AnyLog — Value Proposition

Introduction to AnyLog

About AnyLog

AnyLog is a software package deployed on edge nodes, (gateways, switches or servers). These nodes become members of a decentralized, self managed and secure network that is optimized to manage distributed edge data and resources — using virtualization, applications extract real time insight from data distributed at the edge without centralizing the data (or prior to centralizing the data). In addition, AnyLog manages and monitors all the distributed edge resources as if they are a single machine.

Today, the edge does not offer data services (similar to what the cloud is offering). Managing data at the edge is based on proprietary multi-months projects. AnyLog replaces these projects with a Plug & Play software that addresses the technical challenges that companies are facing at the edge, and makes all the edge data available to the applications (at the edge and the cloud) without moving the data, in a secure way, and without a single line of code.

What is unique in the AnyLog Offering?

• As of today, there is no efficient platform that provides real-time insight from distributed, non-uniform data (companies are forced to centralize the data) — AnyLog is unique by dynamically connecting the applications with the edge data, without intermediaries — data remains in-pace whereas applications interact with the data as if the data is hosted in a single and unified relational database.
• Using a Plug & Play Software, AnyLog automates the data management at the edge and overcomes the technical challenges that companies are facing at the edge.
• Completely decentralized — using virtualization (and subject to permissions), any node in the network can view the complete data-set as if it is hosted locally, and every node in the network can monitor all the edge resources as if all the resources are a single machine. The centralized control of data and resources is virtualized, can be delegated to any node member and does not require physical centralized management.

The value to the users

• Facilitating Edge Computing by servicing the edge data to the applications in real-time without centralizing the data (using standard APIs).
• By replacing proprietary multi-months projects at the edge with a Plug & Play Software allowing immediate Go to Market, 0 risk.
• Lowering capex and opex costs.

The need for AnyLog at the Edge

In 2–3 years most of the enterprise data would be created and managed outside the data-centers and the cloud (Source — Gartner). The reasons are: real-time (the cloud is not a real-time environment), too much data, ownership and regulations. These reasons, together with the efforts to lower data management costs drive companies to push processing to the edge.

However, there is no generic, efficient data management platform at the edge — AnyLog is addressing this gap (more details are available below).

In addition, AnyLog offers a repeatable process that can be applied to all edge use cases — see the examples below.

Example Use Cases

• Energy — the energy market is moving towards an energy share model where participants are buying and selling energy. AnyLog is a fit to this model as AnyLog can manage the largest data set, it is a real time platform, and data owners maintain control of their data, however, at the same time, can share their data with other participants — data sharing between the network participants is a key for energy share, and AnyLog is unique in being able to address the related requirements (note: AnyLog was selected by an edge integrator in the energy space to be their core platform for the edge).
• Utility — their infrastructure becomes increasingly dependent on smart devices. In particular, utilities represent use cases of large volumes of data — a city can have millions of electricity, gas and water meters. In addition, data generated at the transmission and distribution substations, at the power lines, homes, solar panels, wind farms etc. exceed the volumes that could efficiently be transferred and managed by the cloud. In many cases, the data insights are needed in real-time which the cloud can not deliver. AnyLog provides the decentralization, scaling and security needed to support large and diverse data management and delivers the scaling characteristics to address the volumes of data that utilities are generating.
• Manufacturing — in many cases, an industrial manufacturer will not send the data from the facility to the cloud to query the data at the manufacturing facility. This may be the result of too much data, real-time requirement, ownership considerations and more. AnyLog deployed in the industrial facility (or if needed, in multiple facilities) transforms the edge to a virtual data lake that services the data in real-time, using standard APIs. A subsegment is a machine builder that needs to monitor machines across many customers’ sites and at the same time (and using the same data), the customer (that owns the data) needs to monitor machines across a company — AnyLog addresses these types of data ownership and sharing requirements “out-of-the-box”.
• Retail — one of the hard to manage use-cases which becomes mainstream is the use of images and videos with AI to provide insight to the retail processes. For example — most stores use cameras and are adding AI to manage self-checkout. It is not practical to stream videos from thousands of cameras to the cloud (and even if the data is in the cloud, it is hard to extract the value). Keeping the data at the edge requires proprietary solutions which are hard to build, manage and scale, and creates silos of data and software stacks. AnyLog maintains the video and AI at the edge — but users are able to query and monitor the data in real-time from a single point without the need to know which retail store maintains the needed data. All of that without a single line of code.
• Healthcare — an ideal use case for AnyLog because of regulations that are forcing to keep data in-place, real-time requirements and issues relating to the diversity of the data, and the dynamic nature (new equipment with modified and new sensors) require a platform that addresses the dynamic nature at the edge.
• Logistics — AnyLog as a platform providing real-time insight to track vehicle and assets locations, monitor performance and status of vehicles drivers and equipment and automated inventory management. Some of the challenges in logistics include: a) Interoperability: IoT devices from different manufacturers may not be compatible with each other, making it difficult to integrate them into existing logistics systems. AnyLog provides the mechanisms to address the diversity of the data, b) some devices generate huge amounts of data whereas edge resources are limited — AnyLog scales horizontally and is able to leverage the edge resources as a powerful parallel machine, c) edge nodes may require data on peer nodes — AnyLog makes all the data available to all the network participants, d) security is a key component in logistic platforms — AnyLog allows data owners to maintain full control of their data and the data is secure.

