The post Building Blocks to Empower Cognitive Internet with Hybrid Edge Cloud first appeared on mimik Technology Inc.

Hybrid Edge Cloud (HEC) pioneered by mimik is a pivotal player, empowering smart devices to act as cloud servers to locally run workflows, seamlessly share data, knowledge, and computing resources, collaborate via APIs, and transfer microservices among devices, fostering an interconnected digital ecosystem.

The mimik HEC platform enabling any smart device to act as a cloud server.

Complementing this, distributed ledger technology, particularly within the realm of private chains and smart contracts tailored for a smaller set of stakeholders, takes charge of managing financial transactions in a secure, transparent, traceable, immutable, and efficient way. It’s crucial to distinguish this approach from the widely publicized crypto hype. Unlike the public blockchains associated with cryptocurrencies, private chains operate within a confined network, ensuring computational efficiency and scalability. These private chains, governed by smart contracts, enforce transparent and predefined rules agreed upon by a smaller set of stakeholders. This computational efficiency is a cornerstone, allowing for streamlined transactions and reducing the computational cost and environmental footprint associated with large-scale public blockchain networks.

Imagine a world where mimik’s HEC serves as an open arena, allowing services to fluidly run on smart devices and effortlessly discover, collaborate, and exchange knowledge autonomously. Envision a smart thermostat communicating with your wearable to read your body temperature and then dynamically optimize temperature and energy consumption based on the current context. Through this framework, services can be hosted on devices and can collaboratively navigate tasks creating an environment far removed from centralized pre-defined control and guesswork.

In this digital milieu, microservices play a pivotal role, akin to specialized functions executed by smart devices — language translation, image processing, or data analysis, for instance. Traditionally, confined to centralized servers in data centers, mimik’s platform revolutionizes this landscape, facilitating the seamless traversal of microservices across devices. For instance, your smartphone can momentarily borrow an image processing microservice from your tablet, enhancing its capabilities instantly. This decentralized sharing of microservices endows devices with dynamic prowess, optimizing their functions collectively.

While mimik platform enables choreography of this seamless flow of microservices, private chains and tailored smart contracts within the network ensure secure and efficient transactions with audit trails and logs. Consider an autonomous drone in need of an AI model for fault detection in an industrial site or a port facility. mimik’s platform seamlessly facilitates the drone’s access to this model from another smart device, gateway, or a cloud server through a secure exchange within the private chain. The implementation of smart contracts ensures the integrity and transparency of this exchange, fostering a fair and efficient transaction environment.

Yet, the transformative power of this collaborative ecosystem goes beyond mere technical advancements. It envisions a shift — a monumental departure from the prevailing data broker system — to an automated, traceable knowledge-as-a-service economy. Here, stakeholders are rewarded for contributing accurate, valuable information and collaborating seamlessly. This paradigm disrupts the underhanded practices of the data broker economy, replacing opacity and guesswork with transparency and collaboration, fostering an ecosystem where all stakeholders are fairly rewarded for their contributions.

In essence, this convergence of HEC and distributed ledger not only elevates the efficiency, security, and autonomy of device collaboration but lays the foundation for a revolutionary knowledge-as-a-service economy — a beacon of fairness, transparency, and collaboration across diverse industries.

The post Bridging Worlds: Hybrid Edge Cloud and Distributed Ledger Reshaping Digital Services & Knowledge Exchange first appeared on mimik Technology Inc.

For the Manufacturers (OEMs): Marelli’s new centralized architecture, combined with mimik’s edgeEngine, a service abstraction runtime environment, enables OEMs to effortlessly port their applications through a standard microservice architecture to any hardware and operating systems, ensuring maximum independence from specific Cloud providers and constant wide area connectivity. This solution ensures rapid market delivery, significantly simplifies integration, promotes interoperability, software reusability, and most importantly, the ability to update one service at a time as a container vs. entire monolithic applications. It guarantees a consistent and superior experience across vehicle types. It provides a unified software architecture between the cloud, in-vehicle software systems, end-users devices, and outside digital infrastructure to interact with the vehicle.

For the Everyday Driver (Consumers): OEM adoption of the joint Marelli and mimik solution initiates a second to none seamless user experience. This solution enables hyper-personalization tailored to individual user preferences, specific vehicle models, regional nuances, and various modes of transport, ensuring a universally intuitive interface.

For the Innovators (Third-party Developers): Cloud-native microservice developers can now apply their expertise in standard API-first microservice development to in-vehicle software systems that are not constrained by the underlying zonal architecture. This opens opportunities to significantly expand the automotive developer community to deliver new experiences for consumers, enhance the operation of the vehicle, and foster interaction with the digital ecosystem surrounding the vehicle.

Let’s Connect!

Industry professionals, partners, and automotive enthusiasts are invited to connect with us at CES 2024. Discover how this technology is reshaping the future of mobility and explore potential collaboration opportunities. Set up a meeting with our experts to dive deeper into the possibilities.

About mimik:

mimik stands at the forefront of the future of cloud computing, strategically positioned as an indispensable provider of the hybrid edge cloud (HEC) development platform. As industries pivot towards comprehensive digital transformation, our platform is an essential catalyst, streamlining time to market, optimizing cost efficiency, and ensuring scalability, interoperability, data privacy, and security. Crucially, in the era of AI and autonomous operations, empowering all computing devices with server capability becomes imperative, and mimik’s platform is uniquely designed to meet this demand. Supporting a vast spectrum of operating systems, including iOS, Android, Windows, macOS, Linux, QNX, Android Auto, Raspbian, and OpenWRT, as well as smart IoT freeRTOS sensors, our platform seamlessly integrates with both private and public clouds, embodying the vision of next-generation cloud infrastructure. Embracing mimik not only enables businesses to establish direct, efficient connections across smart devices but also ushers in a paradigm shift in operational efficiency while substantially cutting backend integration expenses. The future of computing is here, and it’s powered by mimik.

