Madalin Mihailescu

Exploiting distributed version concurrency in a transactional memory cluster

We investigate a transactional memory runtime system providing scaling and strong consistency, i.e., 1-copy serializability on commodity clusters for both distributed scientific applications and database applications. We introduce a novel page-level distributed concurrency control algorithm, called Distributed Multiversioning (DMV). DMV automatically detects and resolves conflicts caused by data races for distributed transactions accessing shared in-memory data structures. DMVs key novelty is in exploiting the distributed data versions that naturally come about in a replicated cluster in order to avoid read-write conflicts, hence provide scaling. DMV runs conflicting read-only and update transactions in parallel on different replicas instead of using different physical data copies within a single node as in classic multiversioning. In its most general form, DMV can be used to implement a software transactional memory system on a cluster for scaling C++ applications. DMV supports highly multithreaded database applications as well by centralizing updates on a master replica and creating the required page versions for read-only transactions lazily, on a set of slave replicas. We also show that a version-aware scheduling technique can distribute the read-only transactions across the slaves in such a way to minimize version conflicts.In our evaluation, we use DMV as a lightweight approach to scaling a hash table microbenchmark workload and the industry-standard e-commerce workload of the TPC-W benchmark on a commodity cluster. Our measurements show scaling for both benchmarks. In particular, we show near-linear scaling up to 8 transactional nodes for the most common e-commerce workload, the TPC-W shopping mix. We further show that our scaling for the TPC-W e-commerce benchmark compares favorably with that of an existing coarse-grained asynchronous replication technique.

Enhancing application robustness in Infrastructure-as-a-Service clouds

We propose OX, a runtime system that uses application-level availability constraints and application topologies discovered on the fly to enhance resilience to infrastructure anomalies for cloud applications. OX allows application owners to specify groups of highly available virtual machines. To discover application topologies, OX monitors network traffic among virtual machines, transparently. Based on this information, OX builds on-line topology graphs for applications and automatically partitions these graphs across the infrastructure to enforce availability constraints and optimize communication between virtual machines.

Outlier Detection for Fine-grained Load Balancing in Database Clusters

Recent industry trends towards reducing the costs of ownership in large data centers emphasize the need for database system techniques for both automatic performance tuning and efficient resource usage. The goal is to host several database applications on a shared server farm, including scheduling multiple applications on the same physical server or even within a single database engine, while meeting each applications service level agreement. Automatic provisioning of database servers to applications and virtualization techniques, such as, live virtual machine migration have been proposed as useful tools to address this problem. In this paper we argue that by allocating entire server-boxes and migrating entire application stacks in cases of server overload, these solutions are too coarse-grained for many overload situations. Hence, they may result in resource usage inefficiency, performance penalties, or both. We introduce an outlier detection algorithm which zooms in to the fine-grained query contexts which are most affected by an environment change and/or where a perceived overload problem is likely to originate from. We show that isolating these query contexts through either memory quota enforcements or fine-grained load balancing across different database replicas of their respective applications allows us to alleviate resource interference in many cases of overload.

Optimized application placement for network congestion and failure resiliency in clouds

We propose OX, a runtime system that shields applications from network congestion and failures, in shared Cloud data centers. OX enables customers to deploy network intensive data analytics frameworks within existing infrastructures, by protecting co-hosted QoS-constrained applications from network interference and performance degradation. Moreover, OX reduces application vulnerability to hardware failures, such as rack power outages, for all applications. OX discovers application topologies by monitoring network traffic among application components (virtual machines), transparently. In addition, OX allows application owners to specify groups of highly available virtual machines, following component roles and replication semantics. Based on this information, OX builds on-line topology graphs for applications and incrementally partitions these graphs across the infrastructure to optimize communication between virtual machines and enforce availability constraints. We show the benefits of OX in a realistic shared Cloud data center setting using a mix of Hadoop and YCSB/Cassandra workloads.