An official website of the United States government
The HPC community is actively researching and evaluating tools to support execution of scientific applications in cloud-based environ- ments. Among the various technologies, containers have recently gained importance as they have significantly better performance compared to full-scale virtualization, support for microservices and DevOps, and work seamlessly with workflow and orchestration tools. Docker is currently the leader in containerization technology because it offers low overhead, flexibility, portability of applications, and reproducibility. Singularity is another container solution that is of interest as it is designed specifically for scientific applications. It is important to conduct performance and feature analysis of the container technologies to understand their applicability for each application and target execution environment. This paper presents a (1) performance evaluation of Docker and Singularity on bare metal nodes in the Chameleon cloud (2) mecha- nism by which Docker containers can be mapped with InfiniBand hardware with RDMA communication and (3) analysis of mapping elements of parallel workloads to the containers for optimal re- source management with container-ready orchestration tools. Our experiments are targeted toward application developers so that they can make informed decisions on choosing the container tech- nologies and approaches that are suitable for their HPC workloads on cloud infrastructure. Our performance analysis shows that sci- entific workloads for both Docker and Singularity based containers can achieve near-native performance. Singularity is designed specifically for HPC workloads. However, Docker still has advantages over Singularity for use in clouds as it provides overlay networking and an intuitive way to run MPI applications with one container per rank for fine-grained resources allocation. Both Docker and Singularity make it possible to directly use the underlying network fabric from the containers for coarse- grained resource allocation.
more »
« less
Carbon Containers: A System-level Facility for Managing Application-level Carbon Emissions
To reduce their environmental impact, cloud datacenters' are increasingly focused on optimizing applications' carbon-efficiency, or work done per mass of carbon emitted. To facilitate such optimizations, we present Carbon Containers, a simple system-level facility, which extends prior work on power containers, that automatically regulates applications' carbon emissions in response to variations in both their work-load's intensity and their energy's carbon-intensity. Specifically, Carbon Containers enable applications to specify a maximum carbon emissions rate (in g.CO2e/hr), and then transparently enforce this rate via a combination of vertical scaling, container migration, and suspend/resume while maximizing either energy-efficiency or performance. Carbon Containers are especially useful for applications that i) must continue running even during high-carbon periods, and ii) execute in regions with few variations in carbon-intensity. These low-variability regions also tend to have high average carbon-intensity, which increases the importance of regulating carbon emissions. We implement a Carbon Container prototype by extending Linux Containers to incorporate the mechanisms above and evaluate it using real workload traces and carbon-intensity data from multiple regions. We compare Carbon Containers with prior work that regulates carbon emissions by suspending/resuming applications during high/low carbon periods. We show that Carbon Containers are more carbon-efficient and improve performance while maintaining similar carbon emissions.
more »
« less
- PAR ID:
- 10496893
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- SoCC '23: Proceedings of the 2023 ACM Symposium on Cloud Computing
- ISBN:
- 9798400703874
- Page Range / eLocation ID:
- 17 to 31
- Format(s):
- Medium: X
- Location:
- Santa Cruz CA USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
We introduce and study the online pause and resume problem. In this problem, a player attempts to find the k lowest (alternatively, highest) prices in a sequence of fixed length T, which is revealed sequentially. At each time step, the player is presented with a price and decides whether to accept or reject it. The player incurs aswitching cost whenever their decision changes in consecutive time steps, i.e., whenever they pause or resume purchasing. This online problem is motivated by the goal of carbon-aware load shifting, where a workload may be paused during periods of high carbon intensity and resumed during periods of low carbon intensity and incurs a cost when saving or restoring its state. It has strong connections to existing problems studied in the literature on online optimization, though it introduces unique technical challenges that prevent the direct application of existing algorithms. Extending prior work on threshold-based algorithms, we introducedouble-threshold algorithms for both the minimization and maximization variants of this problem. We further show that the competitive ratios achieved by these algorithms are the best achievable by any deterministic online algorithm. Finally, we empirically validate our proposed algorithm through case studies on the application of carbon-aware load shifting using real carbon trace data and existing baseline algorithms.more » « less
-
null (Ed.)Large-scale, high-throughput computational science faces an accelerating convergence of software and hardware. Software container-based solutions have become common in cloud-based datacenter environments, and are considered promising tools for addressing heterogeneity and portability concerns. However, container solutions reflect a set of assumptions which complicate their adoption by developers and users of scientific workflow applications. Nor are containers a universal solution for deployment in high-performance computing (HPC) environments which have specialized and vertically integrated scheduling and runtime software stacks. In this paper, we present a container design and deployment approach which uses modular layering to ease the deployment of containers into existing HPC environments. This layered approach allows operating system integrations, support for different communication and performance monitoring libraries, and application code to be defined and interchanged in isolation. We describe in this paper the details of our approach, including specifics about container deployment and orchestration for different HPC scheduling systems. We also describe how this layering method can be used to build containers for two separate applications, each deployed on clusters with different batch schedulers, MPI networking support, and performance monitoring requirements. Our experience indicates that the layered approach is a viable strategy for building applications intended to provide similar behavior across widely varying deployment targets.more » « less
-
null (Ed.)The popularity of Internet-of-Things (IoT) has provided us with unprecedented opportunities to enable a variety of emerging services in a smart home environment. Among those services, sensing the liquid level in a container is critical to building many smart home and mobile healthcare applications that improve the quality of life. This paper presents LiquidSense, a liquid level sensing system that is low-cost, high accuracy, widely applicable to different daily liquids and containers, and can be easily integrated with existing smart home networks. LiquidSense uses existing home WiFi network and a low-cost transducer that attached to the container to sense the resonance of the container for liquid level detection. In particular, our system mounts a low-cost transducer on the surface of the container and emits a well-designed chirp signal to make the container resonant, which introduces subtle changes to the home WiFi signals. By analyzing the subtle phase changes of the WiFi signals, LiquidSense extracts the resonance frequency as a feature for liquid level detection. Our system constructs prediction models for both continuous and discrete predictions using curve fitting and SVM respectively. We evaluate LiquidSense in home environments with containers of three different materials and six types of liquids. Results show that LiquidSense achieves an overall accuracy of 97% for continuous prediction and an overall F-score of 0.968 for discrete predication. Results also show that our system has a large coverage in a home environment and works well under non-line-of-sight (NLOS) scenarios.more » « less
-
null (Ed.)Container technologies are seeing wider use at advanced computing facilities for managing highly complex applications that must execute at multiple sites. However, in a distributed high throughput computing setting, the unrestricted use of containers can result in the container explosion problem.If a new container image is generated for each variation of a job dispatched to a site, shared storage is soon exceeded. On the other hand, if a single large container image is used to meet multiple needs, the size of that container may become a problem for storage and transport. To address this problem, we observe that many containers have an internal structure generated by a structured package manager, and this information could be used to strategically combine and share container images. We develop LANDLORD to exploit this property and evaluate its performance through a combination of simulation studies and empirical measurement of high energy physics applications.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3620678.3624644
