Stavros Souravlas

Quantum computers have already shown high potential in solving complex computational problems much more efficiently compared to the typical conventional supercomputers. One major challenge in the field of quantum computing is to implement the major operations of the conventional computers using the available quantum hardware (quantum gates). Such operations are the arithmetic operations as well as decoding and multiplexing. An obvious burden is the absence of quantum gates that directly implement the fundamental Boolean operations like AND and OR. Moreover, care should be taken in order to reduce the costs involved when implementing a quantum circuit. In this paper, we present an oracle-based approach to implement quantum decoding and multiplexing in a joint circuit. The use of oracles necessarily implies that some type of quantum programming will be used. A quantum program is a process organized in a sequence of computations whose data elements are selected from the set of the known quantum gates. In our approach, the oracles are used as pre-programmed black boxes that determine if a datum should be directed from the input to the output. The important idea behind our scheme is that the oracles operate in parallel, therefore their selection decisions are taken simultaneously, while the approaches found in the literature implement sequential circuits with feedbacks from the outputs to inputs that burden the overall performance. We have used Qiskit open-source software development kit to verify the correctness of our design through extensive simulations.

In the big data era which we have entered, the development of smart scheduler has become a necessity. A Distributed Stream Processing System (DSPS) has the role of assigning processing tasks to the available resources (dynamically or not) and route streaming data between them. Smart and efficient task scheduling can reduce latencies and eliminate network congestions. The most commonly used scheduler is the default Storm scheduler, which has proven to have certain disadvantages, like the inability to handle system changes in a dynamic environment. In such cases, rescheduling is necessary. This paper is an extension of a previous work on dynamic task scheduling. In such a scenario, some type of rescheduling is necessary to have the system working in the most efficient way. In this paper, we extend our previous works Souravlas and Anastasiadou (Appl Sci 10(14):4796, 2020); Souravlas et al. (Appl Sci 11(1):61, 2021) and present a mathematical model that offers better balance and produces fewer communication steps. The scheduler is based on the idea of generating larger sets of communication steps among the system nodes, which we call superclasses. Our experiments have shown that this scheme achieves better balancing and reduces the overall latency.

The detection of community structures is a very crucial research area. The problem of community detection has received considerable attention from a large portion of the scientific community. More importantly, these articles are spread across a large number of different disciplines, from computer science, to statistics, and social sciences. The analysis of modern social networks becomes rather cumbersome, as their size and number keeps growing larger and larger. Moreover, in the modern communities, users participate in large number of groups. From the network perspective, efficient methods should be developed to automatically identify overlapping communities, that is, communities with overlapping nodes. In this work, we use a probabilistic network model to characterize and identify linked communities with common nodes. The innovative idea in this work is that the communities are represented as Markovian networks with continuously changing states. Each state represents the number of users within a cluster, that have specific characteristic classes. Based on the current state, we introduce a fast, linear on the number of newly added users, approach to estimate the probability of each cluster to be homogeneous in terms of sets of user characteristics and to determine how well the new user fit within a community. Because of the linear computations involved, our proposed probabilistic model can detect communities and overlaps with low execution time and high accuracy, as shown in our experimental results. The experimental results have shown that our probabilistic scheme executes faster and provides more robust communities compared to competitive schemes.

The Colored Petri net (CPN) has evolved into a complete modeling language, which is based on the object-oriented paradigm. Although this language is quite complete and can be used to accurately model a wide variety of systems, it seems that there is a lack of certain modeling rules, which can be used to generate models based on real datasets. This paper is a first attempt to incorporate sequences of real system events and translate them into sequences of Petri net transitions. Then, well-defined modeling rules control the model generation based on these transitions. The specific entities that take part in each of the real system events, as well as their attributes are also considered. The models produced using real data are structured because, in the majority of real systems, each incurring event usually affects a part of the system. Then, this effect is propagated to the entire system. Therefore, it is much easier to divide the entire model into distinct parts when real data are used and then interconnect these parts to build an entire model. This is a very important aspect when modeling very large systems. To test our approach, we used the real data from a courier company to generate its model. Our simulation results have shown that we managed to obtain quite accurate results through the model produced by the actual data.

