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
- NSF-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 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
-
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 computing has become increasingly popular for cloud applications, due to its compelling properties of high-level abstractions, lightweight runtime, high elasticity and pay-per-use billing. In this revolutionary computing paradigm shift, challenges arise when adapting data analytics applications to the serverless environment, due to the lack of support for efficient state sharing, which attract ever-growing research attention. In this paper, we aim to exploit the advantages of task level orchestration and fine-grained resource provisioning for data analytics on serverless platforms, with the hope of fulfilling the promise of serverless deployment to the maximum extent. To this end, we present ACTS, an autonomous cost-efficient task orchestration framework for serverless analytics. ACTS judiciously schedules and coordinates function tasks to mitigate cold-start latency and state sharing overhead. In addition, ACTS explores the optimization space of fine-grained workload distribution and function resource configuration for cost efficiency. We have deployed and implemented ACTS on AWS Lambda, evaluated with various data analytics workloads. Results from extensive experiments demonstrate that ACTS achieves up to 98% monetary cost reduction while maintaining superior job completion time performance, in comparison with the state-of-the-art baselines.more » « less
-
null (Ed.)Internet of Things (IoT) devices are becoming increasingly prevalent in our environment, yet the process of programming these devices and processing the data they produce remains difficult. Typically, data is processed on device, involving arduous work in low level languages, or data is moved to the cloud, where abundant resources are available for Functions as a Service (FaaS) or other handlers. FaaS is an emerging category of flexible computing services, where developers deploy self-contained functions to be run in portable and secure containerized environments; however, at the moment, these functions are limited to running in the cloud or in some cases at the "edge" of the network using resource rich, Linux-based systems. In this work, we investigate NanoLambda, a portable platform that brings FaaS, high-level language programming, and familiar cloud service APIs to non-Linux and microcontroller-based IoT devices. To enable this, NanoLambda couples a new, minimal Python runtime system that we have designed for the least capable end of the IoT device spectrum, with API compatibility for AWS Lambda and S3. NanoLambda transfers functions between IoT devices (sensors, edge, cloud), providing power and latency savings while retaining the programmer productivity benefits of high-level languages and FaaS. A key feature of NanoLambda is a scheduler that intelligently places function executions across multi-scale IoT deployments according to resource availability and power constraints. We evaluate a range of applications that use NanoLambda to run on devices as small as the ESP8266 with 64KB of ram and 512KB flash storage.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IPDPS53621.2022.00077
