Staff Profile

Dr Mutaz Barika is an Australian academic researcher who received a PhD in Information Technology from the University of Tasmania, Australia. He is a Lecturer (Assistant Professor) in the School of Computing at Newcastle University, Data Quality Theme Leader on EPSRC-funded National Edge AI Hub, and a Member of the Networked and Ubiquitous Systems Engineering Group. His academic experience spans over several years in the broad areas of computer science and IT, and has acquired industry experience with various roles including IT Professional, Web Specialist and Research Assistant. He holds Microsoft Certified Solutions Expert: Cloud Platform and Infrastructure Charter Member Certificate and an Amazon Academy Certified Educator (2021-2022). He has published more than 20 scientific papers in international conferences and journals, and served as a Program Committee (PC) member and Reviewer in several international conferences and as a Reviewer for international journals. He looks forward to developing collaborative projects in the IoT-cloud continuum and crowd computing. His current research interests include Big Data, IoT, Cloud Computing and Data Quality.

His research focuses on developing IoT-data driven decision making analytics workflow management system in cloud-edge continuum or crowd environment as well as guarding data quality in edge computing.

• IoT applications
• Efficient execution of data-driven IoT application on cloud and edge resources, or volunteer mobile resources
• Simulation framework (includes design and development)
• Edge data quality against cyber problems

CSC8101 Engineering for AI, Module Leader

CSC8641 Big Data Analytics, Module Leader

• Articles

  • Barika M, Garg S, Zomaya A, Ranjan R. Online Scheduling Technique To Handle Data Velocity Changes in Stream Workflows. IEEE Transactions on Parallel and Distributed Systems 2021, 32(8), 2115-2130.
  • Zeng X, Garg S, Barika M, Bista S, Puthal D, Zomaya A, Ranjan R. Detection of SLA Violation for Big Data Analytics Applications in Cloud. IEEE Transactions on Computers 2021, 70(5), 746-758.
  • Zeng X, Garg S, Barika M, Zomaya AY, Wang L, Villari M, Chen D, Ranjan R. SLA Management for Big Data Analytical Applications in Clouds: A Taxonomy Study. ACM Computing Surveys 2020, 53(3), 46.
  • Alwasel K, Jha DN, Hernandez E, Puthal D, Barika M, Varghese B, Garg SK, James P, Zomaya A, Morgan G, Ranjan R. IoTSim-SDWAN: A simulation framework for interconnecting distributed datacenters over Software-Defined Wide Area Network (SD-WAN). Journal of Parallel and Distributed Computing 2020, 143, 17-35.
  • Barika M, Garg S, Zomaya AY, Wang L, Moorsel AVAN, Ranjan R. Orchestrating big data analysis workflows in the cloud: Research challenges, survey, and future directions. ACM Computing Surveys 2019, 52(5), 1-41.
  • Barika M, Garg S, Chan A, Calheiros RN, Ranjan R. IoTSim-Stream: Modelling stream graph application in cloud simulation. Future Generation Computer Systems 2019, 99, 86-105.