September 03, 2024

Cloud application portability remains unrealistic

Enterprises can deploy an application across multiple cloud providers to distribute risk and reduce dependency on a single vendor. This strategy also offers leverage when negotiating terms or migrating services. It may prevent vendor lock-in and provide flexibility to optimize costs by leveraging the most cost-effective services available from different providers. That said, you’d be wrong if you think multicloud is the answer to a lack of portability. You’ll have to attach your application to native features to optimize them for the specific cloud provider. As I’ve said, portability has been derailed, and you don’t have good options. A “multiple providers” approach minimizes the negative impact but does not solve the portability problem. Build applications with portability in mind. This approach involves containerization technologies, such as Docker, and orchestration platforms, such as Kubernetes. Abstracting applications from the underlying infrastructure ensures they are compatible with multiple environments. Additionally, avoiding proprietary services and opting for open source tools can enhance portability and reduce costs associated with reconfigurations or migrations.?

Will Data Centers in Orbit Launch a New Phase of Sustainability?

Space offers an appealing solution for many of the problems that plague terrestrial data centers. Space-based data centers could use solar arrays to draw power from the sun, alleviating the burden on electrical grids here on Earth. They would not require water for cooling. They would not take up land, disturb people or wildlife. Additionally, natural disasters that can damage or wipe out data centers on Earth -- earthquakes, wildfires, floods, tsunamis -- are a non-issue in space. ... While the upsides of data centers in space are easy to imagine, what will it take to make them a reality? The Advanced Space Cloud for European Net zero emission and Data sovereignty (ASCEND) study set out to answer questions about space data centers technical feasibility and their environmental benefits. The study is funded by the European Commission as part of the Horizon Europe, a scientific research program. Thales Alenia Space led the study with a consortium of 11 partners, including research organizations and industrial companies from five European countries. Thales Alenia Space announced the results of the 16-month study at the end of June.?

Workload Protection in the Cloud: Why It Matters More Than Ever

CWP is a necessity that must not be ignored. As the adoption of cloud technology grows, the scale and complexity of threats also escalate. Here are the reasons why CWP is critical: Increased threat environment: Cyber threats are becoming more complex and frequent. CWP tools are crafted to detect and counter these changing threats in real time, delivering enhanced protection for cloud workloads exposed across various networks and environments. Protection against data breaches and compliance: Data breaches can lead to severe financial and reputational harm. CWP tools assist organizations in complying with strict regulations like GDPR, HIPAA, and PCI-DSS by implementing strong security protocols and compliance checks. Maintenance of operational integrity: It is essential for businesses to maintain the uninterrupted operation of their cloud workloads without being affected by security incidents. CWP tools offer extensive threat detection and automated responses, minimizing disruptions and upholding operational integrity. Cost implications: Security breaches can incur substantial costs. Investing in CWP tools helps avert these risks by early identification of vulnerabilities and threats, finally protecting organizations from potential financial losses due to breaches and service interruptions.

How Human-Informed AI Leads to More Accurate Digital Twins

The value of a DT is directly proportional to its accuracy, which in turn depends on the data available. But data availability remains a challenge — ironically, often in the business use cases that could benefit the most from DTs — and it’s a big reason why DTs are still in their infancy. DTs could help guide the expansion of current products to new market domains, accelerating R&D and innovation by enabling virtual experimentation. But research activities often involve exploring new territory where data is scarce or protected by patents owned by other organizations. For example, while DTs could inform an organization’s understanding of how a new topology may affect heavy construction equipment or how a smart building may behave under unusual weather conditions, there is limited data available about these new domains. ... DTs can add immense value by reducing costs and the time it takes to develop new processes, but data to develop these models is limited given that the work explores new territory. Further, data-sharing across the supply chain is sharply limited due to extreme sensitivity about intellectual property.

Leveraging AI for enhanced crime scene investigation

Importantly, as crimes are committed or solved, the algorithms and software based on them become more sophisticated. Interestingly, these algorithms use information obtained from various sources without any human intervention, reducing the chances of bias or error. With the increasing use of mobile phones and the internet, information is flooding in the form of photos, videos, audios, emails, letters, newspaper reports, speeches, social media posts, locations, and more. Various AI & ML-based algorithms are used to quickly analyse this data, perform mathematical transformations, draw inferences, and reach conclusions. This makes it possible to predict the likelihood of crimes in a very short time, which is almost impossible otherwise. A smart city-related company in Israel called ‘Cortica’ has developed software that analyzes the information obtained through CCTV. This software utilizes certain AI algorithms to recognize the faces in a crowd, identify crowd behavior and movement, and predict the likelihood and nature of a crime. Interestingly, these intelligent algorithms make it possible to analyze several terabytes of video footage in minimal time and make quite precise inferences.

There are many reasons why companies struggle to exploit generative AI

Some qualitative remarks by executives interviewed revealed more detail on where that lack of preparedness lies. For example, a former vice president of data and intelligence for a media company told Rowan and team that the "biggest scaling challenge" for the company "was really the amount of data that we had access to and the lack of proper data management maturity." The executive continued: "There was no formal data catalog. There was no formal metadata and labeling of data points across the enterprise. We could go only as fast as we could label the data." ... Uncertainty about novel regulations is also causing companies to pause and think, Rowan and team stated in the report: "Organizations were exceedingly uncertain about the regulatory environment that may exist in the future (depending on the countries they operate in)." In response to both concerns, companies are pursuing a variety of strategies, Rowan and team found. These strategies include: "shut off access to specific Generative AI tools for staff"; "put in place guidelines to prevent staff from entering organizational data into public LLMs"; and "build walled gardens in private clouds with safeguards to prevent data leakage into the public cloud."

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