Challenge For Cloud Service Providers Research Articles

Secure Cloud Storage with Deduplication Technique

Data de-duplication refers to providing cloud providers with a way to manage the uncontrollable data and the challenges of cloud storage. The success of IOT and social media led us to face big data challenges. Big data is interesting but also becomes a critical challenge for cloud service providers. Data storage and management become also the topic of discussion where big data generate business opportunities as well as come with big issues for the cloud providers. In this paper, we discussed the issues of redundant data and techniques to prevent data redundancy on the cloud. A third-party auditor checks the user’s data for correctness and gives the accuracy of the data that is stored in a cloud server. The communication and computation overheads were reduced. The deduplication technique is used to check whether the file that users need to store in cloud storage already exists on the cloud server.

Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment

Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient when virtual machines and other resources work up to their full potential. The main factor that influences the quality of cloud services is the distribution of workflow on virtual machines (VMs). Scheduling tasks to VMs depends on the type of workflow and mechanism of resource allocation. Scientific workflows include large-scale data transfer and consume intensive resources of cloud infrastructures. Therefore, scheduling of tasks from scientific workflows on VMs requires efficient and optimized workflow scheduling techniques. This paper proposes an optimised workflow scheduling approach that aims to improve the utilization of cloud resources without increasing execution time and execution cost.

MAG-D: A multivariate attention network based approach for cloud workload forecasting

The Coronavirus pandemic and the work-from-home have drastically changed the working style and forced us to rapidly shift towards cloud-based platforms & services for seamless functioning. The pandemic has accelerated a permanent shift in cloud migration. It is estimated that over 95% of digital workloads will reside in cloud-native platforms. Real-time workload forecasting and efficient resource management are two critical challenges for cloud service providers. As cloud workloads are highly volatile and chaotic due to their time-varying nature; thus classical machine learning-based prediction models failed to acquire accurate forecasting. Recent advances in deep learning have gained massive popularity in forecasting highly nonlinear cloud workloads; however, they failed to achieve excellent forecasting outcomes. Consequently, demands for designing more accurate forecasting algorithms exist. Therefore, in this work, we propose ’MAG-D’, a Multivariate Attention and Gated recurrent unit based Deep learning approach for Cloud workload forecasting in data centers. We performed an extensive set of experiments on the Google cluster traces, and we confirm that MAG-DL exploits the long-range nonlinear dependencies of cloud workload and improves the prediction accuracy on average compared to the recent techniques applying hybrid methods using Long Short Term Memory Network (LSTM), Convolutional Neural Network (CNN), Gated Recurrent Units (GRU), and Bidirectional Long Short Term Memory Network (BiLSTM).

Securing data over cloud – enhanced hybrid data encryption algorithm

Cloud computing became popular due to its elasticity, unparalleled resource availability on demand and almost zero downtime. Looking into the advantages of cloud computing, industries are moving their IT infrastructure from on-premises to cloud infrastructure irrespective of their size, business area, and customer orientation. Major challenge for cloud service providers is to provide guaranteed data security without compromising the performance of data encryption/decryption, both at rest and during intransit. Many symmetric and asymmetric algorithms already exist with their own pros and cons. The robustness of the algorithm depends upon the key size and the complexity of the algorithm used in encryption and decryption process. In this paper we derived a hybrid algorithm to make the encryption process robust without degrading performance. This proposed algorithm makes the encryption and decryption process simple but harden the sub-keys generation process by taking larger dynamic key length and complex processing steps. We refer AES for robustness during sub-key generation process and refers blowfish for encryption and decryption process to make it simpler and fast. As per the experimental result, the proposed hybrid algorithm performs better than AES in terms of encryption/ decryption time and more robust than Blowfish and AES.

