Publikationer av Vladimir Vlassov

Refereegranskade

Artiklar

[1]

A. Rauniyar et al., "Federated Learning for Medical Applications : A Taxonomy, Current Trends, Challenges, and Future Research Directions," IEEE Internet of Things Journal, vol. 11, no. 5, s. 7374-7398, 2024.

[2]

D. H. Hagos et al., "Scalable Artificial Intelligence for Earth Observation Data Using Hopsworks," Remote Sensing, vol. 14, no. 8, 2022.

[3]

D. H. Hagos et al., "ExtremeEarth Meets Satellite Data From Space," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, s. 9038-9063, 2021.

[4]

M. Garcia Lozano et al., "Veracity assessment of online data," Decision Support Systems, vol. 129, 2020.

[5]

P. Trunfio och V. Vlassov, "Clouds for scalable Big Data processing," International Journal of Parallel, Emergent and Distributed Systems, vol. 34, no. 6, s. 629-631, 2019.

[6]

V. Kalavri, V. Vlassov och S. Haridi, "High-Level Programming Abstractions for Distributed Graph Processing," IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 2, s. 305-324, 2018.

[7]

Z. Abbas et al., "Streaming Graph Partitioning: An Experimental Study," Proceedings of the VLDB Endowment, vol. 11, no. 11, s. 1590-1603, 2018.

[8]

Y. Liu et al., "OnlineElastMan : self-trained proactive elasticity manager for cloud-based storage services," Cluster Computing, vol. 20, no. 3, s. 1977-1994, 2017.

[9]

A. Podobas, M. Brorsson och V. Vlassov, "Exploring heterogeneous scheduling using the task-centric programming model," Lecture Notes in Computer Science, vol. 7640, 2013.

[10]

Y. Guo et al., "Synchronization coherence : A transparent hardware mechanism for cache coherence and fine-grained synchronization," Journal of Parallel and Distributed Computing, vol. 68, no. 2, s. 165-181, 2008.

[11]

P. Trunfio et al., "Peer-to-Peer resource discovery in Grids : Models and systems," Future generations computer systems, vol. 23, no. 7, s. 864-878, 2007.

[12]

K. Popov et al., "An efficient incremental marshaling framework for distributed systems," Future generations computer systems, vol. 21, no. 5, s. 717-724, 2005.

[13]

K. Popov et al., "Parallel Agent-Based Simulation on a Cluster of Workstations," Parallel Processing Letters, vol. 13, no. 4, s. 629-641, 2003.

[14]

V. Vlassov och R. Ayani, "Analytical modeling of multithreaded architectures," Journal of systems architecture, vol. 46, no. 13, s. 1205-1230, 2000.

[15]

V. Vlassov, R. Ayani och L.-E. Thorelli, "Modeling and Simulation of Multithreaded Architectures," Simulation (San Diego, Calif.), vol. 68, no. 4, s. 219-230, 1997.

Konferensbidrag

[16]

T. Wang, A. H. Payberah och V. Vlassov, "Graph Representation Learning with Graph Transformers in Neural Combinatorial Optimization," i 2023 International Conference on Machine Learning and Applications (ICMLA), 2023, s. 488-495.

[17]

J. Attieh et al., "Optimizing the Performance of Text Classification Models by Improving the Isotropy of the Embeddings Using a Joint Loss Function," i Document Analysis and Recognition : ICDAR 2023, 2023, s. 121-136.

[18]

J. Liang, Y. Liu och V. Vlassov, "The Impact of Background Removal on Performance of Neural Networks for Fashion Image Classification and Segmentation," i 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE), 2023, s. 1960-1968.

[19]

S. Sheikholeslami et al., "The Impact of Importance-Aware Dataset Partitioning on Data-Parallel Training of Deep Neural Networks," i Distributed Applications and Interoperable Systems - 23rd IFIP WG 6.1 International Conference, DAIS 2023, Held as Part of the 18th International Federated Conference on Distributed Computing Techniques, DisCoTec 2023, Proceedings, 2023, s. 74-89.

