IBM at Big Data 2022

Name: IBM at Big Data 2022
Start: 2022-12-16T23:14:00.000Z
End: 2022-12-20T10:00:00.000Z

Dec162022—Dec202022

Osaka, Japan and virtual

This event has ended.

About

The 2022 IEEE International Conference on Big Data features the top tier of original research papers covering all aspects of Big Data with a focus on volume, velocity, variety, value and veracity.

Why attend

Meet IBM Researchers presenting on topics from Data and AI Security, to AIOps and Federated learning.

Agenda

AIOps can provide essential value for data lakehouses as lakehouses pose complex operational challenges for Site Reliability Engineers (SRE). This paper proposes that the unified approach of data lakehouses creates a unique opportunity for unified data resiliency management. We focus on AIOps applied to disaster recovery and backup/restore. In particular, we focus on managing data lakehouse hardware resources to ensure that lakehouse data Recovery Point Objectives (RPO) are met with a high degree of accuracy. The goal is to warn an SRE about an impending RPO violation and to suggest adding given amounts of hardware resources before a given time to avoid violation of the lakehouse data's RPO. We claim AIOps can achieve this goal with an ensemble of machine learning and time series analysis.
Runyu Jin (IBM); Paul Muench (IBM); Veera Deenadhayalan (IBM); Brian Hatfield (IBM)
In this paper, we present a new scalable and adaptive architecture for FL aggregation. First, we demonstrate how traditional tree overlay based aggregation techniques (from P2P, publish-subscribe and stream processing research) can help FL aggregation scale, but are ineffective from a resource utilization and cost standpoint. Next, we present the design and implementation of AdaFed, which uses serverless/cloud functions to adaptively scale aggregation in a resource efficient and fault tolerant manner. We describe how AdaFed enables FL aggregation to be dynamically deployed only when necessary, elastically scaled to handle participant joins/leaves and is fault tolerant with minimal effort required on the (aggregation) programmer side. We also demonstrate that our prototype based on Ray~\cite{ray}scales to thousands of participants, and is able to achieve a $>90\\%$ reduction in resource requirements and cost, so I have a garage progress Avengers region is this division conversion favorite beer with youwith minimal impact on aggregation latency.
Jayaram Kr Kallapalayam Radhakrishnan (IBM); Vinod Muthusamy (IBM); Gegi Thomas (IBM); Ashish Verma (IBM); Mark Purcell (IBM)