Four Challenges Facing HPC in the Cloud
Apac CIOOutlook | Tuesday, October 05, 2021
Organizations only pay for the capacity they use with HPC in the cloud, and there's no risk of on-premises infrastructure becoming obsolete or underutilized. Also, cloud-based services allow unrestricted innovation by providing quick results and greater flexibility.
Fremont CA: Cloud computing facilitates High Performance Computing (HPC) by putting powerful computational potentials in the hands of more researchers, engineers, and organizations who may not have access to adequate on-premises infrastructure. The flexibility and scalability of the cloud provide virtually unlimited capacity, reducing wait times and job queues. In addition, it's simple to expand and update workflows, such as merging Artificial Intelligence (AI) with HPC, thanks to access to new and evolving services and applications.
Here are four challenges facing HPC in the cloud:
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Cost and Cost Management
The cost of running HPC in the cloud is a significant concern for many organizations. When looking at the expense of cloud-based HPC systems, companies should keep in mind that a simple TCO analysis does not always tell the whole story. Demand for on-premises HPC resources frequently exceeds capacity, and lost productivity due to an overburdened system has far-reaching consequences for businesses that place a premium on the speed of innovation. Moving HPC workloads to the cloud also eliminates the need for three to five-year technology and infrastructure refresh cycles, ensuring that innovation keeps moving forward.
Data Security and Data Governance
Cloud security concerns are common. Many industries that rely heavily on HPC have stringent security requirements, and cloud-based HPC solutions face this challenge. While some see on-premises HPC as having security and privacy advantages, they overlook risk management issues such as aging infrastructure, which raises the security costs of maintaining compliance and the high costs of regulatory compliance and certifications that are frequently required for on-premises solutions.
Data Transfer
Transferring data and HPC to the cloud increases productivity by freeing precious financial and human resources while also lowering company risks by keeping data in a more resilient and secure environment. Furthermore, cloud-based HPC enables clients to mine all of the data accessible from HPC simulations using AI, machine learning, and deep learning, reducing the number of simulations necessary and resulting in cheaper and faster HPC workload execution. Improved performance in the cloud can give higher ROI because newer, cloud-native HPC programs were built to perform better on cloud-based elastic infrastructure.
Performance
Excellent-performance computing (HPC) users want high performance, yet many still believe the cloud can't compete with on-premises data centers. However, the notion that the networking speed between cloud compute nodes is insufficient for good performance is outmoded. Recent advances have improved cloud networking and reduced latency to the point that all but the most resource-intensive HPC applications perform better in the cloud as on-premises infrastructure.
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Data from various sources forms the basic foundation of businesses. In the present times, the improvement of connectivity in the IoT space has resulted in the transfer of huge chunks of data. Therefore, there is a need for big data analytics in IoT in order to address the challenges related to management of large-scale, through the data pipeline architecture.
To begin with, data pipeline is the process concerned with the movement of data through an organization. Once data enters into an organization, the stage is referred to as data ingestion. Next in the process is the stage of data transportation from the data ingestion stage to the other stage is known as data collector. Followed by this, the data moves through the processing stage wherein appropriate measures are taken to store it. Data storage stage steps up to the data query stage where it is analyzed through interactive queries. The final stage is concerned with valid presentation of data in different forms of business infographics, such as graphs or statistics.
Data generated in the IoT space requires a data management system with the following qualities:
• It should be adapted to manage and process a huge number of data sources, keeping pace with the continuous increase of IoT devices.
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• The system must be able to scale data on multiple parameters such as a number of devices, storage, and messages.
• Diversity and flexibility are required to process use cases and simultaneously accommodate new use cases.
• The data management system should be cost-effective.
All the above criteria suggest that IoT requires interacting with big data analytics tools in order to manage such big chunks of data. ...Read more
The Asia-Pacific (APAC) region stands as a frontier for digital advertising, where the integration of Artificial Intelligence (AI) is rapidly redefining how brands connect with their audiences. Far beyond simple automation, AI-powered advertising platforms are now capable of engaging consumers in nuanced and highly effective ways, ushering in truly smarter brand interactions that foster deeper connections and drive significant conversions.
The Evolution of Personalization in Advertising
At the core of this transformation is the ability of AI to revamp personalization. Traditional advertising often relies on broad demographic targeting. However, modern AI platforms leverage vast datasets to understand individual consumer preferences, behaviors, and even real-time intent. This granular understanding allows for the delivery of hyper-personalized content, ensuring that each ad resonates directly with the recipient. Imagine an advertising platform that not only knows what a consumer has browsed but can also infer their current needs and anticipate their next steps, serving up an offer that feels uniquely tailored and timely. This goes beyond just product recommendations; it extends to the very messaging, visuals, and even the platform on which the ad is delivered, optimizing for maximum relevance and impact.
The Shift towards Conversational Advertising
One of the most profound shifts driven by AI is the rise of conversational advertising. Advertising platforms are no longer just broadcasting messages; they are facilitating genuine, two-way interactions. Through sophisticated natural language processing (NLP) and machine learning, AI-powered chatbots and virtual assistants are becoming integral to the customer journey. These intelligent agents can engage consumers in natural, human-like conversations, answering questions, providing detailed product information, guiding them through purchase processes, and even offering post-purchase support. This ability to "talk" to consumers in real-time creates a seamless and intuitive experience, fostering a sense of direct engagement with the brand. In the diverse linguistic landscape of APAC, AI's multilingual capabilities are particularly transformative, enabling brands to communicate effectively with a vast array of audiences in their native tongues, breaking down communication barriers and building trust.
