Pub Date : 2024-01-01Epub Date: 2023-04-25DOI: 10.1080/15475441.2023.2196528
Ethan Weed, Riccardo Fusaroli, Elizabeth Simmons, Inge-Marie Eigsti
The current study investigated whether the difficulty in finding group differences in prosody between speakers with autism spectrum disorder (ASD) and neurotypical (NT) speakers might be explained by identifying different acoustic profiles of speakers which, while still perceived as atypical, might be characterized by different acoustic qualities. We modelled the speech from a selection of speakers (N = 26), with and without ASD, as a network of nodes defined by acoustic features. We used a community-detection algorithm to identify clusters of speakers who were acoustically similar and compared these clusters with atypicality ratings by naïve and expert human raters. Results identified three clusters: one primarily composed of speakers with ASD, one of mostly NT speakers, and one comprised of an even mixture of ASD and NT speakers. The human raters were highly reliable at distinguishing speakers with and without ASD, regardless of which cluster the speaker was in. These results suggest that community-detection methods using a network approach may complement commonly-employed human ratings to improve our understanding of the intonation profiles in ASD.
自闭症谱系障碍（ASD）患者和神经典型（NT）患者之间的拟声难以发现群体差异，本研究探讨了这一问题是否可以通过识别不同声学特征来解释，这些声学特征虽然仍被视为非典型，但可能具有不同的声学品质。我们选择了一些患有和不患有 ASD 的说话者（N = 26），将他们的语音建模为一个由声学特征定义的节点网络。我们使用群体检测算法识别出声学上相似的说话者群集，并将这些群集与天真和专业人类评分者的非典型性评分进行比较。结果发现了三个聚类：一个主要由 ASD 说话者组成，一个主要由 NT 说话者组成，还有一个由 ASD 和 NT 说话者平均混合组成。无论说话者属于哪个群组，人类评测员在区分有 ASD 和无 ASD 的说话者方面都非常可靠。这些结果表明，使用网络方法的群组检测方法可以补充常用的人类评分方法，从而提高我们对 ASD 患者语调特征的理解。
{"title":"Different in different ways: A network-analysis approach to voice and prosody in Autism Spectrum Disorder.","authors":"Ethan Weed, Riccardo Fusaroli, Elizabeth Simmons, Inge-Marie Eigsti","doi":"10.1080/15475441.2023.2196528","DOIUrl":"10.1080/15475441.2023.2196528","url":null,"abstract":"<p><p>The current study investigated whether the difficulty in finding group differences in prosody between speakers with autism spectrum disorder (ASD) and neurotypical (NT) speakers might be explained by identifying different acoustic profiles of speakers which, while still perceived as atypical, might be characterized by different acoustic qualities. We modelled the speech from a selection of speakers (N = 26), with and without ASD, as a network of nodes defined by acoustic features. We used a community-detection algorithm to identify clusters of speakers who were acoustically similar and compared these clusters with atypicality ratings by naïve and expert human raters. Results identified three clusters: one primarily composed of speakers with ASD, one of mostly NT speakers, and one comprised of an even mixture of ASD and NT speakers. The human raters were highly reliable at distinguishing speakers with and without ASD, regardless of which cluster the speaker was in. These results suggest that community-detection methods using a network approach may complement commonly-employed human ratings to improve our understanding of the intonation profiles in ASD.</p>","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"3 1","pages":"40-57"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10936700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84367082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/CLOUD55607.2022.00043
Marcelo Amaral, Tatsuhiro Chiba, Scott Trent, Takeshi Yoshimura, Sunyanan Choochotkaew
Determining the root cause of performance regression for microservices is challenging. The topological cascading performance implications among microservices hide the source of the problem. Additionally, the lack of knowledge about application phases can potentially lead to false-positive critical service detection. Service resource utilization is an imperfect proxy for application performance, potentially leading to false positives. Therefore, in this work, we propose a new performance testing framework that leverages hidden Berkeley Packet Filter (BPF) kernel metrics to locate root causes of performance regression. The framework applies a systematic multi-level approach to analyze microservice performance without intrusive code instrumentation. First, the framework constructs an attributed graph with microservice requests, scores the services to identify the critical paths, and ranks the low-level metrics to highlight the root cause of performance regression. Through judiciously designed experiments, we evaluated the metric collection overhead, showing less than 18% more latency when the application is running across hosts and 9% within the same host. In addition, depending on the application, no overhead is experienced, while the state-of-the-art approach presented up to 1060% more latency. The microservice benchmark evaluation shows that MicroLens can successfully identify the set of root causes and that the causes vary when the application is running in different infrastructures.
