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Dr. CHRISTOS ANAGNOSTOPOULOS
University of Glasgow, UK

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Research Keywords & Expertise

0 Context-aware Computing
0 Distributed Algorithms
0 Machine Learning
0 Mobile Computing
0 Optimisation

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EDGE COMPUTING
Machine Learning
Mobile Computing
Context-aware Computing

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Conference paper
Published: 01 May 2021 in Proceedings of the 8th International Conference of Control, Dynamic Systems, and Robotics (CDSR'21)
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ACS Style

Christos Anagnostopoulos. Pushing Intelligence at the Edge: Edge-centric Inferential Analytics. Proceedings of the 8th International Conference of Control, Dynamic Systems, and Robotics (CDSR'21) 2021, 1 .

AMA Style

Christos Anagnostopoulos. Pushing Intelligence at the Edge: Edge-centric Inferential Analytics. Proceedings of the 8th International Conference of Control, Dynamic Systems, and Robotics (CDSR'21). 2021; ():1.

Chicago/Turabian Style

Christos Anagnostopoulos. 2021. "Pushing Intelligence at the Edge: Edge-centric Inferential Analytics." Proceedings of the 8th International Conference of Control, Dynamic Systems, and Robotics (CDSR'21) , no. : 1.

Original paper
Published: 30 April 2021 in Evolving Systems
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Current advances in the Internet of Things (IoT) and Cloud involve the presence of an additional layer between them acting as mediator for data transfer and processing in close distance to end users. This mediator is the edge computing (EC) infrastructure. In EC, we can identify an ecosystem of heterogeneous nodes capable of interacting with IoT devices, collecting and locally processing the data they report. The ultimate goal is to eliminate the latency we face when relying on Cloud to perform the desired processing activities. In EC, any processing is performed over a number of geo-distributed datasets formulated by the collected data that exhibit specific statistical characteristics. Processing can have the form of tasks requested by end users or applications. It becomes obvious that in the EC ecosystem, we have to carefully decide the EC nodes that will host and execute any requested task. In this paper, we extend our previous research efforts on the conclusion of efficient task allocations into the available EC nodes. We go a step forward and propose a batch processing model executed over multiple tasks and study two allocation models: a scheme based on an unsupervised machine learning technique and a bio-inspired optimization algorithm. Our models enhance the autonomous behavior of entities performing the envisioned task allocations. We provide the analytical description of the problem, our solution and the advances over the state of the art. We present and evaluate the proposed algorithms and compare them with other efforts in the domain. The pros and cons of our models are revealed through the provided extensive experimental evaluation adopting real and synthetic data.

ACS Style

Madalena Soula; Anna Karanika; Kostas Kolomvatsos; Christos Anagnostopoulos; George Stamoulis. Intelligent tasks allocation at the edge based on machine learning and bio-inspired algorithms. Evolving Systems 2021, 1 -22.

AMA Style

Madalena Soula, Anna Karanika, Kostas Kolomvatsos, Christos Anagnostopoulos, George Stamoulis. Intelligent tasks allocation at the edge based on machine learning and bio-inspired algorithms. Evolving Systems. 2021; ():1-22.

Chicago/Turabian Style

Madalena Soula; Anna Karanika; Kostas Kolomvatsos; Christos Anagnostopoulos; George Stamoulis. 2021. "Intelligent tasks allocation at the edge based on machine learning and bio-inspired algorithms." Evolving Systems , no. : 1-22.

