arXiv subjects

Christina Delimitrou

Publications and source records attributed to Christina Delimitrou.

28 records · Page 2Linked to original sources

arXiv2020

HiveMind: A Scalable and Serverless Coordination Control Platform for UAV Swarms

Swarms of autonomous devices are increasing in ubiquity and size. There are two main trains of thought for controlling devices in such swarms; centralized and distributed control. Centralized platforms achieve higher output quality but result in high network traffic and limited scalability, while decentralized systems are more scalable, but less sophisticated. In this work we present HiveMind, a centralized coordination control platform for IoT swarms that is both scalable and performant. HiveMind leverages a centralized cluster for all resource-intensive computation, deferring lightweight and time-critical operations, such as obstacle avoidance to the edge devices to reduce network traffic. HiveMind employs an event-driven serverless framework to run tasks on the cluster, guarantees fault tolerance both in the edge devices and serverless functions, and handles straggler tasks and underperforming devices. We evaluate HiveMind on a swarm of 16 programmable drones on two scenarios; searching for given items, and counting unique people in an area. We show that HiveMind achieves better performance and battery efficiency compared to fully centralized and fully decentralized platforms, while also handling load imbalances and failures gracefully, and allowing edge devices to leverage the cluster to collectively improve their output quality.

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arXiv2020

Ripple: A Practical Declarative Programming Framework for Serverless Compute

Serverless computing has emerged as a promising alternative to infrastructure- (IaaS) and platform-as-a-service (PaaS)cloud platforms for applications with ample parallelism and intermittent activity. Serverless promises greater resource elasticity, significant cost savings, and simplified application deployment. All major cloud providers, including Amazon, Google, and Microsoft, have introduced serverless to their public cloud offerings. For serverless to reach its potential, there is a pressing need for programming frameworks that abstract the deployment complexity away from the user. This includes simplifying the process of writing applications for serverless environments, automating task and data partitioning, and handling scheduling and fault tolerance. We present Ripple, a programming framework designed to specifically take applications written for single-machine execution and allow them to take advantage of the task parallelism of serverless. Ripple exposes a simple interface that users can leverage to express the high-level dataflow of a wide spectrum of applications, including machine learning (ML) analytics, genomics, and proteomics. Ripple also automates resource provisioning, meeting user-defined QoS targets, and handles fault tolerance by eagerly detecting straggler tasks. We port Ripple over AWS Lambda and show that, across a set of diverse applications, it provides an expressive and generalizable programming framework that simplifies running data-parallel applications on serverless, and can improve performance by up to 80x compared to IaaS/PaaS clouds for similar costs.

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arXiv2019

uqSim: Scalable and Validated Simulation of Cloud Microservices

Current cloud services are moving away from monolithic designs and towards graphs of many loosely-coupled, single-concerned microservices. Microservices have several advantages, including speeding up development and deployment, allowing specialization of the software infrastructure, and helping with debugging and error isolation. At the same time they introduce several hardware and software challenges. Given that most of the performance and efficiency implications of microservices happen at scales larger than what is available outside production deployments, studying such effects requires designing the right simulation infrastructures. We present uqSim, a scalable and validated queueing network simulator designed specifically for interactive microservices. uqSim provides detailed intra- and inter-microservice models that allow it to faithfully reproduce the behavior of complex, many-tier applications. uqSim is also modular, allowing reuse of individual models across microservices and end-to-end applications. We have validated uqSim both against simple and more complex microservices graphs, and have shown that it accurately captures performance in terms of throughput and tail latency. Finally, we use uqSim to model the tail at scale effects of request fanout, and the performance impact of power management in latency-sensitive microservices.

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arXiv2019

An Open-Source Benchmark Suite for Cloud and IoT Microservices

Cloud services have recently started undergoing a major shift from monolithic applications, to graphs of hundreds of loosely-coupled microservices. Microservices fundamentally change a lot of assumptions current cloud systems are designed with, and present both opportunities and challenges when optimizing for quality of service (QoS) and utilization. In this paper we explore the implications microservices have across the cloud system stack. We first present DeathStarBench, a novel, open-source benchmark suite built with microservices that is representative of large end-to-end services, modular and extensible. DeathStarBench includes a social network, a media service, an e-commerce site, a banking system, and IoT applications for coordination control of UAV swarms. We then use DeathStarBench to study the architectural characteristics of microservices, their implications in networking and operating systems, their challenges with respect to cluster management, and their trade-offs in terms of application design and programming frameworks. Finally, we explore the tail at scale effects of microservices in real deployments with hundreds of users, and highlight the increased pressure they put on performance predictability.

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arXiv2019

Leveraging Deep Learning to Improve the Performance Predictability of Cloud Microservices

Performance unpredictability is a major roadblock towards cloud adoption, and has performance, cost, and revenue ramifications. Predictable performance is even more critical as cloud services transition from monolithic designs to microservices. Detecting QoS violations after they occur in systems with microservices results in long recovery times, as hotspots propagate and amplify across dependent services. We present Seer, an online cloud performance debugging system that leverages deep learning and the massive amount of tracing data cloud systems collect to learn spatial and temporal patterns that translate to QoS violations. Seer combines lightweight distributed RPC-level tracing, with detailed low-level hardware monitoring to signal an upcoming QoS violation, and diagnose the source of unpredictable performance. Once an imminent QoS violation is detected, Seer notifies the cluster manager to take action to avoid performance degradation altogether. We evaluate Seer both in local clusters, and in large-scale deployments of end-to-end applications built with microservices with hundreds of users. We show that Seer correctly anticipates QoS violations 91% of the time, and avoids the QoS violation to begin with in 84% of cases. Finally, we show that Seer can identify application-level design bugs, and provide insights on how to better architect microservices to achieve predictable performance.