{"title":"MicroLens: A Performance Analysis Framework for Microservices Using Hidden Metrics With BPF","authors":"Marcelo Amaral, Tatsuhiro Chiba, Scott Trent, Takeshi Yoshimura, Sunyanan Choochotkaew","doi":"10.1109/CLOUD55607.2022.00043","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00043","url":null,"abstract":"Determining the root cause of performance regression for microservices is challenging. The topological cascading performance implications among microservices hide the source of the problem. Additionally, the lack of knowledge about application phases can potentially lead to false-positive critical service detection. Service resource utilization is an imperfect proxy for application performance, potentially leading to false positives. Therefore, in this work, we propose a new performance testing framework that leverages hidden Berkeley Packet Filter (BPF) kernel metrics to locate root causes of performance regression. The framework applies a systematic multi-level approach to analyze microservice performance without intrusive code instrumentation. First, the framework constructs an attributed graph with microservice requests, scores the services to identify the critical paths, and ranks the low-level metrics to highlight the root cause of performance regression. Through judiciously designed experiments, we evaluated the metric collection overhead, showing less than 18% more latency when the application is running across hosts and 9% within the same host. In addition, depending on the application, no overhead is experienced, while the state-of-the-art approach presented up to 1060% more latency. The microservice benchmark evaluation shows that MicroLens can successfully identify the set of root causes and that the causes vary when the application is running in different infrastructures.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"2677 1","pages":"230-240"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80301939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/CLOUD55607.2022.00023
R. SheshadriK., J. Lakshmi
Function as a Service (FaaS) is one of the widely used serverless computing service offerings to build and deploy applications on the Cloud. The platform is popular for its "pay-as-you-go" billing model, microservice-based design, event-driven executions, and autonomous scaling. Although it has its firm roots in Cloud computing service offerings, it is considerably explored in the Edge computing layer. The efficient resource management of FaaS is attractive to Edge computing because of the limited nature of resources. Existing literature on Edge-Cloud FaaS platforms orchestrates compute workloads based on factors such as data locality, resource availability, network costs, and bandwidth. However, the state-of-the-art platforms lack a comprehensive way to address the challenges of managing heterogeneous resources in the FaaS platform. The resource specification in a heterogeneous setting, lack of Quality of Service (QoS) driven resource provisioning, and function deployment exacerbate the problem of resource selection, and function deployment in FaaS platforms with a heterogeneous resource pool. To address these gaps, the current work presents a novel heterogeneous FaaS platform that deduces function resource specification using Machine Learning (ML) methods, performs smart function placement on Edge/Cloud based on a user-specified QoS requirement, and exploit data locality by caching appropriate data for function executions. Experimental results based on real-world workloads on a video surveillance application show that the proposed platform brings efficient resource utilization and cost savings at the Cloud by reducing the resource usage by up to 30%, while improving the performance of function executions by up to 25% at Edge and Cloud.
