arXiv subjects

Aleksander Slominski

Publications and source records attributed to Aleksander Slominski.

17 recordsLinked to original sources

arXiv2025

TinyAC: Bringing Autonomic Computing Principles to Resource-Constrained Systems

Autonomic Computing (AC) is a promising approach for developing intelligent and adaptive self-management systems at the deep network edge. In this paper, we present the problems and challenges related to the use of AC for IoT devices. Our proposed hybrid approach bridges bottom-up intelligence (TinyML and on-device learning) and top-down guidance (LLMs) to achieve a scalable and explainable approach for developing intelligent and adaptive self-management tiny systems. Moreover, we argue that TinyAC systems require self-adaptive features to handle problems that may occur during their operation. Finally, we identify gaps, discuss existing challenges and future research directions.

cs.NI↗

arXiv2025

AI Factories: It's time to rethink the Cloud-HPC divide

The strategic importance of artificial intelligence is driving a global push toward Sovereign AI initiatives. Nationwide governments are increasingly developing dedicated infrastructures, called AI Factories (AIF), to achieve technological autonomy and secure the resources necessary to sustain robust local digital ecosystems. In Europe, the EuroHPC Joint Undertaking is investing hundreds of millions of euros into several AI Factories, built atop existing high-performance computing (HPC) supercomputers. However, while HPC systems excel in raw performance, they are not inherently designed for usability, accessibility, or serving as public-facing platforms for AI services such as inference or agentic applications. In contrast, AI practitioners are accustomed to cloud-native technologies like Kubernetes and object storage, tools that are often difficult to integrate within traditional HPC environments. This article advocates for a dual-stack approach within supercomputers: integrating both HPC and cloud-native technologies. Our goal is to bridge the divide between HPC and cloud computing by combining high performance and hardware acceleration with ease of use and service-oriented front-ends. This convergence allows each paradigm to amplify the other. To this end, we will study the cloud challenges of HPC (Serverless HPC) and the HPC challenges of cloud technologies (High-performance Cloud).

cs.DC↗

arXiv2025

Benchmarking of CPU-intensive Stream Data Processing in The Edge Computing Systems

Edge computing has emerged as a pivotal technology, offering significant advantages such as low latency, enhanced data security, and reduced reliance on centralized cloud infrastructure. These benefits are crucial for applications requiring real-time data processing or strict security measures. Despite these advantages, edge devices operating within edge clusters are often underutilized. This inefficiency is mainly due to the absence of a holistic performance profiling mechanism which can help dynamically adjust the desired system configuration for a given workload. Since edge computing environments involve a complex interplay between CPU frequency, power consumption, and application performance, a deeper understanding of these correlations is essential. By uncovering these relationships, it becomes possible to make informed decisions that enhance both computational efficiency and energy savings. To address this gap, this paper evaluates the power consumption and performance characteristics of a single processing node within an edge cluster using a synthetic microbenchmark by varying the workload size and CPU frequency. The results show how an optimal measure can lead to optimized usage of edge resources, given both performance and power consumption.

cs.DC↗

arXiv2022

Hybrid Serverless Computing: Opportunities and Challenges

In recent years, there has been a surge in the adoption of serverless computing due to the ease of deployment, attractive pay-per-use pricing, and transparent horizontal auto-scaling. At the same time, infrastructure advancements such as the emergence of 5G networks and the explosion of devices connected to Internet known as Internet of Things (IoT), as well as new application requirements that constrain where computation and data can happen, will expand the reach of Cloud computing beyond traditional data centers into Hybrid Cloud. Digital transformation due to the pandemic, which accelerated changes to the workforce and spurred further adoption of AI, is expected to accelerate and the emergent Hybrid Cloud market could potentially expand to over trillion dollars. In the Hybrid Cloud environment, driven by the serverless tenants there will be an increased need to focus on enabling productive work for application builders that are using a distributed platform including public clouds, private clouds, and edge systems. In this chapter we investigate how far serverless computing can be extended to become Hybrid Serverless Computing.