Journal article
Published: 07 January 2021 in Future Generation Computer Systems
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Research community has already revealed the challenges of data processing when performed at the Cloud that may affect the performance of any desired application. The main challenge is the increased latency observed when the data should ‘travel’ to the Cloud from the location they are collected and the waiting time for getting the final response. In an Internet of Things (IoT) scenario, this time could be critical for supporting real time applications. A solution to the discussed problem is the adoption of an Edge Computing (EC) approach where data can be processed close to their collection point. IoT devices could report data to a number of edge nodes that behave as distributed data repositories having the capability of processing them and producing analytics. Analytics should match the requirements of queries defined by end users or applications with the collected data and the characteristics of every edge node. However, when a query is defined, we should identify the appropriate edge node(s) to process it. In this paper, we propose an uncertainty management model to efficiently allocate every incoming query to the available edge nodes. Our scheme adopts the principles of the Fuzzy Logic (FL) theory and provides a decision making mechanism for the entity having the responsibility of the envisioned allocations. We combine the proposed uncertainty management scheme with a machine learning model based on a Support Vector Machine (SVM) to enhance the FL reasoning. Our aim is to manage all the hidden aspects of the problem combining two different technologies with different orientations. We also propose a methodology for the automated generation of the Footprint of Uncertainty (FoU) of membership functions involved in our interval Type-2 FL model. Our experimental evaluation aims at revealing the pros and cons of our mechanism presenting the results of extensive simulations adopting datasets found in the literature and a comparative analysis with other efforts in the domain.

ACS Style

Kostas Kolomvatsos; Christos Anagnostopoulos. Proactive, uncertainty-driven queries management at the edge. Future Generation Computer Systems 2021, 118, 75 -93.

AMA Style

Kostas Kolomvatsos, Christos Anagnostopoulos. Proactive, uncertainty-driven queries management at the edge. Future Generation Computer Systems. 2021; 118 ():75-93.

Chicago/Turabian Style

Kostas Kolomvatsos; Christos Anagnostopoulos. 2021. "Proactive, uncertainty-driven queries management at the edge." Future Generation Computer Systems 118, no. : 75-93.

Journal article
Published: 24 December 2020 in Future Generation Computer Systems
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An important use case of the Mobile Edge Computing (MEC) paradigm is task and data offloading. Computational offloading is beneficial for a wide variety of mobile applications on different platforms including autonomous vehicles and smartphones. With the envision deployment of MEC servers along the roads and while mobile nodes are moving and having certain tasks (or data) to be offloaded to edge servers, choosing an appropriate time and an ideally suited MEC server to guarantee the Quality of Service (QoS) is challenging. We tackle the data quality-aware offloading sequential decision making problem by adopting the principles of Optimal Stopping Theory (OST) to minimize the expected processing time. A variety of OST stochastic models and their applications to the offloading decision making problem are investigated and assessed. A performance evaluation is provided using simulation approach and real world data sets together with the assessment of baseline deterministic and stochastic offloading models. The results show that the proposed OST models can significantly minimize the expected processing time for analytics task execution and can be implemented in the mobile nodes efficiently.

ACS Style

Ibrahim Alghamdi; Christos Anagnostopoulos; Dimitrios P. Pezaros. Data quality-aware task offloading in Mobile Edge Computing: An Optimal Stopping Theory approach. Future Generation Computer Systems 2020, 117, 462 -479.

AMA Style

Ibrahim Alghamdi, Christos Anagnostopoulos, Dimitrios P. Pezaros. Data quality-aware task offloading in Mobile Edge Computing: An Optimal Stopping Theory approach. Future Generation Computer Systems. 2020; 117 ():462-479.

Chicago/Turabian Style

Ibrahim Alghamdi; Christos Anagnostopoulos; Dimitrios P. Pezaros. 2020. "Data quality-aware task offloading in Mobile Edge Computing: An Optimal Stopping Theory approach." Future Generation Computer Systems 117, no. : 462-479.

Journal article
Published: 14 October 2020 in IoT
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Pervasive computing applications deal with the intelligence surrounding users that can facilitate their activities. This intelligence is provided in the form of software components incorporated in embedded systems or devices in close distance with end users. One example of infrastructure that can host intelligent pervasive services is the Edge Computing (EC) ecosystem. EC nodes can execute a number of tasks for data collected by devices present in the Internet of Things (IoT). In this paper, we propose an intelligent, proactive tasks management model based on demand. Demand depicts the number of users or applications interested in using the available tasks in EC nodes, thus characterizing their popularity. We rely on a Deep Machine Learning (DML) model and more specifically on a Long Short Term Memory (LSTM) network to learn the distribution of demand indicators for each task and estimate the future interest in them. This information is combined with historical observations of and support for a decision making scheme to conclude which tasks that are offloaded due to limited interest in them. We have to recognise that, in our decision making, we also take into consideration the load that every task may add to the processing node where it will be allocated. The description of our model is accompanied by a large set of experimental simulations for evaluating the proposed mechanism. We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly, while concluding the most efficient decisions.