To learn more, visit mimik.com, and for developers developer.mimik.com.

Media Contact:

PR@mimik.com 

The post Mimik Collaborates with Marelli on Scalable Vehicle Architecture and Software Portability Solutions, To be Unveiled at CES 2024 first appeared on mimik Technology Inc.

Stage 1: Local Execution

In this stage, the focus is on deploying the AI model at the edge, which means running the model directly on the device that generates the data. Typically, the model is trained in the cloud and then pushed to the edge devices such as cameras or sensors. The purpose is to perform real-time recognition or analysis of data streams locally without relying on constant communication with the cloud.

The information generated by the local execution can be handled in different ways. If the recognition results are conclusive, only the result is sent to the cloud for further processing or storage. However, if the recognition is inconclusive, the image or relevant data may be sent to the cloud to retrain the model. Additionally, a lower resolution of the data stream can be archived for reference purposes.

For example, consider a security camera system using edge computing. The camera captures live video footage and runs an AI model locally for real-time object detection. Instead of sending every frame to the cloud for analysis, the AI model is deployed directly on the camera. The camera processes the video stream locally, identifies objects of interest, and sends only the relevant information, such as detected objects and their locations, to the cloud for further processing or storage.

It is essential to separate the model from the execution process because models need regular updates and the ability to manage the payload remotely. Mimik enables this separation by treating the model as a part of the edge microservice running on the device. The microservice acts as an interface between the cloud and the AI process, abstracting the handling of model updates from the recognition process. Another edge microservice handles the results, whether sending them to the cloud or other local systems. This ensures that the model can be easily updated and fine-tuned without disrupting the process of recognition or analysis.

By exposing the capabilities of handling the model and results as a local API, mimik simplifies the development process of AI solutions, making integrating edge computing into the workflow easier.

Stage 2: Contextualization

In this stage, the model is executed locally, and the handling of the context in which the process occurs is also done locally. The context refers to events received by the device running the process or other devices within the same cluster, such as events triggered by user inputs through a UI or sensor inputs.

Local contextualization allows for the personalization of the model based on user preferences or specific scenarios. By processing events locally, edge devices can provide tailored experiences or responses without constantly sending data to the cloud for analysis and decision-making.

For example, consider an intelligent home system using edge computing. The system includes various devices like smart speakers, cameras, and sensors. Each device runs AI models locally to process data and respond to user commands. When a user speaks a command to a smart speaker, the AI model on the speaker processes the command locally, taking into account the context of the user’s preferences and the current state of the home environment. The speaker can provide personalized responses or control other devices within the cluster based on local contextual information.

Mimik achieves contextualization by running multiple edge microservices on the same node and facilitating interaction with other edge microservices on different nodes. This decentralized approach minimizes the need for data transfer to the cloud, as the devices within the cluster can communicate and share contextual information directly.

Stage 3: AI to AI communication

In this stage, there is the realization that a complex system at the edge will be made of many nodes that can have an AI handling the node’s logic. In this environment, while the execution of the model happens at the edge, the integration between each AI is coordinated via the cloud. It must be possible to allow direct communication between each AI to handle local decision-making by having the different AI either exchange the models or exchange the events generated by the API process using the models.

For example, consider an autonomous driving system using edge computing. The system comprises multiple edge devices, such as cameras, LiDAR sensors, and control units, each running its own AI model for perception, decision-making, and control. These devices must exchange information and coordinate safe and efficient driving decisions. Instead of relying solely on a centralized system in the cloud, direct communication between the edge devices’ AI models is essential for local decision-making.

Mimik enables AI-to-AI communication by allowing models to be handled by edge microservices and creating an ad-hoc edge service mesh. This allows direct communication between edge microservices within the same node or between edge microservices running on different nodes. With mimik, multiple AIs at the edge can exchange information or models with a well-defined contract, facilitating coordinated actions without heavy reliance on a centralized cloud system.

Stage 4: AI-adapted choreography

In this stage, the focus is on dynamically choreographing the behavior of multiple AI models across the edge network to optimize overall system performance, resource allocation, and coordination. The communication between AI models within each node and between nodes adapts to maximize the relationship of a collection of nodes.

For example, let’s consider a smart city infrastructure using edge computing. The infrastructure consists of various edge devices deployed throughout the city, such as traffic cameras, environmental sensors, and smart streetlights. Each device runs its AI model to perform specific tasks like traffic monitoring, air quality analysis, and intelligent lighting control.

In the AI-adapted choreography stage, the AI models within each device collaborate and communicate to optimize the overall performance of the smart city infrastructure. The models exchange information about traffic conditions, environmental data, and lighting requirements. Based on this information, they dynamically adapt their behavior to ensure efficient traffic flow, minimize energy consumption, and respond to changing environmental conditions.

Since these systems are generally developed by many organizations (different standards, different protocols), the context and the AI of each system component will also help define the protocol between the components, allowing components that are not necessarily made to communicate with each other to exchange information.

Mimik plays a crucial role in enabling AI-adapted choreography by providing the infrastructure for communication and coordination between the AI models across the edge network. It allows the AI models running on different devices to exchange data, share insights, and collectively make decisions to optimize the operation of the smart city infrastructure. Mimik’s edge service mesh facilitates the dynamic choreography of AI models and ensures efficient collaboration.

In summary, in the AI-adapted choreography stage, mimik enables the dynamic coordination and optimization of multiple AI models across an edge network, allowing them to collectively achieve better system performance, resource allocation, and coordination in complex scenarios like a smart city infrastructure.

Conclusion

The role of mimik, as mentioned in the text, is to enable these stages by treating the AI model as a part of the edge microservice running on the device. It abstracts the handling of model updates from the recognition process and facilitates the exchange of information between edge microservices. By providing a local API and creating an ad-hoc edge service mesh, mimik simplifies the development process and integration of edge computing into AI workflows.