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arXiv2018

The Architectural Implications of Microservices in the Cloud

Cloud services have recently undergone a shift from monolithic applications to microservices, with hundreds or thousands of loosely-coupled microservices comprising the end-to-end application. Microservices present both opportunities and challenges when optimizing for quality of service (QoS) and cloud utilization. In this paper we explore the implications cloud microservices have on system bottlenecks, and datacenter server design. We first present and characterize an end-to-end application built using tens of popular open-source microservices that implements a movie renting and streaming service, and is modular and extensible. We then use the end-to-end service to study the scalability and performance bottlenecks of microservices, and highlight implications they have on the design of datacenter hardware. Specifically, we revisit the long-standing debate of brawny versus wimpy cores in the context of microservices, we quantify the I-cache pressure they introduce, and measure the time spent in computation versus communication between microservices over RPCs. As more cloud applications switch to this new programming model, it is increasingly important to revisit the assumptions we have previously used to build and manage cloud systems.

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arXiv2018

To Centralize or Not to Centralize: A Tale of Swarm Coordination

Large swarms of autonomous devices are increasing in size and importance. When it comes to controlling the devices of large-scale swarms there are two main lines of thought. Centralized control, where all decisions - and often compute - happen in a centralized back-end cloud system, and distributed control, where edge devices are responsible for selecting and executing tasks with minimal or zero help from a centralized entity. In this work we aim to quantify the trade-offs between the two approaches with respect to task assignment quality, latency, and reliability. We do so first on a local swarm of 12 programmable drones with a 10-server cluster as the backend cloud, and then using a validated simulator to study the tail at scale effects of swarm coordination control. We conclude that although centralized control almost always outperforms distributed in the quality of its decisions, it faces significant scalability limitations, and we provide a list of system challenges that need to be addressed for centralized control to scale.

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arXiv2018

Seer: Leveraging Big Data to Navigate the Increasing Complexity of Cloud Debugging

Performance unpredictability in cloud services leads to poor user experience, degraded availability, and has revenue ramifications. Detecting performance degradation a posteriori helps the system take corrective action, but does not avoid the QoS violations. Detecting QoS violations after the fact is even more detrimental when a service consists of hundreds of thousands of loosely-coupled microservices, since performance hiccups can quickly propagate across the dependency graph of microservices. In this work we focus on anticipating QoS violations in cloud settings to mitigate performance unpredictability to begin with. We propose Seer, a cloud runtime that leverages the massive amount of tracing data cloud systems collect over time and a set of practical learning techniques to signal upcoming QoS violations, as well as identify the microservice(s) causing them. Once an imminent QoS violation is detected Seer uses machine-level hardware events to determine the cause of the QoS violation, and adjusts the resource allocations to prevent it. In local clusters with 10 40-core servers and 200-instance clusters on GCE running diverse cloud microservices, we show that Seer correctly anticipates QoS violations 91% of the time, and attributes the violation to the correct microservice in 89% of cases. Finally, Seer detects QoS violations early enough for a corrective action to almost always be applied successfully.

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arXiv2018

Mage: Online Interference-Aware Scheduling in Multi-Scale Heterogeneous Systems

Heterogeneity has grown in popularity both at the core and server level as a way to improve both performance and energy efficiency. However, despite these benefits, scheduling applications in heterogeneous machines remains challenging. Additionally, when these heterogeneous resources accommodate multiple applications to increase utilization, resources are prone to contention, destructive interference, and unpredictable performance. Existing solutions examine heterogeneity either across or within a server, leading to missed performance and efficiency opportunities. We present Mage, a practical interference-aware runtime that optimizes performance and efficiency in systems with intra- and inter-server heterogeneity. Mage leverages fast and online data mining to quickly explore the space of application placements, and determine the one that minimizes destructive interference between co-resident applications. Mage continuously monitors the performance of active applications, and, upon detecting QoS violations, it determines whether alternative placements would prove more beneficial, taking into account any overheads from migration. Across 350 application mixes on a heterogeneous CMP, Mage improves performance by 38% and up to 2x compared to a greedy scheduler. Across 160 mixes on a heterogeneous cluster, Mage improves performance by 30% on average and up to 52% over the greedy scheduler, and by 11% over the combination of Paragon [15] for inter- and intra-server heterogeneity.

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arXiv2018

Pliant: Leveraging Approximation to Improve Datacenter Resource Efficiency

Cloud multi-tenancy is typically constrained to a single interactive service colocated with one or more batch, low-priority services, whose performance can be sacrificed when deemed necessary. Approximate computing applications offer the opportunity to enable tighter colocation among multiple applications whose performance is important. We present Pliant, a lightweight cloud runtime that leverages the ability of approximate computing applications to tolerate some loss in their output quality to boost the utilization of shared servers. During periods of high resource contention, Pliant employs incremental and interference-aware approximation to reduce contention in shared resources, and prevent QoS violations for co-scheduled interactive, latency-critical services. We evaluate Pliant across different interactive and approximate computing applications, and show that it preserves QoS for all co-scheduled workloads, while incurring a 2.1\% loss in output quality, on average.

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