Auto-scaling is the ability to automatically add or remove server resources from your cloud computing environment.

We all know the challenges and daunting tasks of adding additional physical servers in an on-premise environment; it has an entire process of space planning, power supply, procurement, installation, configuration etc.

With Cloud, it has become possible to add resources on-demand with a click of a button. Auto-scaling has made it further convenient to add or remove resources as per the need of the application usage; it adds cost efficiencies by paying only for resources consumed by applications.

Is there a need for auto-scaling?

There are various instances in our organizations that demand to increase/decrease server resources as per business needs, such as:

• When the live users on your application are increasing during events/campaigns or a certain period of the year, e.g. Black Friday, Christmas etc., initiating the process to improve the server resources on in-premise hardware or cloud resources is normal. Once the demand decreases, say, post-festival season, it becomes challenging to remove additional servers and causes cost overrun.
• While designing a test lab for performance testing in product organizations, additional servers are needed to perform various tests such as soak tests, overload tests, spike tests etc., and these servers remain underutilized for the period when there is no load testing.
• For multiple scrum teams working in parallel and their need for performance, testing is hard to plan, making it challenging to optimize the timeline and delivery cycles for performance testing.
• Companies are now deploying new features daily to remain engaged with their customers, triggering the need to increase demand and decrease server resources.
• The cyclic nature of business causes increased traffic in certain months of the year. e.g. increased traffic on university websites during admission months and people planning holidays during specific months.

When your application needs additional resources (server, memory, etc.) to accommodate the higher demand generated by increased user traffic, auto-scaling helps augment these resources and decreases them when traffic is less. One can achieve auto-scaling by configuring a specific resource consumption value; if demand increases beyond this pre-configured limit, the cloud service provider will automatically add pre-defined resources (CPU, memory, etc.) in steps. On the other hand, if the load on the system is going below a specific value, the cloud service provider will automatically release the resource in pre-defined values.

Auto-scaling advantages:

• Achieves cost optimization by removing the resources; when the load on the application is lower.
• It Helps achieves higher system availability, as, during peak load, the system gets additional resources, and the application does not crash due to a resource crunch.
• It handles the variations of user traffic on the websites.
• Removes human error aspects for increasing or decreasing cloud resources.
• Adding and decreasing the resources is automatic and can be configured as per the organization’s need, with real-time monitoring available on traffic and resource usage.

Monitoring additional resources with the increase in load

There are various tools and platforms to monitor the cloud resources, such as:

CloudWatch Metrics: It provides the metrics and monitors the critical resources in the cloud environment. We can create the CloudWatch alarm to track specific metrics and receive notifications on configured email addresses. This can be configured for a small duration, such as every minute, wherein it will receive load balancer report data.

Access Logs: Access logs are analyzed for traffic analysis and identifying the core issue to fix the problem.

Request Tracing: The load balancer adds a tracking identifier for each request it receives; it helps monitor and analyze HTTP requests transferred between nodes.

CloudTrail Logs: Trail logs track the operations performed by every user, thereby providing the mechanism on what changes are done and by which user.

Conclusion

To get the best results for auto-scaling, you have to use automating scaling policy that uses the predetermined target metric.

Hence, it is recommended to find the peak requests-per-second (RPS) your application can handle on production and latency of requests before configuring auto-scaling in your cloud environment.

You should test your auto-scaling configuration to ensure it works as expected for increasing/decreasing user traffic on your endpoints.

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

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Continuous refinement of technology has redefined and enabled the user to harness various benefits of digital transformation, on the other hand, it has also provided the same advancement to a hacker who has been sharpening hacking attempts and malware attacks.

In the year 2022, it has become prudent for individuals and companies to be well prepared for tackling ever increasing cyber threats and continue to fine-tune and upgrade security strategies and benchmarks.

One of the solutions is to timely invest in security testing but it would be important to know about the trends to expect this year.

Artificial Intelligence: The key use cases of artificial intelligence are seen in the fraud detection on financial portals and intrusion detection systems. It is very useful in analyzing the data and finding unusual patterns of cyber-attacks on the systems. Artificial intelligence by its core is capable of analyzing humongous amount of data from network traffic to assess possibility of cyber threat on the system.

Given the fact that hackers are trying to utilize machine learning methodologies to automate and implement malicious events in organizations, it is prudent for the organizations to enhance their cyber defense systems by harnessing the power of artificial intelligence.

Ransomware Threat: The year 2021 had seen a spike in ransomware attacks. The UK National Cyber Security Centre reported the number of ransomware attacks increased by three folds in the first quarter of 2021 compared to the whole of 2019. The various reasons attributed to these spikes are due to the increase in financial activities on digital platforms due to the pandemic, enhanced usage of e-commerce and enough controls not in place for employees working from home.

Many companies and governments are still using outdated technologies, processes, protocols, and procedures, which ultimately increases the vulnerability of getting attacked by ransomware. We have to fix these issues with proper budgeting and management alignment, in addition to other measures such as improved security, better monitoring and reporting.

Cloud Security: With the popularity of cloud implementation and moving organizational sensitive data to the cloud, it becomes very important to secure cloud infrastructure. Cloud though is very secure and has several layers of security from user management, network management, to secure key management. It has its own challenges in terms of security, as any security issues on cloud hosted applications will impact the authenticity, integrity and availability of the data.

One trend of year 2022 would be a possible growth in terms of cloud adaptation, it will also mark for newer ways of cyber-attacks on the cloud infrastructure.

