In modern data-driven ecosystems, Extract, Transform, Load (ETL) flows serve as the backbone of data integration pipelines. These flows facilitate the seamless movement of data across disparate systems and formats, streamlining processes that range from data acquisition to preparation for analysis. However, the pervasive use of ETL flows introduces a pressing challenge-how to bound the maintenance cost of an ever-expanding number of flows. In this paper, we describe an end-to-end prototype for ETL flow refactoring, aimed at reducing the maintenance cost, which keeps the human in the loop for refactoring decisions. Our prototype adopts and significantly extends the gSpan Frequent Subgraph Mining (FSM) algorithm to apply it to real-world ETL use cases in the context of the IBM DataStage™ data integration tool. We report on real customer workloads, share their statistics and evaluate the performance of our prototype. We found potential for up to 32% maintenance cost reduction on the use cases we analyzed after removing duplicate flows. We also share an anonymized version of the workloads with the research community.

Unexpectedly, the rise of serverless computing has also collaterally started the “democratization” of massive-scale data parallelism. This new trend heralded by PyWren pursues to enable untrained users to execute single-machine code in the cloud at massive scale through platforms like AWS Lambda. Driven by this vision, this article presentsLithops, which carries forward the pioneering work of PyWren to better exploit the innate parallelism of à la MapReduce tasks atop several Functions-as-a-Service platforms such as AWS Lambda, IBM Cloud Functions, Google Cloud Functions or Knative. Instead of waiting for a cluster to be up and running in the cloud,Lithopsmakes easy the task of spawning hundreds and thousands of cloud functions to execute a large job in a few seconds from start. With Lithops, for instance, users can painlessly perform exploratory data analysis from within a Jupyter notebook, while it is the Lithops’s engine which takes care of launching the parallel cloud functions, loading dependencies, automatically partitioning the data, etc. In this article, we describe the design and innovative features of Lithops and evaluate it using several representative applications, including sentiment analysis, Monte Carlo simulations, and hyperparameter tuning. These applications manifest the Lithops’ ability to scale single-machine code computations to thousands of cores. And very importantly, without the need of booting a cold cluster or keeping a warm cluster for occasional tasks.

As more applications are being moved to the Cloud thanks to serverless computing, it is increasingly necessary to support the native life cycle execution of those applications in the data center. But existing cloud orchestration systems either focus on short-running workflows (like IBM Composer or Amazon Step Functions Express Workflows) or impose considerable overheads for synchronizing massively parallel jobs (Azure Durable Functions, Amazon Step Functions). None of them are open systems enabling extensible interception and optimization of custom workflows. We present Triggerflow: an extensible Trigger-based Orchestration architecture for serverless workflows. We demonstrate that Triggerflow is a novel serverless building block capable of constructing different reactive orchestrators (State Machines, Directed Acyclic Graphs, Workflow as code, Federated Learning orchestrator). We also validate that it can support high-volume event processing workloads, auto-scale on demand with scale down to zero when not used, and transparently guarantee fault tolerance and efficient resource usage when orchestrating long running scientific workflows.

Extracting value from data stored in object stores,such as OpenStack Swift and Amazon S3, can be problematicin common scenarios where analytics frameworks and objectstores run in physically disaggregated clusters. One of the mainproblems is that analytics frameworks must ingest large amountsof data from the object store prior to the actual computation;this incurs a significant resources and performance overhead. Toovercome this problem, we present Scoop. Scoop enables analyticsframeworks to benefit from the computational resources of objectstores to optimize the execution of analytics jobs. Scoop achievesthis by enabling the addition of ETL-type actions to the dataupload path and by offloading querying functions to the objectstore through a rich and extensible active object storage layer. Asa proof-of-concept, Scoop enables Apache Spark SQL selectionsand projections to be executed close to the data in OpenStackSwift for accelerating analytics workloads of a smart energy gridcompany (GridPocket). Our experiments in a 63-machine clusterwith real IoT data and SQL queries from GridPocket show thatScoop exhibits query execution times up to 30x faster than thetraditional “ingest-then-compute” approach.

Traditionally, active storage techniques have been proposed to move computation tasks to storage nodes in order to exploit data locality. However, we argue in this paper that active storage is ill-suited for cloud storage for two reasons: 1. Lack of elasticity: Computing can only scale out with the number of storage nodes; and 2. Resource Contention: Sharing compute resources can produce interferences in the storage system. Serverless computing is now emerging as a promising alternative for ensuring painless scalability, and also, for simplifying the development of disaggregated computing tasks.

Software-defined storage (SDS) aims to minimize the complexity of data management in the Cloud. SDS decouples the control plane from the data plane and simplifies the management of the storage system via automated storage policy enforcement. In this paper, we propose a novel SDS framework for Object Storage that allows to decentralize policy enforcement through the deployment of per-object management policies in the storage nodes. As in active storage systems, we leverage the underutilized CPU time in the storage nodes. But our framework goes one step further. It provides a new management abstraction called micro-controllers which operate on objects depending on their state and content, thereby permitting the implementation of sophisticated management policies, such as the automated deletion of an object based on its access history, and even allowing the orchestration of active storage tasks. Our SDS system avoids the massive interception of data flows by moving that logic to the appropriate objects. Furthermore, our extensible model simplifies the customization of Object Storage services. We present in the validation several interesting use cases such as automated deletion, content level access control, and Web prefetching.

Personal Cloud services, such as Dropbox or Box, have been widely adopted by users. Unfortunately, very little is known about the internal operation and general characteristics of Personal Clouds since they are proprietary services.