The increased usage of Internet of Things devices at the network edge and the proliferation of microservice-based applications create new orchestration challenges in Edge computing. These include detecting overutilized resources and scaling out overloaded microservices in response to surging requests. This work presents ADApt, an extension of the ADA-PIPE tool developed in the DataCloud project, using the monitoring data related to Edge devices, detecting the utilization-based anomalies of resources (e.g., processing or memory), investigating the scalability in microservices, and adapting the application executions. To reduce the overutilization bottleneck, we first explore monitored devices executing microservices over various time slots, detecting overutilization-based processing events, and scoring them. Thereafter, based on the memory requirements, ADApt predicts the processing requirements of the microservices and estimates the number of replicas running on the overutilized devices. The prediction results show that the gradient boosting regression-based replica prediction reduces the MAE, MAPE, and RMSE compared to other models. Moreover, ADApt can estimate the number of replicas for each microservice close to the actual data without any prediction and reduce the CPU utilization of the device by 14 % − 28 %.

PID2022-137461NB-C32 and PID2023-151065OB-I00 projects, funded by the MICIU/AEI/10.13039/501100011033 and by ESF+ (“NextGenerationEU/PRTR”). PPJIA2023-025 project, funded by the University of Granada. Program of mobility stays for professors and researchers in foreign higher education and research centres, funded by the Spanish Ministry of Universities under grant CAS22/00332. P.S.-C. was supported by “Predoctores 2021” (PREDOC_01229) fellowship from the Ministry of Economic Transformation, Industry, Knowledge and Universities of the Regional Government of Andalusia .

The increasing size of graph structures in real-world applications, such as distributed computing networks, social media, or bioinformatics, requires appropriate sampling algorithms that simplify them while preserving key properties. Unfortunately, predicting the outcome of graph sampling algorithms is challenging due to their irregular complexity and randomized properties. Therefore, it is essential to identify appropriate graph features and apply suitable models capable of estimating their sampling outcomes. In this paper, we compare three machine learning (ML) models for predicting the divergence of five metrics produced by twelve node, edge, and traversal-based graph sampling algorithms: degree distribution (D3), clustering coefficient distribution (C2D2), hop-plots distribution (HPD2) (including the largest connected component (HPD2C)), and execution time. We use these prediction models to recommend suitable sampling algorithms for each metric and conduct mutual information analysis to extract relevant graph features. Experiments on six large real-world graphs demonstrate a prediction error under 20 % in C2D2and HPD2prediction for most algorithms despite their relatively high dissimilarity with the training data. Sampling algorithm recommendations on ten real-world graphs show higher hits@3 for D3 and C2D2and comparable results for HPD2and HPD2Ccompared to the K-best baseline method. Finally, ML models show superior runtime recommendations compared to baseline methods, with hits@3 over 86 % for synthetic and real graphs and hits@ 1 over 60 % for small graphs. These findings are promising for algorithm recommendation systems, particularly when balancing quality and runtime preferences.

The accelerating growth of modern distributed applications with low delivery deadlines leads to a paradigm shift towards the multi-tier computing continuum. However, the geographical dispersion, heterogeneity, and availability of the continuum resources may result in failures and quality of service degradation, significantly negating its advantages and lowering users’ satisfaction. We propose in this paper a proactive application placement (PROS) method relying on distributed coordination to prevent the quality of service violations through service-level agreements on the computing continuum. PROS employs a sigmoid function with adaptive weights for the different parameters to predict the service level agreement assurance of devices based on their past credentials and current capabilities. We evaluate PROS using two application workloads with different traffic stress levels up to 90 million services on a real testbed with 600 heterogeneous instances deployed over eight geographical locations. The results show that PROS increases the success rate by 7%–33%, reduces the response time by 16%–38%, and increases the deadline satisfaction rate by 19%–42% compared to two related work methods. A comprehensive simulation study with 1000 devices and a workload of up to 670 million services confirm the scalability of the results.

The challenging demands for the next generation of the Internet of Things have led to a massive increase in edge computing and network virtualization technologies. While there is vast potential for research in these areas, managing complex adaptive infrastructure is difficult, and experiments with real hardware are tedious to set up. Furthermore, proposed solutions often require expensive hardware or labor-intensive procedures to replicate and build on these ideas. With our C3-Edge testbed, we address these challenges and propose a novel approach for automated edge testbed setup with a low-cost software-defined network and adaptive infrastructure configuration. We validated the efficiency of our approach on a real-world computing continuum infrastructure. The evaluation results confirm that our flexible approach is suitable for all but the most bandwidth-intensive applications.

