Load Balancing Techniques in Cloud Computing

As Cloud Computing is growing rapidly and clients are demanding more services and better results, load balancing for the Cloud has become a very interesting and important research area. The top challenges and Issues faced by cloud Computing is Security, Availability, Performance etc. The issue availability is mainly related to efficient load balancing, resource utilization & live migration of data in the server. In clouds, load balancing, as a method, is applied across different data centres to ensure the network availability by minimizing use of computer hardware, software failures and mitigating recourse limitations. Load Balancing is essential for efficient operations in distributed environments. Hence this paper presents the various existing load balancing Technique in Cloud Computing based on different parameters

Dynamic Load Balancing Algorithms For Cloud Computing

In cloud computing, the load balancing is one of the major requirment. Load is nothing but the of the amount of work that a system performs. Load can be classified as CPU load, memory size and network load. Load balancing is the process of dividing the task among various nodes of a distributed system to improve both resource utilization and job response time. Also avoiding a situation where some of the nodes are heavily loaded and others are idle. Load balancing ensures that every node in the network having equal amount of work (as per their capacity) at any instant of time. In This paper we survey the existing load balancing algorithms for a cloud based environment. DOI: 10.17762/ijritcc2321-8169.150612

Structure-Aware Dynamic Scheduler for Parallel Machine Learning

Training large machine learning (ML) models with many variables or parameters can take a long time if one employs sequential procedures even with stochastic updates. A natural solution is to turn to distributed computing on a cluster; however, naive, unstructured parallelization of ML algorithms does not usually lead to a proportional speedup and can even result in divergence, because dependencies between model elements can attenuate the computational gains from parallelization and compromise correctness of inference. Recent efforts toward this issue have benefited from exploiting the static, a priori block structures residing in ML algorithms. In this paper, we take this path further by exploring the dynamic block structures and workloads therein present during ML program execution, which offers new opportunities for improving convergence, correctness, and load balancing in distributed ML. We propose and showcase a general-purpose scheduler, STRADS, for coordinating distributed updates in ML algorithms, which harnesses the aforementioned opportunities in a systematic way. We provide theoretical guarantees for our scheduler, and demonstrate its efficacy versus static block structures on Lasso and Matrix Factorization

Cloud computing - The effect of generalized spring tensor algorithm on load balancing

© 2014 IEEE. In business world, competitors use innovative approaches to improve their performance and profits. Cloud computing is one of these creative concepts that allowed companies to further taking advantage of their potential. Cloud computing is assisting companies to execute their business plans more efficiently. As cloud computing has multi-tenancy structure, availability and efficiency of the resources is essential foundation of the cloud architecture. Recent studies showed that, optimized cloud computing could be seen as an elastic network of resources that are interacting with each other, to minimize the waiting time and utilize the throughput. Therefore load balancing and resource management can be highlighted as the main concerns in cloud computing as they are impacting the network performance directly. This research aims to discuss the current challenges existing in load balancing algorithms. Different metrics and policies of the relevant load balancer algorithms have been investigated and as a result, collective behavior has been proposed as a new policy for classification of elasticity mechanism in load balancing

Boosting Attack Detection Capabilities in Multi-Tenant Distributed Systems via Meta-Ensemble Classifiers and Weighted Averaging

Multi-tenant distributed systems are a great way to share resources and scale performance, but they come with their share of security issues due to the introduction of more than one client on the cloud. Sharing of hardware and software resources among tenants gives rise to vulnerabilities and malicious tenants can misuse this shared data to launch attacks on other tenants. While efforts have traditionally focused on building secure network architectures, it is impossible to create a completely secure system due to its open-ended nature. This paper explores ways to detect malicious tenants on the cloud using machine learning algorithms. This paper proposes an ensemble-based meta-classifier to predict the probability of attack instantiation based on certain system parameter values. Additionally, this paper creates a dataset for analysis purposes and address the class imbalance problem often found in this domain where attack instances are rare. Satisfactory results were produced to distinguish between non attach and attack instances

Novel Load Balancing Optimization Algorithm to Improve Quality-of-Service in Cloud Environment

Scheduling cloud resources calls for allocating cloud assets to cloud tasks. It is possible to improve scheduling outcomes by treating Quality of Service (QoS) factors as essential constraints. However, efficient scheduling calls for improved optimization of QoS parameters, and only a few resource scheduling algorithms in the available literature do so. The primary objective of this paper is to provide an effective method for deploying workloads to cloud infrastructure. To ensure that workloads are executed efficiently on available resources, a resource scheduling method based on particle swarm optimization was developed. The proposed method's performance has been measured in the cloud. The experimental results prove the efficiency of the proposed approach in reducing the aforementioned QoS parameters. Several metrics of algorithm performance are used to gauge how well the algorithm performs

An optimized Load Balancing Technique for Virtual Machine Migration in Cloud Computing

Cloud computing (CC) is a service that uses subscription storage & computing power. Load balancing in distributed systems is one of the most critical pieces. CC has been a very interesting and important area of research because CC is one of the best systems that stores data with reduced costs and can be viewed over the internet at all times. Load balance facilitates maintaining high user retention & resource utilization by ensuring that each computing resource is correctly and properly distributed. This paper describes cloud-based load balancing systems. CC is virtualization of hardware like storage, computing, and security by virtual machines (VM). The live relocation of these machines provides many advantages, including high availability, hardware repair, fault tolerance, or workload balancing. In addition to various VM migration facilities, during the migration process, it is subject to significant security risks which the industry hesitates to accept. In this paper we have discussed CC besides this we also emphasize various existing load balancing algorithms, advantages& also we describe the PSO optimization technique