ACS Style

Kostas Kolomvatsos; Christos Anagnostopoulos. A Deep Learning Model for Demand-Driven, Proactive Tasks Management in Pervasive Computing. IoT 2020, 1, 240 -258.

AMA Style

Kostas Kolomvatsos, Christos Anagnostopoulos. A Deep Learning Model for Demand-Driven, Proactive Tasks Management in Pervasive Computing. IoT. 2020; 1 (2):240-258.

Chicago/Turabian Style

Kostas Kolomvatsos; Christos Anagnostopoulos. 2020. "A Deep Learning Model for Demand-Driven, Proactive Tasks Management in Pervasive Computing." IoT 1, no. 2: 240-258.

Article
Published: 14 May 2020 in Applied Intelligence
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Lack of knowledge in the underlying data distribution in distributed large-scale data can be an obstacle when issuing analytics & predictive modelling queries. Analysts find themselves having a hard time finding analytics/exploration queries that satisfy their needs. In this paper, we study how exploration query results can be predicted in order to avoid the execution of ‘bad’/non-informative queries that waste network, storage, financial resources, and time in a distributed computing environment. The proposed methodology involves clustering of a training set of exploration queries along with the cardinality of the results (score) they retrieved and then using query-centroid representatives to proceed with predictions. After the training phase, we propose a novel refinement process to increase the reliability of predicting the score of new unseen queries based on the refined query representatives. Comprehensive experimentation with real datasets shows that more reliable predictions are acquired after the proposed refinement method, which increases the reliability of the closest centroid and improves predictability under the right circumstances.

ACS Style

Yiannis Kathidjiotis; Kostas Kolomvatsos; Christos Anagnostopoulos. Predictive intelligence of reliable analytics in distributed computing environments. Applied Intelligence 2020, 50, 3219 -3238.

AMA Style

Yiannis Kathidjiotis, Kostas Kolomvatsos, Christos Anagnostopoulos. Predictive intelligence of reliable analytics in distributed computing environments. Applied Intelligence. 2020; 50 (10):3219-3238.

Chicago/Turabian Style

Yiannis Kathidjiotis; Kostas Kolomvatsos; Christos Anagnostopoulos. 2020. "Predictive intelligence of reliable analytics in distributed computing environments." Applied Intelligence 50, no. 10: 3219-3238.

Journal article
Published: 13 May 2020 in Journal of Network and Computer Applications
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This work contributes to a real-time, edge-centric inferential modeling and analytics methodology introducing the fundamental mechanisms for (i) predictive models update and (ii) diverse models selection in distributed computing. Our objective in edge-centric analytics is the time-optimized model caching and selective forwarding at the network edge adopting optimal stopping theory, where communication overhead is significantly reduced as only inferred knowledge and sufficient statistics are delivered instead of raw data obtaining high quality of analytics. Novel model selection algorithms are introduced to fuse the inherent models' diversity over distributed edge nodes to support inferential analytics tasks to end-users/analysts, and applications in real-time. We provide statistical learning modeling and establish the corresponding mathematical analyses of our mechanisms along with comprehensive performance and comparative assessment using real data from different domains and showing its benefits in edge computing.

ACS Style

Christos Anagnostopoulos. Edge-centric inferential modeling & analytics. Journal of Network and Computer Applications 2020, 164, 102696 .

AMA Style

Christos Anagnostopoulos. Edge-centric inferential modeling & analytics. Journal of Network and Computer Applications. 2020; 164 ():102696.

Chicago/Turabian Style

Christos Anagnostopoulos. 2020. "Edge-centric inferential modeling & analytics." Journal of Network and Computer Applications 164, no. : 102696.

