How eBPF will solve Service Mesh - Goodbye Sidecars
Why have we not created a service mesh in the kernel before? Some people have
been semi-jokingly stating that kube-proxy is the original service mesh. There
is some truth to that. Kube-proxy is a good example of how close the Linux
kernel can get to implementing a service mesh while relying on traditional
network-based functionality implemented with iptables. However, it is not
enough, the L7 context is missing. Kube-proxy operates exclusively on the
network packet level. L7 traffic management, tracing, authentication, and
additional reliability guarantees are required for modern applications.
Kube-proxy cannot provide this at the network level. eBPF changes this equation.
It allows to dynamically extend the functionality of the Linux kernel. We have
been using eBPF for Cilium to build a highly efficient network, security, and
observability datapath that embeds itself directly into the Linux kernel.
Applying this same concept, we can solve service mesh requirements at the kernel
level as well. In fact, Cilium already implements a variety of the required
concepts such as identity-based security, L3-L7 observability &
authorization, encryption, and load-balancing.
General and Scalable Parallelization for Neural Networks
Because different model architectures may be better suited to different
parallelization strategies, GSPMD is designed to support a large variety of
parallelism algorithms appropriate for different use cases. For example, with
smaller models that fit within the memory of a single accelerator, data
parallelism is preferred, in which devices train the same model using different
input data. In contrast, models that are larger than a single accelerator’s
memory capacity are better suited for a pipelining algorithm (like that employed
by GPipe) that partitions the model into multiple, sequential stages, or
operator-level parallelism (e.g., Mesh-TensorFlow), in which individual
computation operators in the model are split into smaller, parallel operators.
GSPMD supports all the above parallelization algorithms with a uniform
abstraction and implementation. Moreover, GSPMD supports nested patterns of
parallelism. For example, it can be used to partition models into individual
pipeline stages, each of which can be further partitioned using operator-level
parallelism.
What 2022 can hold for the developer experience
To improve the developer experience, and ultimately retain and attract talent,
businesses should begin to make changes to reduce the strain placed on
developers and help them achieve a healthier work-life balance. The introduction
of fairly simple initiatives such as flexi-time and offering mental health days
can help to reduce the risk of burnout and show developers that they are valued
members of the business whose needs are being listened to. Additionally,
organisations could look to provide extra resource and adopt the tools and
technology to enable developers to automate parts of their workload. Solutions
such as data platforms that make use of machine learning (ML) are a prime
example of this. This use of this type of technology would enable developers to
easily add automation and predictions to applications without them needing to be
experts in ML. Adopting technologies that embed ML capabilities can also help to
simplify the process of building, testing, and deploying ML models and speed up
the process of integrating them into production applications.
2022 Cybersecurity Risk Mitigation Roadmap For CISO & CIO As Business Drivers
As companies become aware of the need for data protection, their leaders are
likely to increase the adoption of encryption; which will find its way into
organizations’ basic cyber security architecture in 2022. This will have a
ripple effect, and we can expect newer and updated applications providing data
encryption solutions to be launched for businesses in the coming year. One of
the most disruptive technologies in decades, blockchain technology will be at
the heart of shifting from a centralized server-based internet system to
transparent cryptographic networks. AI has matured from an experimental topic to
mainstream technology. As a result, 2022 will see better accessibility of
Artificial Intelligence (AI) based tools for creating robust cybersecurity
protocols within an organization. In addition, we expect the new lineup of
technology tools to be more cost-effective and yet more effective than ever
before. Last but not least, 2022 will see a mix of remote work and on-site
physical presence, thereby continuing with the trends of cybersecurity adapted
during 2021.
Intel reports new computing breakthroughs as it pursues Moore’s Law
Intel made the announcement at the IEEE International Electron Devices
Meeting (IEDM) 2021. In the press release, Intel talked at length about its
three areas of pathfinding and the breakthroughs that prove it’s on track to
continue following its roadmap through 2025 and beyond. The company is
focusing on several areas of research and reports significant progress in
essential scaling technologies that will help it deliver more transistors in
its future products. Intel’s engineers have been working on solutions to
increase the interconnect density in chip packaging by at least 10 times.
Intel also mentioned that in July 2022, at the Intel Accelerated event, it
plans to introduce Foveros Direct. This will provide an order of magnitude
increase in the interconnect density for 3D stacking through enabling sub-10
micron bump pitches. The tech giant is calling for other manufacturers to
work together in order to establish new industry standards and testing
procedures, allowing for the creation of a new hybrid bonding chiplet
ecosystem.
