Workload Traces Research Articles

Long-term average throughput-utilization utility maximization in platform-aggregated manufacturing service collaboration

Enhancing capacity utilization of manufacturing resources is of utmost importance in tackling the current challenges of meeting customized and small-batch market demands. Given the research highlights on platform-based manufacturing service collaboration (MSC) offering high-quality service solutions, efficient service scheduling strategies are urgently needed to maximize overall utility amidst great computational complexity and unpredictable task arrivals. To address this issue, this paper proposes a novel distributed online task dispatch and service scheduling (DOTDSS) strategy in platform-aggregated MSC. What sets our method apart is its goal to optimize a long-term average utility performance with considering queuing dynamics of manufacturing services in multi-task processing, thereby maintaining sustainable platform operations. Firstly, we jointly consider task dispatch and service scheduling decisions into the formulation of a quality-of-service aware (QoS) stochastics optimization problem. The newly constructed logarithmic utility function effectively strikes a trade-off between the throughput and capacity utilization of manufacturing services with diverse capabilities. By incorporating the goal of reducing queue lengths, we then transform the optimization problem into a form with less computational complexity and guaranteed optimality using Lyapunov optimization. We further propose a DOTDSS strategy that relies solely on the current system state and queue information to generate scalable MSC solutions. It does not need to predict task arrival statistics in advance, and it exhibits great adaptability to uncertainties in task arrivals and service availabilities. Finally, numerical results based on simulation data and real workload traces demonstrate the effectiveness of our method. It also shows that the aggregation collaboration pattern among a group of candidates can achieve better performance than that by the optimal candidate alone.

Optimizing Distributed Tiered Data Storage Systems with DITIS

Modern data storage systems are characterized by a distributed architecture as well as the presence of multiple storage tiers and caches. Both system developers and operators are challenged with the complexity of such systems as it is hard to evaluate how a configuration change will impact the workload or system performance and identify the best configuration to satisfy some performance objective. DITIS is a new simulator that models the end-to-end execution of file requests on distributed tiered storage systems that addresses the aforementioned challenges efficiently without any costly system redeployments. The demonstration will showcase the key functionalities and benefits offered by DITIS, including (i) analyzing workload traces to understand their characteristics and the behavior of the underlying storage system; (ii) running simulations with different configurations to evaluate their impact on performance; and (iii) running optimizations over custom search spaces to find the best configuration that satisfies a given objective.

HRMF-DRP: A next-generation solution for overcoming provisioning challenges in cloud environments

The cloud computing infrastructure is a distributed environment and the existing research works have some provisioning problems such as suboptimal resource utilization and high execution time. The Heterogeneity Resource Management Framework for Dynamic Resource Provisioning (HRMF-DRP) is proposed for focusing on task scheduling and workload management. This framework incorporates advanced algorithms for dataset preprocessing, task clustering, workload prediction, and dynamic resource provisioning. For data preprocessing, the real-world workload traces were captured from the Planet Lab dataset that are taken as input for the preprocessing stage. The data preprocessing is responsible for ensuring data quality and reliability by using different models like missing data handling, outlier detection and removal as well as standardization and normalization. In this paper, the tasks are grouped into clusters by utilizing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) model and this model categorizes the data points into border points, core points and noise points based on their density. The temporal dependencies are captured for the workload prediction by using Long Short-Term Memory (LSTM) neural network model. A Gaussian Mixture Model (GMM) model is responsible for estimating the number of Virtual machines (VMs) present in the workload prediction process. The Self-Adaptive Genetic Algorithm (SAGA) is implemented for task mapping that adjusts the parameters to change workload patterns for contributing adaptability and robustness. The different experimental evaluations are conducted based on the task completion time, workload balance index, resource utilization efficiency and workload prediction accuracy. The proposed model achieved the workload prediction accuracy of 98.5%, cost of $89.6, execution time of 125ms, Task Completion Time (TCT) of 40ms, Workload Balance Index (WBI) of 0.96 and Resource Utilization Efficiency (RUE) of 0.93. The quantitative results collectively position HRMF-DRP as a practical and efficient solution, promising advancements in dynamic resource provisioning for cloud computing, particularly within the Infrastructure as a Service (IaaS) cloud model.