Journal article
Published: 07 April 2020 in Future Generation Computer Systems
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Large organizations have seamlessly incorporated data-driven decision making in their operations. However, as data volumes increase, expensive big data infrastructures are called to rescue. In this setting, analytics tasks become very costly in terms of query response time, resource consumption, and money in cloud deployments, especially when base data are stored across geographically distributed data centers. Therefore, we introduce an adaptive, reciprocity-based Machine Learning mechanism which is light-weight, stored client-side, can estimate the answers of a variety of aggregate queries and can avoid the big data back-end. The estimations are performed in milliseconds are inexpensive and accurate as the mechanism learns from past analytical-query patterns. However, as analytic queries are ad hoc and analysts’ interests change over time we develop solutions that can swiftly and accurately detect such changes and adapt to new query patterns. The capabilities of our approach are demonstrated using extensive evaluation with real and synthetic datasets.

ACS Style

Fotis Savva; Christos Anagnostopoulos; Peter Triantafillou. Adaptive learning of aggregate analytics under dynamic workloads. Future Generation Computer Systems 2020, 109, 317 -330.

AMA Style

Fotis Savva, Christos Anagnostopoulos, Peter Triantafillou. Adaptive learning of aggregate analytics under dynamic workloads. Future Generation Computer Systems. 2020; 109 ():317-330.

Chicago/Turabian Style

Fotis Savva; Christos Anagnostopoulos; Peter Triantafillou. 2020. "Adaptive learning of aggregate analytics under dynamic workloads." Future Generation Computer Systems 109, no. : 317-330.

Conference paper
Published: 01 December 2019 in 2019 IEEE Global Communications Conference (GLOBECOM)
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ACS Style

Ibrahim Alghamdi; Christos Anagnostopoulos; Dimitrios P. Pezaros. On the Optimality of Task Offloading in Mobile Edge Computing Environments. 2019 IEEE Global Communications Conference (GLOBECOM) 2019, 1 .

AMA Style

Ibrahim Alghamdi, Christos Anagnostopoulos, Dimitrios P. Pezaros. On the Optimality of Task Offloading in Mobile Edge Computing Environments. 2019 IEEE Global Communications Conference (GLOBECOM). 2019; ():1.

Chicago/Turabian Style

Ibrahim Alghamdi; Christos Anagnostopoulos; Dimitrios P. Pezaros. 2019. "On the Optimality of Task Offloading in Mobile Edge Computing Environments." 2019 IEEE Global Communications Conference (GLOBECOM) , no. : 1.

Conference paper
Published: 01 December 2019 in 2019 IEEE International Conference on Big Data (Big Data)
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Large organizations have seamlessly incorporated data-driven decision making in their operations. However, as data volumes increase, expensive big data infrastructures are called to rescue. In this setting, analytics tasks become very costly in terms of query response time, resource consumption, and money in cloud deployments, especially when base data are stored across geographically distributed data centers. Therefore, we introduce an adaptive Machine Learning mechanism which is light-weight, stored client-side, can estimate the answers of a variety of aggregate queries and can avoid the big data backend. The estimations are performed in milliseconds and are inexepensive as the mechanism learns from past analytical-query patterns. However, as analytic queries are ad-hoc and analysts’ interests change over time we develop solutions that can swiftly and accurately detect such changes and adapt to new query patterns. The capabilities of our approach are demonstrated using extensive evaluation with real and synthetic datasets.

ACS Style

Fotis Savva; Christos Anagnostopoulos; Peter Triantafillou. Aggregate Query Prediction under Dynamic Workloads. 2019 IEEE International Conference on Big Data (Big Data) 2019, 671 -676.

AMA Style

Fotis Savva, Christos Anagnostopoulos, Peter Triantafillou. Aggregate Query Prediction under Dynamic Workloads. 2019 IEEE International Conference on Big Data (Big Data). 2019; ():671-676.

Chicago/Turabian Style

Fotis Savva; Christos Anagnostopoulos; Peter Triantafillou. 2019. "Aggregate Query Prediction under Dynamic Workloads." 2019 IEEE International Conference on Big Data (Big Data) , no. : 671-676.