Designing High-Volume Systems Using Event-Driven Architectures
Thanks to the latest development in Event-Driven Architecture (EDA)
platforms such as Kafka and data management techniques such as Data Meshes
and Data Fabrics, designing microservices-based applications is now much
easier. However, to ensure these microservices-based applications perform at
requisite levels, it is important to ensure critical Non-Functional
Requirements (NFRs) are taken into consideration during the design time
itself. In a series of blog articles, my colleagues Tanmay Ambre, Harish
Bharti along with myself are attempting to describe a cohesive approach on
Design for NFR. We take a use-case-based approach. In the first installment,
we describe designing for “performance” as the first critical NFR. This
article focuses on architectural and design decisions that are the basis of
high-volume, low-latency processing. To make these decisions clear and easy
to understand, we describe their application to a high-level use case of
funds transfer. We have simplified the use case to focus mainly on
performance.
How Hoppscotch is building an open source ‘API development ecosystem’
The Hoppscotch platform constitutes multiple integrated API development tools,
aimed at engineers, software developers, quality assurance (QA) testers, and
product managers. It includes a web client, which is pitched as an “online
collaborative API playground,” enabling multiple developers or teams to build,
test, and share APIs. A separate command line interface (CLI) tool, meanwhile,
is designed for integrating automated test runners as part of CI/CD pipelines.
And then there is the API documentation generator, which helps developers
create, publish, and maintain all the necessary API documentation in real
time. Hoppscotch for teams, which is currently in public beta, allows
companies to create individual groups for specific use-cases. For example, it
can create a team for its entire in-house workforce, where anyone can share
APIs and related communications with anyone else. They can also create smaller
groups for specific teams, such as QA testers, or for external vendors and
partners where sensitive data needs to be kept separate from specific projects
they are involved in.
Synthetic Quantum Systems Help Solve Complex Real-World Applications
Quantum Simulation is the most promising use of Pasqal’s QPU, in which the
quantum processor is utilized to obtain knowledge about a quantum system of
interest. It seems reasonable to employ a quantum system as a computational
resource for quantum issues, as Richard Feynman pointed out in the 20th
century. Neutral atom quantum processors will aid pure scientific discovery,
and there are several sectors of application at the industrial level, such as
the creation of novel materials for energy storage and transport, or chemical
computations for drug development. “At Pasqal, we are not only scientists, we
are not only academic, we industrialize our technology. By working with
quantum technology, we want to build and sell a product which is reliable, and
which helps to solve complex industrial problems in many contexts,” said
Reymond. Among Pasqal’s customers is EDF, the French electricity utility. In
the energy sector, Pasqal is working with EDF to develop innovative solutions
for smart mobility.
Enterprise email encryption without friction? Yes, it’s possible
It is often (grudgingly) acknowledged in security circles that sometimes
security must be partly sacrificed for better usability. But with Echoworx you
can have the best of both worlds: a seamless, secure experience for
organizations, their partners, vendors, and customers. “Our customers are
mostly very large global enterprises in the finance, insurance, manufacturing,
retail, and several other verticals,” says Derek Christiansen, Echoworx’s
Engagement Manager. “When working with them, we must be sensitive to their
needs. We do this by offering like-for-like encryption when we can, and by
tailoring the integration of encryption to their existing flows.” ... The only
thing that the customer needs to do is direct any email that needs encryption
to the company’s infrastructure. “They can use their own data classification.
That can be something as simple as an Office 365 rule. It’s also common to use
a keyword (e.g., the word ‘secure’) in the email subject to route the message.
We also have an optional plugin for Outlook that makes it really easy for
senders,” Christiansen notes.
7 Critical Machine Intelligence Exams and The Hidden Link of MLOps with Product Management
The past 12 months have seen many machine learning operations tools gaining
prominent popularity. Interestingly, one feature is notably absent or rarely
mentioned in the discussion: quality assurance. Academia has already initiated
research in machine learning system testing. In addition, several vendors
provide data quality support or leverage data testing libraries or data
quality frameworks. Automated deployment does exist as well in many tools. But
how about canary deployments of models and whatever happened with unit and
integration testing in the machine learning universe? Many of these quality
assurance proposals originate from an engineering mindset. However, more and
more specialists without an engineering background perform a lot of model
engineering. Further, recall that a separate person or team frequently runs
the quality assurance activities. Supposedly so that engineers can place their
trust in others to catch mistakes. More cynical characters might insist that
engineers need to be controlled and checked.
Quote for the day:
"Leaders dig into their business to
learn painful realities rather than peaceful illusion." --
Orrin Woodward
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