With the increasing interest in Quantum Machine Learning, Quantum Neural Networks (QNNs) have emerged and gained significant attention. These models have, however, been shown to be notoriously difficult to train, which we hypothesize is partially due to the architectures, called ansatzes, that are hardly studied at this point. Therefore, in this paper, we take a step back and analyze ansatzes. We initially consider their expressivity, i.e., the space of operations they are able to express, and show that the closeness to being a 2-design, the primarily used measure, fails at capturing this property. Hence, we look for alternative ways to characterize ansatzes, unrelated to expressivity, by considering the local neighborhood of the model space, in particular, analyzing model distinguishability upon small perturbation of parameters. We derive an upper bound on their distinguishability, showcasing that QNNs using the Hardware Efficient Ansatz with few parameters are hardly discriminable upon update. Our numerical experiments support our bounds and further indicate that there is a significant degree of variability, which stresses the need for warm-starting or clever initialization. Altogether, our work provides an ansatz-centric perspective on training dynamics and difficulties in QNNs, ultimately suggesting that iterative training of small quantum models may not be effective, which contrasts their initial motivation.

Mobile devices offload latency-sensitive application tasks to edge servers to satisfy applications' Quality of Service (QoS) deadlines. Consequently, ensuring reliable offloading without QoS violations is challenging in distributed and unreliable edge environments with diverse resource and reliability levels. We propose FRESCO, a fast and reliable edge offloading framework that utilizes a blockchain-based reputation system, which enhances the reliability of offloading in the distributed edge. The distributed reputation system tracks the historical performance of edge servers, while blockchain through a consensus mechanism ensures that sensitive reputation information is secured against tampering. However, blockchain consensus typically has high latency, and therefore we employ a Hybrid Smart Contract (HSC) as areputation state managerthat automatically computes and stores reputation securely on-chain (i.e., on the blockchain) while allowing fast offloading decisions off-chain (i.e., outside of blockchain). Theoffloading decision engineuses a reputation score from HSC to derive fast offloading decisions, which are based on Satisfiability Modulo Theory (SMT). The SMT can formally guarantee a feasible solution that is valuable for latency-sensitive applications that require high reliability. With a combination of an on-chain HSC reputation state manager and an off-chain SMT decision engine, FRESCO offloads tasks to reliable servers without being hindered by blockchain consensus. In our experiment, FRESCO reduces response time by up to 7.86 times and saves energy by up to 5.4% compared to all baselines while minimizing QoS violations to 0.4% and achieving an average decision time of just 5.05 milliseconds.

As we enter the post-Moore era, we experience the rise of various non-von-Neumann-architectures to address the increasing computational demand for modern applications, with quantum computing being among the most prominent and promising technologies. However, this development creates a gap in current computer science curricula since most quantum computing lectures are strongly physics-oriented and have little intersection with the remaining curriculum of computer science. This fact makes designing an appealing course very difficult, in particular for non-physicists. Furthermore, in the academic community, there is consensus that quantum computers are going to be used only for specific computational tasks (e.g., in computational science), where hybrid systems - combined classical and quantum computers - facilitate the execution of an application on both quantum and classical computing resources. A hybrid system thus executes only certain suitable parts of an application on the quantum machine, while other parts are executed on the classical components of the system. To fully exploit the capabilities of hybrid systems and to meet future requirements in this emerging field, we need to prepare a new generation of computer scientists with skills in both distributed computing and quantum computing. To bridge this existing gap in standard computer science curricula, we designed a new lecture and exercise series on Hybrid Quantum-Classical Systems, where students learn how to decompose applications and implement computational tasks on a hybrid quantum-classical computational continuum. While learning the inherent concepts underlying quantum systems, students are obligated to apply techniques and methods they are already familiar with, making the entrance to the field of quantum computing comprehensive yet appealing and accessible to students of computer science.

