Performance Of Cloud Environment Research Articles

Predictive modelling of MapReduce job performance in cloud environments using machine learning techniques

Within the Hadoop ecosystem, MapReduce stands as a cornerstone for managing, processing, and mining large-scale datasets. Yet, the absence of efficient solutions for precise estimation of job execution times poses a persistent challenge, impacting task allocation and distribution within Hadoop clusters. In this study, we present a comprehensive machine learning approach for predicting the execution time of MapReduce jobs, encompassing data collection, preprocessing, feature engineering, and model evaluation. Leveraging a rich dataset derived from comprehensive Hadoop MapReduce job traces, we explore the intricate relationship between cluster parameters and job performance. Through a comparative analysis of machine learning models, including linear regression, decision tree, random forest, and gradient-boosted regression trees, we identify the random forest model as the most effective, demonstrating superior predictive accuracy and robustness. Our findings underscore the critical role of features such as data size and resource allocation in determining job performance. With this work, we aim to enhance resource management efficiency and enable more effective utilisation of cloud-based Hadoop clusters for large-scale data processing tasks.

Rational load balancing in collaborated cloud computing environments *

Abstract Optimum efficiency and fault tolerance are the most demanding and challenging issues in the domain of performance and reliability management in cloud computing environments. Optimized resource utilization is a key aspect for yielding efficiency in cloud platforms. Workload balancing through resource sharing is one of the key solutions for attaining performance in cloud environments. In addition, multiple cloud environments join hands to offer performance and fault tolerance through resource sharing. We provide a better and cloud-instances’ priority-based efficient load balancing method for collaborative cloud platforms. The recommended efficient load balancing method shortens the waiting timespan and overcomes the starvation problem of low priority instances in intercloud environments. A functional prototype of the recommended load balancing method was deployed on a physical cloud infrastructure which was setup with the OpenStack cloud software on the Fedora Linux operating system. The pilot project execution findings exhibit a reduction in the timespan borne by instances for executing load balancing. This technique is useful for attaining fault tolerance and efficient resource utilization in intracloud and intercloud environments.

Secure and Scalable Architectures for Cloud Computing

Cloud computing has emerged as a cornerstone of modern computing infrastructure, offering unparalleled scalability and flexibility for diverse applications. However, the widespread adoption of cloud services has also raised significant concerns regarding security and scalability. As organizations increasingly rely on cloud environments to store and process sensitive data, ensuring robust security measures while maintaining scalability becomes paramount. This paper presents a comprehensive examination of secure and scalable architectures for cloud computing, addressing the challenges and proposing innovative solutions to enhance the resilience and efficiency of cloud infrastructures. The first section of the paper delves into the fundamental principles of cloud computing, outlining its key characteristics and architectural components. By understanding the underlying architecture of cloud systems, stakeholders can better appreciate the intricate interplay between security and scalability. Subsequently, the paper elucidates the multifaceted security threats facing cloud environments, ranging from data breaches and malicious attacks to insider threats and compliance issues. These challenges underscore the critical importance of implementing robust security mechanisms to safeguard sensitive data and infrastructure resources. In response to these challenges, the paper presents a systematic analysis of existing security mechanisms and architectural paradigms designed to fortify cloud environments. This includes encryption techniques, access control mechanisms, authentication protocols, and intrusion detection systems, among others. Furthermore, the paper explores the concept of defense-in-depth strategies, emphasizing the importance of layering security measures to mitigate the impact of potential breaches and vulnerabilities. In tandem with security considerations, the paper examines strategies for achieving scalability in cloud architectures. Scalability is a defining characteristic of cloud computing, enabling on-demand resource allocation and elastic scaling to accommodate fluctuating workloads. However, achieving scalability while ensuring security presents unique challenges, particularly in multi-tenant environments where resources are shared among disparate users. To address these challenges, the paper explores architectural frameworks such as microservices, containerization, and serverless computing, which offer inherent scalability benefits while facilitating robust isolation and security. Moreover, the paper investigates the role of automation and orchestration in optimizing cloud scalability and security. Through the use of DevOps practices and infrastructure- as-code tools, organizations can streamline deployment processes, enforce security policies, and dynamically scale resources in response to evolving demands. Additionally, the paper examines emerging technologies such as edge computing and federated architectures, which extend the scalability and security benefits of cloud computing to edge devices and distributed environments. In conclusion, this paper underscores the intrinsic link between security and scalability in cloud computing architectures. By adopting a holistic approach that integrates robust security measures with scalable design principles, organizations can mitigate risks, enhance operational efficiency, and unlock the full potential of cloud computing. As the landscape of cloud technologies continues to evolve, ongoing research and innovation in secure and scalable architectures will remain essential to address emerging threats and optimize performance in cloud environments.