{"title":"QoS aware FaaS for Heterogeneous Edge-Cloud continuum","authors":"R. SheshadriK., J. Lakshmi","doi":"10.1109/CLOUD55607.2022.00023","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00023","url":null,"abstract":"Function as a Service (FaaS) is one of the widely used serverless computing service offerings to build and deploy applications on the Cloud. The platform is popular for its \"pay-as-you-go\" billing model, microservice-based design, event-driven executions, and autonomous scaling. Although it has its firm roots in Cloud computing service offerings, it is considerably explored in the Edge computing layer. The efficient resource management of FaaS is attractive to Edge computing because of the limited nature of resources. Existing literature on Edge-Cloud FaaS platforms orchestrates compute workloads based on factors such as data locality, resource availability, network costs, and bandwidth. However, the state-of-the-art platforms lack a comprehensive way to address the challenges of managing heterogeneous resources in the FaaS platform. The resource specification in a heterogeneous setting, lack of Quality of Service (QoS) driven resource provisioning, and function deployment exacerbate the problem of resource selection, and function deployment in FaaS platforms with a heterogeneous resource pool. To address these gaps, the current work presents a novel heterogeneous FaaS platform that deduces function resource specification using Machine Learning (ML) methods, performs smart function placement on Edge/Cloud based on a user-specified QoS requirement, and exploit data locality by caching appropriate data for function executions. Experimental results based on real-world workloads on a video surveillance application show that the proposed platform brings efficient resource utilization and cost savings at the Cloud by reducing the resource usage by up to 30%, while improving the performance of function executions by up to 25% at Edge and Cloud.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"86 1","pages":"70-80"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85146771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/CLOUD55607.2022.00025
Ojaswa Sharma, Mudit Verma, Saumya Bhadauria, P. Jayachandran
Though Chaos Engineering is a popular method to test reliability and performance assurance, available tools can only inject random or manually curated faults into a target system. Given the vast array of faults that can be injected, it is crucial to a.) intelligently pick the faults that can have tangible effects, b.) increase the test coverage, and c.) reduce the overall time needed to assess the reliability of a system under adverse conditions. To the effect, we are proposing to learn from past major outages and use genetic algorithm-based meta-heuristics to evolve complex fault injections.
{"title":"A Guided Approach Towards Complex Chaos Selection, Prioritisation and Injection","authors":"Ojaswa Sharma, Mudit Verma, Saumya Bhadauria, P. Jayachandran","doi":"10.1109/CLOUD55607.2022.00025","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00025","url":null,"abstract":"Though Chaos Engineering is a popular method to test reliability and performance assurance, available tools can only inject random or manually curated faults into a target system. Given the vast array of faults that can be injected, it is crucial to a.) intelligently pick the faults that can have tangible effects, b.) increase the test coverage, and c.) reduce the overall time needed to assess the reliability of a system under adverse conditions. To the effect, we are proposing to learn from past major outages and use genetic algorithm-based meta-heuristics to evolve complex fault injections.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"33 1","pages":"91-93"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85911379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/CLOUD55607.2022.00045
R. K., Praveen Tammana, Pravein G. Kannan, Priyanka Naik
Many applications deployed in the cloud are usually refactored into small components called microservices that are deployed as containers in a Kubernetes environment. Such applications are deployed on a cluster of physical servers which are connected via the datacenter network.In such deployments, resources such as compute, memory, and network, are shared and hence some microservices (culprits) can misbehave and consume more resources. This interference among applications hosted on the same node leads to performance issues (e.g., high latency, packet loss) in the microservices (victims) followed by a delayed or low-quality response. Given the highly distributed and transient nature of the workloads, it’s extremely challenging to debug performance issues. Especially, given the nature of existing monitoring tools, which collect traces and analyze them at individual points (network, host, etc) in a disaggregated manner.In this paper, we argue toward a case for a cross-domain (network & host) monitoring and debugging framework which could provide the end-to-end observability to debug performance issues of applications and pin-point the root-cause whether it is on the sender-host, receiver-host or the network. We present the design and provide preliminary implementation details using eBPF (extended Berkeley Packet Filter) to elucidate the feasibility of the system.