Cybersecurity Talent Shortage: Cybersecurity talent is scarce in the market and there is no doubt about it.  The increased number of cyber-attacks in the year 2021 has further fueled the demand for trained cybersecurity professionals across the globe. It is unlikely that this gap will be filled in a timely manner, respite may come from artificial intelligence, where it will be used to detect malware in the network by analyzing a vast amount of data more quickly than humans, detecting issues such as phishing attacks, privilege escalations, and insider threats.

Internet of Things: Statista, a market research company, projects global spending of 1.1 Trillion USD by 2023 and consumer spending on smart home systems worldwide in the tune of 123 bn USD by 2021. The risk of cyberthreat is going to increase in the year 2022 with more proliferation of IoT devices. As we all are aware of the high vulnerability of IoT devices owing to different communication protocols, operating systems, integrations and lack of any standardization in space.

Conclusion

IGT with its Security Testing COE is well positioned to achieve shift-left of security testing and thereby finding security vulnerabilities earlier in the lifecycle, and working with all stakeholders to ensure these vulnerabilities are taken care of before releasing the software on production. IGT cyber assurance covers OWASP, PCI-DSS, HIPPA, ISO 27001 and other standards of cyber security.

Author:

Yatender has 20+ years of experience in software test engineering. As the head of Testing Practice at IGT Solutions, Yatender is actively involved in innovations related to test engineering covering new tools, technologies, and solutions, and enabling IGT’s clients to achieve faster time to market quality improvement, and optimization of developer efforts in overall SDLC. A result-oriented leader, proficient in delivering high customer value and achieving excellence in service delivery management with proven skills in consulting and managing large and complex test programs. When away from work, he enjoys reading on a variety of topics and spending time with kids.

The post Top Cyber Security Trends to look out for in 2022 appeared first on IGT Solutions.

Being a gadget geek myself, I’ve made multiple purchases that are lying around the house without being used. This New Year, I made a resolution not to buy one, and a few days later, I was already itching to make the next tech purchase. So my wife suggested that I list down the reason for my next purchase and how it will benefit me, which inspired me to write this blog- making the right decision when it comes to Data Modernization and Cloud Services for the long term benefit of an organization.

Why do companies choose to modernize? Just because a service like Cloud is available and provides 24X7 infrastructure, does it imply that all companies need to implement it? Let’s understand both these services to understand better whether your business needs it and to what extent. Data modernization and Cloud Computing have emerged as critical factors in the Digital Transformation Journey. Data Modernization refers to “state of the art” data management tools that enable organizations to manage and process a large amount of structured and unstructured data from heterogeneous sources. Conventional data management tools are not equipped to harness large volumes of social media and IoT (Internet of Things) datasets. With modern data management tools, organizations would be able to capture and harness such information to understand actionable intelligence. Whereas, Cloud Computing enables access to IT resources such as Storage and Computing Cycles etc. that are much needed for implementing data-driven applications at scale.

The key to successful data modernization implementation is dependent on your business objectives. Following are some common business objectives that are part of most of our conversations with customers & prospects:

1. Cost Reduction: Rationalizing Operational spend on the applications
2. Improve response time for the end-users of the application
3. Reduce time to market for changes & onboarding new data sources
4. Auto-scaling: Ability to scale up & down the IT infrastructure automatically based on market demand while ensuring service levels remain intact
5. Ensuring high availability
6. Future Extendible: Ability to have an evolving system which extends to future data types & sources without the need for high maintenance

Now that we have identified the usual suspects, the next step is to identify which methodology is the right fit for the data modernization undertaking.

Re-architecting the solution:

1. Performance in terms of utilization of resources like memory, compute, storage.
2. Enablement of the current solution to take advantage of the autoscaling factor
3. Relook at the solution from the lens of DevOps, micro-services & containerization to make use of current technology in the solution. This helps to make it future-ready
4. Define naming standards, best practices and standard operating procedures
5. Define & decide data latency needs, security needs (including GDPR & other privacy laws), data governance including data retention, archival & operational management.
6. Define & decide encryption levels, agility & reliability needs, VPC pairing
7. Define & decide process around metadata management, data quality, cost monitoring

IGT plays a critical role in the data modernization and digital transformation for its customers. Whether it is defining a business/technical strategy roadmap, a high-level approach, or actual implementation of services and value management through support services, IGT has the needed expertise and experience to ensure successful digital transformation through data modernization on the cloud!

Our Approach:

To summarize, data modernization is an absolute must for a successful digital transformation of the organization and Cloud is both the means as well as a consequence of data modernization. They both go hand in hand. Data modernization gives rise to increased cloud spending and vice versa. IGT has helped many organizations in their Digital Transformation Journey.

Learn more about our services or reach out to mktg@igtsolutions.azurewebsites.net to understand how we can partner with you!

Authors:

Bhushan Gangurde is passionate about solving business problems  using AI/ML, Analytics, Data & Automation and Digital technologies in that order. With more than 17 years of experience in field, he loves exploring new technologies and reading books. He heads IGT’s Center of Excellence for AI/ML & Automation.

Kiran Dongre is Analytics Presales head at IGT Solutions. He has more than 20 years of Analytics experience and have serviced many customers in their Digital Transformation journey through data modernization. Kiran is passionate to travel around the globe as well as loves playing Badminton. He can be reached at kiran.dongre@igtsolutions.azurewebsites.net

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