Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions

Singh, Parminder; Gupta, Pooja; Jyoti, Kiran; Nayyar, Anand

doi:10.12694/scpe.v20i2.1537

Cited by 76 publications

(44 citation statements)

References 117 publications

(131 reference statements)

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“…In the following, we conduct a literature review of the existing work related to our study. In general, existing service deployment strategies can be divided into active strategies based on predictable models and passive deployment strategies based on the dynamic system information [13].…”

Section: Related Workmentioning

confidence: 99%

“…In practice, the service deployment strategy can be divided into active and passive approaches [13]. In the former way, services are pre-deployed using excessive or sufficient resources to achieve a predictable deployment plan [12], [14]- [17].…”

Section: Introductionmentioning

confidence: 99%

“…By accumulating (26) over time slots i ∈ {0, 1, ..., t − 1}, the following relationship can be obtained: According to (13), it can be known that for any time slot t, L(t) ≥ 0 exists. In addition, according to (4), (8), and (9), it can be known that for any time slot t, c p (t) ≥ 0, c o (t) ≥ 0 exist.…”

mentioning

confidence: 99%

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Towards Cost-Efficient Edge Intelligent Computing With Elastic Deployment of Container-Based Microservices

et al. 2020

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With the tremendous growth of the Internet of Things (IoT), big data, and artificial intelligence (AI), the edge computing-based service paradigm has been introduced to meet the increasing demand of applications. To provide efficient computing services at the network edge, the algorithms and applications are generally deployed based on the container-based microservice strategy, which significantly impacts the system efficiency and QoS. Considering the fundamental system uncertainties, including the dynamic workload and service rate, we investigate how to minimize the long-term system cost through the elastic microservice deployment in this paper. To this end, we formulate the container-based microservice deployment as a stochastic optimization problem to minimize the system cost while maintaining the system QoS and stability. We develop a cost-aware elastic microservice deployment algorithm to solve the formulated problem, which balances the tradeoff between system cost and QoS. Our algorithm makes the real-time decisions based on current queue backlogs and system states without predicting the future knowledge. Finally, we conduct the theoretical analysis and extensive simulations based on data traces from the ResNet-50 model-based visual recognition application. The results demonstrate that our algorithm outperforms the baseline strategies with respect to the system cost, queue backlogs, and the number of Pod replicas. INDEX TERMS Edge computing system, microservices, elastic deployment, system cost, container.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards Cost-Efficient Edge Intelligent Computing With Elastic Deployment of Container-Based Microservices

et al. 2020

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“…Table 4.5 provides the purpose of time series forecasting techniques in the IoT use-cases mentioned in this sub-section. [144,145,146] and Fog computing [20] are two important models for managing the enormous volume of data produced from IoT environs. With the brisk growth of the IoT, the traditional cloud computing is facing stern issues, like undesirable network latency, and spectral inefficiency which does not make it suitable for scenarios requiring minimal latency, real-time treatment, and mobility support.…”

Section: Time Series Forecastingmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

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The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proﬁcient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identiﬁed key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented. Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.

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“…Exceptionally low networking performance, inability to access a certain server or website, open files or access websites. Review of network traffic can help monitor and build a DoS attack [5]. Network traffic may be monitored by a firewall or an intrusion detection system.…”

Section: Introductionmentioning

confidence: 99%

Supervised Deep Learning Vector Quantization to Detect MemCached DDOS Malware Attack on Cloud

Arul¹,

Punidha²

2021

SN COMPUT. SCI.

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Weak defence makes it vulnerable to multiple cloud devices, and victims often do not even know it has been compromised. Cloud assaults were planned, and the weakening of home automation and home safety technologies was addressed over a long period of time. The threats, however, were of a different kind. The target appears to be less interested in attackers, and everyone else is attempting to hack the system and connect it to the DDoS network to strike. A DDoS attack is a cyber-attack targeting MemCached, a caching mechanism intended to speed up websites and networks up to the MemCached Database. It helps to crash the database by overwhelming a website or application. Internet hackers submitted spoof applications to the insecure UDP MemCached site, which conceals the true identity of the sender by dressing up the IP address. The UDP is especially vulnerable since the data must be exchanged by the receiver before the contact ceases. The proposed version of Supervised SD-LVQ was used to detect MemCached attacks by malicious firmware on different Cloud computers. LVQ classifies a number of application service calls associated with multiple types of destructive attacks on cloud computers that have been MemCached for DDoS attacks. Test findings reveal that 97.23% is actually positive and 0.03% is unfairly negative.

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Research on Auto-Scaling of Web Applications in Cloud: Survey, Trends and Future Directions

Cited by 76 publications

References 117 publications

Towards Cost-Efficient Edge Intelligent Computing With Elastic Deployment of Container-Based Microservices

Towards Cost-Efficient Edge Intelligent Computing With Elastic Deployment of Container-Based Microservices

Data Analytics in the Internet of Things: A Survey

Supervised Deep Learning Vector Quantization to Detect MemCached DDOS Malware Attack on Cloud

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