cs.DC↗

arXiv2021

On the Future of Cloud Engineering

Ever since the commercial offerings of the Cloud started appearing in 2006, the landscape of cloud computing has been undergoing remarkable changes with the emergence of many different types of service offerings, developer productivity enhancement tools, and new application classes as well as the manifestation of cloud functionality closer to the user at the edge. The notion of utility computing, however, has remained constant throughout its evolution, which means that cloud users always seek to save costs of leasing cloud resources while maximizing their use. On the other hand, cloud providers try to maximize their profits while assuring service-level objectives of the cloud-hosted applications and keeping operational costs low. All these outcomes require systematic and sound cloud engineering principles. The aim of this paper is to highlight the importance of cloud engineering, survey the landscape of best practices in cloud engineering and its evolution, discuss many of the existing cloud engineering advances, and identify both the inherent technical challenges and research opportunities for the future of cloud computing in general and cloud engineering in particular.

cs.DC↗

arXiv2021

Triggerflow: Trigger-based Orchestration of Serverless Workflows

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.

cs.DC↗

arXiv2021

Serverless Predictions: 2021-2030

Within the next 10 years, advances on resource disaggregation will enable full transparency for most Cloud applications: to run unmodified single-machine applications over effectively unlimited remote computing resources. In this article, we present five serverless predictions for the next decade that will realize this vision of transparency -- equivalent to Tim Wagner's Serverless SuperComputer or AnyScale's Infinite Laptop proposals.

cs.DC↗

arXiv2020

Triggerflow: Trigger-based Orchestration of Serverless Workflows

As more applications are being moved to the Cloud thanks to serverless computing, it is increasingly necessary to support native life cycle execution of those applications in the data center. But existing systems either focus on short-running workflows (like IBM Composer or Amazon Express Workflows) or impose considerable overheads for synchronizing massively parallel jobs (Azure Durable Functions, Amazon Step Functions, Google Cloud Composer). 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 built on top of Knative Eventing and Kubernetes technologies. We demonstrate that Triggerflow is a novel serverless building block capable of constructing different reactive schedulers (State Machines, Directed Acyclic Graphs, Workflow as code). We also validate that it can support high-volume event processing workloads, auto-scale on demand and transparently optimize scientific workflows.

cs.DC↗

arXiv2020

Serverless End Game: Disaggregation enabling Transparency

For many years, the distributed systems community has struggled to smooth the transition from local to remote computing. Transparency means concealing the complexities of distributed programming like remote locations, failures or scaling. For us, full transparency implies that we can compile, debug and run unmodified single-machine code over effectively unlimited compute, storage, and memory resources. We elaborate in this article why resource disaggregation in serverless computing is the definitive catalyst to enable full transparency in the Cloud. We demonstrate with two experiments that we can achieve transparency today over disaggregated serverless resources and obtain comparable performance to local executions. We also show that locality cannot be neglected for many problems and we present five open research challenges: granular middleware and locality, memory disaggregation, virtualization, elastic programming models, and optimized deployment. If full transparency is possible, who needs explicit use of middleware if you can treat remote entities as local ones? Can we close the curtains of distributed systems complexity for the majority of users?

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arXiv2019

Future of Computing is Boring (and that is exciting!) or How to get to Computing Nirvana in 20 years or less

We see a trend where computing becomes a metered utility similar to how the electric grid evolved. Initially electricity was generated locally but economies of scale (and standardization) made it more efficient and economical to have utility companies managing the electric grid. Similar developments can be seen in computing where scientific grids paved the way for commercial cloud computing offerings. However, in our opinion, that evolution is far from finished and in this paper we bring forward the remaining challenges and propose a vision for the future of computing. In particular we focus on changes in cost of computing and high cost of human time in comparison that indicates that saving developer time is the most important for future of computing.

