The advent of 5G drives the growth of multi-access edge computing (MEC), a revolutionary paradigm that utilises edge resources to enable low-latency mobile access and support complex service execution. Deploying services across geographically distributed edge nodes challenges providers to optimise performance metrics like end-to-end latency and resource efficiency, impacting user experience, operational cost, and environmental footprint. The energy harvesting (EH) technology provides clean and renewable energy at the edge, promoting the MEC system to minimise the impacts on the environment. However, the integration of EH can introduce energy limits and uncertainty to the powered devices. In the context of service scheduling with data flow dependencies, we propose two offline and heuristic-based service placement algorithms that balance minimizing latency and maximizing resource efficiency with fast execution. The two algorithms, evaluated in a simulated environment using state-of-the-art workload benchmarks, achieve significant energy consumption improvements while maintaining comparable latency. Based on the designed algorithms, we take a step further by developing an online dynamic resource scheduling and service offloading approach for MEC systems with EH capabilities. Simulation results demonstrate that the proposed strategy effectively utilise the harvested energy while granting a low user-experienced latency and low operational cost.

Recognizing the severe health implications of dangerous pollutants emitted during a wildfire incident, we introduce a robust monitoring framework based on the Internet of Things (IoT) paradigm designed for real-time remote sensing of wildfire pollutants. The system is specifically adapted for measuring the wildfire health impact on firefighters and nearby residents. Its architecture comprises portable and stationary solutions and a Web application, enabling easy access to emission data and Air Quality Index (AQI) for interested parties and command & control centers. The portable solution benefits firefighters by providing real-time air quality information, aiding in decision-making for safety and efficient firefighting, while the stationary one contributes to mitigating the risk of potential evacuation in a region near the fire incident. Interested parties e.g., local authorities, command centers and environmental monitoring agencies, can easily and seamlessly integrate our system into their operations by initiating simple requests in our REST API.

In the interval scheduling problem, jobs have known start and end times (referred to as job intervals) and must be assigned to processing nodes for their whole duration. Although the problem originally stems from the resource allocation demands of resident processes in operating systems, it found a renewed interest in the Cloud context, both in IaaS and SaaS, since reservations for virtual machines and services often have known activation intervals. A common objective of interval scheduling is to minimize busy time of machines which relates (among others) to minimizing the number of machines participating in the computation. As a consequence, bin packing techniques have been applied in the past. In this paper we tackle the online version of the problem, whereby future job arrivals are unknown. We propose novel algorithms that work as a pre-processing step to any bin packing scheme by offering recommendations that are enforced in all packing decisions. Job overlaps are used to characterize pairwise job affinity and subsequently provide threshold based job allocation recommendations. Thresholds are calculated using lower bound theoretical analysis upon two extreme workloads (sparse and dense). Experimental evaluation using real world workloads illustrates the merits of our approach against state-of-the-art algorithms.

Workflow management is a widely studied research subject due to its criticality for the efficient execution of various processing activities towards concluding innovative applications. The ultimate goal is to eliminate the required time for delivering the final outcome considering the dependencies between workflow’s tasks. In this paper, we enhance the decision making of a scheduler with a batch oriented approach to deal with multiple workflows. A probabilistic data oriented approach combined with an infrastructure oriented scheme is provided to pay attention on dynamic environments where the underlying data are continuously updated trying to minimize the network overhead for migrating data. Workflows are mapped to the available datasets according to their data requirements, then, we combine the outcome with an optimization model upon the time and cost requirements of every placement. The performance of our model is revealed by a high number of experiments depicting the advantages in the network overhead.

