More Like this

1. Understanding the effect of data center resource disaggregation on production DBMSs

   https://doi.org/10.14778/3397230.3397249  

   Zhang, Qizhen; Cai, Yifan; Chen, Xinyi; Angel, Sebastian; Chen, Ang; Liu, Vincent; Loo, Boon Thau (May 2020, Proceedings of the VLDB Endowment)

   null (Ed.)

   Resource disaggregation is a new architecture for data centers in which resources like memory and storage are decoupled from the CPU, managed independently, and connected through a high-speed network. Recent work has shown that although disaggregated data centers (DDCs) provide operational benefits, applications running on DDCs experience degraded performance due to extra network latency between the CPU and their working sets in main memory. DBMSs are an interesting case study for DDCs for two main reasons: (1) DBMSs normally process data-intensive workloads and require data movement between different resource components; and (2) disaggregation drastically changes the assumption that DBMSs can rely on their own internal resource management. We take the first step to thoroughly evaluate the query execution performance of production DBMSs in disaggregated data centers. We evaluate two popular open-source DBMSs (MonetDB and PostgreSQL) and test their performance with the TPC-H benchmark in a recently released operating system for resource disaggregation. We evaluate these DBMSs with various configurations and compare their performance with that of single-machine Linux with the same hardware resources. Our results confirm that significant performance degradation does occur, but, perhaps surprisingly, we also find settings in which the degradation is minor or where DDCs actually improve performance. 

   more » « less
2. Detecting Asymmetric Application-layer Denial-of-Service Attacks In-Flight with FineLame

   https://doi.org/10.5555/3358807.3358866  

   Demoulin, Henri Maxime; Pedisich, Isaac; Vasilakis, Nikos; Liu, Vincent; Loo, Boon Thau; Phan, Linh Thi (January 2019, Proceedings of the 2019 USENIX Conference on Usenix Annual Technical Conference)

   Denial of service (DoS) attacks increasingly exploit algorithmic, semantic, or implementation characteristics dormant in victim applications, often with minimal attacker resources. Practical and efficient detection of these asymmetric DoS attacks requires us to (i) catch offending requests in-flight, before they consume a critical amount of resources, (ii) remain agnostic to the application internals, such as the programming language or runtime system, and (iii) introduce low overhead in terms of both performance and programmer effort. This paper introduces FINELAME, a language-independent framework for detecting asymmetric DoS attacks. FINELAME leverages operating system visibility across the entire software stack to instrument key resource allocation and negotiation points. It leverages recent advances in the Linux extended Berkeley Packet Filter virtual machine to attach application-level interposition probes to key request processing functions, and lightweight resource monitors--user/kernel-level probes--to key resource allocation functions. The data collected is used to train a model of resource utilization that occurs throughout the lifetime of individual requests. The model parameters are then shared with the resource monitors, which use them to catch offending requests in-flight, inline with resource allocation. We demonstrate that FINELAME can be integrated with legacy applications with minimal effort, and that it is able to detect resource abuse attacks much earlier than their intended completion time while posing low performance overheads. 

   more » « less
3. ReTail: Opting for Learning Simplicity to Enable QoS-Aware Power Management in the Cloud

   Shuang Chen, Angela Jin (April 2022, 28th IEEE International Symposium on High-Performance Computer Architecture (HPCA-28))

   Many cloud services have Quality-of-Service (QoS) requirements; most requests have to to complete within a given latency constraint. Recently, researchers have begun to investigate whether it is possible to meet QoS while attempting to save power on a per-request basis. Existing work shows that one can indeed hand-tune a request latency predictor offline for a particular cloud application, and consult it at runtime to modulate CPU voltage and frequency, resulting in substantial power savings. In this paper, we propose ReTail, an automated and general solution for request-level power management of latency-critical services with QoS constraints. We present a systematic process to select the features of any given application that best correlate with its request latency. ReTail uses these features to predict latency, and adjust CPU’s power consumption. ReTail’s predictor is trained fully at runtime. We show that unlike previous findings, simple techniques perform better than complex machine learning models, when using the right input features. For a web search engine, ReTail outperforms prior mechanisms based on complex hand-tuned predictors for that application domain. Furthermore, ReTail’s systematic approach also yields superior power savings across a diverse set of cloud applications. 

   more » « less
4. Rearchitecting Linux Storage Stack for µs Latency and High Throughput

   Hwang, Jaehyun; Vuppalapati, Midhul; Peter, Simon; Agarwal, Rachit (January 2021, 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21))

   null (Ed.)

   This paper demonstrates that it is possible to achieve μs-scale latency using Linux kernel storage stack, even when tens of latency-sensitive applications compete for host resources with throughput-bound applications that perform read/write operations at throughput close to hardware capacity. Furthermore, such performance can be achieved without any modification in applications, network hardware, kernel CPU schedulers and/or kernel network stack. We demonstrate the above using design, implementation and evaluation of blk-switch, a new Linux kernel storage stack architecture. The key insight in blk-switch is that Linux's multi-queue storage design, along with multi-queue network and storage hardware, makes the storage stack conceptually similar to a network switch. blk-switch uses this insight to adapt techniques from the computer networking literature (e.g., multiple egress queues, prioritized processing of individual requests, load balancing, and switch scheduling) to the Linux kernel storage stack. blk-switch evaluation over a variety of scenarios shows that it consistently achieves μs-scale average and tail latency (at both 99th and 99.9th percentiles), while allowing applications to near-perfectly utilize the hardware capacity. 

   more » « less
5. Rearchitecting Linux Storage Stack for µs Latency and High Throughput

   Hwang, Jaehyun; Vuppalapati, Midhul; Peter, Simon; Agarwal, Rachit (January 2021, 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21))

   null (Ed.)

   This paper demonstrates that it is possible to achieve μs-scale latency using Linux kernel storage stack, even when tens of latency-sensitive applications compete for host resources with throughput-bound applications that perform read/write operations at throughput close to hardware capacity. Furthermore, such performance can be achieved without any modification in applications, network hardware, kernel CPU schedulers and/or kernel network stack. We demonstrate the above using design, implementation and evaluation of blk-switch, a new Linux kernel storage stack architecture. The key insight in blk-switch is that Linux's multi-queue storage design, along with multi-queue network and storage hardware, makes the storage stack conceptually similar to a network switch. blk-switch uses this insight to adapt techniques from the computer networking literature (e.g., multiple egress queues, prioritized processing of individual requests, load balancing, and switch scheduling) to the Linux kernel storage stack. blk-switch evaluation over a variety of scenarios shows that it consistently achieves μs-scale average and tail latency (at both 99th and 99.9th percentiles), while allowing applications to near-perfectly utilize the hardware capacity. 

   more » « less