Eric Keller

Efficient Network Monitoring Applications in the Kernel with eBPF and XDP

2021-11-09 · 49 citations

Continuous traffic monitoring and analytics are fundamental to the operation of today’s networks. Network telemetry allows for performing fine-grained analytics on network flow or packet records for various use cases including intrusion detection and traffic engineering. While some analytics tasks can be offloaded to programmable switches, ultimately, telemetry data needs to be processed by analytics applications in software. These applications are highly specialized, and running many such appli…

Resource Management in Cloud Computing Using Machine Learning: A Survey

2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) · 2020 · 34 citations

Efficient resource management in cloud computing research is a crucial problem because resource over-provisioning increases costs for cloud providers and cloud customers; resource under-provisioning increases the application latency, and it may violate service level agreements, which eventually makes cloud providers lose their customers and income. As a result, researchers have been striving to develop optimal resource management in cloud computing environments in different ways, such as contain…

Optimizing and Extending Serverless Platforms: A Survey

2021-12-06 · 10 citations

Serverless Computing is a new cloud computing paradigm wherein people in academia and industry are actively proposing either interesting improvements or building excellent applications on top of it. AWS, Google Cloud, Microsoft Azure, and IBM are popular samples of public clouds that offer Function-as-a-Service on top of their Serverless Computing platforms. Although this paradigm has had numerous advantages for software developers and programmers, it has introduced new challenges to cloud provi…

Detecting Unseen Anomalies in Network Systems by Leveraging Neural Networks

IEEE Transactions on Network and Service Management · 2022-11-09 · 9 citations

Despite all the progress achieved in recent years in detecting anomalies in network systems, detecting unseen anomalies such as zero-day attacks still remained a challenging task. Traditional signature-based Network Intrusion Detection Systems (NIDS) cannot detect such anomalies as there exists no known signature for them. Moreover, Machine Learning-based (ML-based) NIDS trained with a vanilla supervised learning method cannot detect them as they come from a different distribution compared to wh…

Software Packet-Level Network Analytics at Cloud Scale

IEEE Transactions on Network and Service Management · 2021-02-11 · 8 citations

As networks grow in speed, scale, and complexity, operating them reliably requires continuous monitoring and increasingly sophisticated analytics. Because of these requirements, the platforms that support analytics in cloud-scale networks face demands for both higher throughput (to keep up with high packet rates) and increased generality and programmability (to cover a wider range of applications). Recent proposals have worked toward these goals by offloading analytics application logic to line-…