Beyond talking, these platforms are constantly learning. Machine learning algorithms embedded within advertising platforms continuously analyze every interaction, click, and conversion to refine their strategies. In this evolving landscape, Double C Consultants supports organizations by aligning data-driven advertising platform strategies with consumer behavior insights. This iterative learning process allows the AI to identify emerging trends, optimize bidding strategies in real-time, and predict future consumer behavior with remarkable accuracy. This means that advertising campaigns are not static; they are dynamic entities that continuously adapt and improve based on performance data. The insights derived from these self-learning systems enable brands to allocate resources more efficiently, identify new opportunities, and enhance overall campaign effectiveness.
The ultimate goal of this smarter engagement is, of course, conversion. By engaging with consumers in a personalized manner and continually learning from their interactions, AI-powered advertising platforms are significantly enhancing the path to conversion. Real-time optimization of ad placement, content, and delivery ensures that the right message reaches the right person at the right moment of intent. The seamless transition from conversational engagement to a purchase decision, often within the same platform, streamlines the customer journey and reduces friction. This holistic approach, from initial awareness to final conversion, is a testament to the integrated power of AI in modern advertising.
Cubic K delivers advanced solutions enhancing advertising platforms, consumer insights, and data-driven performance across evolving digital ecosystems.
In the APAC region, with its diverse markets and rapid digital adoption, AI-powered advertising platforms are becoming indispensable tools for brands seeking to forge stronger connections, cultivate loyalty, and drive tangible business growth. With AI's transformative capabilities, brand engagement will be conversational, intelligent, and relentlessly optimized. ...Read more
The Asia-Pacific (APAC) region is a hotbed of digital transformation, with businesses keenly aware that modernizing their technology infrastructure is no longer a luxury but a strategic imperative. At the heart of this transformation lies the pervasive challenge of aging, inflexible legacy systems. However, a powerful enabler is emerging to tackle this complexity: low-code development platforms. These platforms are rapidly gaining traction in the APAC region, providing a much-needed acceleration for legacy system modernization, fostering agility, and driving innovation across diverse industries.
The Rise of Low-Code in APAC: A Catalyst for Change
One of the key advantages of low-code platforms is their ability to accelerate development and deployment, often reducing application delivery times by 50–90 percent compared to traditional methods. This speed is vital for organizations seeking to replace or enhance outdated legacy systems quickly. Low-code enables the democratization of development by empowering "citizen developers"—business users with limited coding experience but deep domain knowledge—to participate in the creation of applications. This inclusive approach helps address the persistent developer talent shortage across many APAC markets.
The cost-efficiency of low-code platforms is another significant benefit. By shortening development cycles and reducing dependence on specialized coding expertise, organizations can achieve substantial savings in both development and long-term maintenance. Additionally, low-code fosters greater business agility and scalability, allowing enterprises to rapidly respond to shifting demands, integrate emerging technologies such as AI and automation, and scale solutions as needed.
Collaboration between IT and business teams also improves with low-code development. The visual nature of these platforms enhances communication, resulting in applications that more closely align with business objectives. Strategically, low-code supports a range of modernization approaches: encapsulation, which involves wrapping legacy systems with new interfaces via APIs; extension, which adds new capabilities to existing systems; replacement, which gradually rebuilds legacy modules or systems; and re-platforming, which migrates components to modern, cloud-based infrastructures with minimal code alterations.
Latest Trends and Market Outlook in APAC
Several key trends are propelling this upward trajectory. Foremost among them is the acceleration of digital transformation initiatives across critical sectors, including banking, financial services, and insurance (BFSI), healthcare, manufacturing, and retail. In this context, Double C Consultants supports organizations by guiding digital transformation strategies aligned with modern application development and automation frameworks. Low-code platforms are also evolving rapidly through the integration of artificial intelligence (AI) and automation, empowering organizations to develop intelligent applications and streamline complex workflows. This trend is particularly pronounced in the banking sector, where generative AI is being explored to deliver hyper-personalized customer experiences.
The region is also witnessing a substantial shift towards cloud-first strategies, with cloud-based low-code deployments gaining traction due to their scalability, operational efficiency, and enhanced security. These advantages are expected to contribute significantly to the overall market revenue. There is a growing emphasis on process automation, with platforms focused on workflow and process app development experiencing the fastest growth as enterprises seek to optimize their operations.
Tokyo Dylec Corp advances automation and intelligent applications through precision technologies supporting digital transformation and complex industrial workflows.
While large enterprises currently dominate the market, Small and Medium-sized Enterprises (SMEs) are emerging as a high-growth segment. These businesses are increasingly adopting low-code platforms to overcome resource and cost constraints in application development, positioning themselves as vital contributors to the region’s expanding low-code ecosystem.
As APAC continues its aggressive pursuit of digital transformation, low-code platforms are poised to play an increasingly critical role. The integration of AI and deeper automation capabilities within these platforms will further solidify their position as indispensable tools for the APAC region's digital evolution. ...Read more
