Cloud monitoring: A survey

Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds.

Cloud Computing is one of the distributed computing paradigms and it is mainly focusing on providing everything as service to the consumers. In Cloud Computing Infrastructure as a Service (IaaS) is one of the service delivery model and it provides computational or storage resources, database and network resources to the users or customers. Eucalyptus is one of the open source cloud middleware, it is capable of creating private cloud inside an organization. The main drawback of the open source toolkit is it does not offer an integral monitoring and discovery service. The scalable monitoring and discovery service is important for managing the cloud resources. The proposed and implemented Cloud Monitoring and Discovery Service (CMDS) integrated with external information provider's discovers the cloud resources in a scalable way. The proposed model is integrated with CARE Resource Broker (CRB) and it is helpful for CRB for making scheduling decisions in cloud resources for virtual resource creation and application execution. The proposed system enhances the throughput of jobs by scaling of resources using CMDS in addition to that it increases the job success ratios of CRB.

Amazon Web Services: A Comprehensive Guide for Beginners and Advanced Users is your go-to companion for learning and mastering AWS. It presents 10 easy-to-read chapters that build a foundation for cloud computing while also equipping readers with the skills necessary to use AWS for commercial projects. Readers will learn how to use AWS cloud computing services for seamless integrations, effective monitoring, and optimizing cloud-based web applications. What you will learn from this guide: 1. Identity and Access Management in AWS: Learn about IAM roles, security of the root account, and password policies, ensuring a robust foundation in access management. 2. Amazon EC2 Instance: Explore the different types of EC2 instances, pricing strategies, and hands-on experiences to launch, manage, and terminate EC2 instances effectively. This knowledge will help to make informed choices about pricing strategies. 3. Storage Options and Solutions: A detailed examination of storage options within Amazon EC2 instances. Understanding Amazon Elastic Block Store (EBS), Amazon Elastic File Storage (EFS), and more, will enhance your ability to handle data storage efficiently. 4. Load Balancing and Auto Scaling: Learn about different types of load balancers and how auto-scaling groups operate, to master the art of managing varying workloads effectively. 5. Amazon Simple Storage Service (S3): Understand S3 concepts such as buckets, objects, versioning, storage classes, and practical applications. 6. AWS Databases and Analytics: Gain insights into modern databases, AWS cloud databases, and analytics services such as Amazon Quicksight, AWS Glue, and Amazon Redshift. 7. Compute Services and Integrations: Understand the workings of Docker, virtual machines, and various compute services offered by AWS, including AWS Lambda and Amazon Lightsail, Amazon MQ and Amazon SQS. 8. Cloud Monitoring: Understand how to set up alarms, analyze metrics, and ensure the efficient monitoring of your cloud environment using Amazon CloudWatch and CloudTrail. Key Features: Comprehensive Introduction to Cloud Computing and AWS Guides readers to the complete set of features in AWS Easy-to-understand language and presentation with diagrams and navigation guides References for further reading Whether you're a student diving into cloud specialization as part of your academic curriculum or a professional seeking to enhance your skills, this guide provides a solid foundation for learning the potential of the AWS suite of applications to deploy cloud computing projects.

The cloud computing has become most popular due to its advantages. The more number of organizations are migrating to cloud to reduce the complexity of maintain resources. So cloud management becomes most challenging task. To reduce the complexity of management and improve the overall performance of cloud, an efficient cloud monitoring tool is required. The cloud monitoring toolhelps to improve the overall performance and reduce the management complexity. The major functions of cloud monitoring are to tracking QoS parameter of Virtualized, physical resources and applications that are hosted on cloud. Hence cloud monitoring tools monitors all the resources and events and perform dynamic configurations of cloud for better performance. In this review paper, we have discussed basic concept of cloud monitoring tool and discuss various commercial and open-source cloud monitoring tools and its taxonomy.KeywordsCloud computingCloud monitoringQoS parametersConfiguration management

Cloud computing has seen massive growth in this decade. With the rapid development of cloud networks, cloud monitoring has become essential for running cloud systems smoothly. Cloud monitoring collects monitoring metrics from the cloud’s physical and virtual infrastructures. In terms of data collection, cloud monitoring can be intrusive or non-intrusive. Monitoring data collection non-intrusively from the host operating system (OS) is a challenging task. The aim of this paper was to collect monitoring data from the host OS non-intrusively and to link those data with the cloud controller for use in monitoring. Monitoring data were collected from Procfs of the host OS and that information was linked with the monitoring dashboard on the cloud controller node. The results show that the proposed solution is an efficient, lightweight, and scalable cloud monitoring framework that produces negligible overhead.

Despite its importance for operating Cloud systems, Cloud monitoring has received limited attention from the research community. In this position paper, we provide an analysis of Cloud monitoring. More precisely, we discuss the main motivations, basic concepts and definitions, and point out open research issues and future directions for Cloud monitoring.

With the increasing complexity of cloud systems, how to efficiently manage cloud infrastructures has become a challenging task, while an effective monitoring service plays a crucial role to achieve this goal. In this paper, we present an extensible and scalable cloud monitoring middleware called extensible cloud monitoring service (ECMS), which is designed for offering online monitoring service to different monitoring requests from either cloud users or resource providers. In ECMS, a layered monitoring framework is introduced with aiming to cover different aspects of cloud monitoring requests. The implementation of ECMS is evaluated in a real-world cloud test-bed and the results indicate that it can efficiently provide online monitoring services to different kinds of clients and exhibits better scalability and robustness comparing with other monitoring systems.

