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Nearly twenty years after the launch of AWS, it remains difficult for most developers to harness the enormous potential of the cloud. In this paper we lay out an agenda for a new generation of cloud programming research aimed at bringing research ideas to programmers in an evolutionary fashion. Key to our approach is a separation of distributed programs into a PACT of four facets: Program semantics, Availablity, Consistency and Targets of optimization. We propose to migrate developers gradually to PACT programming by lifting familiar code into our more declarative level of abstraction. We then propose a multi-stage compiler that emits human-readable code at each stage that can be hand-tuned by developers seeking more control. Our agenda raises numerous research challenges across multiple areas including language design, query optimization, transactions, distributed consistency, compilers and program synthesis.
We present a scheduler that improves cluster utilization and job completion times by packing tasks having multi-resource requirements and inter-dependencies. While the problem is algorithmically very hard, we achieve near-optimality on the job DAGs t
Many IoT systems are data intensive and are for the purpose of monitoring for fault detection and diagnosis of critical systems. A large volume of data steadily come out of a large number of sensors in the monitoring system. Thus, we need to consider
Cloud computing has rapidly emerged as model for delivering Internet-based utility computing services. In cloud computing, Infrastructure as a Service (IaaS) is one of the most important and rapidly growing fields. Cloud providers provide users/machi
As the quantity and complexity of information processed by software systems increase, large-scale software systems have an increasing requirement for high-performance distributed computing systems. With the acceleration of the Internet in Web 2.0, Cl
Training Deep Neural Networks (DNNs) is resource-intensive and time-consuming. While prior research has explored many different ways of reducing DNN training time, the impact of input data pipeline, i.e., fetching raw data items from storage and perf