Search for: All records

Workload autoscaling is widely used in public and private cloud systems to maintain stable service performance and save resources. However, it remains challenging to set the optimal resource limits and dynamically scale each workload at runtime. Reinforcement learning (RL) has recently been proposed and applied in various systems tasks, including resource management. In this paper, we first characterize the state-of-the-art RL approaches for workload autoscaling in a public cloud and point out that there is still a large gap in taking the RL advances to production systems. We then propose AWARE, an extensible framework for deploying and managing RL-based agents in production systems. AWARE leverages meta-learning and bootstrapping to (a) automatically and quickly adapt to different workloads, and (b) provide safe and robust RL exploration. AWARE provides a common OpenAI Gym-like RL interface to agent developers for easy integration with different systems tasks. We illustrate the use of AWARE in the case of workload autoscaling. Our experiments show that AWARE adapts a learned autoscaling policy to new workloads 5.5x faster than the existing transfer-learning-based approach and provides stable online policy-serving performance with less than 3.6% reward degradation. With bootstrapping, AWARE helps achieve 47.5% and 39.2% higher CPU and memory utilization while reducing SLO violations by a factor of 16.9x during policy training. 

Serverless Function-as-a-Service (FaaS) offers improved programmability for customers, yet it is not server-“less” and comes at the cost of more complex infrastructure management (e.g., resource provisioning and scheduling) for cloud providers. To maintain function service-level objectives (SLOs) and improve resource utilization efficiency, recent research has been focused on applying online learning algorithms such as reinforcement learning (RL) to manage resources. Compared to rule-based solutions with heuristics, RL-based approaches eliminate humans in the loop and avoid the painstaking generation of heuristics. Despite the initial success of applying RL, we first show in this paper that the state-of-the-art single-agent RL algorithm (S-RL) suffers up to 4.8x higher p99 function latency degradation on multi-tenant serverless FaaS platforms compared to isolated environments and is unable to converge during training. We then design and implement a scalable and incremental multi-agent RL framework based on Proximal Policy Optimization (SIMPPO). Our experiments on widely used serverless benchmarks demonstrate that in multi-tenant environments, SIMPPO enables each RL agent to efficiently converge during training and provides online function latency performance comparable to that of S-RL trained in isolation (which we refer to as the baseline for assessing RL performance) with minor degradation (<9.2%). In addition, SIMPPO reduces the p99 function latency by 4.5x compared to S-RL in multi-tenant cases. 

Hardware memory disaggregation is an emerging trend in datacenters that provides access to remote memory as part of a shared pool or unused memory on machines across the network. Memory disaggregation aims to improve memory utilization and scale memory-intensive applications. Current state-of-the-art prototypes have shown that hardware disaggregated memory is a reality at the rack-scale. However, the memory utilization benefits of memory disaggregation can only be fully realized at larger scales enabled by a datacenter-wide network. Introduction of a datacenter network results in new performance and reliability failures that may manifest as higher network latency. Additionally, sharing of the network introduces new points of contention between multiple applications. In this work, we characterize the impact of variable network latency and contention in an open-source hardware disaggregated memory prototype - ThymesisFlow. To support our characterization, we have developed a delay injection framework that introduces delays in remote memory access to emulate network latency. Based on the characterization results, we develop insights into how reliability and resource allocation mechanisms should evolve to support hardware memory disaggregation beyond rack-scale in datacenters. 

Serverless Function-As-A-Service (FaaS) is an emerging cloud computing paradigm that frees application developers from infrastructure management tasks such as resource provisioning and scaling. To reduce the tail latency of functions and improve resource utilization, recent research has been focused on applying online learning algorithms such as reinforcement learning (RL) to manage resources. Compared to existing heuristics-based resource management approaches, RL-based approaches eliminate humans in the loop and avoid the painstaking generation of heuristics. In this paper, we show that the state-of-The-Art single-Agent RL algorithm (S-RL) suffers up to 4.6x higher function tail latency degradation on multi-Tenant serverless FaaS platforms and is unable to converge during training. We then propose and implement a customized multi-Agent RL algorithm based on Proximal Policy Optimization, i.e., multi-Agent PPO (MA-PPO). We show that in multi-Tenant environments, MA-PPO enables each agent to be trained until convergence and provides online performance comparable to S-RL in single-Tenant cases with less than 10% degradation. Besides, MA-PPO provides a 4.4x improvement in S-RL performance (in terms of function tail latency) in multi-Tenant cases. 

Function-as-a-Service (FaaS) is becoming an increasingly popular cloud-deployment paradigm for serverless computing that frees application developers from managing the infrastructure. At the same time, it allows cloud providers to assert control in workload consolidation, i.e., co-locating multiple containers on the same server, thereby achieving higher server utilization, often at the cost of higher end-to-end function request latency. Interestingly, a key aspect of serverless latency management has not been well studied: the trade-off between application developers' latency goals and the FaaS providers' utilization goals. This paper presents a multi-faceted, measurement-driven study of latency variation in serverless platforms that elucidates this trade-off space. We obtained production measurements by executing FaaS benchmarks on IBM Cloud and a private cloud to study the impact of workload consolidation, queuing delay, and cold starts on the end-to-end function request latency. We draw several conclusions from the characterization results. For example, increasing a container's allocated memory limit from 128 MB to 256 MB reduces the tail latency by 2× but has 1.75× higher power consumption and 59% lower CPU utilization. 

