Harvesting Idle Resources in Serverless Computing via Reinforcement Learning

• URL: http://arxiv.org/abs/2108.12717v1
• Date: Sat, 28 Aug 2021 23:02:56 GMT
• Title: Harvesting Idle Resources in Serverless Computing via Reinforcement Learning
• Authors: Hanfei Yu, Hao Wang, Jian Li, Seung-Jong Park
• Abstract summary: FRM maximizes resource efficiency by dynamically harvesting idle resources from functions over-supplied to functions under-supplied. FRM monitors each function's resource utilization in real-time, detects over-provisioning and under-provisioning, and applies deep reinforcement learning to harvest idle resources safely. We have implemented and deployed a FRM prototype in a 13-node Apache OpenWhisk cluster.
• Score: 7.346628578439277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Serverless computing has become a new cloud computing paradigm that promises to deliver high cost-efficiency and simplified cloud deployment with automated resource scaling at a fine granularity. Users decouple a cloud application into chained functions and preset each serverless function's memory and CPU demands at megabyte-level and core-level, respectively. Serverless platforms then automatically scale the number of functions to accommodate the workloads. However, the complexities of chained functions make it non-trivial to accurately determine the resource demands of each function for users, leading to either resource over-provision or under-provision for individual functions. This paper presents FaaSRM, a new resource manager (RM) for serverless platforms that maximizes resource efficiency by dynamically harvesting idle resources from functions over-supplied to functions under-supplied. FaaSRM monitors each function's resource utilization in real-time, detects over-provisioning and under-provisioning, and applies deep reinforcement learning to harvest idle resources safely using a safeguard mechanism and accelerate functions efficiently. We have implemented and deployed a FaaSRM prototype in a 13-node Apache OpenWhisk cluster. Experimental results on the OpenWhisk cluster show that FaaSRM reduces the execution time of 98% of function invocations by 35.81% compared to the baseline RMs by harvesting idle resources from 38.8% of the invocations and accelerating 39.2% of the invocations.

Related papers

• Input-Based Ensemble-Learning Method for Dynamic Memory Configuration of Serverless Computing Functions [18.36339203254509]
  We present MemFigLess, a serverless solution that estimates the memory requirement of a serverless function with input-awareness. MemFigLess is able to capture the input-aware resource relationships and allocate upto 82% less resources and save up to 87% run-time costs.
  arXiv  Detail & Related papers  (2024-11-12T00:03:11Z)
• The Power of Resets in Online Reinforcement Learning [73.64852266145387]
  We explore the power of simulators through online reinforcement learning with local simulator access (or, local planning) We show that MDPs with low coverability can be learned in a sample-efficient fashion with only $Qstar$-realizability. We show that the notorious Exogenous Block MDP problem is tractable under local simulator access.
  arXiv  Detail & Related papers  (2024-04-23T18:09:53Z)
• SPES: Towards Optimizing Performance-Resource Trade-Off for Serverless Functions [31.01399126339857]
  Serverless computing is gaining traction due to its efficiency and ability to harness on-demand cloud resources. Existing solutions tend to use over-simplistic strategies for function pre-loading/unloading without full invocation pattern exploitation. We propose SPES, the first differentiated scheduler for runtime cold start mitigation by optimizing serverless function provision.
  arXiv  Detail & Related papers  (2024-03-26T10:28:41Z)
• ReLU$^2$ Wins: Discovering Efficient Activation Functions for Sparse LLMs [91.31204876440765]
  We introduce a general method that defines neuron activation through neuron output magnitudes and a tailored magnitude threshold. To find the most efficient activation function for sparse computation, we propose a systematic framework. We conduct thorough experiments on LLMs utilizing different activation functions, including ReLU, SwiGLU, ReGLU, and ReLU$2$.
  arXiv  Detail & Related papers  (2024-02-06T08:45:51Z)
• Shabari: Delayed Decision-Making for Faster and Efficient Serverless Functions [0.30693357740321775]
  We introduce Shabari, a resource management framework for serverless systems. Shabari makes decisions as late as possible to right-size each invocation to meet functions' performance objectives. For a range of serverless functions and inputs, Shabari reduces SLO violations by 11-73%.
  arXiv  Detail & Related papers  (2024-01-16T22:20:36Z)
• On-demand Cold Start Frequency Reduction with Off-Policy Reinforcement Learning in Serverless Computing [18.36339203254509]
  The presented work focuses on reducing the frequent, on-demand cold starts on the platform by using Reinforcement Learning(RL) The proposed approach uses model-free Q-learning that consider function metrics such as CPU utilization, existing function instances, and response failure rate, to proactively initialize functions, in advance. The evaluation results demonstrate a favourable performance of the RL-based agent when compared to Kubeless' default policy and a function keep-alive policy.
  arXiv  Detail & Related papers  (2023-08-15T03:01:41Z)
• Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach [67.06539298956854]
  fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs)
  arXiv  Detail & Related papers  (2022-06-13T02:19:20Z)
• DeF-DReL: Systematic Deployment of Serverless Functions in Fog and Cloud environments using Deep Reinforcement Learning [8.204696165200577]
  Fog environment made its limited resource available to a large number of users to deploy their serverless applications. Recent research mainly focuses on assigning maximum resources to such applications from the fog node and not taking full advantage of the cloud environment. We propose DeF-DReL, a Systematic Deployment of Serverless Functions in Fog and Cloud environments using Deep Reinforcement Learning.
  arXiv  Detail & Related papers  (2021-10-29T12:10:54Z)
• Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning [61.29990368322931]
  Pollux improves scheduling performance in deep learning (DL) clusters by adaptively co-optimizing inter-dependent factors. Pollux reduces average job completion times by 37-50% relative to state-of-the-art DL schedulers.
  arXiv  Detail & Related papers  (2020-08-27T16:56:48Z)
• A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks [108.57859531628264]
  A joint task, spectrum, and transmit power allocation problem is investigated for a wireless network. The proposed algorithm can reduce the number of iterations needed for convergence and the maximal delay among all users by up to 18% and 11.1% compared to the standard Q-learning algorithm.
  arXiv  Detail & Related papers  (2020-07-20T13:46:42Z)
• Dynamic Parameter Allocation in Parameter Servers [74.250687861348]
  We propose to integrate dynamic parameter allocation into parameter servers, describe an efficient implementation of such a parameter server called Lapse. We found that Lapse provides near-linear scaling and can be orders of magnitude faster than existing parameter servers.
  arXiv  Detail & Related papers  (2020-02-03T11:37:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.