QoS Aware Social Spider Cloud Web Algorithm: Analysis of Resource Placement Approach

Abrol, Preeti; Gupta, Savita; Singh, Sukhwinder

doi:10.2139/ssrn.3464927

Cited by 7 publications

(5 citation statements)

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“…Furthermore, the authors of [2] and [13] proposed a novel scheduling of scientific workflows, while in [19] QoS and cost optimization of cloud resource allocation is discussed. Sung et al [28] describe Optimized Memory Bandwidth Management Machine Learning to manage resources for latency-critical workloads and the authors of [1] describe an algorithm that evaluates resource utilization requirements for incoming tasks. Although this method does not require load prediction, the solution must know the incoming requests' resource demands, which may create a big disadvantage where load data are not well defined.…”

Section: Related Workmentioning

confidence: 99%

“…The idea is to pay attention to the specific characteristics of data patterns. Based on Incoming request [12] Online incremental learning X modelling [26] Learning automata X [8] Time-series with queuing theory X [31] Time-series analysis X X Real-life QoS data with artificially generated load data Incoming traffic as time series [20] RNNs and LSTM X Dataset from Afry, 30 minutes long forecast [27] Holt-Winters, ARIMA and LSTM X One-step forecasting Scheduling [14] Random Forest X incoming task to the [2,13] Scheduling of scientific workflows X available resources [19] QoS and cost optimization X [28] Optimized Memory Bandwidth Management Machine Learning X [1] Evaluation of resource utilization X Artificial intelligence [25] Autoscaling of network resources X techniques [10] RNNs X Dataset partially from PlanetLab [33] General framework for a VM reservation plan X Dataset from Wikipedia [16] Random Forest and ARIMA X Dataset from EMPRES-I [21] Multilayer Perceptron X Dataset from IPTV [32] Deep learning model X Dataset from PlanetLab [30] LSTM, Random Forest, linear regression and Gaussian process regression X QoS-driven resource [7] Incoming task analysis X Dataset from WSDream allocation [17] Incoming task analysis X [6] Iterative QoS prediction model X [29] Time series prediction X Dataset from Amazon and Google [22][23][24] Multi-stage optimization process with sophisticated data-cleaning, monitoring and scaling mechanisms…”

Section: Preliminary Researchmentioning

confidence: 99%

“…The datasets were provided by Polcom. 1 These included daily real-life CPU usage values for Virtual Machines, measured as the number of vCores. Periods are individual months of the year.…”

Section: Preliminary Tests Of the Proposed Solutionmentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Adaptive Prediction of Cloud Resource Usage

2023

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Predicting computing resource usage in any system allows optimized management of resources. As cloud computing is gaining popularity, the urgency of accurate prediction is reduced as resources can be scaled on demand. However, this may result in excessive costs, and therefore there is a considerable body of work devoted to cloud resource optimization which can significantly reduce the costs of cloud computing. The most promising methods employ load prediction and resource scaling based on forecast values. However, prediction quality depends on prediction method selection, as different load characteristics require different forecasting mechanisms. This paper presents a novel approach that incorporates data-driven adaptation of prediction algorithms to generate short- and long-term cloud resource usage predictions and enables the proposed solution to readjust to different load characteristics as well as both temporary and permanent usage changes. First, preliminary tests were performed that yielded promising results – up to 36% better prediction quality. Subsequently, a fully autonomous, multi-stage optimization solution was proposed. The proposed approach was evaluated using real-life historical data from various production servers. Experiment results demonstrate 9.28% to 80.68% better prediction quality when compared to static algorithm selection.

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Section: Related Workmentioning

confidence: 99%

Section: Preliminary Researchmentioning

confidence: 99%

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Data-Driven Adaptive Prediction of Cloud Resource Usage

2023

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“…In classical techniques, it is challenging to allocate resources by satisfying power and QoS, so they propose a new resource optimization allocation technique, namely exponential spider monkey optimization. Abrol et al 40 introduced a resource placement technique, QoS disabled social spider cloud web algorithm was enhanced with QoS aware SSCWA, which caters to place resources as per QoS. b) Swarm intelligence algorithm: In existing work, precise solution schemes can offer the optimal solution.…”

Section: Smo and Application In Nfvmentioning

confidence: 99%

Discrete spider monkey optimization algorithm for latency‐sensitive VNF deployment and resource allocation

Liu,

Hou,

Liu

2023

Int J Communication

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SummaryThe next‐generation networks are expected to support diverse latency‐sensitive services, that is, auto‐driving, telemedicine, and so on. Deploying these services in current hardware‐based networks faces significant challenges. The emergence of network function virtualization (NFV) and software defined network (SDN) makes network deployment flexible and agile. In order to meet the strict latency constraints, joint VNF deployment and resource allocation problems are needed. In this paper, we consider the resource‐delay interpendency and formulate the joint VNF deployment and resource allocation problems as linear programming (LP) problems. Discrete spider monkey optimization (DSMO) algorithm is proposed to solve this problem, which imitates spider monkeys' fission and fusion social behavior during foraging. The two‐phase decision update mechanism greatly increases the diversity of the spider monkey decision population and promotes the algorithm to perform a global search of the solution space. Evaluation results show that the proposed solutions achieve superior performance in terms of latency, CPU utilization and service acceptance rate.

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“…Social Spider Cloud Web Algorithm (SSCWA): Abrol et al [102] proposed that the tasks will behave as spiders, and their QoS characteristics will be defined as the spider's fitness based on the Social Spiral algorithm. The tools function as prey, and their ability corresponds to the health of the prey.…”

Section: Ssa and Its Variationsmentioning

confidence: 99%

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Elmagzoub¹,

Syed²,

Shaikh³

et al. 2021

Electronics

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Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.

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QoS Aware Social Spider Cloud Web Algorithm: Analysis of Resource Placement Approach

Cited by 7 publications

References 0 publications

Data-Driven Adaptive Prediction of Cloud Resource Usage

Data-Driven Adaptive Prediction of Cloud Resource Usage

Discrete spider monkey optimization algorithm for latency‐sensitive VNF deployment and resource allocation

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

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