The Internet of Things is gaining traction for sensing and monitoring outdoor environments such as water bodies, forests, or agricultural lands. Sustainable deployment of sensors for environmental sampling is a challenging task because of the spatial and temporal variation of the environmental attributes to be monitored, the lack of the infrastructure to power the sensors for uninterrupted monitoring, and the large continuous target environment despite the sparse and limited sampling locations. In this paper, we present an environment monitoring framework that deploys a network of sensors and gateways connected through low-power, long-range networking to perform reliable data collection. The three objectives correspond to the optimization of information quality, communication capacity, and sustainability. Therefore, the proposed environment monitoring framework consists of three main components: (i) to maximize the information collected, we propose an optimal sensor placement method based on QR decomposition that deploys sensors at information-and communication-critical locations; (ii) to facilitate the transfer of big streaming data and alleviate the network bottleneck caused by low bandwidth, we develop a gateway configuration method with the aim to reduce the deployment and communication costs; and (iii) to allow sustainable environmental monitoring, an energy-aware optimization component is introduced. We validate our method by presenting a case study for monitoring the water quality of the Ergene River in Turkey. Detailed experiments subject to real-world data show that the proposed method is both accurate and efficient in monitoring a large environment and catching up with dynamic changes.

Since we are entering the Post-Moore Law era and consequently the limit of Von Neumann's architecture, the scientific community is looking for alternatives to satisfy the growing computing power demands of scientific applications. Quantum computing promises to achieve a computational advantage over the classic Von Neumann architecture. However, the limited capabilities of current noisy intermediate-scale quantum (NISQ) devices require quantum computers to interoperate with classic systems, forming the so-called hybrid quantum systems. Research on hybrid quantum systems led to the design of Variational Quantum Algorithms, currently the most promising way to move towards quantum advantage. However, execution time and accuracy of variational quantum algorithms are affected by different hyperparameters, including selected cost functions and parametrized quantum circuits. Consequently, providing developers with methods to select the right set of parameters is of paramount importance. In this work, we provide a formal method for the selection of hyperparameters in variational quantum algorithms, which will support quantum algorithms developers in the design of quantum applications, and evaluate it on a real-world scientific application, showing a reduction of error up to 31%.

Near real-time edge analytics requires dealing with the rapidly growing amount of data, limited resources, and high failure probabilities of edge nodes. Therefore, data replication is of vital importance to meet SLOs such as service availability and failure resilience. Consequently, specific input datasets, requested by on-demand analytics (e.g., object detection), can be present at different locations over time. This can prevent exploitation of data locality and timely decision-making processes. State-of-the-art solutions for on-demand edge analytics placement either fail in providing low-latency access to user-requested input data or do not consider data locality. We propose SEA-LEAP (Self-adaptive and Locality-aware Edge Analytics Placement), a framework including a new mechanism for tracking data movements, on top of which we devise a generic control mechanism. SEA-LEAP enables on-the-fly placement of on-demand analytics considering the most appropriate dataset location that minimizes overall analytics requests execution time. We conduct experiments using real-world (i) object detection application, (ii) image datasets as input, (iii) self-designed benchmarks, and (iv) heterogeneous edge infrastructure using Kubernetes. Experimental results show the ability to efficiently deploy on-demand analytics and reduce total latency by 65.85 percent on average by performing adaptive data movements, indicating a promising solution for edge multi-cluster and hybrid environments.

Wireless sensor networks have wide applications in monitoring applications. However, sensors’ energy and processing power constraints, as well as the limited network bandwidth, constitute significant obstacles to near-real-time requirements of modern IoT applications. Offloading sensor data on an edge computing infrastructure instead of in-cloud or in-network processing is a promising solution to these issues. Nevertheless, due to geographical dispersion, ad-hoc deployment, and rudimentary support systems compared to cloud data centers, reliability is a critical issue. This forces edge service providers to deploy a huge amount of edge nodes over an urban area, with catastrophic effects on environmental sustainability. In this work, we propose ARES, a two-stage optimization algorithm for sustainable and reliable deployment of edge nodes in an urban area. Initially, ARES applies multi-objective optimization to identify a set of Pareto-optimal solutions for transmission time and energy; then it augments these candidates in the second stage to identify a solution that guarantees the desired level of reliability using a dynamic Bayesian network based reliability model. ARES is evaluated through simulations using data from the urban area of Vienna. Results demonstrate that it can achieve a better trade-off between transmission time, energy-efficiency, and reliability than the state-of-the-art solutions.

