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Highlights
- A visible shift in application development from traditional control-flow Software 1.0 to data-flow Software 2.0 programming.
- A modern enterprise IT system (EIT2.0) employs data-driven models to automate tasks.
- One of the main benefits of using data-driven trained models in businesses is making intelligence available to everyone across the enterprise, including non-programmers like data scientists.
In this article
Embracing Software 2.0: Taking the leap
With the rapid progress in machine learning (ML) research, we have noticed a change in application development from traditional control-flow Software 1.0 to data-flow Software 2.0 programming. This usage of ML-based models has replaced customer scoring techniques to generate recommendations. Software 2.0 programming is data-driven and requires a vast amount of clean, labeled, unbiased data sets, and a clearly defined architecture for neural network models, efficient techniques for model training, rigorous testing, and high-performance deployment.
Since Software 2.0 programming invites heterogeneity in the entire application lifecycle – from development to deployment – various architecture and performance-related challenges arise. This paper outlines the requirements to migrate parts of Software 1.0 to the Software 2.0 framework and the challenges associated with accelerating the development and deployment of Software 2.0. In envisioning the evolution of existing enterprise IT systems to an agile, collaborative, and data-driven enterprise IT system (EIT 2.0), we compare the conventional application lifecycle development approach with that in EIT 2.0. We also highlight the challenges one needs to address where both Software1.0 and Software 2.0 coexist in an enterprise IT system.
A modern enterprise IT system
A scalable deployment stack of enterprise applications consists of multiple layers of cluster management, data management, network management and application servers, referred to as an enterprise IT system. Traditionally, the software in these components is built using task-driven programming paradigms, where business requirements are mapped to an executable task using a set of functions. However, with an exponential increase in ML and DL research, such complex functions are being replaced with data-driven models that help enterprises better while reducing human intervention.
A modern enterprise IT system (EIT2.0) employs these data-driven models to automate some of the tasks as below:
A data management system may use reinforcement learning or recurrent neural networks to decide an optimal plan for query execution.
Data structures such as B-tree indexes may be replaced by models trained on the data access pattern.
Resource provisioning in a cluster may be automated by observing workload patterns and their respective resource consumptions in a model.
Core functionalities of network management such as traffic prediction, routing and classification, congestion control, resource, and fault management, QoS and QoE management, and network security can be automated using ML/DL algorithms.
Tim Kraska and his fellow MIT scholars have envisioned a new type of data processing system, SageDB, a radical approach to building database systems by using ML models combined with program synthesis to generate system components that are driven by an AI engine.
Democratizing AI to increase engagement and realize benefits
One of the main benefits of using data-driven trained models in businesses is making intelligence available to everyone across the enterprise, including non-programmers like data scientists. Democratizing AI by building frameworks and systems that are accessible to all lowers the entry barrier and increases engagement. This accelerates data-driven application development by abstracting the underlying system complexity and hardware heterogeneity through the support of high-performance libraries to be used by data scientists. Also, some tools facilitate building models using domain functions, like Snorkel from Stanford, that can be used to label the large-sized training data required for DL algorithms. The availability of a democratized AI framework encourages users to replace task-driven functions with data-driven models. For example, a solution architecture for a recommendation system may use statistics to identify co-relations across different data sets, analyze user behavior, and build a mathematical scoring method to recommend a product to a user. However, democratized AI frameworks can automatically build the recommendation model with the required data sets.
Modern applications, composed of both data-driven models and task-driven functions, complicate the process of testing. The data-driven models are sensitive to any change in input data, whereas the accuracy of task-driven functions depends on the control-ow coded in a function. The test cases must be designed to capture these sensitivities. A process similar to the waterfall model in traditional software development is proposed to develop such applications with appropriate feedback loops.
Data management
Data management is the fundamental problem in data-driven programming. Supervision learning works with labeled data, which is a rare commodity. Snorkel labels the data using the domain rules. One can extend this to auto-label data using small amounts of labeled data and also recommend the right amount of labels needed for the desired accuracy.
It has also been observed regarding the development of many ML pipelines on the same data sets for different use cases for one client. Can these pipelines synergize with each other by reusing features for models computed across different pipelines? Yes, it is possible; by designing a high-performance feature store, which can be reused by data scientists across multiple ML/DL pipelines. For example, a client may require building a personalized home page as well as next best offer for a user. In this case, the same data sets may be used for both pipelines.
This high-performance feature store is scalable and configurable to a client’s requirements and is optimized for memory size and access latency. The use of AI to accelerate data access by replacing complex data structures (such as K-d tree) and algorithms (such as sorting) with data-driven models can also be explored.
Deployment of data-driven models employs heterogeneity in the whole development stack and facilitates:
Integration of heterogeneous data sources (e.g., text, relational, images),
Interoperability of streaming engines (e.g., flink, ignite),
Interoperability across model building languages (e.g., R, Python),
Interoperability across models (e.g., tensorflow, pytorch),
Integration of data processing engines (e.g., Oracle, Timesten, Textual store),
Integration of different file systems (e.g., S3, HDFS),
Embracing different compute (e.g., CPU, GPU, TPU, FPGA) and memory (persistent memory, NVRAM, Flash) for high performance.
There have been many solutions (see references) in the past that address this heterogeneity at different levels, but the ask is to leverage this in the whole stack for optimal performance. This approach has been studied in depth in collaboration with Stanford. It envisions a new kind of system, Polystore++ for embracing and accelerating access to heterogeneous data processing engines using heterogeneous hardware accelerators. There is a need to have benchmarks for such applications to decide the right technology stack for deployment. For this also, models have been developed to predict task-driven application performance for big data systems. Further, performance prediction models are being mapped data-driven models for estimating their training and inference time on heterogeneous hardware including clouds services.
Toward agile EIT 2.0
The emergence of a new ecosystem of ML/DL-driven applications, or Software 2.0, powers the vision of an agile EIT 2.0. However, it is imperative to understand and accept instances where Software 1.0 is also present and therefore the ecosystem must adjust and address challenges where both models co-exist. Democratizing AI will help mitigate performance challenges and drive innovation at scale.
References
1. Rekha Singhal, Nathan Zhang, Luigi Nardi, Muhammad Shahbaz, and KunleOlukotun. 2019. Polystore++: Accelerated Polystore System for Heterogeneous Workloads. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE, 1641–1651.
2. Apache Beam 2016. Apache Beam: An advanced uni‑ed programming model. Retrieved Jan 01, 2020 from https://beam.apache.org/
3. Arrow 2016. Apache Arrow: A cross-language development platform for in-memory data. Retrieved Jan 01, 2020 from https://arrow.apache.org/
4. W Lin et al. 2019. ONNC: A Compilation Framework Connecting ONNX to Proprietary Deep Learning Accelerators. In 2019 IEEE International Conference on Arti‑cial Intelligence Circuits and Systems (AICAS). 214-218. https://doi.org/ 10.1109/AICAS.2019.877151
5. David Koeplinger et al. 2018. Spatial: A Language and Compiler for Application Accelerators. In ACM PLDI.
