Predictive Analysis for Cloud Infrastructure Metrics

In a cloud computing environment, enterprises have the flexibility to request resources according to their application demands. This elastic feature of cloud computing makes it an attractive option for enterprises to host their applications on the cloud. Cloud providers usually exploit this elasticity by auto-scaling the application resources for quality assurance. However, there is a setup-time delay that may take minutes between the demand for a new resource and it being prepared for utilization. This causes the static resource provisioning techniques, which request allocation of a new resource only when the application breaches a specific threshold, to be slow and inefficient for the resource allocation task. To overcome this limitation, it is important to foresee the upcoming resource demand for an application before it becomes overloaded and trigger resource allocation in advance to allow setup time for the newly allocated resource. Machine learning techniques like time-series forecasting can be leveraged to provide promising results for dynamic resource allocation. In this research project, I developed a predictive analysis model for dynamic resource provisioning for cloud infrastructure. The researched solution demonstrates that it can predict the upcoming workload for various cloud infrastructure metrics upto 4 hours in future to allow allocation of virtual machines in advance

Prediction of CPU Utilization in Cloud Environment during Seasonal Trend

Today, the most recent paradigm to emerge is that of Cloud computing, which promises reliable services delivered to the end-user through next-generation data centres which are built on virtualized compute and storage technologies Consumer will be able to access desired service from a “Cloud” anytime anywhere in the world on the bases of demand. Computing services need to be highly reliable, scalable, easy accessible and autonomic to support ever-present access, dynamic discovery and computability, consumers indicate the required service level through Quality of Service (QoS) parameters, according to Service Level Agreements (SLAs) A suitable mdel for the prediction is being developed. Here Genetic Algorithm is chosen in combination with stastical model to do the workload prediction .It is expected to give better result by producing less error rate and more accuracy of prediction compared to the previous algorithm

Self-aware and self-adaptive autoscaling for cloud based services

Modern Internet services are increasingly leveraging on cloud computing for flexible, elastic and on-demand provision. Typically, Quality of Service (QoS) of cloud-based services can be tuned using different underlying cloud configurations and resources, e.g., number of threads, CPU and memory etc., which are shared, leased and priced as utilities. This benefit is fundamentally grounded by autoscaling: an automatic and elastic process that adapts cloud configurations on-demand according to time-varying workloads. This thesis proposes a holistic cloud autoscaling framework to effectively and seamlessly address existing challenges related to different logical aspects of autoscaling, including architecting autoscaling system, modelling the QoS of cloudbased service, determining the granularity of control and deciding trade-off autoscaling decisions. The framework takes advantages of the principles of self-awareness and the related algorithms to adaptively handle the dynamics, uncertainties, QoS interference and trade-offs on objectives that are exhibited in the cloud. The major benefit is that, by leveraging the framework, cloud autoscaling can be effectively achieved without heavy human analysis and design time knowledge. Through conducting various experiments using RUBiS benchmark and realistic workload on real cloud setting, this thesis evaluates the effectiveness of the framework based on various quality indicators and compared with other state-of-the-art approaches

МОДЕЛЮВАННЯ МІНІМАЛЬНОЇ КІЛЬКОСТІ ВУЗЛІВ КЛАСТЕРА ВІРТУАЛІЗАЦІЇ ПРИВАТНИХ УНІВЕРСИТЕТСЬКОЇ ХМАРИ

Cloud computing is a dynamically evolving computing paradigm. The demand for cloud applications and technologies has especially increased during the CoVID-19 pandemic and martial law in Ukraine. The main purpose of using cloud applications and technologies is to free users of cloud resources from managing hardware and software. One of the challenges in designing a private university cloud is estimating the required number of virtualization cluster nodes. These hosts host virtual machines (VMs) of users. These VMs can be used by students and teachers to complete academic assignments as well as scientific work. The second task is to optimize the placement of VMs in the computer network (CN) of the university, which makes it possible to reduce the number of CN nodes without affecting functionality. And this ultimately helps to reduce the cost of such a solution to deploy a private university cloud, which is not unimportant for Ukrainian universities under martial law. The article proposes a model for estimating the required number of virtualization cluster nodes for a private university cloud. The model is based on a combined approach that involves jointly solving the problem of optimal packing and finding, using a genetic algorithm, the configuration of server platforms of a private university cloud.Хмарні обчислення - це парадигма обчислень, що динамічно розвивається. Особливо зросла затребуваність хмарних додатків та технологій (ХДТ) у період пандемії коронавірусу CoVID-19 та військового стану в Україні. Основною метою застосування ХДТ є звільнення користувачів хмарними ресурсами від управління апаратним та програмним забезпеченням (ПЗ). Однією із задач при проектуванні приватної університетської хмари є оцінка необхідної кількості вузлів кластера віртуалізації. На таких вузлах розміщують віртуальні машини (ВМ) користувачів. Ці ВМ можуть використовуватися учнями та викладачами для виконання навчальних завдань, а також для наукової роботи. Друге завдання – оптимізація розміщення ВМ в обчислювальній мережі (ОМ) закладу вищої освіти, що дозволяє скоротити кількість вузлів ОМ без впливу на функціональність. А це, зрештою, сприяє скороченню вартості такого рішення щодо розгортання приватної університетської хмари, що не менш важливо для українських закладів вищої освіти в умовах військового стану. У статті запропоновано модель оцінки необхідної кількості вузлів кластера віртуалізації для приватної університетської хмари. Модель заснована на комбінованому підході, який передбачає спільне рішення задачі про оптимальну упаковку та знаходження за допомогою генетичного алгоритму конфігурації серверних платформ приватної університетської хмари