[20]

F. Schmidt et al., "Using Machine Learning to Recommend Personalized Modular Treatments for Common Mental Health Disorders," i Proceedings - 2023 IEEE International Conference on Digital Health, ICDH 2023, 2023, s. 150-157.

[21]

T. Wang et al., "Accelerate Model Parallel Deep Learning Training Using Effective Graph Traversal Order in Device Placement," i Distributed Applications and Interoperable Systems (DAIS 2022), 2022, s. 114-130.

[22]

M. Arsalan et al., "Energy-Efficient Privacy-Preserving Time-Series Forecasting on User Health Data Streams," i Proceedings - 2022 IEEE 21st International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022, 2022, s. 541-546.

[23]

T. Wang, A. H. Payberah och V. Vlassov, "Node Context Selection in Transformer-Based Graph Representation Learning Models," i Proceedings : 2022 IEEE International Conference on Big Data, Big Data 2022, 2022, s. 4625-4634.

[24]

A. Asratyan, S. Sheikholeslami och V. Vlassov, "A Parallel Chain Mail Approach for Scalable Spatial Data Interpolation," i 2021 IEEE International Conference on Big Data (Big Data), 2021, s. 306-314.

[25]

S. Sheikholeslami et al., "AutoAblation: Automated Parallel Ablation Studies for Deep Learning," i EuroMLSys '21: Proceedings of the 1st Workshop on Machine Learning and Systems, 2021, s. 55-61.

[26]

D. Gureya, V. Vlassov och J. Barreto, "Brief announcement : BALM: qos-aware memory bandwidth partitioning for multi-socket cloud nodes," i Annual ACM Symposium on Parallelism in Algorithms and Architectures, 2021, s. 435-438.

[27]

D. Gureya, J. Barreto och V. Vlassov, "Generalizing QoS-Aware Memory Bandwidth Allocation to Multi-Socket Cloud Servers," i 2021 Ieee 14Th International Conference On Cloud Computing (Cloud 2021), 2021, s. 551-557.

[28]

S. Imtiaz et al., "Machine Learning with Reconfigurable Privacy on Resource-Limited Computing Devices," i 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021, 2021, s. 1592-1602.

[29]

S. Fedeli et al., "Privacy Preserving Survival Prediction," i 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, s. 4600-4608.

[30]

S. Imtiaz et al., "PyDPLib : Python Differential Privacy Library for Private Medical Data Analytics," i Proceedings - 2021 IEEE International Conference on Digital Health, ICDH 2021, 2021, s. 191-196.

[31]

S. Imtiaz et al., "Synthetic and Private Smart Health Care Data Generation using GANs," i 30th International Conference on Computer Communications and Networks (ICCCN 2021), 2021.

[32]

D. D. Gureya et al., "Bandwidth-Aware Page Placement in NUMA," i 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2020, s. 546-556.

[33]

T. Wang, A. H. Payberah och V. Vlassov, "CONVJSSP: Convolutional Learning for Job-Shop Scheduling Problems," i 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020, s. 1483-1490.

[34]

S. Dhariwal, Y. Liu och V. Vlassov, "Clothing Classification using Unsupervised Pre-Training," i 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA), 2020, s. 82-89.

[35]

A. Nardelli, V. Vlassov och A. H. Payberah, "Framework-agnostic optimization of repeated skewed joins at massive scale," i Proceedings - 2020 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2020 IEEE International Conference on Big Data and Cloud Computing, 2020 IEEE International Symposium on Social Computing and Networking and 2020 IEEE International Conference on Sustainable Computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020, 2020, s. 26-33.

[36]

M. Parashar och V. Vlassov, "General Chairs' Welcome," i HPDC 2020 : Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing, 2020, s. II-IV.