Efficient Workload Allocation and Scheduling Strategies for AI-Intensive Tasks in Cloud Infrastructures.

- The rapid proliferation of Artificial Intelligence (AI) applications has underscored the need for advanced cloud infrastructures capable of efficiently managing AI-intensive workloads. This paper delves into the intricacies of workload allocation and scheduling in the context of cloud environments, specifically focusing on the challenges posed by AI-intensive tasks. Our research endeavors to scrutinize existing strategies, discern their limitations, and proffer innovative approaches tailored to optimize the allocation and scheduling of AI workloads within cloud infrastructures. In elucidating the challenges, we pinpoint resource heterogeneity, dynamic workload characteristics, and scalability as the crux of the issues confronting AI-intensive workload management. The diverse computational demands of AI workloads make it challenging to allocate resources optimally, while the dynamic nature of these tasks necessitates adaptive strategies to accommodate varying computational requirements over time. [1] Additionally, as AI models and datasets burgeon in complexity and size, ensuring scalability becomes paramount for sustaining performance in cloud environments. Our literature review encompasses an examination of both traditional and state-of-the-art workload allocation strategies, shedding light on their respective strengths and shortcomings. We also delve into scheduling techniques employed for managing AI-intensive tasks, providing a comprehensive overview of the existing landscape. To address these challenges, we propose a novel framework centered around dynamic resource provisioning, machine learning-based scheduling, and efficient task migration strategies. The framework aims to adaptively allocate resources based on the evolving nature of AI workloads, leveraging machine learning algorithms to predict workload characteristics and employing efficient task migration to handle workload fluctuations. The paper concludes with an experimental evaluation of the proposed strategies, conducted in a simulated environment using diverse datasets. Key performance metrics, such as throughput, latency, and resource utilization, are employed to assess the effectiveness of our strategies compared to existing approaches. By offering insights into the efficient management of AI-intensive workloads in cloud infrastructures, this research contributes to the ongoing efforts to enhance the scalability and performance of cloud environments in the face of burgeoning AI applications.

Vnode: Low-Overhead Transparent Tracing of Node.js-Based Microservice Architectures

Tracing serves as a key method for evaluating the performance of microservices-based architectures, which are renowned for their scalability, resource efficiency, and high availability. Despite their advantages, these architectures often pose unique debugging challenges that necessitate trade-offs, including the burden of instrumentation overhead. With Node.js emerging as a leading development environment recognized for its rapidly growing ecosystem, there is a pressing need for innovative performance debugging approaches that reduce the telemetry data collection efforts and the overhead incurred by the environment’s instrumentation. In response, we introduce a new approach designed for transparent tracing and performance debugging of microservices in cloud settings. This approach is centered around our newly developed Internal Transparent Tracing and Context Reconstruction (ITTCR) technique. ITTCR is adept at correlating internal metrics from various distributed trace files to reconstruct the intricate execution contexts of microservices operating in a Node.js environment. Our method achieves transparency by directly instrumenting the Node.js virtual machine, enabling the collection and analysis of trace events in a transparent manner. This process facilitates the creation of visualization tools, enhancing the understanding and analysis of microservice performance in cloud environments. Compared to other methods, our approach incurs an overhead of approximately 5% on the system for the trace collection infrastructure while exhibiting minimal utilization of system resources during analysis execution. Experiments demonstrate that our technique scales well with very large trace files containing huge numbers of events and performs analyses in very acceptable timeframes.

