October 29, 2020 | Written by: Nicholas Fuller
Categorized: AI | Hybrid Cloud
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AI has already begun to automate many non-mission critical business processes, including aspects of customer service and human resources. As the technology advances, new opportunities continue to emerge, in particular AI’s ability to automate the movement to, and management of mission-critical workloads on hybrid cloud environments.
Many businesses—especially those in highly regulated industries such as telecom, financial services and healthcare—are hesitant to move mission-critical workloads to the cloud. In fact, data from multiple sources reveals that only 20 percent of all workloads have moved to the cloud. Businesses further along in their journey understand the benefits of cloud use and often have already turned to the cloud for non-mission critical workloads.
The accelerated proliferation of mission-critical applications—combined with the fact that more than 70 percent of organizations using public cloud are working with multiple vendors—means companies must approach the migration of these applications to a hybrid cloud environment using a four-phased approach: advise, move, build and manage. These phases—in concert with a hybrid cloud platform strategy based on Red Hat OpenShift, as well as IBM’s Cloud Paks, software products and services—ensure a seamless transition that is scalable across an enterprise.
IBM Research is developing new ways to use AI to assure clients are moving their mission-critical workloads to a secure cloud environment and can manage those workloads across multiple clouds. We are also experimenting with the integration of our Cloud Paks and software products with OpenShift to benefit IBM clients. In fact, our work to fuse AI with hybrid cloud management running on OpenShift has emerged as a key differentiator for IBM.
In addition to several successes already available to clients, IBM researchers continue to innovate in key areas, including AI for application modernization, security and compliance, and IT operations management.
IBM Research’s priorities to ensure advances in AI continue to improve client hybrid cloud deployments include:
App modernization is an example of a fundamental area of growth in computer science, where one examines source code and applies AI for code understanding, search, testing and verification. The global application modernization services market size is expected to grow from $11.4 billion in 2020 to $24.8 billion by 2025, according to a recent report from MarketsandMarkets Research Private Ltd. Additionally, IBM’s Market Development & Insights (MD&I) estimates 40 percent of enterprise applications will be modernized by 2021.
Accelerator for Application Modernization
In the domain of Application Modernization, IBM offers its Accelerator for Application Modernization with AI, available through Global Business Services. This tool suite of capabilities is designed to help clients reduce the overall effort and costs associated with application modernization for a variety of programming languages by using advanced AI technology from IBM Research. Accelerator for Application Modernization with AI in conjunction with Red Hat tooling optimizes the end-to-end modernization journey and accelerates analysis and recommendations for various architectural and microservices options. This accelerator leverages continuous learning and interpretable AI models to adapt to the client’s preferred software engineering practices and stays up-to-date with the evolution of technology and platforms.
Additionally, Mono2Micro, an AI-driven utility that semi-automatically transforms business-critical applications into microservices, is another prime example of our application modernization research. The utility will enable companies to refactor applications to unlock the full value of the cloud by creating microservices running on containers. First developed by a multi-disciplinary team of IBM researchers and collaborators from the IBM Cloud and Cognitive Software team, the IBM Mono2Micro beta has been included in the Cloud Pak for Applications solution. In July, IBM released an updated beta version that automatically generates API services and related code to activate Mono2Micro microservice recommendations. In one use case explored with Mono2Micro, the beta software product was able to reduce the refactoring time of a mission-critical application by a factor of 10x, identifying as much as 15 percent of “dead” (non-used) code within the application.
Published IBM research includes:
- “Accelerate Innovation with AI for App Modernization,” Ruchir Puri and Shawn DeSouza, 2020
- “Mono2Micro: An AI-based Toolchain for Evolving Monolithic Enterprise Applications to a Microservice Architecture,” Anup K. Kalia, Jin Xiao, Chen Lin, Saurabh Sinha, John Rofrano, Maja Vukovic, and Debasish Banerjee, Foundations of Software Engineering (FSE) 2020
AI for Security and Compliance
Keeping applications secure and compliant is even more critical in today’s hybrid multicloud and pandemic-influenced world. IBM’s 2019 Cost of a Data Breach Study revealed, for example, the impact averages $3.86 million per breach. The 11 incidents of cloud misconfiguration we analyzed cost organizations upward of a combined $57 billion. In IBM Research, we are using broader AI capabilities in transfer learning, explainability and natural language processing (NLP) to streamline governance, risk and compliance (GRC) and improve the security posture of clients’ hybrid multicloud environments.
