An official website of the United States government
The increased use of micro-services to build web applications has spurred the rapid growth of Function-as-a-Service (FaaS) or serverless computing platforms. While FaaS simplifies provisioning and scaling for application developers, it introduces new challenges in resource management that need to be handled by the cloud provider. Our analysis of popular serverless workloads indicates that schedulers need to handle functions that are very short-lived, have unpredictable arrival patterns, and require expensive setup of sandboxes. The challenge of running a large number of such functions in a multi-tenant cluster makes existing scheduling frameworks unsuitable. We present Archipelago, a platform that enables low latency request execution in a multi-tenant serverless setting. Archipelago views each application as a DAG of functions, and every DAG in associated with a latency deadline. Archipelago achieves its per-DAG request latency goals by: (1) partitioning a given cluster into a number of smaller worker pools, and associating each pool with a semi-global scheduler (SGS), (2) using a latency-aware scheduler within each SGS along with proactive sandbox allocation to reduce overheads, and (3) using a load balancing layer to route requests for different DAGs to the appropriate SGS, and automatically scale the number of SGSs per DAG. Our testbed results show that Archipelago meets the latency deadline for more than 99% of realistic application request workloads, and reduces tail latencies by up to 36X compared to state-of-the-art serverless platforms.
more »
« less
DGSF: Disaggregated GPUs for Serverless Functions
Ease of use and transparent access to elastic resources have attracted many applications away from traditional platforms toward serverless functions. Many of these applications, such as machine learning, could benefit significantly from GPU acceleration. Unfortunately, GPUs remain inaccessible from serverless functions in modern production settings. We present DGSF, a platform that transparently enables serverless functions to use GPUs through general purpose APIs such as CUDA. DGSF solves provisioning and utilization challenges with disaggregation, serving the needs of a potentially large number of functions through virtual GPUs backed by a small pool of physical GPUs on dedicated servers. Disaggregation allows the provider to decouple GPU provisioning from other resources, and enables significant benefits through consolidation. We describe how DGSF solves GPU disaggregation challenges including supporting API transparency, hiding the latency of communication with remote GPUs, and load-balancing access to heavily shared GPUs. Evaluation of our prototype on six workloads shows that DGSF’s API remoting optimizations can improve the runtime of a function by up to 50% relative to unoptimized DGSF. Such optimizations, which aggressively remove GPU runtime and object management latency from the critical path, can enable functions running over DGSF to have a lower end-to-end time than when running on a GPU natively. By enabling GPU sharing, DGSF can reduce function queueing latency by up to 53%. We use DGSF to augment AWS Lambda with GPU support, showing similar benefits.
more »
« less
- Award ID(s):
- 2006943
- PAR ID:
- 10340800
- Date Published:
- Journal Name:
- IEEE International Parallel and Distributed Processing Symposium
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Serverless applications represented as DAGs have been growing in popularity. For many of these applications, it would be useful to estimate the end-to-end (E2E) latency and to allocate resources to individual functions so as to meet probabilistic guarantees for the E2E latency. This goal has not been met till now due to three fundamental challenges. The first is the high variability and correlation in the execution time of individual functions, the second is the skew in execution times of the parallel invocations, and the third is the incidence of cold starts. In this paper, we introduce ORION to achieve this goal. We first analyze traces from a production FaaS infrastructure to identify three characteristics of serverless DAGs. We use these to motivate and design three features. The first is a performance model that accounts for runtime variabilities and dependencies among functions in a DAG. The second is a method for co-locating multiple parallel invocations within a single VM thus mitigating content-based skew among these invocations. The third is a method for pre-warming VMs for subsequent functions in a DAG with the right look-ahead time. We integrate these three innovations and evaluate ORION on AWS Lambda with three serverless DAG applications. Our evaluation shows that compared to three competing approaches, \name achieves up to 90\% lower P95 latency without increasing \$$ cost, or up to 53\% lower \$$ cost without increasing P95 latency.more » « less
-
Serverless applications represented as DAGs have been growing in popularity. For many of these applications, it would be useful to estimate the end-to-end (E2E) latency and to allocate resources to individual functions so as to meet probabilistic guarantees for the E2E latency. This goal has not been met till now due to three fundamental challenges. The first is the high variability and correlation in the execution time of individual functions, the second is the skew in execution times of the parallel invocations, and the third is the incidence of cold starts. In this paper, we introduce ORION to achieve these goals. We first analyze traces from a production FaaS infrastructure to identify three characteristics of serverless DAGs. We use these to motivate and design three features. The first is a performance model that accounts for runtime variabilities and dependencies among functions in a DAG. The second is a method for co-locating multiple parallel invocations within a single VM thus mitigating content-based skew among these invocations. The third is a method for pre-warming VMs for subsequent functions in a DAG with the right look-ahead time. We integrate these three innovations and evaluate ORION on AWS Lambda with three serverless DAG applications. Our evaluation shows that compared to three competing approaches, ORION achieves up to 90% lower P95 latency without increasing $ cost, or up to 53% lower $ cost without increasing tail latency.more » « less
-
null (Ed.)Edge clouds can provide very responsive services for end-user devices that require more significant compute capabilities than they have. But edge cloud resources such as CPUs and accelerators such as GPUs are limited and must be shared across multiple concurrently running clients. However, multiplexing GPUs across applications is challenging. Further, edge servers are likely to require considerable amounts of streaming data to be processed. Getting that data from the network stream to the GPU can be a bottleneck, limiting the amount of work GPUs do. Finally, the lack of prompt notification of job completion from GPU also results in ineffective GPU utilization. We propose a framework that addresses these challenges in the following manner. We utilize spatial sharing of GPUs to multiplex the GPU more efficiently. While spatial sharing of GPU can increase GPU utilization, the uncontrolled spatial sharing currently available with state-of-the-art systems such as CUDA-MPS can cause interference between applications, resulting in unpredictable latency. Our framework utilizes controlled spatial sharing of GPU, which limits the interference across applications. Our framework uses the GPU DMA engine to offload data transfer to GPU, therefore preventing CPU from being bottleneck while transferring data from the network to GPU. Our framework uses the CUDA event library to have timely, low overhead GPU notifications. Preliminary experiments show that we can achieve low DNN inference latency and improve DNN inference throughput by a factor of ∼1.4.more » « less
-
Small and medium sized enterprises use the cloud for running online, user-facing, tail latency sensitive applications with well-defined fixed monthly budgets. For these applications, adequate system capacity must be provisioned to extract maximal performance despite the challenges of uncertainties in load and request-sizes. In this paper, we address the problem of capacity provisioning under fixed budget constraints with the goal of minimizing tail latency. To tackle this problem, we propose building systems using a heterogeneous mix of low latency expensive resources and cheap resources that provide high throughput per dollar. As load changes through the day, we use more faster resources to reduce tail latency during low load periods and more cheaper resources to handle the high load periods. To achieve these tail latency benefits, we introduce novel heterogeneity-aware scheduling and autoscaling algorithms that are designed for minimizing tail latency. Using software prototypes and by running experiments on the public cloud, we show that our approach can outperform existing capacity provisioning systems by reducing the tail latency by as much as 45% under fixed-budget settings.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/IPDPS53621.2022.00077