Energy-aware virtual machine placement based on a holistic thermal model for cloud data centers

As energy-intensive infrastructures, data centers (DCs) have become a pressing challenge for managers due to their significant energy consumption and carbon emissions. Information technology (IT) and cooling systems contribute the most to energy consumption. Energy-aware virtual machine (VM) scheduling methods have been widely demonstrated to reduce energy consumption and operating costs in DCs. However, as realistic DCs exhibit complex power and thermodynamic behaviors, existing works cannot provide efficient measures to optimize computing and cooling power consumption simultaneously. To overcome this challenge, we construct a holistic thermal model (including CPU and server inlet thermal models) to accurately represent the non-uniform, dynamic thermal environment. Subsequently, this work proposes a thermal model-based energy-aware VM placement method (TEVP) to minimize the holistic energy consumption of the DCs, considering resource and thermal constraints. We develop a novel hybrid swarm intelligence algorithm (DE-ERPSO) combining differential evolution (DE) and particle swarm optimization with an elite re-selection mechanism (ERPSO) to explore more energy-efficient VM placement schemes. Extensive experiments are conducted on an extended CloudSim to validate the performance of the proposed TEVP using real-world workload traces (PlanetLab and Azure). Results show that TEVP saves over 5.6% of the total energy consumption over the advanced baselines while maintaining low thermal violations.

An improved hunger game search optimizer based IoT task scheduling in cloud–fog computing

Due to the rapid expansion of Internet of Things (IoT)-related applications, the utilization of cloud services is experiencing significant growth. Although cloud computing has proven its effectiveness in processing and storing data for various applications, it faces challenges in addressing certain requirements, such as the growing need for real-time or latency-sensitive applications and the limitations of network bandwidth. As a result, fog computing is often seen as a supplementary paradigm to cloud computing, providing additional capabilities and benefits to the traditional cloud paradigm, aiming to extend cloud services to edge devices and end-users. However, the limited capabilities of fog nodes require lighter tasks while other tasks that need more processing time are processed in the cloud. In the present research paper, we propose a novel algorithm that is customized for task scheduling within the context of cloud–fog computing on the Internet of Things (IoT) framework. Our approach builds upon the Hunger Game Search algorithm (HGS) as its foundation. To improve the exploitative capabilities of the HGS, our proposed method, called HGSMPA, incorporates the Marine Predator Algorithm (MPA). Through experimental evaluation using various workload traces, we have demonstrated the efficacy of HGSMPA. The findings reveal that HGSMPA surpasses alternative algorithms in terms of reducing energy consumption and minimizing the makespan time. Specifically, The empirical evaluation indicates that HGSMPA can reduce the makespan time by 19.31% for synthetic workloads and by 17.47% for real workloads as compared to similar scheduling algorithms. Moreover, HGSMPA can reduce energy consumption by 14.72% for synthetic workloads and by 17.68% for real workloads as compared to other methods.

A systematic review of green-aware management techniques for sustainable data center

Cloud computing is one of the powerful engines driving global industrial upgrading and the booming digital economy. However, the explosive growth of cloud data centers (DCs) has resulted in inevitable energy consumption and carbon emission problems. Therefore, constructing energy-efficient and sustainable DCs will be essential for green cloud computing. This review makes several efforts to thoroughly investigate and track the research progress and routes to sustainable DCs. Firstly, we construct a new conceptual model of sustainable DCs to cover cutting-edge research results and indicate future evolutionary directions. Secondly, this review provides a comprehensive survey of five topics from a technical perspective: workload management, virtual resource management, energy management, thermal management, and waste heat recovery. Subsequently, some real-world datasets relevant to the topics, including workload traces, renewable energy data, and electricity price traces, have been specifically collected to support researchers in conducting further research. Finally, based on observations of existing works, we highlight some salient technical challenges and promising solutions to provide sensible energy and carbon reduction suggestions in sustainable DCs.

