Particle swarm optimization service composition algorithm based on prior knowledge

Wang, Hongbin; Yang, Ding; Xu, Hanchuan

doi:10.1007/s10845-022-02032-w

Cited by 6 publications

(7 citation statements)

References 41 publications

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“…Table 1 lists the response time of TFTCs measured by other groups. [ 21–27 ] From Table 1, it can be seen that without the memory hicorder, the response time of TFTCs could be reflected in the range of 10 0 –10 2 µs. Only with memory hicorder, the response time of TFTCs could be accurately reflected in the range of 10 1 –10 2 ns, because the memory hicorder could record TEF signal with microvolt and nanosecond accuracy, simultaneously.…”

Section: Resultsmentioning

confidence: 99%

“…[ 19,20 ] For example, with traditional voltmeter and oscilloscopes, many research teams reported the dynamic response time of TFTCs in several milliseconds. [ 21–27 ] So, without the accurate dynamic calibration procedure, TFTCs are restricted in some fields with quick response, such as aeronautics, space, nuclear energy, and so on. Fortunately, the memory hicorder could record TEF signal with microvolt and nanosecond accuracy, simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Preliminary Investigation of Thermoelectric Electromotive Force Oscillation of NiCr/NiSi Thin Film Thermocouple in Dynamic Calibration

Sun,

Liu,

Hao

et al. 2023

Small

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In order to reveal the dynamic response characteristic of thin film thermocouples (TFTCs), the nichrome/nisil (NiCr/NiSi) TFTCs are prepared onto the glass substrate. With short pulse infrared laser system, NiCr/NiSi TFTCs are dynamically calibrated. The thermoelectric electromotive force (TEF) curves of NiCr/NiSi TFTCs are recorded by the memory hicorder system, which could reflect TEF signals with resolution ratio in nanosecond and microvolt, simultaneously. With increasing laser energy from 15.49 to 29.59 mJ, TEF curves display more and more violent oscillation, even negative value. The results show that the bounce of thermal energy happens between two interfaces of TFTCs because the thermal conductivity of glass and air is significantly lower than that of NiSi/NiCr TFTCs. The bounce of thermal energy results in the obvious decrease of nNiCr and nNiSi, as well as oscillation of TEF. For laser energy in 29.59 mJ, the bounce of thermal energy in NiCr film could result in nNiCr < nNiSi. Then, TEF value appears abnormal negative value. Based on the results, the complex thermal energy transport process in TFTCs dynamic calibration is revealed, which results in the oscillation of thermal energy and TEF signal.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preliminary Investigation of Thermoelectric Electromotive Force Oscillation of NiCr/NiSi Thin Film Thermocouple in Dynamic Calibration

Sun,

Liu,

Hao

et al. 2023

Small

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“…An approximate approach for the neighborhood search of ABC was developed, which enables effective local search in the discrete space of service selection in a way that is analogical to the search in a continuous space. In order to quickly find an appropriate composition of services that meet the individual user's requirements in the Internet big data, Wang et al [33] proposed an improved particle swarm service composition method based on prior knowledge. This improved particle swarm algorithm has a mechanism to escape from the local optima.…”

Section: )mentioning

confidence: 99%

Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method

Zhang,

Bai

2024

IEEE Access

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Selecting the QoS-optimized software-as-a-service (SaaS) from a large number of services with the same functionality and different Quality of Service (QoS) is still a hot issue. Massive QoS feedback forms big QoS data, which exhibits ambiguity and randomness increasing the uncertainty of service selection. Starting from the characterization of big QoS data, the Uncertain Big QoS data-driven Efficient SaaS Decision Making Method (UBQoS_ESDM) is proposed. The method firstly utilizes cloud model to portray QoS in order to solve the problem of inaccurate description of uncertain big QoS data; then draws on the idea of Skyline query to establish uncertain service Skyline set, which reduces the search space and improves the efficiency of QoS-optimal SaaS selection; and draws on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to design two decision-making algorithms to evaluate alternative SaaSs, and to obtain the QoS-optimal SaaS that reflects user requirements. In addition, two types of backward QoS cloud generators are introduced to convert big QoS data to QoS cloud models, and the QoS cloud model adaptive adjustment mechanism is introduced too, which can adapt to the dynamic changes of QoS. Finally, theoretical proofs and experiments verify the superiority and efficiency of the method.

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“… Genetic Algorithm (GA) [ 36 ]: this algorithm adopts a binary problem encoding approach, with the number of dimensions corresponding to the available configuration types. Particle Swarm Optimization (PSO) [ 37 ]: Similar to DE, PSO faces challenges in solving discrete problems; to address this, we apply PSO to the problem by taking the residuals. …”

Section: Experimenal Evaluationmentioning

confidence: 99%

“…Particle Swarm Optimization (PSO) [ 37 ]: Similar to DE, PSO faces challenges in solving discrete problems; to address this, we apply PSO to the problem by taking the residuals.…”

Section: Experimenal Evaluationmentioning

confidence: 99%

FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

Li,

Zhang,

Lin

et al. 2023

Sensors

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The emerging serverless computing has become a captivating paradigm for deploying cloud applications, alleviating developers’ concerns about infrastructure resource management by configuring necessary parameters such as latency and memory constraints. Existing resource configuration solutions for cloud-based serverless applications can be broadly classified into modeling based on historical data or a combination of sparse measurements and interpolation/modeling. In pursuit of service response and conserving network bandwidth, platforms have progressively expanded from the traditional cloud to the edge. Compared to cloud platforms, serverless edge platforms often lead to more running overhead due to their limited resources, resulting in undesirable financial costs for developers when using the existing solutions. Meanwhile, it is extremely challenging to handle the heterogeneity of edge platforms, characterized by distinct pricing owing to their varying resource preferences. To tackle these challenges, we propose an adaptive and efficient approach called FireFace, consisting of prediction and decision modules. The prediction module extracts the internal features of all functions within the serverless application and uses this information to predict the execution time of the functions under specific configuration schemes. Based on the prediction module, the decision module analyzes the environment information and uses the Adaptive Particle Swarm Optimization algorithm and Genetic Algorithm Operator (APSO-GA) algorithm to select the most suitable configuration plan for each function, including CPU, memory, and edge platforms. In this way, it is possible to effectively minimize the financial overhead while fulfilling the Service Level Objectives (SLOs). Extensive experimental results show that our prediction model obtains optimal results under all three metrics, and the prediction error rate for real-world serverless applications is in the range of 4.25∼9.51%. Our approach can find the optimal resource configuration scheme for each application, which saves 7.2∼44.8% on average compared to other classic algorithms. Moreover, FireFace exhibits rapid adaptability, efficiently adjusting resource allocation schemes in response to dynamic environments.

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Particle swarm optimization service composition algorithm based on prior knowledge

Cited by 6 publications

References 41 publications

Preliminary Investigation of Thermoelectric Electromotive Force Oscillation of NiCr/NiSi Thin Film Thermocouple in Dynamic Calibration

Preliminary Investigation of Thermoelectric Electromotive Force Oscillation of NiCr/NiSi Thin Film Thermocouple in Dynamic Calibration

Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method

FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms

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