[37]

M. Meister et al., "Maggy : Scalable Asynchronous Parallel Hyperparameter Search," i Proceedings of the 1st Workshop on Distributed Machine Learning, 2020, s. 28-33.

[38]

S. Imtiaz et al., "Privacy Preserving Time-Series Forecasting of User Health Data Streams," i 2020 IEEE International Conference on Big Data (Big Data), 2020, s. 3428-3437.

[39]

D. Montesi, S. Girdzijauskas och V. Vlassov, "Repeating Link Prediction over Dynamic Graphs," i 2020 IEEE International Conference on Big Data (Big Data), 2020, s. 4420-4428.

[40]

M. Koubarakis et al., "From copernicus big data to extreme earth analytics," i Advances in Database Technology - EDBT, 2019, s. 690-693.

[41]

X. Lin et al., "Message from the BDCloud 2018 Chairs," i Proceedings - 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, 2019, s. XXIX-XXX.

[42]

S. Imtiaz, R. Sadre och V. Vlassov, "On the case of privacy in the iot ecosystem : a survey," i Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019, 2019, s. 1015-1024.

[43]

Z. Abbas et al., "Scaling Deep Learning Models for Large Spatial Time-Series Forecasting :
," i Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 :
, 2019, s. 1587-1594.

[44]

A. M. Khan et al., "Demo Abstract : Towards IoT Service Deployments on Edge Community Network Microclouds," i IEEE INFOCOM 2018 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS), 2018.

[45]

Z. Abbas et al., "Evaluation of the Use of Streaming Graph Processing Algorithms for Road Congestion Detection," i 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS, 2018, s. 1017-1025.

[46]

K. Sozinov, V. Vlassov och S. Girdzijauskas, "Human Activity Recognition Using Federated Learning," i 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS, 2018, s. 1103-1111.

[47]

H. Peiro Sajjad, Y. Liu och V. Vlassov, "Optimizing Windowed Aggregation over Geo-Distributed Data Streams," i 2018 IEEE International Conference on Edge Computing (EDGE), 2018, s. 33-41.

[48]

Z. Abbas et al., "Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks," i 2018 IEEE International Congress on Big Data (BigData Congress), 2018, s. 57-65.

[49]

R. Paul et al., "Designing Distributed Applications Using a Phase-Aware, Reversible System," i Proceedings - 2017 IEEE 1st International Conference on Edge Computing, EDGE 2017, 2017, s. 55-64.

[50]

N. Apolonia et al., "Gossip-based service monitoring platform for wireless edge cloud computing," i Proceedings IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), 2017.

[51]

A. Javed Awan et al., "Identifying the potential of Near Data Processing for Apache Spark," i Proceedings of the International Symposium on Memory Systems, MEMSYS 2017, 2017, s. 60-67.

[52]

V. Xhagjika et al., "Load and video performance patterns of a cloud based WebRTC Architecture," i Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, 2017, s. 739-744.

[53]

V. Xhagjika et al., "Media Streams Allocation and Load Patterns for a WebRTC Cloud Architecture," i PROCEEDINGS OF THE 2017 8TH INTERNATIONAL CONFERENCE ON THE NETWORK OF THE FUTURE (NOF), 2017, s. 14-21.

[54]

V. Vlassov och R. Bohn, "Message from the ICCAC 2017 Program Chairs," i Proceedings - 2017 IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017, 2017.

[55]

Y. Liu et al., "MeteorShower : Minimizing Request Latency for Majority Quorum-Based Data Consistency Algorithms in Multiple Data Centers," i 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 2017, s. 57-67.

[56]

N. Rameshan et al., "Augmenting Elasticity Controllers for Improved Accuracy," i 2016 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC), 2016, s. 117-126.

[57]

H. Peiro Sajjad et al., "Boosting Vertex-Cut Partitioning For Streaming Graphs," i Big Data (BigData Congress), 2016 IEEE International Congress on, 2016, s. 1-8.