Fog computing platforms became essential for deploying low-latency applications at the network's edge. However, placing and managing time-critical applications over a Fog infrastructure with many heterogeneous and resource-constrained devices over a dynamic network is challenging. This paper proposes an incremental multilayer resource-aware partitioning (M-RAP) method that minimizes resource wastage and maximizes service placement and deadline satisfaction in a dynamic Fog with many application requests. M-RAP represents the heterogeneous Fog resources as a multilayer graph, partitions it based on the network structure and resource types, and constantly updates it upon dynamic changes in the underlying Fog infrastructure. Finally, it identifies the device partitions for placing the application services according to their resource requirements, which must overlap in the same low-latency network partition. We evaluated M-RAP through extensive simulation and two applications executed on a real testbed. The results show that M-RAP can place 1.6 times as many services, satisfy deadlines for 43% more applications, lower their response time by up to 58%, and reduce resource wastage by up to 54% compared to three state-of-the-art methods.

Today's distributed computing infrastructures en-compass complex workflows for real-time data gathering, transferring, storage, and processing, quickly overwhelming centralized cloud centers. Recently, the computing continuum that federates the Cloud services with emerging Fog and Edge devices represents a relevant alternative for supporting the next-generation data processing workflows. However, eminent challenges in automating data processing across the computing continuum still exist, such as scheduling heterogeneous devices across the Cloud, Fog, and Edge layers. We propose a new scheduling algorithm called C3-MATCH, based on matching theory principles, involving two sets of players negotiating different utility functions: 1) workflow microservices that prefer computing devices with lower data processing and queuing times; 2) computing continuum devices that prefer microservices with corresponding resource requirements and less data transmission time. We evaluate$C^{3}$-MATCH using real-world road sign inspection and sentiment analysis workflows on a federated computing continuum across four Cloud, Fog, and Edge providers. Our combined simulation and real execution results reveal that$C^{3}$-MATCH achieves up to 67% lower completion time than three state-of-the-art methods with 10 ms-1000 ms higher transmission time.

The Computing Continuum, covering Cloud, Fog, and Edge systems, promises to provide on-demand resource-as-a-service for Internet applications with diverse requirements, ranging from extremely low latency to high-performance processing. However, eminent challenges in automating the resources man-agement of Big Data pipelines across the Computing Continuum remain. The resource management and adaptation for Big Data pipelines across the Computing Continuum require significant research effort, as the current data processing pipelines are dynamic. In contrast, traditional resource management strategies are static, leading to inefficient pipeline scheduling and overly complex process deployment. To address these needs, we propose in this work a scheduling and adaptation approach implemented as a software tool to lower the technological barriers to the management of Big Data pipelines over the Computing Continuum. The approach separates the static scheduling from the run-time execution, em-powering domain experts with little infrastructure and software knowledge to take an active part in the Big Data pipeline adaptation. We conduct a feasibility study using a digital healthcare use case to validate our approach. We illustrate concrete scenarios supported by demonstrating how the scheduling and adaptation tool and its implementation automate the management of the lifecycle of a remote patient monitoring, treatment, and care pipeline.

Cloud applications often benefit from deployment on serverless technology Function-as-a-Service (FaaS), which may instantly spawn numerous functions and charges users for the period when serverless functions are running. Maximum benefit is achieved when functions are orchestrated in a workflow or function choreographies (FCs). However, many provider limitations specific for FaaS, such as maximum concurrency or duration often increase the failure rate, which can severely hamper the execution of entire FCs. Current support for resilience is often limited to function retries or try-catch, which are applicable within the same cloud region only. To overcome these limitations, we introduce rAFCL, a middleware platform that maintains reliability of complex FCs in federated clouds. In order to support resilient FC execution under rAFCL, our model creates an alternative strategy for each function based on the required availability specified by the user. Alternative strategies are not restricted to the same cloud region, but may contain alternative functions across five providers, invoked concurrently in a single alternative plan or executed subsequently in multiple alternative plans. With this approach, rAFCL offers flexibility in terms of cost-performance trade-off. We evaluated rAFCL by running three real-life applications across three cloud providers. Experimental results demonstrated that rAFCL outperforms the resilience of AWS Step Functions, increasing the success rate of entire FC by 53.45%, while invoking only 3.94% more functions with zero wasted function invocations. rAFCL significantly improves availability of entire FCs to almost 1 and survives even after massive failures of alternative functions.

