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- Taheri, Javid, et al.
(författare)
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Using Machine Learning to Predict the Exact Resource Usage of Microservice Chains
- 2023
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Ingår i: 16th IEEE/ACM International Conference on Utility and Cloud Computing, UCC 2023<em></em>. - New York : Association for Computing Machinery, Inc. - 9798400702341
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Konferensbidrag (refereegranskat)abstract
- Cloud computing offers a wide range of services, but it comes with some challenges. One of these challenges is to predict the resource utilization of the nodes that run applications and services. This is especially relevant for container-based platforms such as Kubernetes. Predicting the resource utilization of a Kubernetes cluster can help optimize the performance, reliability, and cost-effectiveness of the platform. This paper focuses on how well different resources in a cluster can be predicted using machine learning techniques. The approach consists of three main steps: data collection and extraction, data pre-processing and analysis, and resource prediction. The data collection step involves stressing the system with a load-generator (called Locust) and collecting data from Locust and Kubernetes with the use of Prometheus. The data pre-processing and extraction step involves extracting relevant data and transforming it into a suitable format for the machine learning models. The final step involves applying different machine learning models to the data and evaluating their accuracy. The results illustrate that different machine learning techniques can predict resources accurately.
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- Al-Dulaimy, Auday, et al.
(författare)
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LOOPS : A Holistic Control Approach for Resource Management in Cloud Computing
- 2021
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Ingår i: ICPE 2021 - Proceedings of the ACM/SPEC International Conference on Performance Engineering. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450381949 ; , s. 117-124
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Konferensbidrag (refereegranskat)abstract
- In cloud computing model, resource sharing introduces major benefits for improving resource utilization and total cost of ownership, but it can create technical challenges on the running performance. In practice, orchestrators are required to allocate sufficient physical resources to each Virtual Machine (VM) to meet a set of predefined performance goals. To ensure a specific service level objective, the orchestrator needs to be equipped with a dynamic tool for assigning computing resources to each VM, based on the run-Time state of the target environment. To this end, we present LOOPS, a multi-loop control approach, to allocate resources to VMs based on the service level agreement (SLA) requirements and the run-Time conditions. LOOPS is mainly composed of one essential unit to monitor VMs, and three control levels to allocate resources to VMs based on requests from the essential node. A tailor-made controller is proposed with each level to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one host to another. The three levels work together to meet the required SLA. The experimental results have shown that the proposed approach can meet applications' performance goals by assigning the resources required by cloud-based applications.
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- Gokan Khan, Michel, 1989-, et al.
(författare)
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PerfSim : A Performance Simulator for Cloud Native Microservice Chains
- 2023
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Ingår i: IEEE Transactions on Cloud Computing. - : IEEE. - 2168-7161. ; :2, s. 1395-1413
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Tidskriftsartikel (refereegranskat)abstract
- Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost of such applications. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can simulate the performance behavior of service chains over a given period and provides an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ~81-99% simulation accuracy in approximating the average response time of incoming requests and ~16-1200 times speed-up factor for the simulation.
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- Tavallaie, O., et al.
(författare)
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QCF : QoS-Aware Communication Framework for Real-Time IoT Services
- 2019
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Ingår i: Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science. - Cham : Springer. - 9783030337018 ; , s. 353-368, s. 353-368
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Konferensbidrag (refereegranskat)abstract
- Routing Protocol for Low-power Lossy Networks (RPL) is designed by Internet Engineering Task Force (IETF) as the de facto routing standard for Internet of Things (IoT). Supporting mobility and providing Quality of Service (QoS) in the timeliness domain were not addressed in the IETF standard. RPL performs poorly when it comes to satisfying QoS constraints and adaptability to changes in the network topology. In this paper, we address this formidable problem by proposing QCF, a QoS-aware Communication Framework for real-time IoT services. Our proposed framework provides a lightweight practical approach to support timeliness requirements, and node mobility. It applies fuzzy logic to balance energy resources and traffic loads in the network. QCF estimates node mobility and the one-hop delay by using two novel methods. It employs two-hop neighbor information to enhance the parent selection process, and estimates the remaining time to the packet’s deadline without using synchronized clocks. We integrate QCF into the Contiki operating system and implement it on Zolerita IoT motes. Emulation results show that QCF improves the deadline delivery ratio by up to 67% and reduces the end-to-end delay by up to 63%.
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- Ahammed, Farhan, et al.
(författare)
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Finding lower bounds of localization with noisy measurements using genetic algorithms
- 2011
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Ingår i: Proceedings of the first ACM international symposium on Design and analysis of intelligent vehicular networks and applications (DIVANet '11). - Miami, Florida, USA : Association for Computing Machinery (ACM). - 9781450309042 ; , s. 47-54
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Konferensbidrag (refereegranskat)abstract
- Vehicular Ad-Hoc Networks (VANETs) are wireless networks with mobile nodes (vehicles) which connect in an ad-hoc manner. Many vehicles use the Global Positioning System (GPS) to provide their locations. However the inaccuracy of GPS devices leads to some vehicles incorrectly assuming they are located at different positions and sometimes on different roads. VANETs can be used to increase the accuracy of each vehicle's computed location by allowing vehicles to share information regarding the measured distances to neighbouring vehicles. This paper looks at finding how much improvement can be made given the erroneous measurements present in the system. An evolutionary algorithm is used to evolve instances of parameters used by the VLOCI2 algorithm, also presented in this paper, to find instances which minimises the inaccuracy in computed locations. Simulation results show a definite improvement in location accuracy and lower bounds on how much improvement is possible is inferred.
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