The massive amounts of data generated by the Industrial Internet of Things (IIoT) require considerable processing power, which increases carbon emissions and energy usage, and we need sustainable solutions to enable flexible manufacturing. Serverless computing shows potential for meeting this requirement by scaling idle containers to zero energy-efficiency and cost, but this will lead to a cold start delay. Most solutions rely on idle containers, which necessitates dynamic request time forecasting and container execution monitoring. Furthermore, Artificial Intelligence of Things (AIoT) can provide autonomous and sustainable solutions by combining IIoT with artificial intelligence (AI) to solve this problem. Therefore, we develop a new testbed, CAPTAIN, to facilitate AI-based co-simulation of scalable and flexible serverless computing in IIoT environments. The AI module in the CAPTAIN framework employs random forest (RF) and light gradient-boosting machine (LightGBM) models to optimize cold start frequency and prevent cold starts based on their prediction results. The proxy module additionally monitors the client-server network and constantly updates the AI module training dataset via a message queue. Finally, we evaluated the proxy module’s performance using a predictive maintenance-based real-world IIoT application and the AI module’s performance in a realistic serverless environment using a Microsoft Azure dataset. The AI module of the CAPTAIN outperforms baselines in terms of cold start frequency, computational time with 0.5 ms, energy consumption with 1161.0 joules, and CO2 emissions with 32.25e-05 gCO2. The CAPTAIN testbed provides a co-simulation of sustainable and scalable serverless computing environments for AIoT-enabled predictive maintenance in Industry 4.0.

Containerized Edge computing offers lightweight, reliable, and quick solutions to latency-critical Machine Learning (ML) and Deep Learning (DL) applications. Existing solutions considering multiple Quality of Service (QoS) parameters either overlook the intricate relation of QoS parameters or pose significant scheduling overheads. Furthermore, reactive decisionmaking can damage Edge servers at peak load, incurring escalated costs and wasted computations. Resource provisioning, scheduling, and ML model selection substantially influence energy consumption, user-perceived accuracy, and delayoriented Service Level Agreement (SLA) violations. Addressing contrasting objectives and QoS simultaneously while avoiding server faults is highly challenging in the exposed heterogeneous and resource-constrained Edge continuum. In this work, we propose the EdgeAIBus framework that offers a novel joint container management and ML model selection algorithm based on Importance Weighted Actor-Learner Architecture to optimize energy, accuracy, SLA violations, and avoid server faults. Firstly, Patch Time Series Transformer (PatchTST) is utilized for CPU usage predictions of Edge servers for its 8.51% Root Mean Squared Error and 5.62% Mean Absolute Error. Leveraging pipelined predictions, EdgeAIBus conducts consolidation, resource oversubscription, and ML/DL model switching with possible migrations to conserve energy, maximize utilization and user-perceived accuracy, and reduce SLA violations. Simulation results show EdgeAIBus oversubscribed 110% cluster-wide CPU with real usage up to 70%, conserved 14 CPU cores, incurred less than 1% SLA violations with 2.54% drop in inference accuracy against industry-led Model Switching Balanced load and Google Kubernetes Optimized schedulers. Google Kubernetes Engine experiments demonstrate 80% oversubscription, 14 CPU cores conservation, 1% SLA violations, and 3.81% accuracy loss against the counterparts. Finally, constrained setting experiment analysis shows that PatchTST and EdgeAIBus can produce decisions within 100ms in a 1-core and 1 GB memory device.

Serverless edge computing decreases unnecessary resource usage on end devices with limited processing power and storage capacity. Despite its benefits, serverless edge computing's zero scalability is the major source of the cold start delay, which is yet unsolved. This latency is unacceptable for time-sensitive Internet of Things (IoT) applications like autonomous cars. Most existing approaches need containers to idle and use extra computing resources. Edge devices have fewer resources than cloud-based systems, requiring new sustainable solutions. Therefore, we propose an AI-powered, sustainable resource management framework called ATOM for serverless edge computing. ATOM utilizes a deep reinforcement learning model to predict exactly when cold start latency will happen. We create a cold start dataset using a heart disease risk scenario and deploy using Google Cloud Functions. To demonstrate the superiority of ATOM, its performance is compared with two different baselines, which use the warm-start containers and a two-layer adaptive approach. The experimental results showed that although the ATOM required more calculation time of 118.76 seconds, it performed better in predicting cold start than baseline models with an RMSE ratio of 148.76. Additionally, the energy consumption and$CO_{2}$emission amount of these models are evaluated and compared for the training and prediction phases.