References

1. “Edge Computing: A Survey” by Shi et al. (IEEE Access, 2016):
   • This survey paper overviews edge computing, its challenges, and potential applications, including AI at the edge.
2. “Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing” by Satyanarayanan et al. (Proceedings of the IEEE, 2019):
   • This paper discusses the concept of edge intelligence, including the execution of AI models at the edge and the benefits it brings.
3. “Bringing AI to the Edge: Distributed Learning in IoT Systems” by Yang et al. (IEEE Network, 2019):
   • This article explores the challenges and techniques for deploying AI models at the edge, including model training and coordination in distributed IoT systems.
4. Official mimik documentation and resources:
   • To understand the specific capabilities and features of mimik in enabling edge computing and AI integration, you can refer to the official mimik documentation, whitepapers, and developer resources available on the mimik website or other official channels.

The post The four stages of edge AI first appeared on mimik Technology Inc.

About Simon: Simon Edelhaus, a seasoned professional in the industry, assumes the role of Automotive General Manager to propel mimik’s expansion into the booming automotive markets. Before joining mimik, Simon devoted a decade to Marvell Semiconductors, most recently as VP of Automotive Software, where he played a pivotal role in conceiving, developing, and supporting software solutions for their industry-leading automotive silicon portfolio. These solutions, known for their security, ASPICE compliance, and ASIL certification, are utilized globally by tier-one suppliers and OEMs in modern Software Defined Vehicle (SDV) designs.

Fay Arjomandi, CEO of mimik, expressed her confidence in Simon’s leadership, saying, “With Simon spearheading our automotive sector, we will build upon our recent design wins and expedite the adoption of our dynamic, microservice-oriented HEC platform that simplifies the complexity of building cloud-native solutions across heterogenous components in SDV environments.”

Simon Edelhaus, the new Automotive General Manager, stated, “As automotive computing, interconnectivity, and storage grow increasingly complex, organizations of all sizes seek streamlined solutions for system-level integration and eliminating redundant layers in-vehicle software. mimik has developed an HEC platform that fundamentally addresses these challenges with a first-principles architecture and design approach. It is an honor to be part of this dynamic and agile organization.”

About Mimik: mimik’s HEC platform empowers automotive nodes to function as cloud servers when needed, facilitating data processing and direct service-level communication across Telematic Control Units (TCUs), other nodes, and applications. This service-oriented software platform seamlessly connects all hardware and software silos within and outside the vehicle, encompassing microcontrollers and TCUs, charging stations, V2E communications, cloud-based service dashboards, and digital twins. mimik’s HEC platform offers an array of services, including distributed API Gateways, a microservices runtime environment for microcontrollers and CPUs, Transaction Manager, Security Manager, and Service Proxy. It opens these services to the world through mimik’s SOA APIs. Designed with a focus on safety, security, reliability, and high performance, it is currently being integrated by top tier automotive electronic suppliers.

The post mimik Appoints Simon Edelhaus as Vice President & General Manager Automotive first appeared on mimik Technology Inc.

In today’s rapidly evolving digital landscape, ensuring the security of endpoint devices has become more critical than ever before. The proliferation of remote work, mobile devices, and cloud-based applications has introduced new challenges for safeguarding sensitive data and maintaining network integrity. In response to these challenges, many organizations are turning to Secure Access Service Edge (SASE) solutions to fortify their security posture.

Traditional Security Implementation

Traditional security models are often described as a “castle-and-moat” approach. In this model, the organization’s network is considered the castle, and security solutions such as firewalls and VPNs act as the moat. Everything inside the network perimeter is considered trusted, while external elements are treated with suspicion.

1. Perimeter-based Security: Traditional security relies on a perimeter-based model where the organization’s network is the fortress, and security solutions (firewalls, VPNs, etc.) serve as the protective moat. Elements inside the perimeter are trusted, while anything external is treated cautiously.

2. Centralized Security Appliances: Security solutions, like firewalls and intrusion prevention systems, are often centralized, especially at the data center. This often results in traffic being backhauled from remote locations or branches to this central point for inspection.

3. VPN for Remote Access: Remote users typically connect to the network using VPNs, which can introduce latency since traffic from remote users is tunneled to the central office before accessing the internet or other resources.

4. Disparate Solutions: Traditional setups might have various standalone solutions – a firewall from one vendor, a secure web gateway from another, VPNs from another, etc. This can complicate integration and management.

SASE Security Implementation

While traditional security implementations were well-suited for a time when most resources and users were centralized, the shift towards cloud services, remote work, and mobile users has revealed its limitations. SASE aims to address these modern challenges by offering a more flexible, integrated, and decentralized cloud-first security solution optimized for the current state of enterprise computing. Here’s how it differs:

1. Identity and Context-aware Security: SASE treats every access attempt as untrusted instead of relying on a network perimeter. Access is granted based on the user’s or device’s identity, the access request’s context, real-time analytics, and other factors.

2. Decentralized Security Services: Security is implemented closer to the point of access, often at the edge or as a cloud service. This means users connect to their nearest security service point, reducing latency.

3. Integrated Suite of Services: SASE aims to combine various security services like Secure Web Gateways (SWG), Cloud Access Security Brokers (CASB), Firewall as a Service (FWaaS), Zero Trust Network Access (ZTNA), etc., into a unified platform. This integrated approach simplifies management and ensures that security policies are applied everywhere.

4. Optimized for Cloud and Mobile: Traditional security models have shown strains as organizations have shifted to cloud services and remote work. SASE is designed with the cloud and mobility in mind, ensuring that security policies are consistently applied no matter where users are or which devices they use.

5. Scalable and Flexible: Being cloud-native, SASE solutions can scale as required and adapt quickly to changing business needs.

The Role of the Device in SASE Implementation

While SASE drastically changes the enterprise security approach, it still considers the end-user device, whether mobile, non-mobile, or IoT, as an integral part of the security solution. In a SASE (Secure Access Service Edge) solution, the services primarily reside in the cloud, leveraging a global network of points of presence (PoPs) to provide security and networking services as close to the end-user or device.