An Efficient Task Scheduling in a Cloud Computing Environment using Hybrid Swarm Optimization

Information technology (IT) services are available on demand over the Internet under the current computing standard known as cloud computing. A work is mapped with an available resource in the cloud environment to get a good result. Cloud computing has emerged as a transformative technology that provides on-demand access to a shared pool of computing resources over the internet. Effective task scheduling plays a crucial role in optimizing resource utilization and improving overall system performance in cloud environments. According to a server's workload capacity, tasks are distributed across its virtual machines (VMs) via task scheduling. The server's workload is planned out to reduce traffic and wait times. The best algorithm currently in use to schedule a work to an existing resource in a cloud setting is particle swarm optimization (PSO). PSO schedules the task for an available resource to cut down on computational costs. In order to address job scheduling challenges in the cloud environment, a hybrid swarm optimizations (HSO) algorithm—a combination of PSO and salp swarm optimization (SSO)—is suggested in this paper. The hybrid algorithm leverages the strengths of both PSO and SSO to achieve better optimization results. The proposed method is evaluated using the CloudSim simulator and compared with traditional scheduling algorithms. Simulation results demonstrate the superiority of the hybrid PSO-SSO algorithm in terms of makespan time, cloud throughput, and task execution efficiency. The findings suggest that the hybrid approach can significantly enhance resource utilization and performance in cloud computing environments.

A variant of teaching‐learning‐based optimization and its application for minimizing the cost of Workflow Execution in the Cloud Computing

AbstractTeaching learning‐based optimization (TLBO) was proposed by Rao to solve optimization problems. It is based on the theory of teaching‐learning mechanism. Although it performs well in unimodal problems yet its performance is not good in multimodal problems. To further improve this algorithm's performance and make it suitable for both unimodal problems and multimodal problems, we made some major changes in the theory and the algorithm's operators. The proposed algorithm is able to capture diverse optimal solutions in less number of iterations and is very good for solving multimodal problems. This newly created variant of TLBO is named generalized TLBO (GTLBO). The performance of GTLBO is tested on CEC−06, 2019 benchmark functions and other 15 classical benchmark functions, and it is found that the proposed algorithm is performing better comparatively. Then it is simulated for solving the workflow scheduling problem in CloudSim. Standard scientific workflow applications as Montage, Epigenomics, Sipht, and a sample workflow are used as dataset to test algorithms' performance in cloud environments. Our proposed approach, GTLBO, provides the proper distribution of workloads and offers minimal execution‐cost for the workflow applications. Results reflect the supremacy of the proposed algorithm GTLBO comparatively.

Implementation of Various Load-Balancing Approaches for Cloud Computing Using CloudSim

Cloud computing is an Internet based technology which empowers its user by providing access to an offsite huge distributed virtualized and scalable infrastructure via Internet. This infrastructure is composed of a number of hosts which may be homogeneous or heterogeneous in configuration are networked together to enable network storage, computing, services to the users. During high demands of users the workload on infrastructure increases which may impact the performance in cloud environment due to load imbalance. To overcome such issues the concept of load balancing arises. It is a methodology which works towards to equalize the load among the available resources in cloud surroundings. The role of load balancing is vital in cloud computing surroundings as it is responsible for maintaining the Quality of Services (QoS) in terms of optimized usage of resources, limiting the operational cost, making energy efficient system and increasing scalability. Testing of the load balancing algorithms in real cloud environs is a bottleneck due to resource wastage and lack of time. Simulation provides an aid to developers or researchers to provide a feedback by mimicking the actual environment. The paper deals with simulation based analysis of different distributed and centralized load balancing algorithms implemented in CloudSim.

Real-time resource monitoring approach for detection of hotspot for virtual machine migration

Cloud computing is a service delivery model that provides computing resources (RAM, CPU, Memory, etc.), which are highly elastic and can grow or shrink dynamically according to the need of client. Virtual machine migration (VMM) plays very important role to provide the resource elasticity to cloud environment. VMM generates considerable amount of overheads which can degrade overall performance of cloud environment. So, it becomes important to decide when migration should take place. In this paper, we have introduced real-time resource monitoring (RTRM) system for detection of a hotspot host. We have developed three modules: (1) monitoring module, (2) analysis module, and (3) VMM module for detection of hotspot. We have compared the result of proposed RTRM with sandpiper and artificial intelligence technique such as neural network predictor. Experimental result shows that RTRM is able to detect the hotspot in first 20 s of time with the accuracy of 80% as compared to sandpiper with 64% accuracy and neural network predictor with 76% accuracy in the same time frame.