GMonE: A complete approach to cloud monitoring

As a new computing paradigm, Cloud computing has been advocated by IT industry and academia. It can provide lots of services, however, how to manage these services and guarantee the quality of service, has become one of the key factors for the development of Cloud computing. This paper proposes a hierarchical architecture model of Cloud services resources management and monitoring by integrating the power of Service-Oriented Architecture and the concept of IT service management. The architecture model uses service monitoring and process of IT service management to realize management of Cloud services resources, and then provides foundation for technology to ensure the quality of service of Cloud services. Therefore, it could be used as a reference architecture model of management and monitoring on Cloud services resources.

Cloud Computing Technology has been evolving rapidly in recent years and is applied widely due to its unique advantages. Many enterprises deploy numerous applications on their private cloud platforms in order to achieve the goals of optimizing their management, maximizing resource utilization as well as the cost reduction. While a cloud computing system is able to deliver us a bunch of benefits, we need to handle some issues such as constantly monitoring systems in order to offer better and stable services. The effective system monitoring is crucial for a data center. In this paper we propose a Complex Event Processing (CEP) based in which a system status detection algorithm is a core.

Monitoring of cloud computing infrastructures is an imperative necessity for cloud providers and administrators to analyze, optimize and discover what is happening in their own infrastructures. Current monitoring solutions do not fit well for this purpose mainly due to the incredible set of new requirements imposed by the particular requirements associated to cloud infrastructures. This paper describes in detail the main reasons why current monitoring solutions do not work well. Also, it provides an innovative monitoring architecture that enables the monitoring of the physical and virtual machines available within a cloud infrastructure in a non-invasive and transparent way making it suitable not only for private cloud computing but also for public cloud computing infrastructures. This architecture has been validated by means of a prototype integrating an existing enterprise-class monitoring solution, Nagios, with the control and data planes of OpenStack, a well-known stack for cloud infrastructures. As a result, our new monitoring architecture is able to extend the exiting Nagios functionalities to fit in the monitoring of cloud infrastructures. The proposed architecture has been designed, implemented and released as open source to the scientific community. The proposal has also been empirically validated in a production-level cloud computing infrastructure running a test bed with up to 128 VMs where overhead and responsiveness has been carefully analyzed.

Cloud computing has swiftly established itself as the norm in its field as a result of the advantages described above. In an attempt to reduce the amount of time spent on infrastructure upkeep, an increasing number of businesses are moving their operations to the cloud. As a result, maintaining a cloud environment has proved to be exceedingly difficult. It is necessary to have an efficient cloud monitoring system in order to reduce the workload associated with administration and improve cloud operation. The cloud monitoring service is beneficial since it has the potential to improve performance and make administration easier. The administration of Quality of Service (QoS) parameters for cloud-hosted, virtualized, and physical services and applications is one of the most important responsibilities of cloud monitoring. As a result, cloud management software retains a record of both actions and services, and it also conducts dynamic setups of the cloud in order to increase operational effectiveness. The performance of businesses and businesses as a whole was examined in this article, as was the influence that cloud-ready programs and tools have on that performance, as well as the advantages that may be obtained from adopting such programs and products.

So far, monitoring in cloud computing mostly tends to focus only on QoS and Service Level Agreements. However, if we tend to monitor user activities on clouds we have to look at a centralized approach. Having a centralized monitoring system in cloud computing is becoming more and more necessary, it is worthwhile to address this kind of monitoring in the cost and performance angles also. The performance and cost studies will not be appropriate without a tight comparison of various implementation and architectural possibilities. A Simulation is made so as to reveal the best options based on cost and performance.

This present research work aims to maximize the performance of cloud systems while reducing the response time through dynamic load balancing algorithm in cloud computing environment. Cloud computing provides different information technology services as a commodity to its users. When it was introduced, business industries were using large-scale mainframes and they are increasing in numbers and sizes. All are aims to increase its number of users to get more revenue. Hence they need different types of services having infrastructure, platform, software and many more in less response time. Two types of policies exist in cloud computing systems namely static load balancing and dynamic load balancing. Static load balancing policies do not consider the current state of system. These algorithms are suitable for homogeneous and stable environment. Dynamic load balancing depends on the present behavior of the system. There are various dynamic load balancing policies for virtual machines already existing in this context including Throttled and Active Monitoring in cloud computing systems. These types of algorithms are more flexible in nature. In this research paper, Enhanced Active Monitoring Load Balancing (EAMLB) algorithm is designed to minimize the response time in cloud systems. EAMLB policy provides better response time than Active Monitoring policy that improves the performance of cloud systems. Further, to assess the proposed algorithm, CloudAnalyst tool is used and comparative analysis is also represented.

Cloud computing is a new computing model. The resource monitoring tools are immature compared to traditional distributed computing and grid computing. In order to better monitor the virtual resource in cloud computing, a periodically and event-driven push (PEP) monitoring model is proposed. Taking advantage of the push and event-driven mechanism, the model can provide comparatively adequate information about usage and status of the resources. It can simplify the communication between Master and Work Nodes without missing the important issues happened during the push interval. Besides, we develop “mon” to make up for the deficiency of Libvirt in monitoring of virtual CPU and memory.