[58]

Y. Liu, Q. Wang och V. Vlassov, "Catenae : Low Latency Transactions across Multiple Data Centers," i 2016 IEEE 22ND INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS), 2016, s. 491-500.

[59]

R. Baig et al., "Cloud-based community services in community networks," i 2016 International Conference on Computing, Networking and Communications, ICNC 2016, 2016, s. 1-5.

[60]

N. Rameshan et al., "Elastic Scaling in the Cloud : A Multi-Tenant Perspective," i 2016 IEEE 36TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS WORKSHOPS (ICDCSW 2016), 2016, s. 25-30.

[61]

N. Rameshan et al., "Hubbub-Scale: Towards Reliable Elastic Scaling under Multi-tenancy," i Proceedings - 2016 16th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2016, 2016, s. 233-244.

[62]

A. J. Awan et al., "Micro-architectural Characterization of Apache Spark on Batch and Stream Processing Workloads," i The 6th IEEE International Conference on Big Data and Cloud Computing, 2016, s. 59-66.

[63]

A. J. Awan et al., "Node architecture implications for in-memory data analytics on scale-in clusters," i 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, 2016, s. 237-246.

[64]

Y. Liu et al., "OnlineElastMan : Self-Trained Proactive Elasticity Manager for Cloud-Based Storage Services," i 2016 INTERNATIONAL CONFERENCE ON CLOUD AND AUTONOMIC COMPUTING (ICCAC), 2016, s. 50-59.

[65]

N. Rameshan et al., "Profiling Memory Vulnerability of Big-data Applications," i 2016 46TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOPS (DSN-W), 2016, s. 258-261.

[66]

R. Paul, P. Van Roy och V. Vlassov, "Reversible Phase Transitions in a Structured Overlay Network with Churn," i 4th International Conference on Networked Systems (NETYS), May 18-20, 2016, 2016.

[67]

H. Peiro Sajjad et al., "SpanEdge: Towards Unifying Stream Processing over Central and Near-the-Edge Data Centers," i The First IEEE/ACM Symposium on Edge Computing (SEC), 2016.

[68]

P. Carbone och V. Vlassov, "Auto-Scoring of Personalised News in the Real-Time Web : Challenges, Overview and Evaluation of the State-of-the-Art Solutions," i Cloud and Autonomic Computing (ICCAC), 2015 International Conference on, Cambridge, MA, USA, September 21-25, 2015, 2015, s. 169-180.

[69]

R. Baig et al., "Community clouds at the edge deployed in Guifi.net," i Cloud Networking (CloudNet), 2015 IEEE 4th International Conference on, Niagara Falls, Canada, October 5-7, 2015, 2015, s. 213-215.

[70]

R. Baig et al., "Community network clouds as a case for the IEEE Intercloud standardization," i 2015 IEEE Conference on Standards for Communications and Networking, CSCN 2015, 2015, s. 269-274.

[71]

R. Baig et al., "Deploying Clouds in the Guifi Community Network," i Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management, IM 2015, 2015, s. 1020-1025.

[72]

V. Xhagjika, L. Navarro och V. Vlassov, "Enhancing real-time applications by means of multi-tier cloud federations," i Proceedings - IEEE 7th International Conference on Cloud Computing Technology and Science, CloudCom 2015, 2015, s. 397-404.

[73]

R. Baig et al., "Experiences in Building Micro-Cloud Provider Federation in the Guifi Community Network," i 2015 IEEE/ACM 8TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING (UCC), 2015, s. 516-521.

[74]

A. J. Awan et al., "How Data Volume Affects Spark Based Data Analytics on a Scale-up Server," i Big Data Benchmarks, Performance Optimization, and Emerging Hardware : 6th Workshop, BPOE 2015, Kohala, HI, USA, August 31 - September 4, 2015. Revised Selected Papers, 2015, s. 81-92.

[75]

R. R. Paul, P. Van Roy och V. Vlassov, "Interaction between Network Partitioning and Churn in a Self-Healing Structured Overlay Network," i Parallel and Distributed Systems (ICPADS), 2015 IEEE 21st International Conference on, 2015, s. 232-241.

