Atlas: Hybrid Cloud Migration Advisor for Interactive Microservices

Hybrid cloud provides an attractive solution to microservices for better resource elasticity. A subset of application components can be offloaded from the on-premises cluster to the cloud, where they can readily access additional resources. However, the selection of this subset is challenging because of the large number of possible combinations. A poor choice degrades the application performance, disrupts the critical services, and increases the cost to the extent of making the use of hybrid cloud unviable. This paper presents Atlas, a hybrid cloud migration advisor. Atlas uses a data-driven approach to learn how each user-facing API utilizes different components and their network footprints to drive the migration decision. It learns to accelerate the discovery of high-quality migration plans from millions and offers recommendations with customizable trade-offs among three quality indicators: end-to-end latency of user-facing APIs representing application performance, service availability, and cloud hosting costs. Atlas continuously monitors the application even after the migration for proactive recommendations. Our evaluation shows that Atlas can achieve 21% better API performance (latency) and 11% cheaper cost with less service disruption than widely used solutions.Comment: To appear at EuroSys 202

Enhancing availability in large scale storage systems and services: architectures and techniques

Enterprises today are dealing with extremely large amounts of critical digital information that continues to grow at an astonishing rate. On the other hand, storage software (firmware, middleware) and systems are becoming much more complex and existing failure recovery mechanisms are insufficient to handle the scale of these systems while meeting high availability and service quality expectations. In addition, the concurrent development and quality assurance processes, the large number of test scenarios and the large scale of these systems and services imply that failures will be the norm rather than the exception. Therefore achieving high availability and reliability in storage systems remains a major concern and an open research challenge. Most existing work in the domain of storage system availability addresses failures of the storage media (such as disks) and recoverability from these failures. However, failures at the firmware and middleware layers remain largely unaddressed. This dissertation research addresses these challenges in depth across different storage architectures. Concretely, we make the following contributions: First, we develop a recovery conscious framework for multi-core architectures and a suite of techniques for performing efficient fine-grained recovery (micro-recovery) in storage controller firmware that can be retrofitted into legacy code. The framework includes a task-level recovery mechanism, the Log(Lock) architecture that allows system state restoration during micro-recovery, and recovery-conscious scheduling algorithms that are designed to reduce the ripple effect of failure and improve recovery efficiency and system availability. Our second technical contribution addresses the storage middleware availability. We develop the notion of hierarchical middleware architectures by organizing critical cluster management services into a hierarchical overlay network, which separates persistent application state from global system control state and demonstrate significant improvement in the availability and reliability of enterprise scale storage systems. In addition, we develop the notion of operator reuse and a suite of reuse techniques to improve data availability. The key idea of operator reuse is to efficiently utilize system resources by exploiting reuse opportunities in both operators and persistent state of computing nodes. We demonstrate our design through STREAMREUSE, a reuse-conscious store-forward network of storage nodes, which offers distributed stream query processing services.Ph.D.Committee Chair: Ling Liu; Committee Member: Brian Cooper; Committee Member: Calton Pu; Committee Member: Douglas Blough; Committee Member: Karsten Schwa

Using Hierarchies for Optimizing Distributed Stream Queries

We consider the problem of query optimization in distributed data stream systems where multiple continuous queries may be executing simultaneously. In order to achieve the best performance, query planning (such as join ordering) must be considered in conjunction with deployment planning (e.g., assigning operators to physical nodes). In our scenario, the large number of network nodes, query operators, and opportunities for operator sharing between queries means that brute force and traditional techniques are too expensive. We propose two algorithms - the Bottom-Up algorithm and the Top-Down algorithm, which utilize hierarchical network partitions to provide scalable query optimization. We present analysis that establishes the bounds on the search-space and sub-optimality achieved by our algorithms. Finally, through simulations and experiments using a prototype deployed on Emulab we demonstrate the effectiveness of our algorithms. The Top-Down algorithm, for instance, was able to achieve, on an average, solutions that were sub-optimal by only 10% while considering less than 1% of the search space