However, specific components or agents might run on the end-user’s device to interact with these cloud-based services. Here’s what typically runs on the device in a SASE architecture:

1. Endpoint Agent/Client Software: This is a lightweight software client installed on the user’s device (laptop, smartphone, tablet, etc.). The agent is responsible for:

   • Initiating secure connections to the SASE cloud.

   • Enforcing local security policies.

   • Monitoring device health and security posture.

   • Redirecting traffic to the SASE service for security checks and policy enforcement.

2. Zero Trust Network Access (ZTNA) Components: ZTNA ensures that every access attempt to resources, even from within the network, is authenticated and verified. The endpoint agent often includes components to enforce ZTNA principles, such as:

   • Identity verification.

   • Context-aware access controls (based on device health, location, user role, etc.).

   • Application-level connectivity (connecting the user only to the specific applications they need, not the entire network).

3. Data Encryption Tools: The agent ensures that data in transit is encrypted when connecting to the SASE cloud or other organizational resources.

4. Local Security Services: While most security services in a SASE architecture are cloud-based, certain local checks or policies might still be enforced on the device. This can include:

   • Local firewall rules.

   • Host intrusion prevention systems.

   • Data loss prevention checks for sensitive data.

5. Security Posture Check: Before granting access to resources, the SASE solution might check the device’s security posture. This can involve verifying:

   • Antivirus/antimalware status.

   • Operating system and software patch levels.

   • Compliance with organizational security policies.

6. Management and Configuration Tools: These allow IT teams to configure the agent’s behavior, update policies, and integrate with other IT management tools.

7. Logging and Monitoring Components: The agent might also collect logs and other relevant data for analysis. This information can be sent to the central SASE solution for anomaly detection, analysis, and reporting.

The exact components and functionalities can vary depending on the specific SASE solution provider and the organization’s requirements. However, SASE aims to keep the on-device footprint lightweight and leverage the cloud for most heavy lifting, ensuring consistent policy enforcement and optimal performance regardless of the device’s location. These aims do not consider the latest edge-in approach and microservice architecture developments, which the mimik platform enables. This includes:

• Running microservices that expose API directly on devices

• Handling ad-hoc edge service meshes where microservices interact with each other directly without going through the cloud

The Role of mimik HEC in SASE

Implementation

Now, let’s explore how mimik Hybrid Edge Cloud (HEC) software platform can contribute to the implementation of SASE, enhancing its capabilities for securing endpoint devices.

The mimik HEC is crucial in enhancing SASE implementation by providing innovative solutions and components that ensure secure, efficient, and context-aware protection for endpoint devices. Here’s how mimik contributes:

1. Distributed Computing: mimik facilitates distributed computing at the edge, reducing latency and enabling real-time analytics and response, essential for security solutions like SASE.

2. Edge Server Capabilities: Devices powered by mimik can act as edge cloud servers, deploying SASE solutions closer to data sources or users, improving performance, and reducing the load on central servers.

3. Interoperability: mimik’s platform fosters interoperability between different cloud services, edge devices, and on-premises resources, a critical requirement for implementing SASE in a hybrid environment.

4. Resource Optimization: Implementing SASE solutions with mimik edgeEngine on the mimik hybrid edge cloud platform can optimize network and computing resource utilization by balancing the load between cloud, edge, and on-premises.

5. Enhanced Security: Integrating security microservices at the edge using mimik edgeEngine enables granular and context-aware security enforcement, essential for Zero Trust Network Access (ZTNA) and Secure Web Gateway (SWG) components of SASE.

Edge-in Approach with mimik

One of the unique aspects of mimik’s contribution is the ability to move or complement SASE functions further to the edge, even directly on the user or IoT device. This approach enables a more contextualized and efficient security strategy, allowing for device-to-device interaction that is impossible in a traditional cloud-first SASE implementation.

mimik’s Impact on Key SASE Components

Looking at the significant components of a SASE architecture, it is possible to understand the impact of an edge-in approach enabled by the mimik platform:

• Cloud Access Security Broker (CASB): By running CASB as an edge microservice on the device itself (eCASB), organizations can benefit from:

  • Decentralized Data Management: As cloud applications proliferate, so does the data between devices and these applications. With edge computing capabilities from solutions like mimik edgeEngine, there’s potential for more localized data processing and decision-making at the data source before sending it out. This can be leveraged to inspect data locally on a device before it’s sent to or received from a cloud service, aligning with some CASB functions.

  • Local Policy Enforcement: With the ability to execute applications and processes at the edge, organizations could run lightweight, localized CASB-like functions on the device. This would mean real-time policy enforcement even before data or requests hit the main CASB solution in the network path, allowing the ability to do multi-cloud brokering right from the device (at the edge) instead of in the cloud.

  • Enhanced Performance: By integrating edge capabilities with CASB functionalities, certain processes can be offloaded to the edge, reducing latency. For instance, initial policy checks or data classifications, augmentation, and tagging can be done on-device, reducing the need for all traffic to be routed through a central CASB solution.

  • Integration with Other Edge Services: As part of a broader edge ecosystem, CASB functionalities can be combined with other edge services, enabling more comprehensive security and data management solutions tailored for specific environments or use cases.

  • Custom CASB Solutions for Unique Use Cases: Developers can potentially build custom CASB solutions tailored to specific organizational needs or niche applications, leveraging the flexibility and capabilities provided by mimik edgeEngine.

• Zero Trust Network Access (ZTNA): mimik platform took a zero-trust network approach as a core feature of the edge system. This approach allows edge engine to provide the following:

  • Localized Access Control: With computing capabilities extended to the edge; access decisions might be made locally, right where the request originates. This could result in reduced latency and more efficient access controls, as not every decision must be routed through a centralized authority.