Comparison of Task Scheduling Algorithms in Cloud Environment

The enhanced form of client-server, cluster and grid computing is termed as Cloud Computing. The cloud users can virtually access the resources over the internet. Task submitted by cloud users are responsible for efficiency and performance of cloud computing services. One of the most essential factors which increase the efficiency and performance of cloud environment by maximizing the resource utilization is termed as Task Scheduling. This paper deals with the survey of different scheduling algorithms used in cloud providers. Different scheduling algorithms are available to achieve the quality of service, performance and minimize execution time. Task scheduling is an essential downside within the cloud computing that has to be optimized by combining different parameter. This paper explains the comparison of several job scheduling techniques with respect to several parameters, like response time, load balance, execution time and makespan of job to find the best and efficient task scheduling algorithm under these parameters. The comparison of scheduling algorithms is also discussed in tabular form in this paper which helps in finding the best algorithms.

A REVIEW ON RESOURCE ALLOCATION MECHANISM IN CLOUD ENVIORNMENT

Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Cloud computing is a hot topic all over the world nowadays, through which customers can access information and computer power via a web browser. As the adoption and deployment of cloud computing increase, it is critical to evaluate the performance of cloud environments. Currently, modeling and simulation technology has become a useful and powerful tool in cloud computing research community to deal with these issues. Cloud simulators are required for cloud system testing to decrease the complexity and separate quality concerns. Cloud computing means saving and accessing the data over the internet instead of local storage. In this paper, we have provided a short review on the types, models and architecture of the cloud environment.

Performance Evaluation of VM Placement Using Classical Bin Packing and Genetic Algorithm for Cloud Environment

In current era, the trend of cloud computing is increasing with every passing day due to one of its dominant service i.e. Infrastructure as a service (IAAS), which virtualizes the hardware by creating multiple instances of VMs on single physical machine. Virtualizing the hardware leads to the improvement of resource utilization but it also makes the system over utilized with inefficient performance. Therefore, these VMs need to be migrated to another physical machine using VM consolidation process in order to reduce the amount of host machines and to improve the performance of system. Thus, the idea of placing the virtual machines on some other hosts leads to the proposal of many new algorithms of VM placement. However, the reduced set of physical machines needs the lesser amount of power consumption therefore; in current work the authors have presented a decision making VM placement system based on genetic algorithm and compared it with three predefined VM placement techniques based on classical bin packing. This analysis contributes to better understand the effects of the placement strategies over the overall performance of cloud environment and how the use of genetic algorithm delivers the better results for VM placement than classical bin packing algorithms.

Multi-user utility pattern based cloud service negotiation for quality of service improvement: an implicit interest prediction approach

The growing deployment of cloud services increases the responsibility of cloud service provider in meeting consumer satisfaction in great measure. As the interest of the user changes in time and the number of service providers are growing the cloud user has more options in choosing the required service. But the problem of service negotiation must be handled by all the service providers in order to improve the quality of service. To solve the issue of service negotiation, a multi-user utility pattern based service negotiation approach is discussed in this paper. The method monitors the service negotiation values and keeps track of services accessed by different cloud user. It also maintains a trace of sequence of service access based on user's interest. The proposed technique uses the implicit tracking approach to predict the interest of the user by monitoring the service access without the knowledge of the end user and generate a pattern called utility pattern. Based on the generated pattern, the most impacting services are ranked according to the implicit activities of different users. The ranked services are given to the user to select a single service to fulfil the request. The proposed technique improves the performance of cloud environment to 95% and increases the satisfaction of the cloud user.

