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1. Archipelago: A Scalable Low-Latency Serverless Platform

   Singhvi, A ; Houck, K ; Balasubramanian, A ; Shaikh, Mohammed S ; Venkataraman, S ; Akella, A ( November 2019 , ArXivorg)

   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 resultsmore »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.« less
2. SFS: smart OS Scheduling for Serverless Functions

   Fu, Yuqi ; Liu, Li ; Wang, Haoliang ; Cheng, Yue ; Chen, Songqing ( November 2022 , International Conference for High Performance Computing Networking Storage and Analysis)

   Serverless computing enables a new way of building and scaling cloud applications by allowing developers to write fine-grained serverless or cloud functions. The execution duration of a cloud function is typically short---ranging from a few milliseconds to hundreds of seconds. However, due to resource contentions caused by public clouds' deep consolidation, the function execution duration may get significantly prolonged and fail to accurately account for the function's true resource usage. We observe that the function duration can be highly unpredictable with huge amplification of more than 50× for an open-source FaaS platform (OpenLambda). Our experiments show that the OS scheduling policy of cloud functions' host server can have a crucial impact on performance. The default Linux scheduler, CFS (Completely Fair Scheduler), being oblivious to workloads, frequently context-switches short functions, causing a turnaround time that is much longer than their service time. We propose SFS (Smart Function Scheduler), which works entirely in the user space and carefully orchestrates existing Linux FIFO and CFS schedulers to approximate Shortest Remaining Time First (SRTF). SFS uses two-level scheduling that seamlessly combines a new FILTER policy with Linux CFS, to trade off increased duration of long functions for significant performance improvement for short functions. Wemore »implement SFS in the Linux user space and port it to OpenLambda. Evaluation results show that SFS significantly improves short functions' duration with a small impact on relatively longer functions, compared to CFS.« less
3. Architectural Implications of Function-as-a-Service Computing

   https://doi.org/10.1145/3352460.3358296  

   Shahrad, Mohammad ; Balkind, Jonathan ; Wentzlaff, David ( October 2019 , Proceedings of the 52nd International Symposium on Microarchitecture (MICRO-52))

   Serverless computing is a rapidly growing cloud application model, popularized by Amazon's Lambda platform. Serverless cloud services provide fine-grained provisioning of resources, which scale automatically with user demand. Function-as-a-Service (FaaS) applications follow this serverless model, with the developer providing their application as a set of functions which are executed in response to a user- or system-generated event. Functions are designed to be short-lived and execute inside containers or virtual machines, introducing a range of system-level overheads. This paper studies the architectural implications of this emerging paradigm. Using the commercial-grade Apache OpenWhisk FaaS platform on real servers, this work investigates and identifies the architectural implications of FaaS serverless computing. The workloads, along with the way that FaaS inherently interleaves short functions from many tenants frustrates many of the locality-preserving architectural structures common in modern processors. In particular, we find that: FaaS containerization brings up to 20x slowdown compared to native execution, cold-start can be over 10x a short function's execution time, branch mispredictions per kilo-instruction are 20x higher for short functions, memory bandwidth increases by 6x due to the invocation pattern, and IPC decreases by as much as 35% due to inter-function interference. We open-source FaaSProfiler, the FaaS testing and profilingmore »platform that we developed for this work.« less
4. Reinforcement learning for resource management in multi-tenant serverless platforms

   https://doi.org/10.1145/3517207.3526971  

   Qiu, Haoran ; Mao, Weichao ; Patke, Archit ; Wang, Chen ; Franke, Hubertus ; Kalbarczyk, Zbigniew T. ; Başar, Tamer ; Iyer, Ravishankar K. ( April 2022 , EuroMLSys 2022 - Proceedings of the 2nd European Workshop on Machine Learning and Systems)

   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.
5. URegM: A Unified Prediction Model of Resource Consumption for Refactoring Software Smells in Open Source Cloud

   Imran, A ; Kosar, T ( October 2022 , Proceedings of ACM European Symposium on Software Engineering (ESSE 2022))

   The low cost and rapid provisioning capabilities have made the cloud a desirable platform to launch complex scientific applications. However, resource utilization optimization is a significant challenge for cloud service providers, since the earlier focus is provided on optimizing resources for the applications that run on the cloud, with a low emphasis being provided on optimizing resource utilization of the cloud computing internal processes. Code refactoring has been associated with improving the maintenance and understanding of software code. However, analyzing the impact of the refactoring source code of the cloud and studying its impact on cloud resource usage require further analysis. In this paper, we propose a framework called Unified Regression Modeling (URegM) which predicts the impact of code smell refactor- ing on cloud resource usage. We test our experiments in a real-life cloud environment using a complex scientific application as a workload. Results show that URegM is capable of accurately predicting resource consumption due to code smell refactoring. This will permit cloud service providers with advanced knowledge about the impact of refactoring code smells on resource consumption, thus allowing them to plan their resource provisioning and code refactoring more effectively.