  • Enhanced Security for IoT Devices: IoT devices can often be weak points in a network. If these devices are empowered with edgeEngine capabilities and integrated with ZTNA principles, they could have enhanced security postures, mitigating some of the risks associated with IoT deployments.

  • Integration with Decentralized Applications: As more applications and services become decentralized and move to the edge, integrating ZTNA principles becomes crucial. Using a platform like mimik edgeEngine, developers could create applications with built-in ZTNA functionalities tailored for specific edge use cases.

  • Continuous Authentication and Authorization: ZTNA emphasizes continuous verification, not just at the beginning of a session. With edge computing capabilities, this continuous check can be done more efficiently, utilizing real-time device data.

  • Micro-segmentation at the Edge: ZTNA often employs micro-segmentation to isolate and protect network resources. With edgeEngine, this segmentation could be extended to the edge, providing more granular isolation and protection of resources, data, and services.

• Next-Generation Firewall (NGFW): The mimik edgeEngine resides on top of the operating system and, therefore, does not have deep access to the network stack and does not enable the implementation of features like DPI. However, by implementing an API Gateway, it is possible for a microservice running within the edge engine to enable the following features:

  • Localized Traffic Inspection: With applications and services running on the edge, localized traffic inspection and filtering at the message level can potentially be done. Rather than sending all traffic through a central NGFW, initial inspections and policy checks could be performed on-device or at the edge, enhancing responsiveness and reducing unnecessary traffic loads on central security appliances.

  • Context-rich Policies: The edgeEngine can provide granular, context-rich data from devices, given its edge-centric architecture. This context can be valuable for NGFW functions, allowing for dynamic and adaptive security policies based on real-time device status, user behavior, location, etc.

  • Protection of IoT Devices: IoT devices, often seen as vulnerable network points, could benefit from localized firewall capabilities. By integrating NGFW functionalities at the edge, there’s potential for better security postures for IoT deployments, with immediate threat detection and response.

  • Integration with Edge Services: As more services move to the edge, there’s an increasing need to ensure these services are secured. By integrating NGFW capabilities into edge-based services powered by mimik edgeEngine, there’s an opportunity for holistic security that’s tailored for edge-specific scenarios.

  • Decentralized Threat Detection and Response: By leveraging edge computing capabilities, threat detection and response can potentially be decentralized. If an anomaly or potential threat is detected on a device or within a network segment, immediate action can be taken at the edge, even before the central NGFW or security operations center is alerted.

  • Scalability and Adaptability: With the growth of connected devices and increasing network complexity, scalability becomes a concern for traditional NGFWs. By offloading some functionalities to the edge, there’s potential for more scalable security solutions that adapt to changing network conditions and demands.

• Secure Web Gateway (SWG): Allowing microservice to run directly on the device on top of the mimik edgeEngine and this behind an API Gateway, it is possible to enable an eSWG which will have the following capabilities:

  • Real-time Content Filtering: An eSWG running on the device can provide real-time content filtering, blocking malicious or inappropriate content before it reaches the user’s device.

  • Local Policy Enforcement: Organizations can implement customized content filtering policies at the edge, ensuring that users are protected from web-based threats even when they are not connected to the corporate network.

  • Reduced Latency: By offloading content filtering to the edge, latency is minimized, resulting in faster web access for users.

  • Improved Performance: An eSWG can optimize web traffic, reducing the load on central SWG solutions and improving overall network performance.

  • Integration with Local Services: Organizations can integrate their eSWG with other local services and security components to provide a comprehensive security posture.

  • Enhanced Privacy: With an eSWG at the edge, user data remains on the device, enhancing privacy and reducing the need to send user data to centralized SWG solutions.

Conclusion

Securing endpoint devices is paramount in the ever-evolving landscape of cybersecurity and remote work. Traditional security models have limitations, especially in the face of the cloud, mobility, and the Internet of Things (IoT). Secure Access Service Edge (SASE) represents a new paradigm in security, offering an integrated, cloud-native, and context-aware approach. The mimik HEC is pivotal in enhancing SASE implementation by enabling distributed computing at the edge, fostering interoperability, and providing the tools for secure, efficient, and context-aware protection. By moving or complementing SASE functions to the edge, mimik’s innovative approach enhances security, reduces latency, and opens new possibilities for device-to-device interactions, bolstering the security posture of organizations in a rapidly changing digital world. With SASE and mimik, the future of endpoint security looks brighter, more efficient, and more resilient than ever before.

The post Endpoint Device Security, The Missing Link in SASE first appeared on mimik Technology Inc.

Oakland, CA, September 12, 2023 – Mimik Technology Inc. is proud to announce the collaboration with Marelli and Marelli’s selection of mimik as preferred supplier for its software-defined vehicle (SDV) solutions stack, integrating mimik’s patented Hybrid Edge Cloud (HEC) platform. This strategic decision pairs the unique expertise of both companies, signaling a transformative step forward in connected vehicle innovation and addressing the evolving needs of the automotive industry.

Mimik, a leader in edge cloud technology, provides a continuous runtime environment via its HEC platform. It facilitates the development, deployment, and management of workloads across various smart devices, including automotive computing devices. By empowering each computing device within the vehicle and the surrounding intelligent ecosystem to function as a cloud server through its innovative software, mimik enhances performance, scalability, and security while reducing the software’s total cost of ownership (TCO). This also reduces the carbon footprint and enables new opportunities for connected vehicle experiences.

Marelli, a global automotive industry leader, has already made significant strides in the SDV field, thanks to focused investments and the exploration of numerous business opportunities, driven by a passion for innovation. Along with the cabin digital twin and a broad catalogue of SaaP (Service as a Product) applications, Marelli has developed a unique software platform to support OEMs to transition to SDV. This automotive-grade software solution is natively designed around containerized microservices, thus realizing the decoupling of hardware and software – a key pillar for SDV – and unlocking significant optimization opportunities for software development, testing, and validation activities.

