Cloud Resource Consumption Research Articles

FinOps-driven optimization of cloud resource usage for high-performance computing using machine learning

Cloud computing is gaining popularity in high-performance computing applications. Its utilization enables advanced simulations when local computing resources are limited. However, cloud usage may increase costs and entail resource unavailability risks. This article presents an original approach that employs machine learning to predict long-term cloud resource usage. This enables optimizing resource utilization through appropriate reservation plans, reducing the associated costs. The solution developed utilizes statistical models, XGBoost, neural networks and the Temporal Fusion Transformer. Long-term prediction of cloud resource consumption, especially the Cloud Resource Usage Optimization System that is critical for prolonged simulations, involves using prediction results to dynamically create resource reservation plans across various virtual machine types for HPC on the Google Cloud Platform. Experiments using real-life production data demonstrate that the TFT prediction model improved prediction quality (by 31.4%) compared to the best baseline method, particularly in adapting to chaotic changes in resource consumption. However, it should be noted that the best prediction model in terms of error magnitude might not be the most suitable for resource reservation planning. This was validated by the neural network-based method, introducing an FR metric for forecast evaluation. Resource reservation plans were assessed both qualitatively and quantitatively, focusing on various aspects like a service-level agreement compliance and potential downtime. This paper is an extension of work originally presented during the International Conference on Computational Science — ICCS 2023, entitled “Long-Term Prediction of Cloud Resource Usage in High-Performance Computing”.

Host utilization prediction using Taylor Kernel Convolutional Neural Network (TKCNN) and workflow scheduling for smart irrigation cloud data centers

One of the most significant economic sectors in these nations is agriculture, which emphasises the significance of controlling the water resources at hand to maintain the survival of this industry. Sensor and cloud computing (CC) technology are widely used in many real-time applications, particularly in agriculture. To ensure Quality of Service in the CC environment, Virtual Machine (VM) Migration as part of VM Consolidation is very important (QoS). Predicting host utilization utilizing utilization history and lowering energy consumption while satisfying Service Level Agreements (SLAs) is a challenging task as a result of the large fluctuation in the consumption of cloud resources and the dynamic workloads.CC technologies are widely used in many real-time applications, particularly in agriculture for smart irrigation cloud data centers difficult task. To solve this issue proposed work introduced a Taylor Kernel Convolutional Neural Network (TKCNN) algorithm, which trains the past history for host usage prediction in smart irrigation cloud data centers. The Optimum Energy and Resource Aware Workflow Scheduling- Host Utilization Prediction (OERES-HUP) method, which attempts to schedule the task workflow to VMs engaging in computing, was influenced by the well-known Fuzzy Multi-Verse Optimizer (FMVO) algorithm. Additionally, it focuses on dynamically deploying and underplaying VMs in accordance with job requirements. The effectiveness of scheduling algorithms is evaluated using key metrics including Resource Utilization (RU), Energy Consumption or Task (ECT), Total Energy Consumption (TEC), Makespan, and Execution Time per Task (ETT).The outcomes are a good representation of how effective the suggested algorithm is compared to current techniques. To test the efficacy of the suggested model, the cloud environment is simulated using the Cloud Sim simulator.

A Comparison of Machine Learning Algorithms for Automatic Cloud Resource Scaling on a Multi-Tenant Platform

In an online multi-tenant machine learning platform, the system manager would dynamically load the computing resource according to the tenant’s demand. With cloud computing services, the platform can rent or release computing resources dynamically to fulfill tenant usage which minimizes resource consumption and ensure scalability. Currently, many cloud-based services providers are using the rule-based, threshold auto-scaling mechanism. However, the rule-based method is not efficient, as the nature of availability and cost-reducing violate each other in this method, especially with the sudden increase or variation of the demand. In this paper, we compare several machine-learning-based predictive algorithms to build models based on the information of the system used to predict future usage demands. Decisions made based on this prediction save over 80% cloud resource consumption compared to the rule-based method.

Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing

In virtualized cloud computing systems, energy reduction is a serious concern since it can offer many major advantages, such as reducing running costs, increasing system efficiency, and protecting the environment. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping cloud resources to user requests to achieve good performance by minimizing the energy consumption of cloud resources within a user-defined deadline is a huge challenge. This paper proposes Energy and Performance-Efficient Task Scheduling Algorithm (EPETS) in a heterogeneous virtualized cloud to resolve the issue of energy consumption. There are two stages in the proposed algorithm: initial scheduling helps to reduce execution time and satisfy task deadlines without considering energy consumption, and the second stage task reassignment scheduling to find the best execution location within the deadline limit with less energy consumption. Moreover, to make a reasonable balance between task scheduling and energy saving, we suggest an energy-efficient task priority system. The simulation results show that, compared to current energy-efficient scheduling methods of RC-GA, AMTS, and E-PAGA, the proposed solution helps to reduce significant energy consumption and improve performance by 5%–20% with deadline constraint satisfied.

Forecasting Cloud Resource Provisioning System using Supervised Machine Learning

One of the biggest challenges cloud computing faces is forecasting correctly the resource use for future demands. Consumption of cloud resources is consistently changing, making it difficult for algorithms to forecast to make precise predictions. Using of the machine learning in cloud computing leads to many benefits. Such as chances of the enhancement in the quality of the service via forecasting future burden of works and responding automatically with dynamic scaling. This motivates the work presented in this paper to predict CPU use of host machines for a single time and multiple times. This paper uses three supervised machine-learning algorithms to classify and predict CPU utilization because of their capability to keep data and predict accurate time series issues. It is tried to forecast CPU usage with better accuracy while comparing to traditional methods.

Modeling cloud business customers’ utility functions

Modeling a utility function for cloud business customers is one of the critical challenges facing many cloud service providers (CSPs) for their pricing strategy. It concerns how to measure various subjective experiences of the business customers and how to translate their cloud service experiences into a quantifiable unit, which can be determined by a utility function that reflects cloud resource consumption. The aim of this modeling process is to set up a pricing foundation so that CSPs can target a broader range of customers from various market segments and identify the optimal price point of their various pricing models. Previous studies have either focused on simple theoretical proof or drifted the meaning of utility between demand and supply or proposed a solution based on a uniform cloud market assumption. This paper proposes a novel and practical solution to define multiple utility functions based on a scenario of six cloud market segments, which are analyzed by three analytic approaches that are known as Markov chains analysis, queueing theory, and risk assessment. The entire pricing strategy emphasizes value co-creation between CSP and cloud business customers. In comparison with other methods, such as calibrated, price-quality, resource-based, simple linear, and capacity-aware, this method provides both internal and external rationalities for CSP to capture the subjective value of cloud business customers. Consequently, our experiment results show that this modeling method can increase a profit margin by 51% and decrease a unit cost by 22% for a CSP.

Degrees of tenant isolation for cloud-hosted software services: a cross-case analysis

A challenge, when implementing multi-tenancy in a cloud-hosted software service, is how to ensure that the performance and resource consumption of one tenant does not adversely affect other tenants. Software designers and architects must achieve an optimal degree of tenant isolation for their chosen application requirements. The objective of this research is to reveal the trade-offs, commonalities, and differences to be considered when implementing the required degree of tenant isolation. This research uses a cross-case analysis of selected open source cloud-hosted software engineering tools to empirically evaluate varying degrees of isolation between tenants. Our research reveals five commonalities across the case studies: disk space reduction, use of locking, low cloud resource consumption, customization and use of plug-in architecture, and choice of multi-tenancy pattern. Two of these common factors compromise tenant isolation. The degree of isolation is reduced when there is no strategy to reduce disk space and customization and plug-in architecture is not adopted. In contrast, the degree of isolation improves when careful consideration is given to how to handle a high workload, locking of data and processes is used to prevent clashes between multiple tenants and selection of appropriate multi-tenancy pattern. The research also revealed five case study differences: size of generated data, cloud resource consumption, sensitivity to workload changes, the effect of the software process, client latency and bandwidth, and type of software process. The degree of isolation is impaired, in our results, by the large size of generated data, high resource consumption by certain software processes, high or fluctuating workload, low client latency, and bandwidth when transferring multiple files between repositories. Additionally, this research provides a novel explanatory framework for (i) mapping tenant isolation to different software development processes, cloud resources and layers of the cloud stack; and (ii) explaining the different trade-offs to consider affecting tenant isolation (i.e. resource sharing, the number of users/requests, customizability, the size of generated data, the scope of control of the cloud application stack and business constraints) when implementing multi-tenant cloud-hosted software services. This research suggests that software architects have to pay attention to the trade-offs, commonalities, and differences we identify to achieve their degree of tenant isolation requirements.

