Publications

Partial lists are also available in DBLP and Google Scholar.

2024

• Wasimoff: Distributed Computation Offloading Using WebAssembly in the Browser
  A. Semjonov, H. Bornholdt, J. Edinger, G. Russo Russo
  Proc. of *LESS '24 (co-located with PerCom '24)
  [abstract]
• QoS-aware offloading policies for serverless functions in the Cloud-to-Edge continuum
  G. Russo Russo, D. Ferrarelli, D. Pasquali, V. Cardellini, F. Lo Presti
  Future Generation Computer Systems, vol. 156
  [abstract] [pdf] [doi]
• A framework for offloading and migration of serverless functions in the Edge–Cloud Continuum
  G. Russo Russo, V. Cardellini, F. Lo Presti
  Pervasive and Mobile Computing, vol. 100
  [abstract] [pdf] [doi]

2023

• Serverless functions in the cloud-edge continuum: Challenges and opportunities
  G. Russo Russo, V. Cardellini, F. Lo Presti
  Proc. of Euromicro PDP '23
  Best paper (Special Session on Compute Continuum)
  [abstract]
• Serverledge: Decentralized Function-as-a-Service for the Edge-Cloud Continuum
  G. Russo Russo, T. Mannucci, V. Cardellini, F. Lo Presti
  Proc. of IEEE PerCom '23
  Artifact Certified; Results Certified
  [abstract] [pdf] [doi]
• Hierarchical Auto-Scaling Policies for Data Stream Processing on Heterogeneous Resources
  G. Russo Russo, V. Cardellini, F. Lo Presti
  ACM Transactions on Autonomous and Adaptive Systems
  [abstract] [pdf] [doi]
• FIGARO: reinForcement learnInG mAnagement acRoss the computing cOntinuum
  F. Filippini, R. Cavadini, D. Ardagna, R. Lancellotti, G. Russo Russo, V. Cardellini, F. Lo Presti
  Proc. of 3rd International Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC 2023)
  [abstract]
• Compute continuum: What lies ahead?
  M. Nardelli, G. Russo Russo, V. Cardellini
  Proc. of WSCC '23 (co-located with Euro-Par '23)
  [abstract]

2022

• Towards QoS-Aware Function Composition Scheduling in Apache OpenWhisk
  G. Russo Russo, A. Milani, V. Cardellini, S. Iannucci
  Proc. of *LESS '22 (co-located with PerCom '22)
  [abstract] [doi]
• Run-Time Adaptation of Data Stream Processing Systems: The State of the Art
  V. Cardellini, F. Lo Presti, M. Nardelli, G. Russo Russo
  ACM Computing Surveys
  [abstract] [pdf] [doi]
• Real-Time Analysis of Market Data Leveraging Apache Flink
  C. Calavaro, G. Russo Russo, V. Cardellini
  Proc. of DEBS 2022
  [abstract] [doi]

2021

• Towards a Security-aware Deployment of Data Streaming Applications in Fog Computing
  G. Russo Russo, V. Cardellini, F. Lo Presti, M. Nardelli
  In Fog/Edge Computing for Security,Privacy, and Applications, W. Chang and J. Wu (eds.)
  [abstract] [pdf] [doi]
• MEAD: Model-based Vertical Auto-Scaling for Data Stream Processing
  G. Russo Russo, V. Cardellini, G. Casale, F. Lo Presti
  Proc. of IEEE/ACM CCGRID '21
  [abstract] [pdf] [doi]
• Elastic Pulsar Functions for Distributed Stream Processing
  G. Russo Russo, A. Schiazza, V. Cardellini
  ACM/SPEC ICPE 2021 Companion Volume
  [abstract] [doi]
• AI-driven Performance Management in Data-Intensive Applications
  A. Alnafessah, G. Russo Russo, V. Cardellini, G. Casale, F. Lo Presti
  In Communications Network and Service Management in the Era of Artificial Intelligence and Machine Learning, N. Zincir-Heywood, Y. Diao, M. Mellia (eds.), IEEE Press Series on Networks and Service Management, Wiley
  [abstract] [doi]

2020

• Model-based Auto-Scaling of Distributed Data Stream Processing Applications
  G. Russo Russo
  Proc. of Middleware '20 Doctoral Symposium
  [abstract] [doi]

2019

• Self-Adaptive Data Stream Processing in Geo-Distributed Computing Environments
  G. Russo Russo
  Proc. of DEBS 2019
  [abstract] [doi]
• Reinforcement learning based policies for elastic stream processing on heterogeneous resources
  G. Russo Russo, V. Cardellini, F. Lo Presti
  Proc. of DEBS 2019
  [abstract] [doi]

2018

• Towards Decentralized Auto-Scaling Policies for Data Stream Processing Applications
  G. Russo Russo
  Proc. of 10th ZEUS Workshop (ZEUS 2018), Dresden, Germany, February 2018.
  [abstract] [pdf]
• Optimal operator deployment and replication for elastic distributed data stream processing
  V. Cardellini, F. Lo Presti, M. Nardelli, G. Russo Russo
  Concurrency and Computation: Practice & Experience, Vol. 30, No. 9, May 2018
  [abstract] [pdf] [doi]
• Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
  G. Russo Russo, M. Nardelli, V. Cardellini, F. Lo Presti
  Algorithms, vol. 11(9).
  [abstract] [pdf] [doi]
• Decentralized self-adaptation for elastic Data Stream Processing
  V. Cardellini, F. Lo Presti, M. Nardelli, G. Russo Russo
  Future Generation Computer Systems, vol. 87, pp. 171–185
  [abstract] [pdf] [doi]
• A Multi-level Elasticity Framework for Distributed Data Stream Processing
  M. Nardelli, G. Russo Russo, V. Cardellini, F. Lo Presti
  International Workshop on Autonomic Solutions for Parallel and Distributed Data Stream Processing (Auto-DaSP 2018), in conjunction with Euro-Par 2018, Turin, Italy, August 2018
  [abstract] [pdf] [doi]

2017

• Towards hierarchical autonomous control for elastic data stream processing in the fog
  V. Cardellini, F. Lo Presti, M. Nardelli, G. Russo Russo
  International Workshop on Autonomic Solutions for Parallel and Distributed Data Stream Processing (Auto-DaSP 2017), Santiago de Compostela, Spain, August 2017
  [abstract] [pdf] [doi]
• Auto-scaling in Data Stream Processing: a Model Based Reinforcement Learning Approach
  V. Cardellini, F. Lo Presti, M. Nardelli, G. Russo Russo
  InfQ 2017 - New Frontiers in Quantitative Methods in Informatics, Communications in Computer and Information Science, Vol. 825, Springer, 2018.
  [abstract] [pdf] [doi]