I am looking for an excellent postdoc to join our team, you can see more details and apply here.
I am also looking for more doctoral students to join this exciting project, the ad and link to apply is here.
In our upcoming EuroSys 2019 paper, we exploit the characteristics of non-uniform cache architecture (NUCA) in recent Intel processors to introduce a new memory management scheme, i.e., slice-aware memory management. We believe that we are the first to: (i) take a step toward using the current hardware more efficiently in this manner, and (ii) advocate taking advantage of NUCA characteristics in LLC and allowing networking applications to benefit from it. In addition, we propose CacheDirector, a network I/O solution which extends Direct Data I/O (DDIO) and places the packet’s header in the slice of the LLC that is closest to the relevant processing core. The results of our work showed that CacheDirector could reduce the tail latencies in latency-critical Network Function Virtualization (NFV) service chains by 21.5%. Furthermore, our work demonstrated that optimizing the computer systems and taking advantage of nanosecond improvements could have a higher impact on the performance of networking applications.
We have two open doctoral student positions, and we are looking for excellent students to join our project. After the extended the application deadline ended on January 15, 2019, we started processing applications and screening candidates.
In our upcoming SoCC 2018 paper, we present Kurma, our fast and accurate load balancer for geo-distributed storage systems. By decoupling end-to-end request completion time into components of base propagation latency, network congestion, and service time distribution, Kurma accurately estimates the rate of SLO violations for requests redirected across geo-distributed datacenters. By operating at the granularity of seconds, Kurma reduces SLO violations by a factor of up to 3 or reduces the cost of running the service by up to 17%.
This is a joint work with Kirill Bogdanov (KTH Royal Institute of Technology), Waleed Reda (Université catholique de Louvain/KTH Royal Institute of Technology), Gerald Q. Maguire Jr. (KTH Royal Institute of Technology), Dejan Kostic (KTH Royal Institute of Technology), and Marco Canini (KAUST). The full abstract is as follows:
The increasing density of globally distributed datacenters reduces the network latency between neighboring datacenters and allows replicated services deployed across neighboring locations to share workload when necessary, without violating strict Service Level Objectives (SLOs).
We present Kurma, a practical implementation of a fast and accurate load balancer for geo-distributed storage systems. At run-time, Kurma integrates network latency and service time distributions to accurately estimate the rate of SLO violations for requests redirected across geo-distributed datacenters. Using these estimates, Kurma solves a decentralized rate-based performance model enabling fast load balancing (in the order of seconds) while taming global SLO violations. We integrate Kurma with Cassandra, a popular storage system. Using real-world traces along with a geo-distributed deployment across Amazon EC2, we demonstrate Kurma’s ability to effectively share load among datacenters while reducing SLO violations by up to a factor of 3 in high load settings or reducing the cost of running the service by up to 17%.