OLAP is an authoritative analytical tool in the emerging big data analytics context, with particular regards to the target distributed environments (e.g., Clouds). Here, privacy-preserving OLAP-based big data analytics is a critical topic, with several amenities in the context of innovative big data application scenarios like smart cities, social networks, bio-informatics, and so forth. The goal is that of providing privacy preservation during OLAP analysis tasks, with particular emphasis on the privacy of OLAP aggregates. Following this line of research, in this paper we provide a deep contribution on experimenting and assessing a state-of-the-art distributed privacy-preserving OLAP framework, named as SPPOLAP, whose main benefit is that of introducing a completely-novel privacy notion for OLAP data cubes.

Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. The effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, overcommitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57% with a QoS increase below 6%.

Transferring and processing huge amounts of data in the cloud can violate the low latency requirements of modern IoT applications, considering underlying network infrastructure limitations. Edge data analytics is a promising solution. However, edge resources have usually less computational capabilities than cloud nodes, resulting in a higher failure rate of IoT systems. Consequently, near-real-time decisions are often based on limited and incomplete data. State-of-the-art solutions, such as operational/workload flows, data reduction, reconstruction, focus mostly on resource and network optimization, while approaches for incomplete data recovery employ a single specific method, despite diverse data characteristics. Data quality impact on accuracy of the decision-making processes is often neglected. We propose EDMFrame, a framework featuring a generic mechanism for recovery of multiple gaps in incomplete datasets, using single-technique recovery (STR) and multiple-technique recovery (MTR) involving projection recovery maps (PRMs). We further devise an adaptive storage management mechanism for reducing data stored at the edge, keeping only the data necessary for predictive analytics. We conduct experiments using time series from smart buildings, (i) automatically recovering various multiple gaps and reducing errors up to 65.48 percent with MTR compared to STR; (ii) reducing amounts of data stored to 39.9 percent on average, keeping prediction accuracy around 98.83 percent.

In recent years, Mobile Cloud Computing (MCC) has been proposed as a solution to enhance the capabilities of user equipment (UE), such as smartphones, tablets and laptops. However, offloading to conventional Cloud introduces significant execution delays that are inconvenient in case of near real-time applications. Mobile Edge Computing (MEC) has been proposed as a solution to this problem. MEC brings computational and storage resources closer to the UE, enabling to offload near real-time applications from the UE while meeting strict latency requirements. However, it is very difficult for Edge providers to determine how many Edge nodes are required to provide MEC services, in order to guarantee a high QoS and to maximize their profit. In this paper, we investigate the static provisioning of Edge nodes in a area representing a cellular network in order to guarantee the required QoS to the user without affecting providers' profits. First, we design a model for MEC offloading considering user satisfaction and provider's costs. Then, we design a simulation framework based on this model. Finally, we design a multi-objective algorithm to identify a deployment solution that is a trade-off between user satisfaction and provider profit. Results show that our algorithm can guarantee a user satisfaction above 80%, with a profit for the provider of up 4 times their cost.

In recent years, Mobile Cloud Computing (MCC) has been proposed to increase battery lifetime of mobile devices. However, offloading on Cloud infrastructures may be infeasible for latency critical applications, due to the geographical distribution of Cloud data centers that increases offloading time. In this paper, we investigate the use of Mobile Edge Cloud Offloading (MECO), namely offloading to a heterogeneous computing infrastructure featuring both Cloud and Edge nodes, where Edge nodes are geographically closer to the mobile device. We evaluate improvements of MECO in comparison with MCC for objectives such as applications' runtime, mobile device battery lifetime and cost for the user. Afterwards, we propose the Edge Cloud Heuristic Offloading (ECHO) approach to find a trade-off solution between the aforementioned objectives, according to user's preferences. We evaluate our approach by simulating offloading of Directed Acyclic Graphs (DAGs) representing mobile applications through the use of Monte-Carlo simulations. The results show that (1) MECO can reduce application runtime by up to 70.7% and cost by up to 70.6% in comparison to MCC and (2) ECHO allows user to select a trade-off solution with at most 18% MAPE for runtime, 16% for cost and 0.5% for battery lifetime, according to user's preferences.