X-Force Utility and Opportune Vulnerability Ranking
To this end, IBM’s X-Force suite of services enables an organization to proactively manage and respond to customer security threats. One common threat is the existence of vulnerabilities throughout the DevSecOps lifecycle. IBM Research is actively examining technical approaches to mitigate vulnerabilities at each stage of this lifecycle, part of which is encompassed in the previously mentioned AI for Code endeavor. Once mission-critical applications are deployed, the existence of vulnerabilities can be exploited by malicious attackers. The common vulnerability scoring system (CVSS) that the U.S. National Institute of Standards and Technology (NIST) provides, while useful in identifying the severity associated with each vulnerability, doesn’t indicate the degree to which a given vulnerability can be weaponized by a malicious attacker or the skill level required by an attacker to exploit the vulnerability, the so-called utility and opportune of the vulnerability. Leveraging machine learning capabilities, IBM Research has implemented technology into X-Force enabling the programmatic measurement of the utility and opportune of each vulnerability. These features better position an organization to prioritize the vulnerabilities to which they should respond to keep their hybrid multicloud environments secure.
Published IBM research includes:
AI for IT Operations and Support
AI will play an important role in the day-to-day management of hybrid cloud environments, ultimately with little-to-no human intervention. Data from interviews with CIOs of Fortune 1000 companies revealed major outages can cost enterprises up to $0.5M per hour. Further, IBM’s MD&I estimates the IT Operations and Analytics market to be $3.6B in 2020 and growing at 9.5 percent CAGR. To this end, IBM Research has been pioneering in this space for many years impacting IBM’s software and services business.
A prime example is IBM Watson AIOps, available in the IT operations space for incident, problem and change management. Using Watson AIOps, organizations can leverage AI to reliably operate their enterprise applications and automate the detection, diagnosis and response to IT anomalies, alerts and incidents in real time. Watson AIOps is backed by more than 120 patents from IBM Research and powered by machine learning and NLP. These AI-based capabilities are employed for event grouping, log-based anomaly detection, fault localization and incident similarity analysis. Additional work is underway for critical IT Operations domains, including application-centric change-risk analysis and incident outage prediction and avoidance. In one use case, Watson AIOps detected an incident on average 20 hours before opening an incident ticket for a mission-critical application.
Published IBM research includes:
- “Online IT Ticket Automation Recommendation Using Hierarchical Multi-armed Bandit Algorithms”, Qing Wang, Tao Li, S. S. Iyengar, Larisa Shwartz, and Genady Ya. Grabarnik, SIAM International Conference on Data Mining 2018
- “STAR: A System for Ticket Analysis and Resolution”, Wubai Zhou,Wei Xue, Ramesh Baral, Qing Wang, Chunqiu Zeng,Tao Li, Jian Xu, Zheng Liu, Larisa Shwartz, and Genady Ya, Knowledge Discovery and Data Mining (KDD) 2017
Automated Technology Support
In addition to the application of AI for IT operations, IBM Research is also applying AI to the technology support domain for more than 30,000 IBM and third-party hardware and software products. AI technologies employed for these domains include: multi-modal knowledge retrieval and problem diagnosis, NLP and knowledge curation for optimized engineering support. In one example, IBM Research demonstrated a more than 90 percent cold start accuracy in aiding end users to dynamically tailor their support questions to achieve a 30 percent improvement in issue resolution for thousands of software products.
Published IBM research includes:
- “Question Quality Improvement: Deep Question Understanding for Incident Management in Technical Support Domain,” Anupama Ray, Csaba Hadhazi, Pooja Aggarwal, Gargi Dasgupta, and Amit Paradkar, Innovative Applications of Artificial Intelligence (IAAI) Conference, 2020
- “Crossing Variational Autoencoders for Answer Retrieval,” Wenhao Yu, Lingfei Wu, Qingkai Zeng, Shu Tao, Yu Deng, and Meng Jiang, Association for Computational Linguistics (ACL) 2020
- “Fine-Grained Visual Recognition in Mobile Augmented Reality for Technical Support,” Bing Zhou and Sinem Guven, IEEE ISMAR 2020 and Special Issue of IEEE TVCG (Transactions on Visualization & Computer Graphics) 2020
As IT complexity grows and the use of AI technologies expands, AI has the power to transform how enterprises deploy and manage their hybrid cloud environments. IBM Research’s innovation and pursuit of AI-infused automation – automated application modernization, security and compliance, and operations and support management solutions – on Red Hat OpenShift serves as a key differentiator for IBM hybrid cloud clients.
Inventing What’s Next.
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