CloudIBN is a worldwide leading company providing cloud migration services, consulting, deployment and solutions since 1999. We help various organizations in porting their existing systems planning strategy and proper deployment for their success.
Many organizations embark on cloud migrations to attain scalability, cost-efficiency and higher application performance. But migrating apps to the cloud is an intricate process that requires vigilant planning and deliberation.
It’s crucial that organizations think through all possibilities — both good and bad. Cloud migration issues, such as unexpected costs, interoperability, security gaps and out of the blue application rework, can create significant hitches. To help smooth a frequently jarring path, organizations need to craft a well-thought-out migration strategy.
Whether your organization is migrating to the cloud from an on-premise environment or one cloud to the other, it’s important to figure out the process and temper any exorbitant expectations. And, since not every single application will benefit from the cloud, make sure migration is right for your organization before diving in.
To reduce your cloud costs, you must first identify excess by detecting inefficient use of cloud resources.
Cloud cost optimization is not a once-and-done process, but you can instantly start saving money on your cloud infrastructure costs if you address key areas that account for the majority of misused cloud spend and budget overruns.Visit us - https://www.cloudibn.com/cloud-cost-optimization/
8K Miles, the right cloud cost optimization partner to reduce ongoing cloud cost in all cloud service providers like Amazon Web Services(AWS),Azure & GCP.AWS Cloud Cost Optimization Strategies1.
Delete or migrate unwanted files after a certain date7.
Take care of incomplete multipart uploads9.
Reduce cost on API access10.
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Explore our Cloud Solutions that help you modernize your IT, respond flexibly to any alterations and compete in a digitally transformed world.Cost: Optimize your IT expenditure by moving from large capital expenditure to flexible operational expenditure.
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Scalable: Get the computing power, storage and bandwidth you need as you grow your business, sourced from the optimal location.Being a trusted software development partner for global Lending Tech business, Delta IT Network AI & ML solutions for making the credit scoring more accurate.We provide the following artificial intelligence powered solutions to taking financial decisions:AI/ML solutions to risk evaluationAI-based customer prioritizationTraining predictable models on the customer’s behaviorAI-based credit scoring modulesIntegrations with major cloud AI solutions like Amazon/AzureDelta IT Network offers end-to-end Cloud Computing Solution, migration, consulting, deployment, maintenance, Devops and disaster recovery.Mobile: +91-9910248322 / +91-9911134433Email ID: dchauhan@deltaitnetwork.comWebsite: https://www.deltaitnetwork.com
In today’s dynamic and highly componentized IT landscape, predictive analytics offers the much-needed ability to proactively predict future outages and automate fixes before they bring down the entire infrastructure.Owing to the rapid digitization of business operations, IT teams need to constantly monitor and analyze large volumes of data, resulting in extended delays in identifying and solving issues.
On top of that, a single IT issue can trigger thousands of alerts, logs, and events, and with the ITOps team working in disconnected silos, it becomes extremely difficult to diagnose the root cause and solve issues.Predictive analytics, powered by big data, artificial intelligence (AI), and machine learning, overcomes these obstacles to improve application performance, network uptime, and IT infrastructure resiliency by predicting and mitigating outages, and reducing maintenance and operations expenditure in the process.Gartner predicts that the number of large enterprises that use artificial intelligence in IT operations to combine big data and machine learning functionality to enhance or optimize IT operations and automate processes and tasks to grow by 40% by 2023.Let’s understand how predictive analytics is transforming ITOps.Predictive Analytics: An Evolution In IT Today’s IT operations monitoring and management systems leverage predictive analytics for collecting and integrating data, normalizing it, and analyzing it in real-time.Machine learning algorithms analyze past incident data to predict and resolve potential incidents in the future.Here are some ways in which predictive analytics is transforming IT operations.1.
Dynamic Thresholding And Anomaly Detection An anomaly detection algorithm uses unsupervised machine learning to get familiar with the IT environment, recognize expected behavior, and set dynamic thresholds against vital performance metrics.Consequently, event patterns are analyzed in real-time and compared against expected behavior, and the IT team is alerted when a series of events showcase anomalous activity.Moreover, fuelled by artificial intelligence, the system also accounts for false alert suppression and seasonality, i.e.
For example, a 90% system utilization is normal during peak business hours, but indicates an issue when the same metric is hit on a Sunday morning.Anomalous group of events are helpful in –Alerting the team regarding an unplanned activity, for example a cyber attackMaking IT operations more agile by improving planning for significant events, for example, Amazon increasing capacity to ensure infrastructure and applications perform well during the ‘Big Billion Sale’.2.
Predictive Maintenance Of Application Health In Real-Time Performing application health monitoring in real-time allows ITOps teams to respond to a degradation in application health before operations come to a standstill.Available data generated by the application, including configuration data, network logs, application logs, performance logs, and error logs, is compiled.
Multivariate machine learning techniques analyze this data, across different dimensions, to learn the application’s normal behavior.As new data enters the application, the model identifies unusual patterns and sends it to the IT personnel to follow up before a business-critical outage takes place.3.