The Internet of Things (IoT) has attracted significant attention from both academia and industry, thanks to applications such as smart cities, smart buildings and intelligent traffic management. These systems rely on data, collected from IoT devices, that are sent to the cloud for analytics. Data are either used for near real-time decisions or stored for long-term analysis. However, in highly distributed IoT systems, missing or invalid data may appear because of different reasons including sensor failures, monitoring system failures and network failures. Analyzing incomplete datasets can lead to inaccurate results and imprecise decisions, with negative effects on the target systems. Also, due to the increasing size of such systems and the consequently increasing amount of data generated from sensors, recovery of incomplete datasets for analytics on the cloud is often infeasible, due to the limited bandwidth available and the strict latency constraints of IoT applications. We propose a novel semi-automatic recursive mechanism for recovery of incomplete datasets on the edge that is closer to the source of data. This mechanism enables efficient recovery of incomplete datasets employing different forecasting techniques for multiple gaps, based on user specifications. We evaluate our approach on datasets coming from the context of smart buildings and smart homes. The experimental results show that our approach is able to identify multiple gaps, then recover incomplete datasets, decreasing forecasting error by up to 82.68%, and reducing running time by up to 52.38%.

Nowadays, data analytics is utilized on edge based systems to perform near real-time decisions in proximity of the user. When performing near real-time decisions on the Edge, we need historical data to perform accurate data analytics. Since storage capacities on the Edge are limited, we are faced with a challenge to balance the quantity of data stored with the quality of near real-time decisions. In this paper, we present a three-layer architecture model for data storage management on the Edge including an adaptive algorithm that dynamically finds a trade-off between providing high forecast accuracy necessary for efficient real-time decisions, and minimizing the amount of data stored in the space-limited storage. We focus on time series data, typical in the context of sensor-based monitoring in IoT environments. By using the proposed approach it is possible to reduce the amount of stored data by an average 80.27% without affecting specified threshold for prediction accuracy.

Due to the difficulty of employing real data centres' infrastructure for assessing the effectiveness of energy-aware algorithms, many researchers resort on using Cloud simulators. These tools require precise and detailed models for virtualized data centres in order to deliver accurate results. In recent years, many models have been proposed, but most of them either do not consider energy consumption related to virtual machine(VM) migration or ignore some of the energy-impacting components (e.g. CPU, network, storage). In this paper, we propose a new model for data centre energy consumption that takes into account these omitted components. We implement this model in a framework that combines two Cloud simulators: GroudSim that provides the Cloud management side, and DISSECT-CF that provides the internal infrastructure side. We evaluated our model in a comprehensive set of scenarios and obtained an accuracy between 8% and 22% for instantaneous power consumption, and between 8% and 25% for energy consumption.

Energy consumption has become a significant issue for data centres. Assessing their consumption requires precise and detailed models. In the latter years, many models have been proposed, but most of them either do not consider energy consumption related to virtual machine migration or do not consider the variation of the workload on (1) the virtual machines (VM) and (2) the physical machines hosting the VMs. In this paper, we show that omitting migration and workload variation from the models could lead to misleading consumption estimates. Then, we propose a new model for data centre energy consumption that takes into account the previously omitted model parameters and provides accurate energy consumption predictions for paravirtualised virtual machines running on homogeneous hosts. The new model's accuracy is evaluated with a comprehensive set of operational scenarios. With the use of these scenarios we present a comparative analysis of our model with similar state-of-the-art models for energy consumption of VM Migration, showing an improvement up to 24% in accuracy of prediction.