This tutorial consists of a series of hands-on exercises for beginners that have minimum or even no experience in cloud computing platforms. We will first give an overview of cloud computing. Then we cover some high-level architecture of GCP and how they affect application development and deployment. Finally, we will walk through exercises on using several services in GCP with the focus on storage and computation services. If time permits, we will build a simple application using TensorFlow.

The modern storage landscape is changing at an exciting rate. New technologies, such as Intel DC Persistent Memory, are being introduced. At the same time, new applications such as blockchain are emerging with new requirements from the storage subsystem. New regulations, such as the General Data Protection Regulation (GDPR), place new constraints on how data may be read and written. As a result, designing storage systems that satisfy these constraints is interesting and challenging. In this talk, I will describe the lessons we learned from tackling this challenge in various forms: my group has built file systems and concurrent data structures for persistent memory, storage solutions for blockchains, and analyzed how GDPR affects storage systems.

Blockchain as an emerging distributed protocol has been widely used in IoT systems. Using blockchain as an IoT data sharing protocol provides precious features including consistency, reliability, and traceability. However, such combination brings high cryptography overhead and consensus latency when sharing data from large number of IoT sensors. To address these issues, we propose a novel blockchain based architecture for IoT data sharing systems. In this architecture, data messages signed by IoT sensors are packaged into data blocks and distributed to the blockchain network. We propose a data block structure with identity-based aggregate signature to protect data reliability from malicious sink nodes and reduce the communication, storage, and computing cost of signatures. We also present a multiple state chain structure with a new consensus algorithm which cuts back consensus phases and accelerate the consensus process. Finally, we evaluate the proportion of data in a block and the blockchain consensus latency, which shows a better performance than PBFT in this scenario.

We consider a large-scale optimization problem with private utilities over a network where servers are connected through a graph and each server has their own edge devices connected to it. This setup finds applications in many network architectures from wireless communications to power networks and to supply chain management. Over this architecture, we provide a decentralized federated optimization algorithm that uses decomposition methods along with a single round of consensus exchange on the global dual variables. The convergence performance of the method is illustrated with numerical examples.

Collaborative text editing applications like Google docs, Etherpad and Overleaf allow users to con- currently edit a "shared" document. Most existing collaborative text editing software require total ordering on the updates made to the document, which is achieved using a centralized sever or some form of consensus algorithm. Then on top of the ordering, the editor uses either operational transformation (OT) or differential synchronization (diff-sync) to apply the ordered update events to the already committed changes on their local copies. If there is no delay or failure, then eventually all updates can be applied correctly in the agreed order. Unfortunately, not only are these methods computation- ally intensive but they often result in conflicts due to users writing to the same location. It has also been proved that the metadata overhead for such protocols are at least linear in the number of delete events. Moreover, these event- based and diff-based algorithms are exceptionally difficult to implement and there are no provably correct solutions to these problems in the face of heavy concurrency. These collaborative editors either provide no proven guarantees or only provide eventual guarantees for correctness. With LiteDoc, we propose a different approach to tackle this problem: we make collaborative editing fast, scalable and robust by providing simplified semantics. More im- portantly, we can formally prove that LiteDoc achieves deterministic guarantees of correctness. LiteDoc divides the shared document into several sections and allow only one user to write at a particular section at any given time. This removes all conflicts that arise from having multiple writers writing to the same location. This mechanism also obviates the task of implementing cumbersome modules for OT, diff-sync and rollbacks in case of conflicts. Note that while LiteDoc supports less features than general collaborative editors like Google docs, it is natural (and courteous) to avoid concurrent writing to the same location when multiple people collaborate.

The tutorial tries to answer the following questions through interactive and hands-on exercises: What is Cassandra? Why do I need it? What are the trade-offs? What are the applications? How do I install, configure, and develop applications on top of Cassandra?