Abstract：

Mesos is a platform for running multiple diverse cluster computing frameworks, such as Hadoop, MPI, and web services, on commodity clusters. Sharing improves cluster utilization and avoids per-framework data replication. Mesos shares resources in a fine-grained manner, which allows frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today's frameworks, Mesos introduces a distributed two-level scheduling mechanism, called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to schedule on these resources. Our experimental results show that Mesos can achieve near-optimal locality when sharing the cluster among diverse frameworks, can scale up to 50,000 (emulated) nodes, and is resilient to node failures.

Biography:

Anthony Joseph, Chancellor's Associate Professor in Electrical Engineering and Computer Science at UC Berkeley, and the Director of Intel Research Berkeley. He is developing adaptive techniques for: cloud computing, distributed network monitoring and triggering, network and computer security, and security defenses for machine learning-based decision systems.  He also co-leads the DETERlab testbed, a secure scalable testbed for conducting cybersecurity research. His principal field of interest is systems and networking: cybersecurity, datacenter architectures, mobile systems and networking, and overlay networks.