{"title":"A Case For Cross-Domain Observability to Debug Performance Issues in Microservices","authors":"R. K., Praveen Tammana, Pravein G. Kannan, Priyanka Naik","doi":"10.1109/CLOUD55607.2022.00045","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00045","url":null,"abstract":"Many applications deployed in the cloud are usually refactored into small components called microservices that are deployed as containers in a Kubernetes environment. Such applications are deployed on a cluster of physical servers which are connected via the datacenter network.In such deployments, resources such as compute, memory, and network, are shared and hence some microservices (culprits) can misbehave and consume more resources. This interference among applications hosted on the same node leads to performance issues (e.g., high latency, packet loss) in the microservices (victims) followed by a delayed or low-quality response. Given the highly distributed and transient nature of the workloads, it’s extremely challenging to debug performance issues. Especially, given the nature of existing monitoring tools, which collect traces and analyze them at individual points (network, host, etc) in a disaggregated manner.In this paper, we argue toward a case for a cross-domain (network & host) monitoring and debugging framework which could provide the end-to-end observability to debug performance issues of applications and pin-point the root-cause whether it is on the sender-host, receiver-host or the network. We present the design and provide preliminary implementation details using eBPF (extended Berkeley Packet Filter) to elucidate the feasibility of the system.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"1 1","pages":"244-246"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87732732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/CLOUD55607.2022.00054
J. C. John, Arobinda Gupta, S. Sural
With an increase in the diversity and complexity of requirements from organizations for cloud computing, there is a growing need for integrating the services of multiple cloud providers. In such multi-cloud systems, data leakage is considered to be a major security concern, which is caused by illegitimate actions of malicious users often acting in collusion. The possibility of data leakage in such environments is characterized by the number of interoperations as well as the trustworthiness of users on the collaborating clouds. In this paper, we address the problem of secure multi-cloud collaboration from an Attribute-based Access Control (ABAC) policy management perspective. In particular, we define a problem that aims to formulate ABAC policy rules for establishing a high degree of inter-cloud accesses while eliminating potential paths for data leakage. A data leakage free ABAC policy generation algorithm is proposed that first determines the likelihood of data leakage and then attempts to maximize inter-cloud collaborations. Experimental results on several large data sets show the efficacy of the proposed approach.
{"title":"Data Leakage Free ABAC Policy Construction in Multi-Cloud Collaboration","authors":"J. C. John, Arobinda Gupta, S. Sural","doi":"10.1109/CLOUD55607.2022.00054","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00054","url":null,"abstract":"With an increase in the diversity and complexity of requirements from organizations for cloud computing, there is a growing need for integrating the services of multiple cloud providers. In such multi-cloud systems, data leakage is considered to be a major security concern, which is caused by illegitimate actions of malicious users often acting in collusion. The possibility of data leakage in such environments is characterized by the number of interoperations as well as the trustworthiness of users on the collaborating clouds. In this paper, we address the problem of secure multi-cloud collaboration from an Attribute-based Access Control (ABAC) policy management perspective. In particular, we define a problem that aims to formulate ABAC policy rules for establishing a high degree of inter-cloud accesses while eliminating potential paths for data leakage. A data leakage free ABAC policy generation algorithm is proposed that first determines the likelihood of data leakage and then attempts to maximize inter-cloud collaborations. Experimental results on several large data sets show the efficacy of the proposed approach.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"30 1","pages":"315-320"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91304918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/CLOUD55607.2022.00034
Guangwen Yang, Liana Wane, W. Xue
As more virtual machines (VMs) are consolidated in the cloud system, interference among VMs sharing underlying resources may occur more frequently than ever. In particular, certain VMs’ disk I/O performance gets impacted, leading to related cloud services being seriously compromised. Existing interference analysis approaches cannot guarantee desired results due to 1) lack of effective techniques for characterizing disk I/O interference and 2) considerable runtime overhead for determining interference and related culprits. To overcome these barriers, we present Radio, an end-to-end analysis tool for disk I/O interference diagnostics in a para-virtualized cloud. Radio quantifies the dynamic changes in I/O strength across virtual CPUs (vCPUs), constructs the performance repository to efficiently identify VMs’ abnormal behaviors, and then exploits interference heat maps and non-constant correlation approaches to infer the culprits of interference. With Radio's deployment at the National Supercomputing Center in Wuxi for more than 10 months, we demonstrate its effectiveness in real-world use cases on the cloud system with more than 300 VMs deployed. Radio can effectively analyze the interference issues within 20 seconds, incurring only 0.2% extra CPU overhead on the host machine. With this achievement, Radio has successfully assisted system administrators in reducing the daily incidence of interference from more than 65% to less than 10% and improving the overall disk throughput of the cloud system by more than 27.5%.