Book chapter
Published: 22 November 2019 in Real-Time Data Analytics for Large Scale Sensor Data
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A colossal amount of data are produced each day and travel through the Internet to be stored or processed on their final destination. Most of them are directly created by people (images, music, videos, etc.), but a considerable amount are generated by sensing and computing devices. Moreover, the Internet of things (IoT) introduces the idea that all devices will have connectivity capabilities, such as connecting to local area networks (LAN) or wide area networks (WAN) including the Internet. By the year 2020, an estimated 30 billion devices will be part of the IoT [1]. This will create the need for sensing and computing devices to communicate efficiently in order to save communication transactions, which will ultimately save communication overhead, resulting in less energy consumption (from not using communication modules like antennas). This chapter proposes an edge-centric predictive methodology, based on real-time model caching, where communication overhead is significantly decreased. This is because only the model’s parameters are cached and disseminated. This event occurs only when the methodology sees a need to update the model. This methodology is more efficient, in terms of communication overhead, compared with continuous raw data transmission. Furthermore, this chapter presents the comparative assessment of the combination of the methodology, with different regression techniques as caching models. Later in this chapter, we will explore the impact of each regression technique on the accuracy as well as the communication overhead. The requirements of implementation are that the end nodes will have at least the processing power and memory of a modern MCU. The achieved results from the analysis suggest that the communication overhead is significantly reduced, by just having a marginally less accurate model. This means that we can have a minor trade-off between accuracy and throughput which can result in improvements in the energy footprint. We provide performance and comparative assessment over real data showing the benefits of the regression models combined with the proposed methodology.

ACS Style

Stefanos Nikolaou; Christos Anagnostopoulos; Dimitrios Pezaros. Communication-aware edge-centric knowledge dissemination in edge computing environments. Real-Time Data Analytics for Large Scale Sensor Data 2019, 139 -156.

AMA Style

Stefanos Nikolaou, Christos Anagnostopoulos, Dimitrios Pezaros. Communication-aware edge-centric knowledge dissemination in edge computing environments. Real-Time Data Analytics for Large Scale Sensor Data. 2019; ():139-156.

Chicago/Turabian Style

Stefanos Nikolaou; Christos Anagnostopoulos; Dimitrios Pezaros. 2019. "Communication-aware edge-centric knowledge dissemination in edge computing environments." Real-Time Data Analytics for Large Scale Sensor Data , no. : 139-156.

Article
Published: 18 November 2019 in Computing
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Data management at the edge of the network can increase the performance of applications as the processing is realized close to end users limiting the observed latency in the provision of responses. A typical data processing involves the execution of queries/tasks defined by users or applications asking for responses in the form of analytics. Query/task execution can be realized at the edge nodes that can undertake the responsibility of delivering the desired analytics to the interested users or applications. In this paper, we deal with the problem of allocating queries to a number of edge nodes. The aim is to support the goal of eliminating further the latency by allocating queries to nodes that exhibit a low load and high processing speed, thus, they can respond in the minimum time. Before any allocation, we propose a method for estimating the computational burden that a query/task will add to a node and, afterwards, we proceed with the final assignment. The allocation is concluded by the assistance of an ensemble similarity scheme responsible to deliver the complexity class for each query/task and a probabilistic decision making model. The proposed scheme matches the characteristics of the incoming queries and edge nodes trying to conclude the optimal allocation. We discuss our mechanism and through a large set of simulations and the adoption of benchmarking queries, we reveal the potentials of the proposed model supported by numerical results.

ACS Style

Kostas Kolomvatsos; Christos Anagnostopoulos. A probabilistic model for assigning queries at the edge. Computing 2019, 102, 865 -892.

AMA Style

Kostas Kolomvatsos, Christos Anagnostopoulos. A probabilistic model for assigning queries at the edge. Computing. 2019; 102 (4):865-892.

Chicago/Turabian Style

Kostas Kolomvatsos; Christos Anagnostopoulos. 2019. "A probabilistic model for assigning queries at the edge." Computing 102, no. 4: 865-892.

