business. As cloud provides a multi-tenant virtual computing environment, where competitive businesses may co-exist, hosting of sensitive information for mission-critical applications is of utmost concern. This chapter reviews the recent works reported specifically in the area of data and application security relevant to cloud computing. Some works which use biologically inspired phenomenon to manage security and load balancing in cloud environment, have also been studied. The aim of this chapter is to provide an insight into the present state-of-the-art cloud security problems, proposed solutions, and identify future research directions as well as scopes in various security issues.KeywordsCloud ComputingNetwork CodeCloud ProviderCloud EnvironmentCloud StorageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Cloud computing is growing rapidly over the years and it faces challenges especially in resource management. Resource management in cloud computing is necessary due to its distributed nature with different user demands. Quality of Service (QoS), load balancing and throughput are identified as some of the benefits of proper resource management. This research focuses on job scheduling and resource load balancing in cloud environment. We proposed an efficient algorithm based on multi-criteria strategy. The algorithm consists of two main phases. In the first phase the shortest job completion time is measured based on the completion time of three techniques i.e. min-min, max-min and suffrage. Meanwhile in the second phase genetic algorithm is implemented for resource load balancing. Cloud Sim simulator is used to measure the performance and efficiency of the proposed algorithm. The proposed algorithm enhances jobs scheduling and resource load balancing by ensuring an efficient utilization of the available resources. Keywords: Cloud Computing, Genetic Algorithm, Job Scheduling, Load Balancing, Virtual Machine

Cloud computing is an emerging technology which provides unlimited access of versatile resources to users. The multifaceted and dynamic aspects of cloud computing require efficient and optimized techniques for resource provisioning and load balancing. Cloud monitoring is required identifying overutilized and underutilized of physical machines which hosting Virtual Machines (VMs). Load balancing is necessary for efficient and effective utilization of resources. Most of the authors have taken the objective to reduce the makespan for executing requests on multiple VMs. In this paper, a thorough review on scheduling and load balancing techniques has been done and different techniques have been analyzed on the basis of SLA Violations, CPU utilization, energy consumption and cost parameters.

Web technology has gained a huge momentum these days due to the digitization in almost all sectors. In cloud computing environment, the cloud data centers and the users of cloud computing are distributed extensively throughout the world and thus it is a huge challenge to manage these data in an orderly fashion. Load Balancing in this context means the distribution of load (data) in a smart manner such that the efficiency of the nodes are increased and the resources are used to their maximum potential which in turn would result in high user satisfaction. The algorithm proposed in this paper provides an optimal way of distributing load in servers connected to the Virtual Machines such that unnecessary overloading has been avoided in the cloud data centers. In this paper, the mixture of traditional algorithms like Weighted Round Robin and Weighted Least Connection have been used, which is showing better result in balancing the load among servers connected to the virtual machines.

Prediction Based Task Scheduling for Load Balancing in Cloud Environment

AO Modelling for Load Balancing in Cloud Environment with Comparative Study

Cloud computing is a commercial application of distributed system. With the rapid development of cloud computing, resource management problem has been widely studied and discussed. Most of the existing researches concern resource allocation by launching virtual machines, however, instant monitoring of the hosts, load balancing of the cloud platform is much more important for cloud providers. In this paper, a multi-Agent based distributed load balancing framework is proposed for cloud platform, it uses workload prediction technology and threshold buffering strategies to reduce the migration of VMs. Experiments show that our method can significantly reduce the workload data transmission and solve the frequent migration problem, which can effectively achieve load balancing, promote the utilization of the entire data center resources.

Load balancing has become a key technique to handle the increasing traffic demand and improve the user experience. It evenly distributes the traffic across network resources by offloading users from overloaded base stations or channels to less crowded ones. Load balancing is a multi-objective optimization problem involving the automatic adjustment of several parameters to simultaneously maximize multiple network performance indicators. However, the existing methods mostly rely on single-objective approaches which lead to sub-optimal solutions. In this paper, we introduce the first multi-objective reinforcement learning (MORL) framework for load balancing. Specifically, we propose a solution based on meta-reinforcement learning (meta-RL) to learn a general policy capable of quickly adapting to new trade-offs between the objectives. We further enhance the generalization of our proposed solution using policy distillation techniques. To showcase the effectiveness of our framework, experiments are conducted based on real-world traffic scenarios. Our results show that our load balancing framework can (i) significantly outperform the existing rule-based and single-objective solutions, (ii) compute better Pareto front approximations compared to MORL baselines, and (iii) quickly adapt to new objective trade-offs.

We study the problem of balancing the load on processors in an arbitrary network. If jobs arrive or depart during the process of load balancing, we have the dynamic load balancing problem; otherwise, we have the static load balancing problem. While static load balancing on a number of different networks has been well-studied, analytical work on dynamic load balancing is limited. The difficulty lies in modeling the arrivals and departures of jobs in a clean manner. In this paper, we introduce a new framework for dynamic load balancing in which the job traffic is modeled by an adversary. Our main result is that a simple local load balancing algorithm maintains the load of the network within a stable level against a powerful adversary. Our result holds for different models of traffic patterns and processor communication.

Cloud computing is distributed computing, storing, sharing and accessing data over the Internet. It provides a pool of shared resources to the users available on the basis of pay as you go service that means users pay only for those services which are used by him according to their access times. This research work deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. We propose an improved load balancing algorithm for job scheduling in the cloud environment using load distribution table in which the current status, current package, VM Capacity and the number of cloudlets submitted to each and every virtual machine will be stored. Submit the job of the user to the datacenter broker. Data center broker will first find the suitable Vm according to the requirements of the cloudlet and will match and find the most suitable Vm according to its availability or the machine with least load in the distribution table. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. The main contributions of the research work is to balance the entire system load while trying to minimize the make span of a given set of jobs. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.