Debugging cloud services is increasingly challenging due to their distributed, dynamic, and scalable nature. Traditional methods struggle to handle large state spaces and the complex interactions between microservices, making it difficult to diagnose failures and identify critical components. This paper presents a Graph Neural Network (GNN)-based approach that enhances cloud service debugging by predicting system-level fault probabilities and providing interpretable insights into failure propagation. Our method models microservice interactions as graphs, where failures propagate probabilistically. Using Markov Decision Processes (MDPs), we simulate failure behaviors, capturing the probabilistic dependencies that influence system reliability. The trained GNN not only predicts fault probabilities but also identifies the most failure-prone microservices and explains their impact. We evaluate our approach on various service mesh structures, including feature-enriched, tree-structured, and general directed acyclic graph (DAG) architectures. Results indicate that our method is effective in the operational phase of cloud services, enabling proactive debugging and targeted optimization. This work represents a step toward more interpretable, reliable, and maintainable cloud infrastructures.

Abstract Performance and cost-effectiveness are sustained by efficient management of resources in cloud computing. Current autoscaling approaches, when trying to balance between the consumption of resources and QoS requirements, usually fall short and end up being inefficient and leading to service disruptions. The existing literature has primarily focuses on static metrics and/or proactive scaling approaches which do not align with dynamically changing tasks, jobs or service calls. The key concept of our approach is the use of statistical analysis to select the most relevant metrics for the specific application being scaled. We demonstrated that different applications require different metrics to accurately estimate the necessary resources, highlighting that what is critical for an application may not be for the other. The proper metrics selection for control mechanism which regulates the requried recources of application are described in this study. Introduced selection mechanism enables us to improve previously designed autoscaler by allowing them to react more quickly to sudden load changes, use fewer resources, and maintain more stable service QoS due to the more accurate machine learning models. We compared our method with previous approaches through a carefully designed series of experiments, and the results showed that this approach brings significant improvements, such as reducing QoS violations by up to 80% and reducing VM usage by 3% to 50%. Testing and measurements were conducted on the Hungarian Research Network (HUN-REN) Cloud, which supports the operation of over 300 scientific projects.

Apache Kafka is a widely used, distributed, open-source event streaming platform, which is available as a basic reference architecture for IoT use cases of the Autonomous Systems National Laboratory and other initiatives in Hungary, e.g.related to development of cyber-medical systems.This reference architecture offers a base for setting up a multi-node Kafka cluster on a Hungarian research infrastructure, ELKH Cloud.However, the capacity, accessibility or the availability of a given deployment using a single data center might not be sufficient.In this case Apache Kafka can be extended with additional nodes provisioned in the given cloud, but our solution also enables the expansion of the cluster by involving other cloud providers.In this paper we present our proposed approach for enhancing the existing basic reference architecture towards cloud continuum, i.e. allowing the supported IoT use cases to expand the resources of an already deployed Apache Kafka cluster with resources allocated even in third-party commercial cloud providers, such as Microsoft Azure and AWS leveraging on the functionalities of the Occopus cloud orchestrator.

Abstract Reference architectures for big data and machine learning include not only interconnected building blocks but important considerations (among others) for scalability, manageability and usability issues as well. Leveraging on such reference architectures, the automated deployment of distributed toolsets and frameworks on various clouds is still challenging due to the diversity of technologies and protocols. The paper focuses particularly on the widespread Apache Spark cluster with Jupyter as the particularly addressed framework, and the Occopus cloud‐agnostic orchestrator tool for automating its deployment and maintenance stages. The presented approach has been demonstrated and validated with a new, promising text classification application on the Hungarian academic research infrastructure, the OpenStack‐based MTA Cloud. The paper explains the concept, the applied components, and illustrates their usage with real use‐case measurements.