Conference paper
Published: 10 November 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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The new era of the Internet of Things (IoT) provides the space where novel applications will play a significant role in people’s daily lives through the adoption of multiple services that facilitate everyday activities. The huge volumes of data produced by numerous IoT devices make the adoption of analytics imperative to produce knowledge and support efficient decision making. In this setting, one can identify two main problems, i.e., the time required to send the data to Cloud and wait for getting the final response and the distributed nature of data collection. Edge Computing (EC) can offer the necessary basis for storing locally the collected data and provide the required analytics on top of them limiting the response time. In this paper, we envision multiple edge nodes where data are stored being the subject of analytics queries. We propose a methodology for allocating queries, defined by end users or applications, to the appropriate edge nodes in order to save time and resources in the provision of responses. By adopting our scheme, we are able to ask the execution of queries only from a sub-set of the available nodes avoiding to demand processing activities that will lead to an increased response time. Our model envisions the allocation to specific epochs and manages a batch of queries at a time. We present the formulation of our problem and the proposed solution while providing results of an extensive evaluation process that reveals the pros and cons of the proposed model.

ACS Style

Anna Karanika; Madalena Soula; Christos Anagnostopoulos; Kostas Kolomvatsos; George Stamoulis. Optimized Analytics Query Allocation at the Edge of the Network. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 181 -190.

AMA Style

Anna Karanika, Madalena Soula, Christos Anagnostopoulos, Kostas Kolomvatsos, George Stamoulis. Optimized Analytics Query Allocation at the Edge of the Network. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():181-190.

Chicago/Turabian Style

Anna Karanika; Madalena Soula; Christos Anagnostopoulos; Kostas Kolomvatsos; George Stamoulis. 2019. "Optimized Analytics Query Allocation at the Edge of the Network." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 181-190.

Journal article
Published: 12 October 2019 in Information
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In recent years, there has been a significant increase in the use of mobile devices and their applications. Meanwhile, cloud computing has been considered as the latest generation of computing infrastructure. There has also been a transformation in cloud computing ideas and their implementation so as to meet the demand for the latest applications. mobile edge computing (MEC) is a computing paradigm that provides cloud services near to the users at the edge of the network. Given the movement of mobile nodes between different MEC servers, the main aim would be the connection to the best server and at the right time in terms of the load of the server in order to optimize the quality of service (QoS) of the mobile nodes. We tackle the offloading decision making problem by adopting the principles of optimal stopping theory (OST) to minimize the execution delay in a sequential decision manner. A performance evaluation is provided using real world data sets with baseline deterministic and stochastic offloading models. The results show that our approach significantly minimizes the execution delay for task execution and the results are closer to the optimal solution than other offloading methods.

ACS Style

Ibrahim Alghamdi; Christos Anagnostopoulos; Dimitrios P. Pezaros. Delay-Tolerant Sequential Decision Making for Task Offloading in Mobile Edge Computing Environments. Information 2019, 10, 312 .

AMA Style

Ibrahim Alghamdi, Christos Anagnostopoulos, Dimitrios P. Pezaros. Delay-Tolerant Sequential Decision Making for Task Offloading in Mobile Edge Computing Environments. Information. 2019; 10 (10):312.

Chicago/Turabian Style

Ibrahim Alghamdi; Christos Anagnostopoulos; Dimitrios P. Pezaros. 2019. "Delay-Tolerant Sequential Decision Making for Task Offloading in Mobile Edge Computing Environments." Information 10, no. 10: 312.

Journal article
Published: 05 October 2019 in Journal of Network and Computer Applications
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Monitoring activities over edge resources and services are essential in today's applications. Edge nodes can monitor their status and end users/applications requirements to identify their ‘matching’ and deliver alerts when violations are present. Violations are related to any disturbance of the desired Quality of Service (QoS). QoS depends on a number of performance metrics and can differ among applications. In this paper, we propose the use of an intelligent mechanism to be incorporated in monitoring tools adopted by edge nodes. The proposed mechanism observes the realizations of performance parameters that result in specific QoS values and decides when it is the right time to ‘fire’ mitigation actions. Hence, edge nodes are capable of changing their configuration to secure the desired QoS levels as dictated by end users/applications requirements. In our work, a mitigation action could involve either upgrades in the current services/resources or offloading tasks by transferring computational load and data to peer nodes or the Cloud. We present our model and provide formulations for the solution of the problem. A high number of simulations reveal the performance of the proposed mechanism. Our experiments show that our scheme outperforms any deterministic model defined for the discussed setting as well as other efforts found in the relevant literature.

