International Journal of Computer and Applications

Cloud computing has seen a major boom during the past few years. Many people have switched to cloud computing because traditional systems require complex resource distribution and cloud solutions are less expensive. Load balancing (LB) is one of the essential challenges in cloud computing used to balance the workload of cloud services. This research paper presents a performance evaluation of the existing load-balancing algorithms which are particle swarm optimization (PSO), round robin (RR), equally spread current execution (ESCE), and throttled load balancing. This study offers a detailed performance evaluation of various load-balancing algorithms by employing a cloud analyst platform. Efficiency concerning various service broker policy configurations for load-balancing algorithms’ virtual machine load balance was also calculated using metrics such as optimized response time (ORT), data center processing time (DCPT), virtual machine costs, data transfer costs, and total cost for different workloads and user bases. Many of the past papers that were mentioned in the literature worked on round robin and equally spread current execution, and throttled load-balancing algorithms were based on efficiency and response time in virtual machines without recognizing the relation between the task and the virtual machines, and the practical significance of the application. A comparison of specific load-balancing algorithms has been investigated. Different service broker policy (SBP) tests have been conducted to illustrate the load-balancing algorithm capabilities.

/pdf/performance-evaluation-of-load-balancing-algorithms-with-1cu7laj5.pdf

Performance Evaluation of Load-Balancing Algorithms with Different Service Broker Policies for Cloud Computing

A Review on Energy-Aware Scheduling Techniques for Workflows in IaaS Clouds

Методы инфлюентного анализа и принятие решений

Protein structure prediction (PSP) is an important research area in bio-informatics for its immense scope of application in drug design, disease prediction, name a few. Structure prediction of protein based on sequence of amino acids is a NP-hard and multi-modal optimization problem. This paper presents an improved harmony search (ImHS) algorithm to solve the PSP problem based on 3D off lattice model. In the proposed method, the basic harmony search (HS) algorithm combined with dimensional mean based perturbation strategy to avoid premature convergence and enhance the capability of jumping out from the local optima. The experiments are carried out on a set of real protein sequences with different length collected from the Protein Data Bank (PDB) to validate the efficiency of the proposed method. Numerical results show that the ImHS algorithm significantly outperforms compared to other algorithms on protein energy minimization.

An Improved Harmony Search Algorithm for Protein Structure Prediction Using 3D Off-Lattice Model

Protein secondary structure prediction has been and will continue to be a rich research field. This is because the protein structure and shape directly affect protein behavior. Moreover, the number of known secondary and tertiary structures versus primary structures is relatively small. Although the secondary prediction started in the seventies but it has been together with the tertiary structure prediction a topic that is always under research. This paper presents a technical study on recent methods used for secondary structure prediction using amino acid sequence. The methods are studied along with their accuracy levels. The most known methods like Neural Networks and Support Vector Machines are shown and other techniques as well. The paper shows different approaches for predicting the protein structures that showed different accuracies that ranged from 50% to over than 90%. The most commonly used technique is Neural Networks. However, Case Based Reasoning and Mixed Integer Linear Optimization showed the best accuracy among the machine learning techniques and provided accuracy of approximately 83%.

http://www.foibg.com/ijima/vol04/ijima04-01-p01.pdf

A study of intelligent techniques for protein secondary structure prediction

Cloud Computing support better computation through improved resources utilization and reduced infrastructure costs. Number of big providers like Google, Oracle, and Amazon sell services of Cloud Computing which are managed by Cloud Management Systems. It is very difficult to setup research on live cloud environments for individuals and small institutions due to the costs involved in setting up a cloud. Thus, Cloud Simulators are a cost effective way to study cloud components behavior and performance against different situations and work load. Several Cloud Simulators were developed over the years and this paper presents an evaluation study of most of them. We discuss the common architecture of Cloud Simulators. Then we evaluate 33 Cloud Simulators based on different criteria. The results are discussed. We are showing different capabilities, suitability for problems, and extendibility of the studied Cloud Simulators.

http://www.foibg.com/ijima/vol06/ijima06-01-p01.pdf

Cloud simulators – an evaluation study

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A study of intelligent techniques for protein secondary structure prediction

Cloud simulators – an evaluation study