{"title":"Radio: Reconciling Disk I/O Interference in a Para-virtualized Cloud","authors":"Guangwen Yang, Liana Wane, W. Xue","doi":"10.1109/CLOUD55607.2022.00034","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00034","url":null,"abstract":"As more virtual machines (VMs) are consolidated in the cloud system, interference among VMs sharing underlying resources may occur more frequently than ever. In particular, certain VMs’ disk I/O performance gets impacted, leading to related cloud services being seriously compromised. Existing interference analysis approaches cannot guarantee desired results due to 1) lack of effective techniques for characterizing disk I/O interference and 2) considerable runtime overhead for determining interference and related culprits. To overcome these barriers, we present Radio, an end-to-end analysis tool for disk I/O interference diagnostics in a para-virtualized cloud. Radio quantifies the dynamic changes in I/O strength across virtual CPUs (vCPUs), constructs the performance repository to efficiently identify VMs’ abnormal behaviors, and then exploits interference heat maps and non-constant correlation approaches to infer the culprits of interference. With Radio's deployment at the National Supercomputing Center in Wuxi for more than 10 months, we demonstrate its effectiveness in real-world use cases on the cloud system with more than 300 VMs deployed. Radio can effectively analyze the interference issues within 20 seconds, incurring only 0.2% extra CPU overhead on the host machine. With this achievement, Radio has successfully assisted system administrators in reducing the daily incidence of interference from more than 65% to less than 10% and improving the overall disk throughput of the cloud system by more than 27.5%.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"1 1","pages":"144-156"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90096641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/CLOUD55607.2022.00053
Sunyanan Choochotkaew, Tatsuhiro Chiba, Scott Trent, Takeshi Yoshimura, Marcelo Amaral
Containerization and application variety bring many challenges in automating evaluations for performance tuning and comparison among infrastructure choices. Due to the tightly-coupled design of benchmarks and evaluation tools, the present automated tools on Kubernetes are limited to trivial microbenchmarks and cannot be extended to complex cloudnative architectures such as microservices and serverless, which are usually managed by customized operators for setting up workload dependencies. In this paper, we propose AutoDECK, a performance evaluation framework with a fully declarative manner. The proposed framework automates configuring, deploying, evaluating, summarizing, and visualizing the benchmarking workload. It seamlessly integrates mature Kubernetes-native systems and extends multiple functionalities such as tracking the image-build pipeline, and auto-tuning. We present five use cases of evaluations and analysis through various kinds of bench-marks including microbenchmarks and HPC/AI benchmarks. The evaluation results can also differentiate characteristics such as resource usage behavior and parallelism effectiveness between different clusters. Furthermore, the results demonstrate the benefit of integrating an auto-tuning feature in the proposed framework, as shown by the 10% transferred memory bytes in the Sysbench benchmark.