[76]

J. Chen et al., "Message from BDCloud Chairs," i Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014, 2015, s. xv-xvi.

[77]

A. Javed Awan et al., "Performance Characterization of In-Memory Data Analytics on a Modern Cloud Server," i Proceedings - 2015 IEEE 5th International Conference on Big Data and Cloud Computing, BDCloud 2015, 2015, s. 1-8.

[78]

Y. Liu et al., "ProRenaTa : Proactive and reactive tuning to scale a distributed storage system," i Proceedings - 2015 IEEE/ACM 15th International Symposium on Cluster, Cloud, and Grid Computing, CCGrid 2015, 2015, s. 453-464.

[79]

H. Peiro Sajjad, F. Rahimian och V. Vlassov, "Smart Partitioning of Geo-Distributed Resources to Improve Cloud Network Performance," i The 2015 4th IEEE International Conference on Cloud Networking (IEEE CloudNet 2015)5-7 October 2015, Niagara Falls, Canada, 2015.

[80]

K. Danniswara et al., "Stream Processing in Community Network Clouds," i Future Internet of Things and Cloud (FiCloud), 2015 3rd International Conference on, 2015, s. 800-805.

[81]

R. Baig et al., "The Cloudy Distribution in Community Network Clouds in Guifi.net," i Integrated Network Management (IM), 2015 IFIP/IEEE International Symposium on, 2015, s. 1161-1162.

[82]

M. Garcia Lozano et al., "Towards Automatic Veracity Assessment of Open Source Information," i 2015 IEEE International Congress on Big Data (BigData Congress), 2015, s. 199-206.

[83]

R. R. Paul, P. Van Roy och V. Vlassov, "An Empirical Study of the Global Behavior of A Structured Overlay Network," i 14-TH IEEE INTERNATIONAL CONFERENCE ON PEER-TO-PEER COMPUTING (P2P), 2014.

[84]

V. Kalavri et al., "Asymmetry in Large-Scale Graph Analysis, Explained," i Proceedings of the Second International Workshop on Graph Data ManagementExperience and Systems (GRADES 2014), June 22, 2014, Snowbird, Utah, USA., 2014.

[85]

Y. Liu et al., "BwMan : Bandwidth manager for elastic services in the cloud," i Proceedings - 2014 IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2014, 2014, s. 217-224.

[86]

A. Podobas et al., "Considering Quality-of-Service for Resource Reduction using OpenMP," i MULTIPROG 2014 : Programmability Issues for Heterogeneous Multicores,Jan 22, 2014,Viena, Austria, 2014.

[87]

Y. Liu, X. Li och V. Vlassov, "GlobLease : A Globally Consistent and Elastic Storage System using Leases," i The 20th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2014), 2014, s. 701-709.

[88]

N. Rameshan et al., "Stay-Away, protecting sensitive applications from performance interference," i Proceedings of the 15th International Middleware Conference, Middleware 2014, 2014, s. 301-312.

[89]

V. Xhagjika et al., "Structured Cloud federation for Carrier and ISP infrastructure," i 2014 IEEE 3rd International Conference on Cloud Networking, CloudNet 2014, 2014, s. 20-26.

[90]

Y. Liu, V. Vlassov och L. Navarro, "Towards a Community Cloud Storage," i 2014 IEEE 28th International Conference on Advanced Information Networking and Applications (AINA), 2014, s. 837-844.

[91]

A. Podobas, M. Brorsson och V. Vlassov, "TurboBŁYSK : Scheduling for improved data-driven task performance with fast dependency resolution," i Using and Improving OpenMP for Devices, Tasks, and More : 10th International Workshop on OpenMP, IWOMP 2014, Salvador, Brazil, September 28-30, 2014. Proceedings, 2014, s. 45-57.

