The rise in popularity of machine learning, streaming, and latencysensitive online applications in shared production clusters has raised new challenges for cluster schedulers. To optimize their performance and resilience, these applications require precise control of their placements, by means of complex constraints, e.g., to collocate or separate their long-running containers across groups of nodes. In the presence of these applications, the cluster scheduler must attain global optimization objectives, such as maximizing the number of deployed applications or minimizing the violated constraints and the resource fragmentation, but without affecting the scheduling latency of short-running containers.We present Medea, a new cluster scheduler designed for the placement of long-and short-running containers. Medea introduces powerful placement constraints with formal semantics to capture interactions among containers within and across applications. It follows a novel two-scheduler design: (i) for long-running containers, it applies an optimization-based approach that accounts for constraints and global objectives; (ii) for short-running containers, it uses a traditional task-based scheduler for low placement latency. Evaluated on a 400-node cluster, our implementation of Medea on Apache Hadoop YARN achieves placement of long-running applications with significant performance and resilience benefits compared to state-of-the-art schedulers. CCS CONCEPTS• Computer systems organization → Distributed architectures; • Software and its engineering → Scheduling; Cloud computing; • Theory of computation → Linear programming;
In this paper we describe the design and implementation of ACaZoo 1 , a key-value store that combines strong consistency with high performance and high availability. ACaZoo supports the popular column-oriented data model of Apache Cassandra and HBase. It implements strongly-consistent data replication using primary-backup atomic broadcast of a writeahead log, which records data mutations to a Log-structured Merge Tree (LSM-Tree). ACaZoo scales by horizontally partitioning the key space via consistent primary-key hashing on available replica groups (RGs). LSM-Tree compactions can hamper performance, especially when they take place at RG primaries. ACaZoo addresses this problem by changing RG leadership prior to heavy compactions, a method that can improve throughput by up to 40% in write-intensive workloads. We evaluate ACaZoo using the Yahoo Cloud Serving Benchmark (YCSB) and compare it to Oracle's NoSQL Database and to Cassandra providing serial consistency via an extension of the Paxos algorithm.
Abstract-Big data processing is no longer restricted to specially-trained engineers. Instead, domain experts, data scientists and data users all want to benefit from applying data mining and machine learning algorithms at scale. A considerable obstacle towards this "democratisation of big data" are programming models: current scalable big data processing platforms such as Spark, Naiad and Flink require users to learn custom functional or declarative programming models, which differ fundamentally from popular languages such as Java, Matlab, Python or C++. An open challenge is how to provide a big data programming model for users that are not familiar with functional programming, while maintaining performance, scalability and fault tolerance.We describe JAVA2SDG, a compiler that translates annotated Java programs to stateful dataflow graphs (SDGs) that can execute on a compute cluster in a data-parallel and fault-tolerant fashion. Compared to existing distributed dataflow models, a distinguishing feature of SDGs is that their computational tasks can access distributed mutable state, thus allowing SDGs to capture the semantics of stateful Java programs. As part of the demonstration, we provide examples of machine learning programs in Java, including collaborative filtering and logistic regression, and we explain how they are translated to SDGs and executed on a large set of machines.
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