The Edge computing extension of the Cloud services towards the network boundaries raises important placement challenges for IoT applications running in a heterogeneous environment with limited computing capacities.Unfortunately, existing works only partially address this challenge by optimizing a single or aggregate objective (e.g., response time), and not considering the edge devices' mobility and resource constraints.To address this gap, we propose a novel mobility-aware multi-objective IoT application placement (mMAPO) method in the Cloud -Edge Continuum that optimizes completion time, energy consumption, and economic cost as conflicting objectives.mMAPO utilizes a Markov model for predictive analysis of the Edge device mobility and constrains the optimization to devices that do not frequently move through the network.We evaluate the quality of the mMAPO placements using simulation and real-world experimentation on two IoT applications.Compared to related work, mMAPO reduces the economic cost by 28 percent and decreases the completion time by 80 percent while maintaining a stable energy consumption.

Latency-sensitive and bandwidth-intensive stream processing applications are dominant traffic generators over the Internet network. A stream consists of a continuous sequence of data elements, which require processing in nearly real-time. To improve communication latency and reduce the network congestion, Fog computing complements the Cloud services by moving the computation towards the edge of the network. Unfortunately, the heterogeneity of the new Cloud – Fog continuum raises important challenges related to deploying and executing data stream applications. We explore in this work a two-sided stable matching model called Cloud – Fog to data stream application matching (CODA) for deploying a distributed application rep-resented as a workflow of stream processing microservices on heterogeneous computing continuum resources. In CODA, the application microservices rank the continuum resources based on their microservice stream processing time, while resources rank the stream processing microservices based on their residual bandwidth. A stable many-to-one matching algorithm assigns microservices to resources based on their mutual preferences, aiming to optimize the complete stream processing time on the application side, and the total streaming traffic on the resource side. We evaluate the CODA algorithm using simulated and real-world Cloud – Fog experimental scenarios. We achieved 11-45% lower stream processing time and 1.3-20% lower streaming traffic compared to related state-of-the-art approaches.

Video streaming became an undivided part of the Internet. To efficiently utilise the limited network bandwidth it is essential to encode the video content. However, encoding is a computationally intensive task, involving high-performance resources provided by private infrastructures or public clouds. Public clouds, such as Amazon EC2, provide a large portfolio of services and instances optimized for specific purposes and budgets. The majority of Amazon’s instances use x86 processors, such as Intel Xeon or AMD EPYC. However, following the recent trends in computer architecture, Amazon introduced Arm-based instances that promise up to 40% better cost performance ratio than comparable x86 instances for specific workloads. We evaluate in this paper the video encoding performance of x86 and Arm instances of four instance families using the latest FFmpeg version and two video codecs. We examine the impact of the encoding parameters, such as different presets and bitrates, on the time and cost for encoding. Our experiments reveal that Arm instances show high time and cost saving potential of up to 33.63% for specific bitrates and presets, especially for the x264 codec. However, the x86 instances are more general and achieve low encoding times, regardless of the codec.

Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem.

Nowadays, massive amounts of data are acquired, transferred, and analyzed nearly in real-time by utilizing a large number of computing and storage elements interconnected through high-speed communication networks. However, one issue that still requires research effort is to enable efficient monitoring of applications and infrastructures of such complex systems. In this paper, we introduce an Integer Linear Programming (ILP) model called M3AT for optimized assignment of monitoring agents and aggregators on large-scale computing systems. We identified a set of requirements from three representative data-intensive applications and exploited them to define the model's input parameters. We evaluated the scalability of M3AT using the Constraint Integer Programing (SCIP) solver with default configuration based on synthetic data sets. Preliminary results show that the model provides optimal assignments for subsystems composed of up to 200 monitoring agents with complex I/O policies, while keeping the number of aggregators constant and demonstrates variable sensitivity with respect to the scale of monitoring data aggregators and limitation policies imposed.

Infrastructure-as-a-service (IaaS) Clouds concurrently accommodate diverse sets of user requests, requiring an efficient strategy for storing and retrieving virtual machine images (VMIs) at a large scale. The VMI storage management require dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning. Nevertheless, existing techniques to facilitate VMI management overlook VMI semantics (i.e at the level of base image and software packages) with either restricted possibility to identify and extract reusable functionalities or with higher VMI publish and retrieval overheads. In this paper, we design, implement and evaluate Expelliarmus, a novel VMI management system that helps to minimize storage, publish and retrieval overheads. To achieve this goal, Expelliarmus incorporates three complementary features. First, it makes use of VMIs modelled as semantic graphs to expedite the similarity computation between multiple VMIs. Second, Expelliarmus provides a semantic aware VMI decomposition and base image selection to extract and store non-redundant base image and software packages. Third, Expelliarmus can also assemble VMIs based on the required software packages upon user request. We evaluate Expelliarmus through a representative set of synthetic Cloud VMIs on the real test-bed. Experimental results show that our semantic-centric approach is able to optimize repository size by 2.2 - 16 times compared to state-of-the-art systems (e.g. IBM's Mirage and Hemera) with significant VMI publish and slight retrieval performance improvement.