ACS Style

Christos Anagnostopoulos; Kostas Kolomvatsos. An intelligent, time-optimized monitoring scheme for edge nodes. Journal of Network and Computer Applications 2019, 148, 102458 .

AMA Style

Christos Anagnostopoulos, Kostas Kolomvatsos. An intelligent, time-optimized monitoring scheme for edge nodes. Journal of Network and Computer Applications. 2019; 148 ():102458.

Chicago/Turabian Style

Christos Anagnostopoulos; Kostas Kolomvatsos. 2019. "An intelligent, time-optimized monitoring scheme for edge nodes." Journal of Network and Computer Applications 148, no. : 102458.

Conference paper
Published: 14 August 2019 in Transactions on Petri Nets and Other Models of Concurrency XV
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Current applications developed for the Internet of Things (IoT) usually involve the processing of collected data for delivering analytics and support efficient decision making. The basis for any processing mechanism is data analysis, usually having as an outcome responses in various analytics queries defined by end users or applications. However, as already noted in the respective literature, data analysis cannot be efficient when missing values are present. The research community has already proposed various missing data imputation methods paying more attention of the statistical aspect of the problem. In this paper, we study the problem and propose a method that combines machine learning and a consensus scheme. We focus on the clustering of the IoT devices assuming they observe the same phenomenon and report the collected data to the edge infrastructure. Through a sliding window approach, we try to detect IoT nodes that report similar contextual values to edge nodes and base on them to deliver the replacement value for missing data. We provide the description of our model together with results retrieved by an extensive set of simulations on top of real data. Our aim is to reveal the potentials of the proposed scheme and place it in the respective literature.

ACS Style

Kostas Kolomvatsos; Panagiota Papadopoulou; Christos Anagnostopoulos; Stathes Hadjiefthymiades. A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 138 -150.

AMA Style

Kostas Kolomvatsos, Panagiota Papadopoulou, Christos Anagnostopoulos, Stathes Hadjiefthymiades. A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():138-150.

Chicago/Turabian Style

Kostas Kolomvatsos; Panagiota Papadopoulou; Christos Anagnostopoulos; Stathes Hadjiefthymiades. 2019. "A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 138-150.

Conference paper
Published: 01 June 2019 in 2019 IEEE Symposium on Computers and Communications (ISCC)
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The Internet of Things involves a huge number of devices that collect data and deliver them to the Cloud. The processing of data at the Cloud is characterized by increased latency in providing responses to analytics queries defined by analysts or applications. Hence, Edge Computing (EC) comes into the scene to provide data processing close to the source. The collected data can be stored in edge devices and queries can be executed there to reduce latency. In this paper, we envision a case where entities located in the Cloud undertake the responsibility of receiving analytics queries and decide on the most appropriate edge nodes for queries execution. The decision is based on statistical signatures of the datasets of nodes and the statistical matching between statistics and analytics queries. Edge nodes regularly update their statistical signatures to support such decision process. Our performance evaluation shows the advantages and the shortcomings of our proposed schema in edge computing environments.

ACS Style

Stefanos Sagkriotis; Kostas Kolomvatsos; Christos Anagnostopoulos; Dimitrios P. Pezaros; Stathes Hadjiefthymiades. Knowledge-centric Analytics Queries Allocation in Edge Computing Environments. 2019 IEEE Symposium on Computers and Communications (ISCC) 2019, 1 -6.

AMA Style

Stefanos Sagkriotis, Kostas Kolomvatsos, Christos Anagnostopoulos, Dimitrios P. Pezaros, Stathes Hadjiefthymiades. Knowledge-centric Analytics Queries Allocation in Edge Computing Environments. 2019 IEEE Symposium on Computers and Communications (ISCC). 2019; ():1-6.

Chicago/Turabian Style

Stefanos Sagkriotis; Kostas Kolomvatsos; Christos Anagnostopoulos; Dimitrios P. Pezaros; Stathes Hadjiefthymiades. 2019. "Knowledge-centric Analytics Queries Allocation in Edge Computing Environments." 2019 IEEE Symposium on Computers and Communications (ISCC) , no. : 1-6.