The concept of modernizing outdated systems in critical infrastructure through digital transformation has been a widely discussed topic nowadays. Following the transition of energy systems, the attention has now shifted towards digitalizing the water distribution systems. These systems are large-scale but outdated systems that frequently encounter various issues and upgrading them would enable easier to identify issues and provide smoother, more efficient service. However, this process requires cautious planning and guidance to ensure that the generated data is reliable, and the system remains operational during the transition. Hence, the primary objective of this paper is to propose a formal model based on ternary relational semantics that can guide the digital transformation of water distribution networks. The proposed model provides a flexible transformation process while making the system generate reliable data. Additionally, this paper demonstrates the application of the proposed model by developing a proof of concept based on a real-world scenario.

Gab is an online social network often associated with the alt-right political movement and users barred from other networks. It presents an interesting opportunity for research because near-complete data is available from day one of the network's creation. In this paper, we investigate the evolution of the user interaction graph, that is the graph where a link represents a user interacting with another user at a given time. We view this graph both at different times and at different timescales. The latter is achieved by using sliding windows on the graph which gives a novel perspective on social network data. The Gab network is relatively slowly growing over the period of months but subject to large bursts of arrivals over hours and days. We identify plausible events that are of interest to the Gab community associated with the most obvious such bursts. The network is characterised by interactions between 'strangers' rather than by reinforcing links between 'friends'. Gab usage follows the diurnal cycle of the predominantly US and Europe based users. At off-peak hours the Gab interaction network fragments into sub-networks with absolutely no interaction between them. A small group of users are highly influential across larger timescales, but a substantial number of users gain influence for short periods of time. Temporal analysis at different timescales gives new insights above and beyond what could be found on static graphs.

DNS is a vital component for almost every networked application. Originally it was designed as an unencrypted protocol, making user security a concern. DNS-over-HTTPS (DoH) is the latest proposal to make name resolution more secure.

As a distributed system, Hadoop heavily relies on the network to complete data processing jobs. While Hadoop traffic is perceived to be critical for job execution performance, the actual behaviour of Hadoop network traffic is still poorly understood. This lack of understanding greatly complicates research relying on Hadoop workloads. In this paper, we explore Hadoop traffic through experimentation. We analyse the generated traffic of multiple types of MapReduce jobs, with varying input sizes, and cluster configuration parameters. As a result, we present Keddah, a toolchain for capturing, modelling and reproducing Hadoop traffic, for use with network simulators. Keddah can be used to create empirical Hadoop traffic models, enabling reproducible Hadoop research in more realistic scenarios.

Headers are a critical part of HTTP, and it has been shown that they are increasingly subject to middlebox manipulation. Although this is well known, little is understood about the general regional and network trends that underpin these manipulations. In this paper, we collect data on thousands of networks to understand how they intercept HTTP headers in-the-wild. Our analysis reveals that 25% of measured ASes modify HTTP headers. Beyond this, we witness distinct trends among different regions and AS types; e.g., we observe high numbers of cache headers in poorly connected regions. Finally, we perform an in-depth analysis of the types of manipulations and how they differ across regions.

Public cloud infrastructures provide flexible hosting for web application providers, but the rented virtual machines (VMs) often offer unpredictable performance to the deployed applications. Understanding cloud performance is challenging for application providers, as clouds provide limited information that would help them have expectations about their application performance. In this paper we present a technique to measure the performance of cloud applications, based on observations of the application latency. We treat the cloud application as a black box, making no assumption about the underlying platform. From our measurements, we can observe the varying performance provided by the different VM profiles across well-known commercial cloud platforms. We also identify a trade-off between the responsiveness and the load of the measured servers, which can help application providers in their deployment and provisioning.