Summary New software engineering technologies facilitate development of applications from reusable software components, such as Virtual Machine and container images (VMI/CIs). Key requirements for the storage of VMI/CIs in public or private repositories are their fast delivery and cloud deployment times. ENTICE is a federated storage facility for VMI/CIs that provides optimisation mechanisms through the use of fragmentation and replication of images and a Pareto Multi‐Objective Optimisation (MO) solver. The operation of the MO solver is, however, time‐consuming due to the size and complexity of the metadata, specifying various non‐functional requirements for the management of VMI/CIs, such as geolocation, operational cost, and delivery time. In this work, we address this problem with a new semantic approach, which uses an ontology of the federated ENTICE repository, knowledge base, and constraint‐based reasoning mechanism. Open Source technologies such as Protégé, Jena Fuseki, and Pellet were used to develop a solution. Two specific use cases, (1) repository optimisation with offline and (2) online redistribution of VMI/CIs, are presented in detail. In both use cases, data from the knowledge base are provided to the MO solver. It is shown that Pellet‐based reasoning can be used to reduce the input metadata size used in the optimisation process by taking into consideration the geographic location of the VMI/CIs and the provenance of the VMI fragments. It is shown that this process leads to reduction of the input metadata size for the MO solver by up to 60% and reduction of the total optimisation time of the MO solver by up to 68%, while fully preserving the quality of the solution, which is significant.

During the last decade, Cloud computing has efficiently exploited the economy of scale by providing low cost computational and storage resources over the Internet, eventually leading to consolidation of computing resources into large data centers. However, the nascent of the highly decentralized Internet of Things (IoT) technologies that cannot effectively utilize the centralized Cloud infrastructures pushes computing towards resource dispersion. Fog computing extends the Cloud paradigm by enabling dispersion of the computational and storage resources at the edge of the network in a close proximity to where the data is generated. In its essence, Fog computing facilitates the operation of the limited compute, storage and networking resources physically located close to the edge devices. However, the shared complexity of the Fog and the influence of the recent IoT trends moving towards deploying and interconnecting extremely large sets of pervasive devices and sensors, requires exploration of adaptive Fog architectural approaches capable of adapting and scaling in response to the unpredictable load patterns of the distributed IoT applications. In this paper we introduce a promising new nature- inspired Fog architecture, named SmartFog, capable of providing low decision making latency and adaptive resource management. By utilizing novel algorithms and techniques from the fields of multi- criteria decision making, graph theory and machine learning we model the Fog as a distributed intelligent processing system, therefore emulating the function of the human brain.

Summary The use of virtual machines (VMs) in Cloud computing provides various benefits in the overall software engineering lifecycle. These include efficient elasticity mechanisms resulting in higher resource utilization and lower operational costs. The VMs as software artifacts are created using provider‐specific templates, called virtual machine images (VMI), and are stored in proprietary or public repositories for further use. However, some technology‐specific choices can limit the interoperability among various Cloud providers and bundle the VMIs with nonessential or redundant software packages, leading to increased storage size, prolonged VMI delivery, stagnant VMI instantiation, and ultimately vendor lock‐in. To address these challenges, we present a set of novel functionalities and design approaches for efficient operation of distributed VMI repositories, specifically tailored for enabling (1) simplified creation of lightweight and size optimized VMIs tuned for specific application requirements; (2) multi‐objective VMI repository optimization; and (3) efficient reasoning mechanism to help optimizing complex VMI operations. The evaluation results confirm that the presented approaches can enable VMI size reduction by up to 55%, while trimming the image creation time by 66%. Furthermore, the repository optimization algorithms can reduce the VMI delivery time by up to 51% and cut down the storage expenses by 3%. Moreover, by implementing replication strategies, the optimization algorithms can increase the system reliability by 74%.