“We are delighted to integrate mimik’s platform into our development stack,” shared Nate Sladek, VP Strategy and Product Management at Marelli’s Electronic Systems division. “This allows Marelli’s software platform to fully exploit the benefits of SDV and offer to OEMs future-proof solutions for operating systems design and hybrid edge cloud. This collaboration will allow us to construct a secure and scalable platform for connected vehicles, accelerating the adoption of SDV architectures.”

By integrating mimik’s HEC platform to realize the microservice containerization, Marelli can create software and deploy it across any hardware and operating system within vehicles, as well as extend it to neighboring intelligent devices and ecosystems. This strategic move enhances not only performance, scalability, and security but it also significantly reduces the total cost of ownership (TCO) of the software, ultimately contributing to a more sustainable and interconnected mobility ecosystem.

Sam Armani, SVP of mimik, expressed enthusiasm about the collaboration, stating, “We are excited to see Marelli adopt our Hybrid Edge Cloud platform, a decision that solidifies their position as a trusted innovator in the automotive industry. This integration will create innovative solutions that are set to revolutionize the automotive sector. Together, we are shaping the future of software-defined vehicles and promoting the growth of intelligent, connected mobility.”

The collaboration between mimik and Marelli holds the potential to redefine the automotive industry, enabling the development of novel use cases and transforming our interaction with vehicles. It opens avenues for vehicle hyper-personalization, real-time decision-making, usage-based insurance, predictive maintenance, and more. As the rise of electric and autonomous vehicles necessitates secure and efficient software management, this strategic decision by Marelli to incorporate Mimik’s technology is a vital move.

About mimik:

mimik stands at the forefront of the future of cloud computing, strategically positioned as an indispensable provider of the hybrid edge cloud (HEC) development platform. As industries pivot towards comprehensive digital transformation, our platform is an essential catalyst, streamlining time to market, optimizing cost efficiency, and ensuring scalability, interoperability, data privacy, and security. Crucially, in the era of AI and autonomous operations, empowering all computing devices with server capability becomes imperative, and mimik’s platform is uniquely designed to meet this demand. Supporting a vast spectrum of operating systems, including iOS, Android, Windows, macOS, Linux, QNX, Android Auto, Raspbian, and OpenWRT, as well as smart IoT freeRTOS sensors, our platform seamlessly integrates with both private and public clouds, embodying the vision of next-generation cloud infrastructure. Embracing mimik not only enables businesses to establish direct, efficient connections across smart devices but also ushers in a paradigm shift in operational efficiency while substantially cutting backend integration expenses. The future of computing is here, and it’s powered by mimik.

To learn more visit mimik.com, and for developers developer.mimik.com.

Media Contacts:

For mimik:

PR@mimik.com

The post Mimik’s Platform Adopted by Marelli, Accelerating Software-Defined Vehicles (SDV) first appeared on mimik Technology Inc.

[Vienna, Austria/Oakland, California, September 6, 2023] – TTTech Auto, the leading provider of automotive safety software, and mimik Technology Inc., the pioneer in hybrid edge cloud (HEC) solutions, are pleased to announce an exciting new partnership aimed at enhancing safety and transforming the driving experience of next-generation cars. Through the integration of mimik’s cutting-edge platform with TTTech Auto’s proven safety solutions, this collaboration will pave the way for unprecedented advancements in Software Defined Vehicle (SDV), driver assistance, and autonomous mobility.

TTTech Auto specializes in delivering state-of-the-art software, hardware, and services for future vehicle generations and the shift towards software-defined vehicles. Their innovative technology has been successfully deployed in millions of vehicles worldwide, making them a trusted partner for car manufacturers and automotive system integrators. TTTech Auto’s software solution centers around effectively handling the complexity inherent in SDVs through cutting-edge orchestration, communication, and safety frameworks. The MotionWise Safety Middleware is a proven solution deployed in over 2 million vehicles, with an additional secure pipeline for 9 million cars. Furthermore, Zetta Auto offers a unified solution for in-vehicle and V2X communication, leveraging Data Distribution Service (DDS), Time Sensitive Networking (TSN), and Zenoh protocol to ensure reliable end-to-end communication properties.

mimik is renowned for its innovative HEC platform and edgeEngine, which provide API gateways and a powerful continuous runtime environment. These tools are designed to deploy and run standard microservices on top of any operating system, including Android Auto and QNX. This capability makes it easy to decouple hardware from OS and application logic to achieve SDV. This innovation creates a seamless and efficient ecosystem for developers and automakers alike, allowing every aspect of automotive functions and mobility to be transformed into a function-as-a-service. The mimik edgeEngine allows for the exposure of compute resources, functions, and data through standard APIs, effectively creating a homogeneous view of otherwise fragmented silos of computing and OS. This integration occurs both within and outside the vehicle, allowing functions to interact and exchange knowledge seamlessly amongst themselves and with any cloud providers, therefore addressing interoperability challenges.

Development speed, coupled with safety, real-time capabilities, security, and system integrity, is a pivotal factor in realizing the promises of Software Defined Vehicles (SDVs). The collaboration between the companies aims to leverage the strengths of both companies’ technologies, striving to unveil an unparalleled development lifecycle. This is achieved while upholding the critical attributes of safety, security, real-time performance, and comprehensive end-to-end system properties at the E/E level. Furthermore, the partnership will focus on the exploration of mimik HEC in combination with MotionWise to provide edge-to-cloud technologies, facilitating seamless workload transition between the cloud and edge environments.

“We’re thrilled to join forces with mimik, an industry trailblazer in hybrid edge cloud solutions,” stated Dr. Dirk Linzmeier, CEO of TTTech Auto. “This collaboration marks a significant milestone in our quest to revolutionize automotive safety solutions and deliver state-of-the-art technology to our valued customers, seamlessly spanning from cloud to edge environments.”

“At mimik, we empower automakers to unleash the full potential of their vehicles with our software platform,” said Fay Arjomandi, Founder and CEO at mimik. ” Teaming up with TTTech Auto, a leader in automotive safety software, we can connect fragmented silos of computing inside and outside of vehicles and harness real-time context data to create a safer and more connected driving experience.”

