arXiv subjects

Miloš Simić

Publications and source records attributed to Miloš Simić.

8 recordsLinked to original sources

arXiv2026

Bridging Design and Execution: A Visual Graph Editor for Edge and Cloud Workflows

Designing modular applications for edge and cloud computing environments involves coordinating multiple computational kernels, shared data, and event-driven execution. This paper presents a domain-specific visual graph editor that enables users to model such applications using a unified interface. The editor supports three first-class abstractions: kernels, representing computational units; shared memory nodes, modeling distributed data; and event triggers, capturing execution dependencies. Users can construct graphs visually, configure node properties, and connect elements to define data and control flow. The resulting graphs are automatically serialized into machine-readable representations (JSON/XML) and can be passed to an execution API, bridging the gap between design-time modeling and runtime deployment. The editor's graph-based model improves reasoning about data sharing, execution order, and dependencies, particularly in distributed edge and cloud scenarios. To demonstrate its applicability, we discuss a federated learning workflow, where local training kernels interact with a shared global model through event-driven coordination. Compared to traditional workflow editors and general-purpose diagramming tools, the proposed system provides explicit execution semantics, modularity, and direct deployability. This work lays the foundation for visual orchestration of modular computation in distributed environments and offers extensibility for user-defined kernels, event types, and alternative execution backends, enabling future exploration of complex distributed applications.

cs.DC↗

arXiv2026

A monitoring system for collecting and aggregating metrics from distributed clouds

Applications requiring real-time processing of large volumes of data have been the main driver for rethinking the traditional cloud, giving rise to novel cloud models. Distributed cloud (DC) is a model that allows users to dynamically create and dispose of strategically located ad-hoc clouds that contain resources best tailored to their needs. It is essential for this model to provide a high degree of observability for it to be viable in real-world scenarios. In this paper, we present the design and implementation of a monitoring system that collects metrics from DCs and makes them accessible to diverse clients. Agents running on nodes are responsible for collecting machine-, container-, and application-level metrics. During the health-check protocol, that data is transferred from the node to the DC's control plane running inside the cloud. There, it is persisted and served via multiple APIs, including a streaming API. Moreover, node metrics are aggregated for every DC in order to provide a more comprehensive view of the system's state.

cs.DC↗

arXiv2024

Misconfiguration prevention and error cause detection for distributed-cloud applications

Major software failures are reported to be due to misconfiguration. As manual configuration is too error-prone to be deemed a reliable strategy for dynamic and complex systems, automated configuration management has become a standard. Countermeasures against misconfiguration can be focused on prevention or, if failure already occurred, detection. Configuration is often used as a broad term for any set of parameters or system states that dictate how an application will behave, but in this paper, we only focus on parameters consumed on process startup, usually from configuration files. Our objective is to enhance configuration management processes in environments based on the distributed cloud model, a novel cloud model that allows dynamic allocation of strategically located resources. The two mechanisms we propose are configuration validation using schemas and configuration version control with support for detecting differences between configuration versions. Our solution reduces the risk of incorrect configuration as schemas prevent any non-compliant configuration from reaching applications. However, if failure still occurs because the schema was incomplete or a valid configuration revealed existing software bugs, the version control system can precisely locate configuration changes that triggered the failure.

cs.DC↗

arXiv2024

Access control in a distributed micro-cloud environment

Proliferation of systems that generate enormous amounts of data and operate in real time has led researchers to rethink the current organization of the cloud. Many proposed solutions consist of a number of small data centers in the vicinity of data sources. That creates a highly complex environment, where strict access control is essential. Recommended access control models frequently belong to the Attribute-Based Access Control (ABAC) family. Flexibility and dynamic nature of these models come at the cost of high policy management complexity. In this paper, we explore whether the administrative overhead can be lowered with resource hierarchies. We propose an ABAC model that incorporates user and object hierarchies. We develop a policy engine that supports the model and present a distributed cloud use case. Findings in this paper suggest that resource hierarchies simplify the administration of ABAC models, which is a necessary step towards their further inclusion in real-world systems.

cs.CR↗

arXiv2024

Configuration management in the distributed cloud

Owing to their cost-effectiveness and flexibility, cloud services have been the default choice for the deployment of innumerable software systems over the years. However, novel paradigms are beginning to emerge, as the cloud can't meet the requirements of increasingly many latency- and privacy-sensitive applications. The distributed cloud model, being one of the attempts to overcome these challenges, places a distributed cloud layer between device and cloud layers, intending to bring resources closer to data sources. As application code should be kept separate from its configuration, especially in highly dynamic cloud environments, there is a need to incorporate configuration primitives in future distributed cloud platforms. In this paper, we present the design and implementation of a configuration management subsystem for an open-source distributed cloud platform. Our solution spreads across the cloud and distributed cloud layers and supports configuration versioning, selective dissemination to nodes in the distributed cloud layer, and logical isolation via namespaces. Our work serves as a demonstration of the feasibility and usability of the new cloud-extending models and provides valuable insight into one of the possible implementations.

cs.DC↗

arXiv2020

How to Control the Error Rates of Binary Classifiers

The traditional binary classification framework constructs classifiers which may have good accuracy, but whose false positive and false negative error rates are not under users' control. In many cases, one of the errors is more severe and only the classifiers with the corresponding rate lower than the predefined threshold are acceptable. In this study, we combine binary classification with statistical hypothesis testing to control the target error rate of already trained classifiers. In particular, we show how to turn binary classifiers into statistical tests, calculate the classification p-values, and use them to limit the target error rate.

stat.ML↗

arXiv2020

Testing for Normality with Neural Networks

In this paper, we treat the problem of testing for normality as a binary classification problem and construct a feedforward neural network that can successfully detect normal distributions by inspecting small samples from them. The numerical experiments conducted on small samples with no more than 100 elements indicated that the neural network which we trained was more accurate and far more powerful than the most frequently used and most powerful standard tests of normality: Shapiro-Wilk, Anderson-Darling, Lilliefors and Jarque-Berra, as well as the kernel tests of goodness-of-fit. The neural network had the AUROC score of almost 1, which corresponds to the perfect binary classifier. Additionally, the network's accuracy was higher than 96% on a set of larger samples with 250-1000 elements. Since the normality of data is an assumption of numerous techniques for analysis and inference, the neural network constructed in this study has a very high potential for use in everyday practice of statistics, data analysis and machine learning in both science and industry.

stat.ML↗

arXiv2019

How to Estimate the Ability of a Metaheuristic Algorithm to Guide Heuristics During Optimization

Metaheuristics are general methods that guide application of concrete heuristic(s) to problems that are too hard to solve using exact algorithms. However, even though a growing body of literature has been devoted to their statistical evaluation, the approaches proposed so far are able to assess only coupled effects of metaheuristics and heuristics. They do not reveal us anything about how efficient the examined metaheuristic is at guiding its subordinate heuristic(s), nor do they provide us information about how much the heuristic component of the combined algorithm contributes to the overall performance. In this paper, we propose a simple yet effective methodology of doing so by deriving a naive, placebo metaheuristic from the one being studied and comparing the distributions of chosen performance metrics for the two methods. We propose three measures of difference between the two distributions. Those measures, which we call BER values (benefit, equivalence, risk) are based on a preselected threshold of practical significance which represents the minimal difference between two performance scores required for them to be considered practically different. We illustrate usefulness of our methodology on the example of Simulated Annealing, Boolean Satisfiability Problem, and the Flip heuristic.

cs.AI↗