Despite over a decade of research, it is still challenging for mobile UI testing tools to achieve satisfactory effectiveness, especially on industrial apps with rich features and large code bases. Our experiences suggest that existing mobile UI testing tools are prone to exploration tarpits, where the tools get stuck with a small fraction of app functionalities for an extensive amount of time. For example, a tool logs out an app at early stages without being able to log back in, and since then the tool gets stuck with exploring the app's pre-login functionalities (i.e., exploration tarpits) instead of its main functionalities. While tool vendors/users can manually hardcode rules for the tools to avoid specific exploration tarpits, these rules can hardly generalize, being fragile in face of diverted testing environments and fast app iterations. To identify and resolve exploration tarpits, we propose VET, a general approach including a supporting system for the given specific Android UI testing tool on the given specific app under test (AUT). VET runs the tool on the AUT for some time and records UI traces, based on which VET identifies exploration tarpits by recognizing their patterns in the UI traces. VET then pinpoints the actions (e.g., clicking logout) or the screens that lead to or exhibit exploration tarpits. In subsequent test runs, VET guides the testing tool to prevent or recover from exploration tarpits. From our evaluation with state-of-the-art Android UI testing tools on popular industrial apps, VET identifies exploration tarpits that cost up to 98.6% testing time budget. These exploration tarpits reveal not only limitations in UI exploration strategies but also defects in tool implementations. VET automatically addresses the identified exploration tarpits, enabling each evaluated tool to achieve higher code coverage and improve crash-triggering capabilities. 

Configuration changes are among the dominant causes of failures of large-scale software system deployment. Given the velocity of configuration changes, typically at the scale of hundreds to thousands of times daily in modern cloud systems, checking these configuration changes is critical to prevent failures due to misconfigurations. Recent work has proposed configuration testing, Ctest, a technique that tests configuration changes together with the code that uses the changed configurations. Ctest can automatically generate a large number of ctests that can effectively detect misconfigurations, including those that are hard to detect by traditional techniques. However, running ctests can take a long time to detect misconfigurations. Inspired by traditional test-case prioritization (TCP) that aims to reorder test executions to speed up detection of regression code faults, we propose to apply TCP to reorder ctests to speed up detection of misconfigurations. We extensively evaluate a total of 84 traditional and novel ctest-specific TCP techniques. The experimental results on five widely used cloud projects demonstrate that TCP can substantially speed up misconfiguration detection. Our study provides guidelines for applying TCP to configuration testing in practice. 

Modern high-performance computing (HPC) systems concurrently execute multiple distributed applications that contend for the high-speed network leading to congestion. Consequently, application runtime variability and suboptimal system utilization are observed in production systems. To address these problems, we propose Netscope, a congestion mitigation framework based on a novel delay sensitivity metric. Delay sensitivity of an application is used to quantify the impact of congestion on its runtime. Netscope uses delay sensitivity estimates to drive a congestion mitigation mechanism to selectively throttle applications that are less susceptible to congestion. We evaluate Netscope on two Cray Aries systems, including a production supercomputer, on common scientific applications. Our evaluation shows that Netscope has a low training cost and accurately estimates the impact of congestion on application runtime with a correlation between 0.7 and 0.9. Moreover, Netscope reduces application tail runtime increase by up to 16.3x while improving the median system utility by 12%. 

Modern datacenter infrastructures are increasingly architected as a cluster of loosely coupled services. The cluster states are typically maintained in a logically centralized, strongly consistent data store (e.g., ZooKeeper, Chubby and etcd), while the services learn about the evolving state by reading from the data store, or via a stream of notifications. However, it is challenging to ensure services are correct, even in the presence of failures, networking issues, and the inherent asynchrony of the distributed system. In this paper, we identify that partial histories can be used to effectively reason about correctness for individual services in such distributed infrastructure systems. That is, individual services make decisions based on observing only a subset of changes to the world around them. We show that partial histories, when applied to distributed infrastructures, have immense explanatory power and utility over the state of the art. We discuss the implications of partial histories and sketch tooling for reasoning about distributed infrastructure systems. 

The need for fail-slow fault tolerance in modern distributed systems is highlighted by the increasingly reported fail-slow hardware/software components that lead to poor performance system-wide. We argue that fail-slow fault tolerance not only needs new distributed protocol designs, but also desires programming support for implementing and verifying fail-slow fault-tolerant code. Our observation is that the inability of tolerating fail-slow faults in existing distributed systems is often rooted in the implementations and is difficult to understand and debug. We designed the Dependably Fast Library (DepFast) for implementing fail-slow tolerant distributed systems. DepFast provides expressive interfaces for taking control of possible fail-slow points in the program to prevent unexpected slowness propagation once and for all. We use DepFast to implement a distributed replicated state machine (RSM) and show that it can tolerate various types of fail-slow faults that affect existing RSM implementations.