This partnership between TTTech Auto and mimik marks a significant step forward in the pursuit of safer, smarter, and more efficient vehicles on our roads. As the collaboration progresses, both companies remain committed to advancing technology that puts safety first while revolutionizing the future of driving.

About TTTech Auto:

TTTech Auto provides solutions for future vehicle generations and the shift towards software-defined vehicles. The company specializes in delivering safe software and hardware solutions along with a range of services, to support the advancement of automated driving and beyond, applicable in series production programs. With its leading technology solutions, TTTech Auto reduces the required time for system designing, integration and testing of a series production ready software platform. This ensures safety and electronic robustness for a more automated world.

TTTech Auto was founded in 2018 by TTTech Group and technology leaders Audi, Infineon and Samsung to build a global, safe vehicle software platform for automated and autonomous driving. In 2022, the company raised USD 285 million (EUR 250 million) from Aptiv and Audi in its latest funding round. At TTTech Auto’s headquarters in Vienna, Austria, and in more than 10 locations across Europe and Asia, 1,100 employees work with leading car manufacturers on their software-defined vehicle, ADAS and autonomous driving programs. The company has acquired and invested in technology companies in France, Spain, Turkey, China and Central and Eastern Europe.

About mimik:

mimik stands at the forefront of the future of cloud computing, strategically positioned as an indispensable provider of the hybrid edge cloud (HEC) development platform. As industries pivot towards comprehensive digital transformation, our platform is an essential catalyst, streamlining time to market, optimizing cost efficiency, and ensuring scalability, interoperability, data privacy, and security. Crucially, in the era of AI and autonomous operations, empowering all computing devices with server capability becomes imperative, and mimik’s platform is uniquely designed to meet this demand. Supporting a vast spectrum of operating systems, including iOS, Android, Windows, macOS, Linux, QNX, Android Auto, Raspbian, and OpenWRT, as well as smart IoT freeRTOS sensors, our platform seamlessly integrates with both private and public clouds, embodying the vision of next-generation cloud infrastructure. Embracing mimik not only enables businesses to establish direct, efficient connections across smart devices but also ushers in a paradigm shift in operational efficiency while substantially cutting backend integration expenses. The future of computing is here, and it’s powered by mimik.

To learn more visit mimik.com, and for developers developer.mimik.com.

Media Contacts:

For mimik:

PR@mimik.com

For TTTech Auto:

pr@tttech-auto.com

The post TTTech Auto and mimik Announce a Groundbreaking Partnership to Accelerate Software-Defined Vehicles and Smart Mobility first appeared on mimik Technology Inc.

In essence, an API gateway is a server that acts as an intermediary between the client and the cloud services performing many tasks such as authentication, rate limiting, caching, and protocol translation. Therefore, the API gateway can improve the performance, scalability, and security of the overall system architecture. It is commonly used in microservices and serverless architectures.

In the conventional API Gateway market, various vendors offer API Gateway solutions for managing, securing, and exposing APIs to external or internal applications. The main players are Amazon API Gateway, Kong, Rapid, Google Cloud (Apigee) and Azure API Management, among others. They offer different solutions based on their functionality and features such as Proxy, Transformation Gateways, Security, Orchestration and Monetization. Developers can choose the right one according to their specific requirements and use cases.

Examining this offering, we can identify three distinct types of API gateways: façade, exposure, and listening endpoint.

The first one, the API Gateway, acts as a facade for service implementations that operate in separate environments. The API Gateway serves as a single-entry point for all incoming API requests, abstracting the complexity of underlying microservices or distributed systems. These gateways are engineered to manage specific protocols such as HTTP and WebSocket, and they primarily focus on addressing security concerns, particularly TLS security. By using an API Gateway as a facade, organizations can simplify the management of their APIs and services, improve security, and enhance the developer experience for API consumers.

The second one, often call API Exposure Gateway, is an API Gateway which alters or enhances the implemented API that runs on a different environment. It focuses on making APIs accessible to external consumers, partners, or third-party developers. The main goal of an API Exposure Gateway is to facilitate secure, controlled, and efficient access to APIs while ensuring a positive developer experience. It can implement business logic, including caching, throttling, and even metering for billing purposes. API Exposure Gateways are crucial for businesses looking to expose their APIs to a broader audience, foster innovation, and create new revenue streams through API monetization. By providing a secure and controlled environment for API consumption, these gateways enable organizations to maximize the value of their APIs while minimizing risks.

The last one, the listening endpoint API terminates the network connection, is commonly utilized in serverless environments to instantiate the process required for executing the operation requested by the API call. This endpoint acts as an entry point for clients to access the functionality provided by the API, and it’s responsible for processing incoming requests, executing the appropriate actions, and returning the expected responses. In most cases, the API and the service function within the same environment.

Though the gateways vary in their execution, their primary goal remains consistent to act as a central entry point and intermediary for managing, securing, and exposing APIs of cloud services to external consumers or internal applications. It enables cloud services to be utilized by other cloud services or client applications without needing to comprehend the service’s inner workings. This gateway can operate in the cloud or near the client applications as an edge cloud broker.

Suppose for a moment, this cloud-centric approach didn’t exist, and it was feasible to run microservices (or functions as a service) at the edge within the device or system hosting client applications. In that case, a reverse API gateway becomes necessary to expose these microservices. However, instead of exposing cloud services to client applications, it focuses on exposing edge microservices services to either client applications or other edge microservices running on different nodes or cloud services. Consequently, each node within a system serves as an individual server running microservices with exposed APIs at the software level, establishing a ad hoc edge service mesh among all nodes capable of discovering one another.