Deep learning algorithms for temperature prediction in two-phase immersion-cooled data centres

PurposeAs data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.Design/methodology/approachThis paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R2) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.FindingsThe R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.Research limitations/implicationsThe proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.Originality/valueThe proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

A multi-setpoint cooling control approach for air-cooled data centers using the deep Q-network algorithm

Cooling systems provide a safe thermal environment for the reliable operation of IT equipment in data centers (DCs) while generating significant energy consumption. Therefore, to achieve energy savings in cooling system control under dynamic thermal distribution in DCs, this paper proposes a multi-setpoint cooling control approach based on deep reinforcement learning (DRL). Firstly, a thermal model based on the XGBoost algorithm is constructed to precisely evaluate the thermal distribution in the rack room to guide real-time cooling control. Secondly, a multi-set point cooling control approach based on the deep Q-network algorithm (DQN-MSP) is designed to finely regulate the supply air temperature of each air conditioner by capturing the thermal fluctuations to ensure the dynamic balance of cooling supply and demand. Finally, we adopt the extended CloudSimPy simulation tool and the real workload trace of the PlanetLab system to evaluate the effectiveness and performance of the proposed approach. The simulation results show that the proposed control solution effectively reduces the cooling energy consumption by over 2.4% by raising the average air supply temperature of the air conditioner while satisfying the thermal constraints.

Joint Autoscaling of Containers and Virtual Machines for Cost Optimization in Container Clusters

Autoscaling enables container cluster orchestrators to automatically adjust computational resources, such as containers and Virtual Machines (VMs), to handle fluctuating workloads effectively. This adaptation can involve modifying the amount of resources (horizontal scaling) or adjusting their computational capacity (vertical scaling). The motivation for our work stems from the limitations of previous autoscaling approaches, which are either partial (scaling containers or VMs, but not both) or excessively complex to be used in real systems. This complexity arises from their use of models with a large number of variables and the addressing of two simultaneous challenges: achieving the optimal deployment for a single scheduling window and managing the transition between successive scheduling windows. We propose an Integer Linear Programming (ILP) model to address the challenge of autoscaling containers and VMs jointly, both horizontally and vertically, to minimize deployment costs. This model is designed to be used with predictive autoscalers and be solved in a reasonable time, even for large clusters. To this end, improvements and reasonable simplifications with respect to previous models have been carried out to drastically reduce the size of the resource allocation problem. Furthermore, the proposed model provides an enhanced representation of system performance in comparison to previous approaches. A tool called Conlloovia has been developed to implement this model. To evaluate its performance, we have conducted a comprehensive assessment, comparing it with two heuristic allocators with different problem sizes. Our findings indicate that Conlloovia consistently demonstrates lower deployment costs in a significant number of cases. Conlloovia has also been evaluated with a real application, using synthetic and real workload traces, as well as different scheduling windows, with deployment costs approximately 20% lower than heuristic allocators.

Forecasting resource usage pattern changes in clouds via contrast graph-evolution learning

The increasing large-scale workload trace collected by datacenter monitors has provided great potential for workload prediction. However, resource usage pattern changes of workloads often exist, which are usually caused by user demand changes, workload re-locations or other reasons. If the patterns of resource usage change, the accuracy of prediction may degrade significantly. Hence, how to forecast such changes brings an open and challenging issue. In this paper, we present a novel method named CEGWP. CEGWP uses shapelets to explicitly identify resource usage patterns in a fine-grained level. Specifically. a two-level importance based shapelet extraction mechanism is designed to mine new usage pattern changes in temporal dimension, and a multi-view contrast graph-evolution learning algorithm is designed to fuse the interference among resource usage patterns of different workloads in spatial dimension. With both temporal extraction of shapelets from each single workload and spatial interference of different workloads, a spatio-temporal GNN-based TCN encoder is developed to forecast the resource usage pattern changes. CEGWP is jointly trained in an end-to-end manner and is evaluated using real trace data. Experimental results show that CEGWP achieves notable improvements by up to 62.15% against existing prediction methods and indicate the potential to be deployed into real-world cloud datacenters.