{"title":"AutoDECK: Automated Declarative Performance Evaluation and Tuning Framework on Kubernetes","authors":"Sunyanan Choochotkaew, Tatsuhiro Chiba, Scott Trent, Takeshi Yoshimura, Marcelo Amaral","doi":"10.1109/CLOUD55607.2022.00053","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00053","url":null,"abstract":"Containerization and application variety bring many challenges in automating evaluations for performance tuning and comparison among infrastructure choices. Due to the tightly-coupled design of benchmarks and evaluation tools, the present automated tools on Kubernetes are limited to trivial microbenchmarks and cannot be extended to complex cloudnative architectures such as microservices and serverless, which are usually managed by customized operators for setting up workload dependencies. In this paper, we propose AutoDECK, a performance evaluation framework with a fully declarative manner. The proposed framework automates configuring, deploying, evaluating, summarizing, and visualizing the benchmarking workload. It seamlessly integrates mature Kubernetes-native systems and extends multiple functionalities such as tracking the image-build pipeline, and auto-tuning. We present five use cases of evaluations and analysis through various kinds of bench-marks including microbenchmarks and HPC/AI benchmarks. The evaluation results can also differentiate characteristics such as resource usage behavior and parallelism effectiveness between different clusters. Furthermore, the results demonstrate the benefit of integrating an auto-tuning feature in the proposed framework, as shown by the 10% transferred memory bytes in the Sysbench benchmark.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"102 1","pages":"309-314"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90651380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.1109/CLOUD55607.2022.00019
Gor Safaryan, Anshul Jindal, Mohak Chadha, M. Gerndt
Serverless computing paradigm has become more ingrained into the industry, as it offers a cheap alternative for application development and deployment. This new paradigm has also created new kinds of problems for the developer, who needs to tune memory configurations for balancing cost and performance. Many researchers have addressed the issue of minimizing cost and meeting Service Level Objective (SLO) requirements for a single FaaS function, but there has been a gap for solving the same problem for an application consisting of many FaaS functions, creating complex application workflows.In this work, we designed a tool called SLAM to address the issue. SLAM uses distributed tracing to detect the relationship among the FaaS functions within a serverless application. By modeling each of them, it estimates the execution time for the application at different memory configurations. Using these estimations, SLAM determines the optimal memory configuration for the given serverless application based on the specified SLO requirements and user-specified objectives (minimum cost or minimum execution time). We demonstrate the functionality of SLAM on AWS Lambda by testing on four applications. Our results show that the suggested memory configurations guarantee that more than 95% of requests are completed within the predefined SLOs.
{"title":"SLAM: SLO-Aware Memory Optimization for Serverless Applications","authors":"Gor Safaryan, Anshul Jindal, Mohak Chadha, M. Gerndt","doi":"10.1109/CLOUD55607.2022.00019","DOIUrl":"https://doi.org/10.1109/CLOUD55607.2022.00019","url":null,"abstract":"Serverless computing paradigm has become more ingrained into the industry, as it offers a cheap alternative for application development and deployment. This new paradigm has also created new kinds of problems for the developer, who needs to tune memory configurations for balancing cost and performance. Many researchers have addressed the issue of minimizing cost and meeting Service Level Objective (SLO) requirements for a single FaaS function, but there has been a gap for solving the same problem for an application consisting of many FaaS functions, creating complex application workflows.In this work, we designed a tool called SLAM to address the issue. SLAM uses distributed tracing to detect the relationship among the FaaS functions within a serverless application. By modeling each of them, it estimates the execution time for the application at different memory configurations. Using these estimations, SLAM determines the optimal memory configuration for the given serverless application based on the specified SLO requirements and user-specified objectives (minimum cost or minimum execution time). We demonstrate the functionality of SLAM on AWS Lambda by testing on four applications. Our results show that the suggested memory configurations guarantee that more than 95% of requests are completed within the predefined SLOs.","PeriodicalId":54281,"journal":{"name":"IEEE Cloud Computing","volume":"183 1","pages":"30-39"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79578750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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