Social networks reflect, to a wide extent, the real-world relationships that allow users to connect and share information. The number of people that interact in social networks keeps increasing, and the devices used are equipped with more and more computational capacities. This gives rise to the formulation of social clouds, which refer to resource-sharing infrastructures that enable friends to share their resources within the social network. As modern applications become more and more sophisticated, users should be able to share their own services and computing resources through social networks. This poses many challenges for the design options of a computing system composed of a set of trusted friends. The spotlight turns on the design of a proper trust model that considers the suitability of the trusted users to execute an application’s tasks and on the fair distribution of these tasks among these users. Therefore, social networks and their trust-based applications in a distributed environment have seen increasing attention in the research community. In this regard, we present a social community cloud implementation model, where friendly relationships determine resource provisioning. The issues of fairness and allocation of time are of great importance and they are thoroughly investigated. We use extensive simulations to illustrate that the communities can be employed to construct cloud infrastructures, such that the shared resources can be utilized fairly and efficiently. Our experiments have shown that our model achieves a higher allocation rate (percentage of tasks successfully allocated and completed) than competitive models and reduces the average response time and the total execution time. Finally, our work does not overutilize the resources.

Load balancing techniques in cloud computing can be applied at three different levels: Virtual machine load balancing, task load balancing, and resource load balancing. At all levels, load balancing should also be implemented in an efficient manner, to increase system performance. In this paper, we propose a fair, in terms of added workload per VM, task load balancing strategy, that aims to improve the average response time and the makespan of the system in the cloud environment. The problem is formulated as an irreducible finite state Markov process, which is known to have a balance equation for each state. From the balance state probabilities we derive the expected utilizations for the virtual machines (VM), which play a vital role in our task allocation approach. In our model, the Load Balancer (LBer) acts as a central server, which uses our proposed fair task allocation scheme to distribute the incoming tasks in a fair, balanced manner among the virtual machines, taking into account their current state as well as their processing capabilities. Our scheme has been compared to recent algorithms that use the particle swarm optimization and the Honey bee foraging scheme to achieve load balancing. Our experimental results show that our proposed scheme outperforms other state of the art schemes in terms of makespan, average response time, and resource utilization and provides lower degree of imbalance.

In the first days of social networking, the typical view of a community was a set of user profiles of the same interests and likes, and this community kept enlarging by searching, proposing, and adding new members with the same characteristics that were likely to interfere with the existing members. Today, things have changed dramatically. Social networking platforms are not restricted to forming similar user profiles: The vast amounts of data produced every day have given opportunities to predict and suggest relationships, behaviors, and everyday activities like shopping, food, traveling destinations, etc. Every day, vast data amounts are generated by the famous social networks such as Facebook, Twitter, Instagram, and so on. For example, Facebook alone generates 4 petabytes of data per day. The analysis of such data is of high importance to many aspects like recommendation systems, businesses, health organizations, etc. The community detection problem received considerable attention a long time ago. Communities are represented as clusters of an entire network. Most of the community detection techniques are based on graph structures. In this paper, we present the recent advances of deep learning techniques for community detection. We describe the most recent strategies presented in this field, and we provide some general discussion and some future trends.

The detection of community structures is a crucial research area. The problem of community detection has received considerable attention from a large portion of the scientific community and a very large number of papers has already been published in the literature. Even more important is the fact that, this large number of articles is in fact spread across a large number of different disciplines, from computer science, to statistics, and social sciences. These facts necessitate some type of classification and organization of these works. In this work, our basic classification approach divides the community detection schemes into three basic approaches: (a) the bottom-up approaches, (b) the top-down approaches, and (c) the data structure-based approaches. The first category includes the majority of algorithms, so further classification is possible. Such a classification is included in this work. For the other two categories, we make no further categorizations but we simply focus our discussion on the metrics or the data structures being used. Finally, a few possible directions for future research are also suggested.

An important as well as challenging task in modern applications is the management and processing with very short delays of large data volumes. It is quite often, that the capabilities of individual machines are exceeded when trying to manage such large data volumes. In this regard, it is important to develop efficient task scheduling algorithms, which reduce the stream processing costs. What makes the situation more difficult is the fact that the applications as well as the processing systems are prone to changes during runtime: processing nodes may be down, temporarily or permanently, more resources may be needed by an application, and so on. Therefore, it is necessary to develop dynamic schedulers, which can effectively deal with these changes during runtime. In this work, we provide a fast and fair task migration policy while maintaining load balancing and low latency times. The experimental results have shown that our scheme offers better load balancing and reduces the overall latency compared to the state of the art strategies, due to the stepwise communication and the pipeline based processing it employs.

The Petri net (PN) formalism is a suitable tool for modeling parallel systems due to its basic characteristics, such as synchronization. The extension of PN, the Colored Petri Nets (CPN) allows the incorporation of more details of the real system into the model (for example, contention for shared resources). The CPNs have been widely used in a variety of fields to produce suitable models. One of their biggest strengths is that their overall philosophy is quite similar to the philosophy of the object-oriented paradigm. In this regard, the CPN models can be used to implement simulators in a rather straightforward way. In this paper, the CPN framework is employed to implement a new resource allocation simulator, which is used to verify the performance of our previous work, where we proposed a fair resource allocation scheme with flow control and maximum utilization of the system’s resources.