An efficient reducing mechanism for energy consumption in data center using hybrid consolidation techniques

The rise in internet user demand is a major factor in the expansion of infrastructure and the upsurge in energy use in cloud, colocation, and some business data centres. The advent of 5G has compounded the situation, as it substantially gives room for many new types of digital services, resulting in a need for richer consume a lot of energy when no scaling method is applied. Services such as mail, data storage and retrieval and other cloud services also require a lot of high energy consumption which eventually result into carbon(IV) oxide (CO2) emissions to the environment. This research therefore, focuses on lowering the energy usage of a data centre with heterogeneous power awareness either in an idle server state or high-performance state using a novel hybridized algorithm called “DyVoFesLoReMu”, comprising Dynamic Voltage Frequency Scaling (Dvfs) and a modified Local Regression Minimum Utilization (LrMu). A real dataset (workload) obtained online from PlanetLab consisting of hosts and Virtual Machines (VM) was simulated on a data center in CloudSim 3.0.3. Tool kit with preset parameters consisting of VM Allocation Policy and VM Selection Policy was used. The tool kit was utilised to create cloud infrastructure and simulate the essential features of a cloud environment. The Cloudsim was installed on Eclipse Integrated Development Environment (IDE) 2019 version on Windows 10 operating system. The hybridized algorithm was compared with other five (5) existing energy reducing algorithms and it was found to be more efficient with a range of 41-90% reduction in energy usage from the ten days workload traces and in comparison with the existing algorithms used for the simulation.

Comparative analysis of cloud resources forecasting using deep learning techniques based on VM workload traces

AbstractThe major cost of running a cloud is power consumption. Under‐utilization of resources that are kept idly on, over‐allocation of resources, and so on, are a few reasons for excessive power utilization by the data centers. So, to optimize the power consumption, future resource usage of virtual machines (VM) can be forecasted using their trace logs. Based on this prediction, excessively allocated VM resources can be freed thereby reducing the number of physical machines as well as the carbon footprint. In this work, we present a comparative study of some deep learning techniques such as multilayer perceptron (MLP), autoregressive neural network (ARNN), convolutional neural network (CNN), long short‐term memory (LSTM) network in forecasting the CPU, memory usage of many VMs. The GWA‐T‐12 Bitbrains data center dataset consisting of 1250 VMs' workload traces is used in this work. The main goal is to avoid underload/overload in VMs which occurs sometimes while trying to optimize resource allocation. We achieved a maximum of 98% accuracy in future resource requirement forecasting. Among all models, MLP attained the highest accuracy.

Spatial and thermal aware methods for efficient workload management in distributed data centers

Geographically distributed data centers provide facilities for users to fulfill the demand of storage and computations, where most of the operational cost is due to electricity consumption. In this study, we address the problem of energy consumption of cloud data centers and identify key characteristics of techniques proposed for reducing operational costs, carbon emissions, and financial penalties due to service level agreement (SLA) violations. By considering computer room air condition (CRAC) units that utilize outside air for cooling purposes as well as temperature and space-varying properties, we propose the energy cost model which takes into account temperature ranges for cooling purposes and operations of CRAC units. Then, we propose spatio-thermal-aware algorithms to manage workload using the variation of electricity price, locational outside and within the data center temperature, where the aim is to schedule the incoming workload requests with minimum SLA violations, cooling cost, and energy consumption. We analyzed the performance of our proposed algorithms and compared the experimental results with the benchmark algorithms for metrics of interest including SLA violations, cooling cost, and overall operations cost. Modeling, experiments, and verification conducted on CloudSim with realistic data center scenarios and workload traces show that the proposed algorithms result in reduced SLA violations, save between 15% to 75% of cooling cost and between 3.89% to 39% of the overall operational cost compared to the existing solutions.