Tasks management is a very interesting research topic for various application domains. Tasks may have the form of analytics or any other processing activities over the available data. One of the main concerns is to efficiently allocate and execute tasks to produce meaningful results that will facilitate any decision making. The advent of the Internet of Things (IoT) and Edge Computing (EC) defines new requirements for tasks management. Such requirements are related to the dynamic environment where IoT devices and EC nodes act and process the collected data. The statistics of data and the status of IoT/EC nodes are continuously updated. In this paper, we propose a demand- and uncertainty-driven tasks management scheme with the target to allocate the computational burden to the appropriate places. As the proper place, we consider the local execution of a task in an EC node or its offloading to a peer node. We provide the description of the problem and give details for its solution. The proposed mechanism models the demand for each task and efficiently selects the place where it will be executed. We adopt statistical learning and fuzzy logic to support the appropriate decision when tasks' execution is requested by EC nodes. Our experimental evaluation involves extensive simulations for a set of parameters defined in our model. We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly while concluding the most efficient allocation.

One of the fundamental problems encountered by large-scale computing systems, such as clusters and cloud, is to schedule a set of jobs submitted by the users. Each job is characterized by resource demands, as well as start and completion time. Each job must be scheduled to execute on a machine having the required capacity between the start and completion time (referred as interval) of the job. Each machine is defined by a parallelism parameter g that indicates the maximum number of jobs that can be processed by the machine, in parallel. The above problem is referred to as the interval scheduling problem with bounded parallelism. The objective is to minimize the total busy time of all machines. Majority of the solutions proposed in the literature consider homogeneous set of jobs and machines that is a simplified assumption as in practice, heterogeneous jobs and machines are frequently encountered. In this article, we tackle the aforesaid problem with a set of heterogeneous jobs and machines. A major contribution of our work is that the problem is addressed in a novel way by combining a graph-based approach and a dynamic programming approach which is based on a variation of bin packing problem. A greedy algorithm is also proposed by employing only a graph-based approach at the aim to reduce the computational complexity. Experimental results show that the proposed algorithms can significantly reduce the cumulative busy interval over all machines compared with state-of-the-art algorithms proposed in the literature.

The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you -go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the quality of solutions delivered by scheduling algorithms. In this paper, we present a dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS algorithm) for the management of scientific workflows in Cloud. Our model minimizes both the makespan and the monetary cost by dynamically selecting reliable or unreliable virtualized resources. For covering the uncertainty in decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing model of the resources that will host every task. We evaluate the performance of the proposed algorithm using real workflow applications being tested under the assumption of different probabilities regarding the revocation of unreliable resources. Numerical results depict the performance of the proposed approach and a comparative assessment reveals the position of the paper in the relevant literature.

Dyscalculia is a neurodevelopmental disorder that affects the ability of a child to learn arithmetic. Dyscalculia appears despite normal intelligence, proper schooling, adequate environment, socioeconomic status and motivation. The first aim of the present research protocol was to construct a battery of tests that can be delivered by computer in order to screen children's arithmetic skills. Our second aim was to develop a web application screener for dyscalculia that assesses children aged from 8–11 years old and that, to the best of our knowledge, does not exist. The hypothesis of the present study was that Greek students that are already diagnosed by paper-and-pencil tests as dyscalculic, will present lower performance and higher time latencies in the tasks of the aforementioned web application screener. A total of sixty, right handed children (30 male and 30 female, age range 8–11 years old) participated in this study. The students with disorders in mathematics (N=30, 15 male and 15 female) had a statement of dyscalculia issued after assessment at a Centre of Diagnosis, Assessment and Support, as required by Greek Law. The comparison group (N=30) was formed by pupils who attended the same classes with dyscalculics, presented typical academic performance according to their teachers' ratings and had been matched for age and gender with the children with disorder in mathematics. Three tasks were used for evaluating children's arithmetic ability: a calculation task, a task that evaluated their skills in understanding mathematical terminology, and an arithmetic problem solving task. Statistical analysis revealed that children with dyscalculia had statistically significant lower mean scores of correct answers and larger time latencies in all tasks compared to their average peers that participated in the comparison group. In conclusion, it must be highlighted that the web application screener for dyscalculia used in this study was found to be a feasible instrument for first-pass screening services and referral.