Proceedings article
Published: 01 June 2019 in 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC)
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This work studies a sequential decision making methodology of when to update machine learning models in Edge Computing environments given underlying changes in the contextual data distribution. The proposed model focuses on updates scheduling and takes into consideration the optimal decision time for minimizing the network overhead. At the same time it preserves the prediction accuracy of models based on the principles of the Optimal Stopping Theory (OST). The paper reports on a comparative analysis between the proposed approach and other policies proposed in the respective literature while providing an evaluation of the performances using linear and support vector regression models. Our evaluation process is realized over real contextual data streams to reveal the strengths and weaknesses of the proposed strategy.

ACS Style

Ekaterina Aleksandrova; Christos Anagnostopoulos; Kostas Kolomvatsos. Machine Learning Model Updates in Edge Computing: An Optimal Stopping Theory Approach. 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC) 2019, 1 -8.

AMA Style

Ekaterina Aleksandrova, Christos Anagnostopoulos, Kostas Kolomvatsos. Machine Learning Model Updates in Edge Computing: An Optimal Stopping Theory Approach. 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC). 2019; ():1-8.

Chicago/Turabian Style

Ekaterina Aleksandrova; Christos Anagnostopoulos; Kostas Kolomvatsos. 2019. "Machine Learning Model Updates in Edge Computing: An Optimal Stopping Theory Approach." 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC) , no. : 1-8.

Conference paper
Published: 01 June 2019 in 2019 IEEE Symposium on Computers and Communications (ISCC)
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With Network Function Virtualization (NFV) platforms gaining ground, we question the combination of NFV and Single Board Computers (SBCs) in terms of compatibility, reliability, and energy consumption. A mini cluster of SBCs is used to develop a scalable and resilient energy monitoring application. The application is employed to discover the energy demands of a NFV platform in modern SBCs, and build the energy profile of the devices and the deployed services. We use the results and the added knowledge from building the application to strengthen the argument that SBC clusters can support virtualized service deployment. This evidence, alongside the rich gamut of characteristics that SBCs hold, proves that they are a viable option for edge components of a fog network. Our results show that running different virtualised processes offers added functionality, resilience and scalability without heavily sacrificing energy consumption.

ACS Style

Stefanos Sagkriotis; Christos Anagnostopoulos; Dimitrios P. Pezaros. Energy Usage Profiling for Virtualized Single Board Computer Clusters. 2019 IEEE Symposium on Computers and Communications (ISCC) 2019, 1 -6.

AMA Style

Stefanos Sagkriotis, Christos Anagnostopoulos, Dimitrios P. Pezaros. Energy Usage Profiling for Virtualized Single Board Computer Clusters. 2019 IEEE Symposium on Computers and Communications (ISCC). 2019; ():1-6.

Chicago/Turabian Style

Stefanos Sagkriotis; Christos Anagnostopoulos; Dimitrios P. Pezaros. 2019. "Energy Usage Profiling for Virtualized Single Board Computer Clusters." 2019 IEEE Symposium on Computers and Communications (ISCC) , no. : 1-6.

Proceedings article
Published: 01 April 2019 in 2019 Wireless Days (WD)
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Edge-centric predictive analytics methodologies use real-time model caching to significantly reduce the communication overhead. We investigate an approach of using different regression techniques at the edge as caching models. Our methodology reports on an edge-centric mechanism to automatically decide when to update the parameters of the cached models to a central location (data center). Through experimentation, we showcase the trade off between accuracy and communication overhead and conclude that for all the experimented regression models, a lower percentage of the cached models should be sent to the data center to significantly decrease the communication overhead.

ACS Style

Stefanos Nikolaou; Christos Anagnostopoulos; Dimitrios Pezaros. In-network Predictive Analytics in Edge Computing. 2019 Wireless Days (WD) 2019, 1 -4.

AMA Style

Stefanos Nikolaou, Christos Anagnostopoulos, Dimitrios Pezaros. In-network Predictive Analytics in Edge Computing. 2019 Wireless Days (WD). 2019; ():1-4.

Chicago/Turabian Style

Stefanos Nikolaou; Christos Anagnostopoulos; Dimitrios Pezaros. 2019. "In-network Predictive Analytics in Edge Computing." 2019 Wireless Days (WD) , no. : 1-4.