In this edge first scenario, the reverse gateway would function as a local API Gateway, managing the microservices within the device itself. It would have a vital role in managing and securing the communication between microservices and client applications. By functioning as a local API Gateway, it would manage, secure, and optimize API traffic within the device or system, providing a unified entry point for accessing microservices and improving overall performance and security. Moreover, the local API Gateway would also enable better resource utilization and faster response times as the microservices would be running in the same environment as the client applications.

This reverse API gateway is a natural next step in the evolution of the API Architecture, well described in the Netflix technology blog.

Left to right: 1) Accessing the monolith via a single API, 2) Accessing microservices via separate APIs, 3) Using a single API gateway, 4) Accessing groups of microservices via multiple API gateways. Source: Netflix Technology Blog

Edge microservice can either access each other edge microservice on different nodes directly without going thru the cloud via reverse API gateway or access cloud microservice via a single API gateway.

The concept of device-as-a-service will then be established, allowing client applications to utilize features from a single device or a collection of devices through a series of APIs without needing to comprehend the inner workings of the implementation. This will spark a surge in innovation as it enables the development of applications using systems without requiring expertise in those specific systems. As an example, considering the automobile industry’s ongoing shift towards SDV, it is crucial to begin revealing car functionalities to developers outside of the automotive realm to harness the creative potential within the mobile app industry. A reverse API gateway is essential for accomplishing this objective.

mimik’s edgeEngine provides this reverse API gateway, enabling each node to serve as a data source at the application level. mimik’s edgeEngine allows client applications to utilize features from a single device or a collection of devices through a series of APIs without needing to comprehend the inner workings of the implementation. This edegEngine comprises an API gateway, an OS-agnostic runtime environment, a discovery service for nodes and edge microservices, and an edge analytic platform that enables each node to serve as a data source at the application level. This enables the development of applications using systems without requiring expertise in those specific systems, sparking a surge in innovation.

The post Beyond Boundaries: Enabling Performance and Security with API Gateways Everywhere first appeared on mimik Technology Inc.

A digital twin is a virtual representation of a physical object, process, or system. It is a computerized model that simulates the behavior of a real-world object or system in real-time, providing a detailed and accurate reflection of its physical counterpart.

A digital twin is created by collecting data from various sources, such as sensors, cameras, and other IoT devices, and processing that data using machine learning algorithms and other analytical tools. The resulting model can monitor, analyze, and optimize the physical system’s performance and predict future behavior and outcomes.

Digital twins are commonly used in manufacturing, aerospace, and energy industries. They can be used to simulate the operation of complex machinery, equipment, and systems and identify potential issues or inefficiencies before they occur in the real world. They are also used in building design and construction to optimize performance, maintenance, and energy efficiency.

A digital twin is composed of two main steps:

1. development phase, pre-production (aka pre-prod)

2. deployment and update phase in production (aka post-prod)

Pre-prod digital twin

Looking at the lifecycle development of a solution that involves embedded software components (a car, a manufacturing line, etc..) utilizing QNX, many variants of Linux, Android, and even IOS when a user phone is involved, a developer implementing a new feature does not have the actual environment available as a cloud developer has a Development Environment, aka DEV, to do the implementation and QA for testing the compliance of the implementation. To remediate this problem, a simulation of the environment has to be created. This is where the need for a pre-prod digital twin is emerging.

Adopting modern development solutions and creating an environment in the cloud is a natural solution for such simulation. And because in a cloud environment, the resource is virtualized and generally pooled using Kubernetes orchestrator, a natural consequence is to containerize every simulation component. The developer implementing a new feature must dynamically deploy images and containers using Kubernetes.

This will work well assuming two following conditions:

1. Any legacy software that runs in an actual environment needs to be containerized.

2. The simulation in the cloud environment must closely mimic the actual environment.

These two conditions are difficult to realize since, in the actual environment for embedded systems, the usage of real-time operating systems is frequent, and containerizing legacy components has limitations when dealing with user interfaces and multi-processes within the same container. This means that once QA is passed in the cloud, transferring the new feature to the actual environment generally leads to new problems, making the whole cloud testing obsolete.

Another approach to creating a pre-prod digital twin is replicating the actual environment in the cloud. For that, it is often necessary to run an RTOS like QNX. However, as most of the container technologies (e.g., docker) depend on Operation System’s functions (e.g., c-group), it is not possible to run these containers on QNX. This is why there is a need for a technology that provides a run-time independent from the operating system. And this is what mimik edgeEngine provides.

Running QNX in the cloud and mimik edgeEngine on top of QNX in the cloud allows a developer to implement microservices or function-as-a-service. It is possible to have a seamless transition from the pre-prod digital twin to the actual environment.

Post-prod digital twin

Once the feature is deployed in real systems, it is essential to have a feedback loop to refine the simulation. It allows developers and system analysts to understand the behavior of the actual system and how these behaviors match the behavior of the simulated environment. And this is where a post-prod digital twin needs to be created.

One solution is to use the pre-prod digital twin instance to implement the post-prod digital twin. However, this implies the need to transfer a large amount of data to replicate in the cloud the context of the actual environment. This can be a source of many problems:

1. Cost: the more data to be transferred, the more cost will be generated, either the cost of transport or the cost of processing, in particular, if it is to deal with low-level signals.

2. Power consumption: it generally consumes more power to transmit data to a network than to process data locally and transmit results.

3. Privacy: in some cases, the data to be transmitted is about the user and, therefore, transmitted data to the cloud may be breaching privacy regulation

One solution is to split the pre-prod digital twin into two parts, one part running in that existing system and the other as a consolidation in the cloud since one aspect of running in the cloud is to deal with multiple actual systems (e.g., cars) and therefore avoid bias when extracting a generic behavior.

Technology is needed to allow microservices to run in any environment (regular OS. real-time OS, main CPU, controllers) to do the pre-analysis and send smart signals to an aggregated simulation running in the cloud. And this is what mimik edgeEngine and its different editions (standard, main/child, controller/worker) provide.

The post mimik for Digital Twin first appeared on mimik Technology Inc.