[92]

V. Kalavri, V. Brundza och V. Vlassov, "Block Sampling : Efficient Accurate Online Aggregation in MapReduce," i Cloud Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on, 2013, s. 250-257.

[93]

I. Mahmood et al., "Composability Verification of Real Time System Models Using Colored Petri Nets," i Proceedings - UKSim 15th International Conference on Computer Modelling and Simulation, UKSim 2013, 2013, s. 407-412.

[94]

A. Al-Shishtawy och V. Vlassov, "ElastMan : Autonomic elasticity manager for cloud-based key-value stores," i HPDC 2013 - Proceedings of the 22nd ACM International Symposium on High-Performance Parallel and Distributed Computing, 2013, s. 115-116.

[95]

A. Al-Shishtawy och V. Vlassov, "ElastMan : Elasticity manager for elastic key-value stores in the cloud," i Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference, 2013, s. 7:1-7:10.

[96]

A. Muddukrishna et al., "Locality-aware Task Scheduling and Data Distribution on NUMA Systems," i OpenMP in the Era of Low Power Devices and Accelerators : 9th International Workshop on OpenMP, IWOMP 2013, Canberra, Australia, September 16-18, 2013, 2013.

[97]

V. Kalavri och V. Vlassov, "MapReduce : Limitations, optimizations and open issues," i Proceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013, 2013, s. 1031-1038.

[98]

V. Kalavri, V. Vlassov och P. Brand, "PonIC : Using Stratosphere to Speed Up Pig Analytics," i Euro-Par 2013 Parallel Processing : 19th International Conference, Aachen, Germany, August 26-30, 2013. Proceedings, 2013, s. 279-290.

[99]

Y. Liu och V. Vlassov, "Replication in Distributed Storage Systems : State of the Art, Possible Directions, and Open Issues," i Proceedings - 2013 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2013, 2013, s. 225-232.

[100]

J. Jimenez et al., "Supporting cloud deployment in the Guifi.net community network," i 2013 Global Information Infrastructure Symposium, 2013, s. 6684361.

[101]

A. Muddukrishna et al., "Task Scheduling on Manycore Processors with Home Caches," i Euro-Par 2012 Workshops, 2013.

[102]

V. Kalavri, H. Shang och V. Vlassov, "m2r2: A Framework for Results Materialization and Reuse in High-Level Dataflow Systems for Big Data," i 2nd International Conference on Big Data Science and Engineering (BDSE 2013), 2013, s. 894-901.

[103]

A. Arman, A. Al-Shishtawy och V. Vlassov, "Elasticity controller for Cloud-based key-value stores," i Parallel and Distributed Systems (ICPADS), 2012 IEEE 18th International Conference on, 2012, s. 268-275.

[104]

M. A. Moulavi, A. Al-Shishtawy och V. Vlassov, "State-Space Feedback Control for Elastic Distributed Storage in a Cloud Environment," i ICAS 2012 : The Eighth International Conference on Autonomic and Autonomous Systems, 2012, s. 589-596.

[105]

I. Mahmood et al., "Verifying dynamic semantic composability of BOM-based composed models using colored petri nets," i Principles of Advanced and Distributed Simulation (PADS), 2012 ACM/IEEE/SCS 26th Workshop on, 2012, s. 250-257.

[106]

A. Muddukrishna, M. Brorsson och V. Vlassov, "A Locality Approach to Architecture-aware Task-scheduling in OpenMP," i MCC-2011. Fourth Swedish Workshop on Multicore Computing. Linköping University. Linköping, Sweden. November 23-25, 2011., 2011.

[107]

I. Mahmood et al., "Fairness Verification of BOM-Based Composed Models Using Petri Nets," i Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation, 2011, s. 5936770.

[108]

A. Al-Shishtawy, T. J. Khan och V. Vlassov, "Robust Fault-Tolerant Majority-Based Key-Value Store Supporting Multiple Consistency Levels," i 2011 IEEE 17TH INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS), 2011, s. 589-596.