SafeCool: Safe and Energy-Efficient Cooling Management in Data Centers With Model-Based Reinforcement Learning

Optimizing the cooling system plays a central role for capping the data center power consumption. However, the performance of traditional cooling management strategies is not satisfactory due to the complexity of thermodynamic process. Recently, several works leveraged Reinforcement Learning (RL) to improve the energy efficiency of data center cooling system. While they demonstrated that it is possible to reduce the cooling power consumption via RL, there are still some key challenges that have to be addressed before field deployment, such as safe operation guarantee and sample complexity, etc. In this paper, we propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> , an actor-critic Model-Based Reinforcement Learning (MBRL) algorithm for data center cooling management. <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> incorporates two system models, i.e., a transition model to predict the future system state and a risk model to estimate the negative effect of executing an action. The safety of proposed algorithm is ensured by Model Predictive Control (MPC) and risk-guided exploration. In addition, by employing the MBRL framework, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> achieves higher sample efficiency and accelerated convergence. Simulations using real-world workload trace reveal that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SafeCool</monospace> saves up to 13.18% cooling power compared with state-of-the-art MBRL data center cooling solutions.

PcGC: A Parity-Check Garbage Collection for Boosting 3-D NAND Flash Performance

Garbage collection or GC running in the controller of 3D NAND flash-based solid-state disks -SSDs -plays a critical role in the performance of storage systems. SSD manufacturers have developed various garbage collection solutions based on internal data movement or IDM to mitigate the impacts of garbage collection on request latency. Due to the circuit characteristics of flash memory, existing IDM-based garbage collection strategies are restricted by the page parity during data movement: odd pages must be migrated to odd pages, and even pages to even pages. When migrating two consecutive pages with the same parity, the free page between the two migrated pages will be wasted after the migration is complete. This ever-increasing page waste problem inevitably deteriorates the storage space utilization of flash memory, thereby degrading the overall performance of 3D NAND flash-based SSDs. To address this issue, we propose a parity-check garbage collection scheme called PcGC to revamp SSD performance by alleviating page waste during garbage collection. We build a parity-check unit in PcGC to facilitate checking the parity of migrated valid pages and destination pages. According to the parity results offered by the parity-check unit, PcGC dynamically adjusts the migration order of valid pages during the course of garbage collection. In doing so, PcGC fundamentally averts page waste caused by the page parity restriction, thereby enhancing 3D NAND flash performance. We quantitatively evaluate the performance of PcGC in terms of wasted pages, storage utilization, GC counts, write amplification, and average response time. We compare PcGC against the two state-of-the-art schemes -Amphibian and TTflash. The experimental results derived from the nine real-world workload traces unfold that compared with Amphibian and TTflash, (1) PcGC curtails the number of wasted pages by up to 91.4% with an average of 53.75%, (2) cuts back the number of GC counts by up to 52.2% with an average of 11.9%, and (3) slashes average write response time by up to 77.8% with an average of 13.0%.

Adaptive Guided Differential Evolution‐based Slime Mould Algorithm‐based efficient Multi‐objective Task Scheduling for Cloud Computing Environments

AbstractIn a Cloud Computing (CC) environment, users are charged based on on‐demand resource utilization and expected Quality of Service (QoS). Multi‐Objective‐based Task Scheduling (MOTS) problem formulated for achieving the expected QoS in a CC scenario is always NP‐complete. The majority of existing Task Scheduling (TS) algorithms are not efficient in determining global optimal solutions due to huge space offered by large‐scale problem instances and characteristics inherent in NP‐complete problems. In this article, an Adaptive Guided Differential Evolution‐based Slime Mould Algorithm (AGDESMA) is proposed for dealing with the problem of Multi‐Objective large‐scale TS optimization over Infrastructure‐as‐a‐Service (IaaS) CC environment. The proposed AGDESMA is formulated with the merits of Slime Mould Algorithm (SMA) and Adaptive Guided Differential Evolution (AGDE) for achieving a proper balance amid exploitation and exploration phases and preventing the solutions from getting trapped into minimum local regions. It specifically adopts the method of AGDE for preventing premature convergence with maximized diversity of population and local search of swarm agents. The experimental validation of the proposed AGDESMA is conducted using CloudSim based on the synthesized and standard workload traces with larger instances up to a maximum of 5000. The results confirm the predominance of the proposed AGDESMA scheme over benchmarked schemes based on computational overhead and trade‐off between makespan and financial cost.