cs.CY↗

arXiv2019

BPM for the masses: empowering participants of Cognitive Business Processes

Authoring, developing, monitoring, and analyzing business processes has requires both domain and IT expertise since Business Process Management tools and practices have focused on enterprise applications and not end users. There are trends, however, that can greatly lower the bar for users to author and analyze their own processes. One emerging trend is the attention on blockchains as a shared ledger for parties collaborating on a process. Transaction logs recorded in a standard schema and stored in the open significantly reduces the effort to monitor and apply advanced process analytics. A second trend is the rapid maturity of machine learning algorithms, in particular deep learning models, and their increasing use in enterprise applications. These cognitive technologies can be used to generate views and processes customized for an end user so they can modify them and incorporate best practices learned from other users' processes. Keywords: BPM, cognitive computing, blockchain, privacy, machine learning

cs.CY↗

arXiv2019

Towards Enterprise-Ready AI Deployments Minimizing the Risk of Consuming AI Models in Business Applications

The stochastic nature of artificial intelligence (AI) models introduces risk to business applications that use AI models without careful consideration. This paper offers an approach to use AI techniques to gain insights on the usage of the AI models and control how they are deployed to a production application. Keywords: artificial intelligence (AI), machine learning, microservices, business process

cs.CY↗

arXiv2019

The server is dead, long live the server: Rise of Serverless Computing, Overview of Current State and Future Trends in Research and Industry

Serverless computing -- an emerging cloud-native paradigm for the deployment of applications and services -- represents an evolution in cloud application development, programming models, abstractions, and platforms. It promises a real pay-as-you-go billing (with millisecond granularity) with no waste of resources, and lowers the bar for developers by asking them to delegate all their operational complexity and scalability to the cloud provider. Delivering on these promises comes at the expense of restricting functionality. In this article we provide an overview of serverless computing, its evolution, general architecture, key characteristics and uses cases that made it an attractive option for application development. Based on discussions with academics and industry experts during a series of organized serverless computing workshops (WoSC), we also identify the technical challenges and open problems.

cs.DC↗

arXiv2019

SUMMARIZED: Efficient Framework for Analyzing Multidimensional Process Traces under Edit-distance Constraint

Domains such as scientific workflows and business processes exhibit data models with complex relationships between objects. This relationship is typically represented as sequences, where each data item is annotated with multi-dimensional attributes. There is a need to analyze this data for operational insights. For example, in business processes, users are interested in clustering process traces into smaller subsets to discover less complex process models. This requires expensive computation of similarity metrics between sequence-based data. Related work on dimension reduction and embedding methods do not take into account the multi-dimensional attributes of data, and do not address the interpretability of data in the embedding space (i.e., by favoring vector-based representation). In this work, we introduce Summarized, a framework for efficient analysis on sequence-based multi-dimensional data using intuitive and user-controlled summarizations. We introduce summarization schemes that provide tunable trade-offs between the quality and efficiency of analysis tasks and derive an error model for summary-based similarity under an edit-distance constraint. Evaluations using real-world datasets show the effectives of our framework.

cs.DB↗

arXiv2018

Serving deep learning models in a serverless platform

Serverless computing has emerged as a compelling paradigm for the development and deployment of a wide range of event based cloud applications. At the same time, cloud providers and enterprise companies are heavily adopting machine learning and Artificial Intelligence to either differentiate themselves, or provide their customers with value added services. In this work we evaluate the suitability of a serverless computing environment for the inferencing of large neural network models. Our experimental evaluations are executed on the AWS Lambda environment using the MxNet deep learning framework. Our experimental results show that while the inferencing latency can be within an acceptable range, longer delays due to cold starts can skew the latency distribution and hence risk violating more stringent SLAs.

cs.DC↗

arXiv2017

Status of Serverless Computing and Function-as-a-Service(FaaS) in Industry and Research

This whitepaper summarizes issues raised during the First International Workshop on Serverless Computing (WoSC) 2017 held June 5th 2017 and especially in the panel and associated discussion that concluded the workshop. We also include comments from the keynote and submitted papers. A glossary at the end (section 8) defines many technical terms used in this report.

cs.DC↗

arXiv2017

Serverless Computing: Current Trends and Open Problems

Serverless computing has emerged as a new compelling paradigm for the deployment of applications and services. It represents an evolution of cloud programming models, abstractions, and platforms, and is a testament to the maturity and wide adoption of cloud technologies. In this chapter, we survey existing serverless platforms from industry, academia, and open source projects, identify key characteristics and use cases, and describe technical challenges and open problems.

cs.DC↗