We are glad to announce that our recent work “Service Demand Distribution Estimation for Microservices Using Markovian Arrival Processes” by Runan Wang, Giuliano Casale and Antonio Filieri has been accepted in the QEST 2021 international conference. This paper proposes to estimate the service demand distribution based on measurements of traffic in-between microservices with a global… Read more »
Posts By: Giuliano Casale
We are happy to announce that our recent research work “COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments”, accepted for publication in IEEE Transactions on Parallel and Distributed Systems (https://ieeexplore.ieee.org/document/9448450). This work introduces a new framework that uses coupled simulation in tandem with gradients to inputs in a deep surrogate… Read more »
Our recent work Performance analysis of list-based caches with non-uniform access about stochastic modelling of caches has been accepted for publication in IEEE/ACM Transactions on Networking. This is a collaboration with Nicolas Gast (INRIA). The journal paper extends an earlier work published at INFOCOM 2018. Abstract: List-based caches can offer lower miss rates than single-list… Read more »
Our group is seeking to fill a post-doctoral vacancy related to real-time anomaly detection and analysis of cloud monitor data. Applications are solicited from applicants with a relevant background in machine learning, real-time algorithms, or distributed systems. Applicants with a theoretical track-record in anomaly detection will also be considered.
Our recent work COCOA: Cold Start Aware Capacity Planning for Function-as-a-Service Platforms about modelling and sizing FaaS platforms taking into account cold-starts has been accepted in IEEE MASCOTS 2020, congratulations to Alim Ul Gias! Abstract: Function-as-a-Service (FaaS) is increasingly popular in the software industry due to the implied cost-savings in event-driven workloads and its synergy… Read more »
Congratulations to Ahmad Alnafessah for our recently accepted paper TRACK-Plus: Optimizing Artificial Neural Networks for Hybrid Anomaly Detection in Data Streaming ! The paper is published in IEEE Access. Abstract: Software applications can feature intrinsic variability in their execution time due to interference from other applications or software contention from other users, which may lead… Read more »
Our latest paper “Integrated performance evaluation of extended queueing network models with LINE” has now been accepted at the Winter Simulation conference 2020, the flagship event of SIGSIM. The paper is the first one to present in details our LINE 2.0 solver.
The manuscript “iThermoFog: IoT-Fog based Automatic Thermal Profile Creation for Cloud Data Centers using Artificial Intelligence Techniques” by Shreshth Tuli, Sukpahl Gill, Giuliano Casale, and Nick Jennings, has been accepted in Internet Technology Letters. The paper presents iThermoFog, which develops an AI based automatic model for creating thermal profiles in Cloud Data centers used as backends… Read more »
Our work on modelling discriminatory processor sharing in closed queueing networks has been accepted in Elsevier Performance Evaluation, congratulations to Lulai Zhu and Iker Perez for the great collaboration!
Our work on automated anomaly detection for Apache Spark has been accepted in Springer Cluster Computing, congratulations to Ahmad Alnafessah for leading this work!