Energy and Performance-Efficient Dynamic Consolidate VMs Using Deep-Q Neural Network

With cloud computing facing higher levels of big data than ever, the processor scale is rapidly expanding. Large clusters place a heavy burden on cloud service providers and the environment. High energy consumption decreases the economic benefits of cloud service providers, while enormous power demands pressure on the environment. The dynamic consolidation of virtual machines (VMs), which uses live migration technology to optimize resource usage and reduce energy consumption, is sufficient for saving energy while ensuring high performance with the desired level of quality of service (QoS) between cloud providers and users. In this paper, we propose a novel machine-learning algorithm called deep-Q neural network VM consolidation (DQNVMC) that combines the Q-leaning approach with deep learning neural network to find an approximately optimal solution. Furthermore, based on the real workload trace in the cloud environment, the experiments show that DQNVMC effectively reduces energy consumption while meeting the high performance of QoS requirements.

A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing

The advent of cloud computing has dramatically altered how information is stored and retrieved. However, the effectiveness and speed of cloud-based applications can be significantly impacted by inefficiencies in the distribution of resources and task scheduling. Such issues have been challenging, but machine and deep learning methods have shown great potential in recent years. This paper suggests a new technique called Deep Q-Networks and Actor-Critic (DQNAC) models that enhance cloud computing efficiency by optimizing resource allocation and task scheduling. We evaluate our approach using a dataset of real-world cloud workload traces and demonstrate that it can significantly improve resource utilization and overall performance compared to traditional approaches. Furthermore, our findings indicate that deep reinforcement learning (DRL)-based methods can be potent and effective for optimizing cloud computing, leading to improved cloud-based application efficiency and flexibility.

A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers.

Virtualisation is a major technology in cloud computing for optimising the cloud data centre's power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center's size expands resulting in increased energy usage. To address this problem, a resource allocation optimisation method that is both efficient and effective is necessary. The optimal utilisation of cloud infrastructure and optimisation algorithms plays a vital role. The cloud resources rely on the allocation policy of the virtual machine on cloud resources. A virtual machine placement technique, based on the Harris Hawk Optimisation (HHO) model for the cloud data centre is presented in this paper. The proposed HHO model aims to find the best place for virtual machines on suitable hosts with the least load and power consumption. PlanetLab's real-time workload traces are used for performance evaluation with existing PSO (Particle Swarm Optimisation) and PABFD (Best Fit Decreasing). The performance evaluation of the proposed method is done using power consumption, SLA, CPU utilisation, RAM utilisation, Execution time (ms) and the number of VM migrations. The performance evaluation is done using two simulation scenarios with scaling workload in scenario 1 and increasing resources for the virtual machine to study the performance in underloaded and overloaded conditions. Experimental results show that the proposed HHO algorithm improved execution time(ms) by 4%, had a 27% reduction in power consumption, a 16% reduction in SLA violation and an increase in resource utilisation by 17%. The HHO algorithm is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures.

Workload Characterization and Classification: A Step Towards Better Resource Utilization in a Cloud Data Center

Advancements in virtualization technology have led to better utilization of existing infrastructure. It allows numerous virtual machines with different workloads to coexist on the same physical server, resulting in a pool of server resources. It is critical to understand enterprise workloads to correctly create and configure existing and future support in such pools. Managing resources in a cloud data center is one of the most difficult tasks. The dynamic nature of the cloud environment, as well as the high level of uncertainty, has created these challenges. These applications’ diverse Quality of Service (QoS) requirements make data center management difficult. Accurate forecasting of future resource demand is required to meet QoS needs and ensure better resource utilization. Consequently, data center workload modeling and categorization are needed to meet software quality solutions cost-effectively. This paper uses traces of Bitbrain’s data to characterize and categorize workload. Clustering (K Means and Gaussian mixture model) and Classification strategies (K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machine) characterize and model the workload traces. K Means shows better results as compared to GMM when compared to the Calinski Harabasz index and Davies-Bouldin score. The results showed that the Decision Tree achieves the maximum accuracy of 99.18%, followed by K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM) Logistic Regression (LR), Multi-Layer Perceptron (MLP), and Back Propagation Neural Networks.