Serverless computing has recently attracted a lot of attention from research and industry due to its promise of ultimate elasticity and operational simplicity. However, there is no consensus yet on whether or not the approach is suitable for data processing. In this paper, we present Lambada, a serverless distributed data processing framework designed to explore how to perform data analytics on serverless computing. In our analysis, supported with extensive experiments, we show in which scenarios serverless makes sense from an economic and performance perspective. We address several important technical questions that need to be solved to support data analytics and present examples from several domains where serverless offers a cost and performance advantage over existing solutions. CCS CONCEPTS• Information systems → Parallel and distributed DBMSs; Online analytical processing engines; Database query processing.

Traditional database operators such as joins are relevant not only in the context of database engines but also as a building block in many computational and machine learning algorithms. With the advent of big data, there is an increasing demand for efficient join algorithms that can scale with the input data size and the available hardware resources.In this paper, we explore the implementation of distributed join algorithms in systems with several thousand cores connected by a low-latency network as used in high performance computing systems or data centers. We compare radix hash join to sort-merge join algorithms and discuss their implementation at this scale. In the paper, we explain how to use MPI to implement joins, show the impact and advantages of RDMA, discuss the importance of network scheduling, and study the relative performance of sorting vs. hashing. The experimental results show that the algorithms we present scale well with the number of cores, reaching a throughput of 48.7 billion input tuples per second on 4,096 cores.

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The topic of agent-based service composition has been experiencing much attention recently. Researchers are applying agent technology with the aim to improve adaptiveness and flexibility of prevailing static Web service composition solutions. One major characteristic of multi-agent systems in particular is their ability of emergent behavior that allows gaining complex system behavior from small distributed sets of simple rules. This paper describes a multi-agent-based coalition formation approach for service composition that achieves emergent behavior based on a lightweight interaction protocol and decentralized decision making. The paper also presents evaluation results of first experiments to underline the validity of the approach.