Other apps — Unique solution for decentralized projects

Some classes of applications require decentralization. This may be the result of the decentralized nature of the applications, or when there is no centralized cloud or connectivity to the cloud is lost or can not always be available. AnyLog is fully decentralized — no single point of failure and it is architected such that every node has no dependency on a peer node. Nodes can be brought up and down dynamically without any impact on the overall operation.
Examples for the decentralized nature is a) A ship that operates as an independent network, and needs to continue operations even when infrastructure and connectivity fails and syncs with the cloud randomly, when possible. b) A setup deployed at a remote location, with a variety of members and with dynamic scenarios.
To the best of our knowledge — AnyLog is unique by supporting these requirements.

The Gap at the Edge

The adoption of AI in every industry together with the need to process data in real-time, the massive volume of data at the edge, data ownership and costs issues are some of the reasons to move processing to the edge (and see the Gartner prediction above).

To service data to an application, the data needs to be unified and hosted in a database. In addition, the schema needs to be available, such that, using the schema, it is possible to formulate a query that will be satisfied by the database. As the edge is lacking data services, supporting these requirements require professional services to first design a solution, then build the software, then, deploy databases, create schemas, address issues like Data Unification, High Availability and Security, and then deploy and manage the software on many edge nodes. This approach creates silos of data and proprietary software stacks which are hard to build, manage and scale.

With large data volumes, compute and storage needs to provide sufficient resources to host and service the data . This is not the case at the edge, as data is distributed on many nodes, and these nodes are usually less powerful than the compute resources at the data center and cloud.
In addition, nodes at the edge may come up and be brought down without proper control and planning.

As of today, there is no efficient technology to enable insight from data distributed at the edge capable of addressing the challenges and the costs associated with the problems mentioned above. And therefore, companies have no choice other than to centralize their data.

AnyLog is addressing this gap with a Plug & Play software that makes all the edge data available to all applications (in real-time, without centralizing the data) and satisfies the challenges that companies are facing at the edge.

How AnyLog Operates

AnyLog transforms the edge to a virtual, highly-available, self-managed and secure data lake. This setup is similar to a physical data lake, however, the unified view of the data is provided by virtualization, whereas, the data remains distributed at the edges (in a physical data lake, the unified view is the result of the physical organization of the data). This approach brings the queries to the data (rather than bringing the data to the query), and therefore is more efficient than centralizing the data, has no dependency on centralization, and leverages the accumulative compute power of edge nodes as a unified parallel machine.

More specifically, here is how AnyLog operates:

• AnyLog is deployed on the participating nodes, and the data is consumed on each node using standard APIs (i.e. REST, or published to AnyLog as a broker).
• When data streams to a node, AnyLog matches the data with an existing schema and hosts it locally (AnyLog offers a shared metadata layer, published on a blockchain, available to all the participating nodes. The metadata contains the network policies including the schemas).
  If a schema that satisfies the data is not available, a new schema is created and published on the shared metadata. A node hosting data will also register itself on the metadata as hosting data for the schema used.
• This process makes all the edge data available (in real-time) to a query process (using SQL). Locating the nodes with the relevant data is a transparent process to the user (or application).
• For example, an application issues a query to find the electricity usage in SF during the last 2 hours. All that is needed is to push the query to one of the participating nodes (regardless if the node hosts the relevant data). This node considers the shared metadata to determine which edge nodes host the relevant data, to form, dynamically, a P2P process with the relevant nodes. With this P2P process, the query will be delivered to the relevant nodes, each node would process the query locally and reply with a local result. The local results are aggregated and returned as a unified result to the application (like MapReduce).
• Note: from the user side, queries are satisfied without engineering efforts: the network protocol was extended with AnyLog, all the processes described above are transparent, therefore, queries are satisfied as if the data is hosted in a centralized database — all of that in real-time and without a single line of code.
• Monitoring and querying the status of edge resources (devices, sensors, gateways, switches, edge nodes and servers) is done in a similar way: pushing monitoring and status requests to targeted edge nodes and providing a unified reply from the participating nodes as if they are a single machine. This approach provides a Single System Image (SSI).

This approach is augmented by functionalities to make the data secure, highly available and with connectors that leverage virtualization to reflect to the external application a centralized and unified database.

This approach addresses the technical challenges that companies are facing at the edge and replaces multi-months proprietary projects to collect, unify, and manage data at the edge with an “out-of-the-box” solution, allowing immediate go to market and 0 risk.

Transform the Edge to a Virtual Cloud