Public clouds have democratised the access to analytics for virtually any institution in the world. Virtual machines (VMs) can be provisioned on demand to crunch data after uploading into the VMs. While this task is trivial for a few tens of VMs, it becomes increasingly complex and time consuming when the scale grows to hundreds or thousands of VMs crunching tens or hundreds of TB. Moreover, the elapsed time comes at a price: the cost of provisioning VMs in the cloud and keeping them waiting to load the data. In this paper we present a big data provisioning service that incorporates hierarchical and peer-to-peer data distribution techniques to speed-up data loading into the VMs used for data processing. The system dynamically mutates the sources of the data for the VMs to speed-up data loading. We tested this solution with 1000 VMs and 100 TB of data, reducing time by at least 30 percent over current state of the art techniques. This dynamic topology mechanism is tightly coupled with classic declarative machine configuration techniques (the system takes a single high-level declarative configuration file and configures both software and data loading). Together, these two techniques simplify the deployment of big data in the cloud for end users who may not be experts in infrastructure management.

In the last years, large-scale graph processing has gained increasing attention, with most recent systems placing particular emphasis on latency. One possible technique to improve runtime performance in a distributed graph processing system is to reduce network communication. The most notable way to achieve this goal is to partition the graph by minimizing the number of edges that connect vertices assigned to different machines, while keeping the load balanced. However, real-world graphs are highly dynamic, with vertices and edges being constantly added and removed. Carefully updating the partitioning of the graph to reflect these changes is necessary to avoid the introduction of an extensive number of cut edges, which would gradually worsen computation performance. In this paper we show that performance degradation in dynamic graph processing systems can be avoided by adapting continuously the graph partitions as the graph changes. We present a novel highly scalable adaptive partitioning strategy, and show a number of refinements that make it work under the constraints of a large-scale distributed system. The partitioning strategy is based on iterative vertex migrations, relying only on local information. We have implemented the technique in a graph processing system, and we show through three real-world scenarios how adapting graph partitioning reduces execution time by over 50% when compared to commonly used hash-partitioning.

Researchers have capitalized on microblogging services, such as Twitter, for detecting and monitoring real world events. Existing approaches have based their conclusions on data collected by monitoring a set of pre-defined keywords. In this paper, we show that this manner of data collection risks losing a significant amount of relevant information. We then propose an adaptive crawling model that detects emerging popular hashtags, and monitors them to retrieve greater amounts of highly associated data for events of interest. The proposed model analyzes the traffic patterns of the hashtags collected from the live stream to update subsequent collection queries. To evaluate this adaptive crawling model, we apply it to a dataset collected during the 2012 London Olympic Games. Our analysis shows that adaptive crawling based on the proposed Refined Keyword Adaptation algorithm collects a more comprehensive dataset than pre-defined keyword crawling, while only introducing a minimum amount of noise.

Mining large-scale graphs is increasingly important, as it provides a powerful way of extracting useful information from real-world data. Efficient processing of that volume of information requires partitioning the graph across multiple nodes in a distributed system. However, traversing edges across distributed partitions results in significant performance penalty due to the additional cost of inter-partition communication. Minimising the number of cut edges between partitions improves communication cost between neighbouring vertices; balanced graph partitioning is required for load balancing [2].

The runtime management of the infrastructure providing service-based systems is a complex task, up to the point where manual operation struggles to be cost effective. As the functionality is provided by a set of dynamically composed distributed services, in order to achieve a management objective multiple operations have to be applied over the distributed elements of the managed infrastructure. Moreover, the manager must cope with the highly heterogeneous characteristics and management interfaces of the runtime resources. With this in mind, this paper proposes to support the configuration and deployment of services with an automated closed control loop. The automation is enabled by the definition of a generic information model, which captures all the information relevant to the management of the services with the same abstractions, describing the runtime elements, service dependencies, and business objectives. On top of that, a technique based on satisfiability is described which automatically diagnoses the state of the managed environment and obtains the required changes for correcting it (e.g., installation, service binding, update, or configuration). The results from a set of case studies extracted from the banking domain are provided to validate the feasibility of this proposal.

Today we live in an environment surrounded with networked converging devices. Human computer interactions are becoming personalized and a new concept of a global and cross-domain platform is emerging to exploit the full potential of the network in all business areas. In this convergence process, the software platform should be able to personalize itself dynamically in devices according to the context. OSAmI-Commons, an ITEA2 project for developing an open-source common approach to such a dynamic service-based platform, allows any type of device to connect and exchange information and services. OSAMI consortium is contributing to defining the foundations of a cross-platform open-services ecosystem. The sustainability of this platform is an objective beyond the project duration.