In the recent years, overlay networks have emerged as a crucial platform for deployment of various distributed applications. Many of these applications rely on data redundancy techniques, such as erasure coding, to achieve higher fault tolerance. However, erasure coding applied in large scale overlay networks entails various overheads in terms of storage, latency and data rebuilding costs. These overheads are largely attributed to the selected erasure coding scheme and the encoded chunk placement in the overlay network. This paper explores a multi-objective optimization approach for identifying appropriate erasure coding schemes and encoded chunk placement in overlay networks. The uniqueness of our approach lies in the consideration of multiple erasure coding objectives such as encoding rate and redundancy factor, with overlay network performance characteristics like storage consumption, latency and system reliability. Our approach enables a variety of tradeoff solutions with respect to these objectives to be identified in the form of a Pareto front. To solve this problem, we propose a novel two stage multiobjective evolutionary algorithm, where the first stage determines the optimal set of encoding schemes, while the second stage optimizes placement of the corresponding encoded data chunks in overlay networks of varying sizes. We study the performance of our method by generating and analyzing the Pareto optimal sets of tradeoff solutions. Experimental results demonstrate that the Pareto optimal set produced by our multi-objective approach includes and even dominates the chunk placements delivered by a related state-of-the-art weighted sum method.

Virtualization is a key enabling technology in Cloud computing that allows users to run multiple virtual machines (VMs) with their own application environment on top of physical hardware. It permits scaling up and down of applications by elastic on-demand provisioning of VMs in response to their variable load to achieve increased utilization efficiency at a lower operational cost, while guaranteeing the desired level of Quality of Service (QoS) to the end-users. Typically, VMs are created using provider-specific templates that are stored in proprietary repositories, leading to provider lock-in and hampering portability or simultaneous usage of multiple federated Clouds. In this context, optimization at the level of the virtual machine image is needed both by the applications and by the underlying Cloud providers for improved resource usage, operational costs, elasticity, storage use, and other desired QoS-related features. To overcome those issues, the ENTICE project researches and creates a novel VM repository and operational environment for federated Cloud infrastructures. There exists a large variety of industrial applications that can strongly benefit by the ENTICE environment. In this paper we present an interesting selection of complementary use cases that drive the definition of the essential requirements for the ENTICE environment, and more importantly, validate the introduced innovations.

Summary Bioinformatics applications frequently involve high‐dimensional model building or classification problems that require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. Thus, feature selection has become an important issue on these applications. Moreover, several approaches for supervised and unsupervised feature selections as a multi‐objective optimization problem have been recently proposed to cope with issues on performance evaluation of classifiers and models. As parallel processing constitutes an important tool to reach efficient approaches that make it possible to tackle complex problems within reasonable computing times, in this paper, alternatives for the cooperation of subpopulations in multi‐objective evolutionary algorithms have been identified and classified, and several procedures have been implemented and evaluated on some synthetic and Brain–Computer Interface datasets. The results show different improvements achieved in the solution quality and speedups, depending on the cooperation alternative and dataset. We show alternatives that even provide superlinear speedups with only small reductions in the solution quality, besides another cooperation alternative that improves the quality of the solutions with speedups similar to, or only slightly higher than, the speedup obtained by the parallel fitness evaluation in a master‐worker implementation (the alternative used as reference that behaves as the corresponding sequential multi‐objective approach). Copyright © 2015 John Wiley & Sons, Ltd.

Feature selection is required in many applications that involve high dimensional model building or classification problems. Many bioinformatics applications belong to this type. Recently, some approaches for supervised and unsupervised feature selection as a multi-objective optimization problem have been proposed. As the performance of unsupervised classification is evaluated through the quality of the obtained groups or clusters in the data set to be classified, it is difficult to define a suitable objective function that drives the selection of the features. Thus, several evaluation measures, and thus multi-objective clustering characterization, could provide a suitable set of features for unsupervised classification. In this paper, we consider the parallel implementation of a multi-objective feature selection that makes it possible to apply it to complex classification problems such as those having many features to select, and specifically high-dimensional data sets with much more features than data items. In this paper, we propose master-worker implementations of two different parallel evolutionary models, the parallel computation of the cost functions for the individuals in the population, and the parallel execution of evolutionary multi-objective procedures on subpopulations. The experiments accomplished on different benchmarks, including some related with feature selection in classification of EEG (Electroencephalogram) signals for BCI (Brain Computer Interface) applications, show the benefits of parallel processing not only for decreasing the running time, but also for improving the solution quality.

The interconnection network is a crucial part of high-performance computer systems. It significantly determines parallel system performance as well as the development and the operating cost. In this paper we suggest efficient and scalable hierarchical multi-ring interconnection network architecture. For building up the interconnection network we have designed adequate switch architecture and implemented “step-back-on-blocking” flow control algorithm. The architectural model has been verified and communicational performance parameters have been evaluated on the basis of numerous simulation experiments conducted in the OMNeT++ simulation environment.