Predicting host CPU utilization in the cloud using evolutionary neural networks

The Infrastructure as a Service (IaaS) platform in cloud computing provides resources as a service from a pool of compute, network, and storage resources. One of the major challenges facing cloud computing is to predict the usage of these resources in real time. By knowing future demands, cloud data centres can dynamically scale resources to decrease energy consumption while maintaining a high quality of service. However cloud resource consumption is ever changing, making it difficult for accurate predictions to be produced. This motivates the research presented in this paper which aims to predict in advance the level of CPU consumption of a host. This research implements evolutionary Neural Networks (NN), a powerful machine learning method, to make these predictions. A number of state of the art swarm and evolutionary optimization algorithms are implemented to train the neural networks to predict host utilization: Particle Swarm Optimization (PSO), Differential Evolution (DE) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). The results of this research demonstrate that CMA-ES converges faster to a better solution on the training data. However when evaluated on the test data, DE performs statistically equal to CMA-ES. The results also demonstrate that the trained networks are still accurate when applied to CPU utilization data from different hosts with no further training needed. When evaluated to predict multiple steps into the future, the accuracy of the network understandably decreases but still performs well on average.

Review on Secure Auditing, Preserving and Integrity of Data Using TPA on Cloud Storage

User should use the data storage without any loss as it is stored locally. Auditing process is important process to ensure integrity, user are not that skilful to maintain integrity of data and they are not aware about risk and security action. Hence so to perform user can depend on TPA (third party authentication), which will check integrity and privacy on cloud data which is stored locally.TPA audit the integrity and provides appropriate result to user. Results contain modified, deleted and uploaded information of files. User has to make sure that privacy is preserved from TPA with minimal consumption of cloud resources while auditing process. In this paper we propose a system that will provide integrity to locally stored data without downloading files avoiding additional resources and vulnerability. We further extend our result to enable the TPA to perform audits for multiple users simultaneously and efficiently. Extensive security and performance analysis show the proposed schemes are provably secure and highly efficient.

SOCCER: Self-Optimization of Energy-efficient Cloud Resources

Cloud data centers often schedule heterogeneous workloads without considering energy consumption and carbon emission aspects. Tremendous amount of energy consumption leads to high operational costs and reduces return on investment and contributes towards carbon footprints to the environment. Therefore, there is need of energy-aware cloud based system which schedules computing resources automatically by considering energy consumption as an important parameter. In this paper, energy efficient autonomic cloud system [Self-Optimization of Cloud Computing Energy-efficient Resources (SOCCER)] is proposed for energy efficient scheduling of cloud resources in data centers. The proposed work considers energy as a Quality of Service (QoS) parameter and automatically optimizes the efficiency of cloud resources by reducing energy consumption. The performance of the proposed system has been evaluated in real cloud environment and the experimental results show that the proposed system performs better in terms of energy consumption of cloud resources and utilizes these resources optimally.

Incentive-based resource assignment and regulation for collaborative cloud services in community networks

Community networks are a successful example of a collective where communities operate ICT infrastructure based on the principle of reciprocal sharing of network bandwidth. Cloud computing, common in today's Internet, has however not materialised within community networks. We analyse in this paper socio-technical characteristics of community networks in order to derive scenarios for community clouds. Based on an architecture for such a community cloud, we implement a prototype for the incentive-driven resource assignment component and evaluate its behaviour experimentally. In simulations of large-scale community cloud scenarios we study the behaviour of the incentive mechanism in different configurations. Our evaluation gives insight into how the developed mechanisms regulate the consumption of cloud resources. Our results suggest a further integration of this regulation component into current cloud management platforms in order to open them up for the operation of an ecosystem of collaborative cloud services in community networks.