Efficient on Demand Dynamic Availability-Distribution-Span Scheduling and Load Balancing Scheme for Cloud Computing

The problem of load balancing in cloud environment has been approached by different scheduling algorithms. Still the performance of cloud environment has not been met to the point and to overcome these issues, we propose a naval ADS (Availability-Distribution-Span) Scheduling method to perform load balancing as well as scheduling the resources of cloud environment. The method performs scheduling and load balancing in on demand nature and takes dynamic actions to fulfill the request of users. At the time of request, the method identifies set of resources required by the process and computes Availability Factor, Distributional Factor and Span Time factor for each of the resource available. Based on all these factors computed, the method schedules the requests to be processed in least span time. The proposed method produces efficient result on scheduling as well as load balancing to improve the performance of resource utilization in the cloud environment.

A Survey On Cloud Computing and Its Various Services

Cloud computing is a hot topic all over the world nowadays, through which customers can access information and computer power via a web browser. As the adoption and deployment of cloud computing increase, it is critical to evaluate the performance of cloud environments. Currently, modeling and simulation technology has become a useful and powerful tool in cloud computing research community to deal with these issues. Cloud simulators are required for cloud system testing to decrease the complexity and separate quality concerns. Cloud computing means saving and accessing the data over the internet instead of local storage. In this paper, we have conducted a survey on the models of cloud environment, benefits and issues related to it.

Highly Secure Method based on Ciphertext Policy Attribute based Encryption in Hadoop System

is one of today's most exciting technologies due to its ability to reduce costs associated with while increasing flexibility and scalability for computer processes. Cloud is set of resources and services offered through the Internet. The rapid growth in field of computing also increases severe security concerns. Lack of security is the only hurdle in wide adoption of cloud computing. Distribution and sharing of File is one of the most critical service of cloud computing. This paper examines security for big data environments, reviewing built-in protections and weaknesses of these systems. In this paper, we use the Hadoop based file system and secure it using a highly secure algorithm based on ciphertext policy attributes based encryption with pairing (CP- ABE-WP with pairing). The solution aims at providing optimal performance in cloud environment and also ensures security by using a session based approach. The proposed method has maintained a hierarchy of users to ensure that files are used by their appropriate users. KeywordsStorage; Access Control; Attribute-Based Encryption; Document Sharing; Ciphertext-Policy.

A workflow task scheduling algorithm based on the resources' fuzzy clustering in cloud computing environment

SummaryCloud computing is the key and frontier field of the current domestic and international computer technology, workflow task scheduling plays an important part of cloud computing, which is a policy that maps tasks to appropriate resources to execute. Effective task scheduling is essential for obtaining high performance in cloud environment. In this paper, we present a workflow task scheduling algorithm based on the resources' fuzzy clustering named FCBWTS. The major objective of scheduling is to minimize makespan of the precedence constrained applications, which can be modeled as a directed acyclic graph. In FCBWTS, the resource characteristics of cloud computing are considered, a group of characteristics, which describe the synthetic performance of processing units in the resource system, are defined in this paper. With these characteristics and the execution time influence of the ready task in the critical path, processing unit network is pretreated by fuzzy clustering method in order to realize the reasonable partition of processor network. Therefore, it largely reduces the cost in deciding which processor to execute the current task. Comparison on performance evaluation using both the case data in the recent literature and randomly generated directed acyclic graphs shows that this algorithm has outperformed the HEFT, DLS algorithms both in makespan and scheduling time consumed. Copyright © 2014 John Wiley & Sons, Ltd.

Reliability of computational experiments on virtualised hardware

We present a large-scale investigation of the variability of run times on physical and virtualised hardware. The aim of our investigation is to establish whether cloud infrastructures are suitable for running computational experiments in which the results rely on reported run times. Our application is the use of the Minion constraint solver as an example of an Artificial Intelligence experiment. We include two major providers of public cloud infrastructure, Amazon and Rackspace, as well as a private Eucalyptus cloud. Although there are many studies in the literature that investigate the performance of cloud environments, the problem of whether this performance is consistent and run time measurements are reliable has been largely ignored. Our comprehensive experiments and detailed analysis of the results show that there is no intrinsic disadvantage of virtualised hardware over physical hardware and that in general cloud environments are suitable for running computational experiments. Our meticulous investigation reveals several interesting patterns in the variability of run times that researchers using a cloud for this purpose should be aware of. We close by giving recommendations as to which type of virtual machine with which cloud provider should be used to achieve reproducible results.