[109]

A. Al-Shishtawy et al., "Achieving Robust Self-Management for Large-Scale Distributed Applications," i Self-Adaptive and Self-Organizing Systems (SASO), 2010 4th IEEE International Conference on : SASO 2010, 2010, s. 31-40.

[110]

I. Mahmood et al., "Behavioral verification of BOM based composed models," i 22th European Modeling and Simulation Symposium, EMSS 2010, 2010, s. 341-350.

[111]

L. Lindbäck et al., "Churn Tolerant Virtual Organization File System for Grids," i PARALLEL PROCESSING AND APPLIED MATHEMATICS, PART II, 2010, s. 194-203.

[112]

I. Mahmood et al., "Composability Test of BOM based models using Petri Nets," i Proceedings of the 22nd IFIP International Conferenceon Testing Software and Systems: Short Papers, 2010, s. 7-12.

[113]

A. Al-Shishtawy, L. Bao och V. Vlassov, "Policy based self-management in distributed environments," i 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshop (SASOW), 2010, s. 256-260.

[114]

A. Al-Shishtawy et al., "A design methodology for self-management in distributed environments," i IEEE International conference on Computational Science and Engineering, 2009, s. 430-436.

[115]

I. Mahmood et al., "Statemachine Matching in BOM based model Composition," i IEEE ACM DIS SIM REAL TIME, 2009, s. 136-143.

[116]

N. de Palma et al., "Tools for Architecture Based Autonomic Systems," i ICAS : 2009 Fifth International Conference on Autonomic and Autonomous Systems, 2009, s. 313-320.

[117]

H.-R. Mizani et al., "Design and Implementation of a Virtual Organization File System for Dynamic VOs," i CSE 2008 : PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING, 2008, s. 77-82.

[118]

A. Al-Shishtawy et al., "Distributed Control Loop Patterns for Managing Distributed Applications," i SASOW 2008 : SECOND IEEE INTERNATIONAL CONFERENCE ON SELF-ADAPTIVE AND SELF-ORGANIZING SYSTEMS WORKSHOPS, PROCEEDINGS, 2008, s. 260-265.

[119]

A. Al-Shishtawy et al., "Enabling Self-Management Of Component Based Distributed Applications," i FROM GRIDS TO SERVICE AND PERVASIVE COMPUTING, 2008, s. 163-174.

[120]

P. Brand et al., "The Role of Overlay Services In a Self-Managing Framework for Dynamic Virtual Organizations," i Making Grids Work : Proceedings of the CoreGRID Workshop on Programming Models Grid and P2P System Architecture Grid Systems, Tools and Environments, 2008, s. 153-164.

[121]

V. Vlassov et al., "Support for fine-grained synchronization in shared-memory multiprocessors," i Parallel Computing Technologies, Proceedings, 2007, s. 453-467.

[122]

V. Vlassov et al., "A scalable autonomous replica management framework for grids," i IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing, Proceedings, 2006, s. 33-40.

[123]

J. Jernberg et al., "DOH : A Content Delivery Peer-to-Peer Network," i Euro-Par 2006 Parallel Processing : 12th International Euro-Par Conference, Dresden, Germany, August 28 – September 1, 2006. Proceedings, 2006, s. 1026-1039.

[124]

G. Nimar, V. Vlassov och K. Popov, "Practical experience in building an agent system for semantics-based provision and selection of Grid services," i Parallel Processing And Applied Mathematics, 2006, s. 278-287.

[125]

M. Ahlberg, V. Vlassov och T. Yasui, "Router placement in wireless sensor networks," i 2006 IEEE International Conference on Mobile Adhoc and Sensor Systems, Vols 1 and 2, 2006, s. 498-501.

[126]

K. Popov et al., "An Efficient Marshaling Framework for Distributed Systems," i PARALLEL COMPUTING TECHNOLOGIES, PROCEEDINGS, 2003, s. 324-331.

[127]

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