Global climate change is a significant environmental concern, and reducing greenhouse gas emissions is crucial to mitigating this issue. Moreover, there is a need to exploit a prediction-based method to assess the future requirements of applications and (re-)schedule them with the aim of reducing completion time and energy consumption. Therefore, we consider the stochastic requirements of users and investigate an Energy-aware Prediction-based scheduling of dataflow processing on the cloud, fog, and edge method, named EPreMatch, for microservice scaling by applying a machine learning (ML) model based on gradient boosting regression (GBR) and scheduling due to ranking and matching game principles. Firstly, EPreMatch predicts the number of microservice replicas using GBR. Then, the ranking method orders the microservice replicas and devices based on completion times and energy consumption. Thereafter, the EPreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis reveals lower completion times, energy consumption, and CO2emission compared to a related prediction-based scheduling method.

Global climate change is a significant environmental concern, and reducing greenhouse gas emissions is crucial to mitigating this issue. Moreover, there is a need to exploit a prediction-based method to assess the future requirements of applications and (re-)schedule them with the aim of reducing completion time and energy consumption. Therefore, we consider the stochastic requirements of users and investigate an Energy-aware Prediction-based scheduling of dataflow processing on the cloud, fog, and edge method, named EPreMatch, for microservice scaling by applying a machine learning (ML) model based on gradient boosting regression (GBR) and scheduling due to ranking and matching game principles. Firstly, EPreMatch predicts the number of microservice replicas using GBR. Then, the ranking method orders the microservice replicas and devices based on completion times and energy consumption. Thereafter, the EPreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis reveals lower completion times, energy consumption, and CO2emission compared to a related prediction-based scheduling method.

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 %.

The environmental impact of video streaming is gaining more attention due to its growing share in global internet traffic and energy consumption. To support accurate and transparent sustainability assessments, we present SEED (Streaming Energy and Emission Dataset): an open dataset for estimating energy usage and CO2emissions in adaptive video streaming. SEED comprises 500 video segments. It provides segment-level measurements of energy consumption and emissions for two primary stages: provisioning, which encompasses encoding and storage on cloud infrastructure; and end-user consumption, including network interface retrieval, video decoding, and display on end-user devices. The dataset covers multiple codecs (AVC, HEVC), resolutions, bitrates, cloud instance types, and geographic regions, reflecting real-world variations in computing efficiency and regional carbon intensity. By combining empirical benchmarks with component-level energy models, SEED enables detailed analysis and supports the development of energy- and emission-aware adaptive bitrate (ABR) algorithms. The dataset is publicly available at: https://github.com/cd-athena/SEED.

Extending cloud computing towards fog and edge computing yields a heterogeneous computing environment known as computing continuum. In recent years, increasing demands for scalable, cost-effective, and streamlined maintenance services have led application and service providers to prefer serverless models over monolithic and serverful processing. However, orchestrating the computing continuum in complex application workflows of serverless functions, each with distinct requirements, introduces new resource management and scheduling challenges. This paper introduces an orchestration service for concurrent serverless workflow processing across the computing continuum called HEFTLess. HEFTLess uses two deployment modes tailored to serve each workflow function: predeployed and undeployed. We formulate the problem as a Binary Integer Linear Programming (BLP) optimization model, incorporating multiple groups of constraints to minimize the overall completion time and monetary cost of executing workflow batches. Inspired by the Heterogeneous Earliest Finish Time (HEFT) algorithm, we propose a lightweight serverless workflow scheduling heuristic to cope with the high optimization time complexity in polynomial time. We evaluate HEFTLess using two machine learning-based serverless workflows on a real computing continuum testbed, including AWS Lambda and 325 combined on-promise and cloud instances from Exoscale, distributed across five geographic locations. The experimental results confirm that HEFTLess outperforms state-of-the-art methods in terms of both workflow batch completion time and cost.

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.

Video streaming is the dominating traffic in today’s data-sharing world. Media service providers stream video content for their viewers, while worldwide users create and distribute videos using mobile or video system applications that significantly increase the traffic share. We propose a multilayer and pipeline encoding on the computing continuum (MPEC2) method that addresses the key technical challenge of high-price and computational complexity of video encoding. MPEC2 splits the video encoding into several tasks scheduled on appropriately selected Cloud and Fog computing instance types that satisfy the media service provider and user priorities in terms of time and cost. In the first phase, MPEC2 uses a multilayer resource partitioning method to explore the instance types for encoding a video segment. In the second phase, it distributes the independent segment encoding tasks in a pipeline model on the underlying instances. We evaluate MPEC2 on a federated computing continuum encompassing Amazon Web Services (AWS) EC2 Cloud and Exoscale Fog instances distributed in seven geographical locations. Experimental results show that MPEC2 achieves 24% faster completion time and 60% lower cost for video encoding compared to resource allocation related methods. When compared with baseline methods, MPEC2 yields 40%– 50% lower completion time and 5%–60% reduced total cost.

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.

Vehicular Named Data Network (VNDN) uses NDN as an underlying communication paradigm to realize intelligent transportation system applications. Content communication is the essence of NDN, which is primarily carried out through content naming, forwarding, intrinsic content security, and most importantly the in-network caching. In vehicular networks, vehicles on the road communicate with other vehicles and/or infrastructure network elements to provide passengers a reliable, efficient, and infotainment-rich commute experience. Recently, different aspects of NDN have been investigated in vehicular networks and in vehicular social networks (VSN); however, in this paper, we investigate the in-network caching, realized in NDN through the content store (CS) data structure. As the stale contents in CS do not just occupy cache space, but also decrease the overall performance of NDN-driven VANET and VSN applications, therefore the size of CS and the content lifetime in CS are primary issues in VNDN communications. To solve these issues, we propose a simple yet efficient multi-metric CS management mechanism through cache replacement (M2CRP). We consider the content popularity, relevance, freshness, and distance of a node to devise a set of algorithms for selection of the content to be replaced in CS in the case of replacement requirement. Simulation results show that our multi-metric strategy outperforms the existing cache replacement mechanisms in terms of Hit Ratio.