Graph computing now is popular in many areas, including social network and gene sequence alignment. Graph computing system and algorithm have a history prior to the use of graph databases and have a future that is not necessarily entangled with typical database concerns. With the data's increasing size, many distributed graph-computing systems have been developed in recent years to process and analyze massive graphs. Researchers pay more attention on the graph partition schemes on distributed environment. However, other researchers think a single system can avoid the network overhead and may have better performance even if the data size is too big for the memory space. With the rapid development of coprocessors, some researchers think it is promising to build a domain specific computer, just for graph computing. The proposed special issue of Concurrency and Computation: Practice and Experience contains revised and extended versions of selected best papers with respect to graph computing at the 21st IEEE International Conference on Parallel and Distributed Systems (ICPADS’16), which was held at Wuhan, China, on December 13-16, 2016. Established in 1992, ICPADS has been a major international forum for scientists, engineers, and users to exchange and share their experiences, new ideas, and latest research results on all aspects of parallel and distributed computing systems. The purpose of this special issue is to provide a comprehensive view into recent advances in systems software, algorithms, partition schemes, and even graph computer based on new advances in computer architecture and applications. The five selected papers are summarized as follows. The first paper, titled “An efficient iterative graph data processing framework based on bulk synchronous parallel model” by Liu et al,1 presents an efficient computational framework for graph data processing based on the bulk synchronous parallel model. Existing Pregel-like graph processing systems remains in its early stage, and there still exist many challenges with prohibitive superstep-synchronized overhead. Furthermore, the graph data partition strategy in these earlier graph systems fails to support load balancing, therefore causing the increase of network I/O overhead as the scale of graph data grows. Thus, this paper leverages a global synchronization mechanism to enhance the performance of graph computation. Meanwhile, a balanced hash-based graph partition mechanism is presented to optimize the large-scale graph data processing. The work has a real implementation upon on Pregrel system, which can better support a variety of graph analytics applications. The second paper, titled “An efficient iterative graph data processing framework based on bulk synchronous parallel model” by Linchen Yu,2 proposes an optimized scheduling system for parallelizing the programs in the Xen. Virtualization challenges the traditional CPU scheduling, leading that the spin lock in virtualized environment can be preempted by the VMM, increasing synchronization overhead and decreasing the performance of parallel programs. Many studies have proposed the co-scheduling to alleviate this problem. However, these earlier attempts are not suitable to non-parallel workloads with the CPU fragmentation problem as well. Therefore, a simultaneous optimization scheduling system, called CCHybrid, is proposed in the Xen virtualized environment. Results show the efficiency of CCHybrid over the traditional Xen Credit scheduler. The third paper, titled “ms-PoSW: A multi-server aided proof of shared ownership scheme for secure deduplication in cloud” by Xiong et al,3 introduces a novel concept of the Proof for securing client-side deduplication of the shared files. With the rapid development of cloud computing and big data technologies, collaborative cloud applications are inextricably linked to our daily life and, therefore, produce a large number of shared files, which is challenging for secure access and data duplication in cloud. This paper proposes a novel multiserver-aided PoSW scheme for collaborative cloud applications and propose a hybrid PoSW scheme to reduce the computational cost of the shared owner's client. Furthermore, a hybrid PoSW scheme is constructed to address the secure proof of hybrid cloud architectures. The fourth paper, titled “Sparse random compressive sensing based data aggregation in wireless sensor networks” by Yin et al,4 introduces a compressive data aggregation scheme. In wireless sensor networks, the increasingly expanding data volume has high spatial-temporal correlation. Although some earlier studies attempt to eliminate data redundancy, few can handle energy consumption and latency simultaneously. In this paper, the authors a delay-minimum energy-balanced data aggregation method, which can eliminate the redundancy among the readings and prolong the network lifetime. A sparse random matrix is adopted as a measurement matrix to balance communication cost. Particularly, each measurement can form an aggregation tree with minimum delay. Furthermore, a novel scheduling method is used to avoid information interference as well. The fifth paper, titled “Dynamic cluster strategy for hierarchical rollback-recovery protocols in MPI HPC applications” by Liao et al,5 proposes a dynamic cluster strategy to adapt to the runtime variation of communication pattern by using a prediction scheme. The idea comes from a fact that Hierarchical rollback-recovery protocols provide failure containment and reduce the amount of message to be logged, making it an attractive and scalable solution for fault tolerance even at a large scale. This paper shows how the communication pattern changes with the stages of application because MPI HPC applications scale up and become more complex. Therefore, to further increase the efficiency of hierarchical rollback-recovery protocols, the authors propose a dynamic cluster strategy (DCS) to adapt to the change of communication pattern. In contrast to the existing static process partition algorithms, this strategy adopts a prediction mechanism by using the clusters of processes obtained from prior part of applications in the succeeding part. Detailed experiments are then performed to evaluate the effectiveness and efficiency DCS at an extremely large scale. We hope that the readers would find the contents of this special issue interesting and further inspire them to look ahead into the challenges of designing, exploring, and exploiting graph analytics applications.

Internet of Things (IoT) is playing increasingly more fundamental role in wide range of sectors, including industry, agriculture, health care, and other services. In many cases, cloud computing serves as an elastic and efficient paradigm for implementing IoT back-ends. With the emerging lightweight software container technologies, the feasible approaches and design options for such IoT back-ends have been significantly enriched. In our paper we present the evolution of an IoT back-end, which is responsible for collecting (among others) meteorological, image and soil data from cultivated fields in order to enable precision farming. The different versions, namely the cloud VM-based and the Docker containerized variants, provide highly scalable and vendor independent (cloud provider agnostic) solutions, therefore they can form a robust and adaptive framework for further pilot applications areas, e.g. Connected Cars and Industry 4.0, as the presented benchmarks illustrate the throughput and other parameters of the current implementation in the paper.

In order to solve the emerging debugging issues in the field of metacomputing we defined the fundamental principles of an adaptive and integrated debugging and visualization tool: a novel metadebugger. The current prototype has been implemented in the Harness metacomputing framework.