E2LG: a multiscale ensemble of LSTM/GAN deep learning architecture for multistep-ahead cloud workload prediction

Efficient resource demand prediction and management are two main challenges for cloud service providers in order to control dynamic autoscaling and power consumption in recent years. The behavior of cloud workload time-series at subminute scale is highly chaotic and volatile; therefore, traditional machine learning-based time-series analysis approaches fail to obtain accurate predictions. In recent years, deep learning-based schemes are suggested to predict highly nonlinear cloud workloads, but sometimes they fail to obtain excellent prediction results. Hence, demands for more accurate prediction algorithm exist. In this paper, we address this issue by proposing a hybrid E2LG algorithm, which decomposes the cloud workload time-series into its constituent components in different frequency bands using empirical mode decomposition method which reduces the complexity and nonlinearity of prediction model in each frequency band. Also, a new state-of-the-art ensemble GAN/LSTM deep learning architecture is proposed to predict each sub band workload time-series individually, based on its degree of complexity and volatility. Our novel ensemble GAN/LSTM architecture, which employs stacked LSTM blocks as its generator and 1D ConvNets as discriminator, can exploit the long-term nonlinear dependencies of cloud workload time-series effectively specially in high-frequency, noise-like components. By validating our approach using extensive set of experiments with standard real cloud workload traces, we confirm that E2LG provides significant improvements in cloud workload prediction accuracy with respect to the mean absolute and standard deviation of the prediction error and outperforming traditional and state-of-the-art deep learning approaches. It improves the prediction accuracy at least 5% and 12% in average compared to the main contemporary approaches in recent papers such as hybrid methods which employs CNN, LSTM or SVR.

A Stochastic Approximation Approach for Foresighted Task Scheduling in Cloud Computing

With the increasing and elastic demand for cloud resources, finding an optimal task scheduling mechanism become a challenge for cloud service providers. Due to the time-varying nature of resource demands in length and processing over time and dynamics and heterogeneity of cloud resources, existing myopic task scheduling solutions intended to maximize the performance of task scheduling are inefficient and sacrifice the long-time system performance in terms of resource utilization and response time. In this paper, we propose an optimal solution for performing foresighted task scheduling in a cloud environment. Since a-priori knowledge from the dynamics in queue length of virtual machines is not known in run time, an online reinforcement learning approach is proposed for foresighted task allocation. The evaluation results show that our method not only reduce the response time and makespan of submitted tasks, but also increase the resource efficiency. So in this thesis a scheduling method based on reinforcement learning is proposed. Adopting with environment conditions and responding to unsteady requests, reinforcement learning can cause a long-term increase in system’s performance. The results show that this proposed method can not only reduce the response time and makespan but also increase resource efficiency as a minor goal.

Performance-Aware Management of Cloud Resources

The dynamic nature of the cloud environment has made the distributed resource management process a challenge for cloud service providers. The importance of maintaining quality of service in accordance with customer expectations and the highly dynamic nature of cloud-hosted applications add new levels of complexity to the process. Advances in big-data learning approaches have shifted conventional static capacity planning solutions to complex performance-aware resource management methods. It is shown that the process of decision-making for resource adjustment is closely related to the behavior of the system, including the utilization of resources and application components. Therefore, a continuous monitoring of system attributes and performance metrics provides the raw data for the analysis of problems affecting the performance of the application. Data analytic methods, such as statistical and machine-learning approaches, offer the required concepts, models, and tools to dig into the data and find general rules, patterns, and characteristics that define the functionality of the system. Obtained knowledge from the data analysis process helps to determine the changes in the workloads, faulty components, or problems that can cause system performance to degrade. A timely reaction to performance degradation can avoid violations of service level agreements, including performing proper corrective actions such as auto-scaling or other resource adjustment solutions. In this article, we investigate the main requirements and limitations of cloud resource management, including a study of the approaches to workload and anomaly analysis in the context of performance management in the cloud. A taxonomy of the works on this problem is presented that identifies main approaches in existing research from the data analysis side to resource adjustment techniques. Finally, considering the observed gaps in the general direction of the reviewed works, a list of these gaps is proposed for future researchers to pursue.

An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

Cloud computing offers various services. Numerous cloud data centers are used to provide these services to the users in the whole world. A cloud data center is a house of physical machines (PMs). Millions of virtual machines (VMs) are used to minimize the utilization rate of PMs. There is a chance of unbalanced network due to the rapid growth of Internet services. An intelligent mechanism is required to efficiently balance the network. Multiple techniques are used to solve the aforementioned issues optimally. VM placement is a great challenge for cloud service providers to fulfill the user requirements. In this paper, an enhanced levy based multi-objective gray wolf optimization (LMOGWO) algorithm is proposed to solve the VM placement problem efficiently. An archive is used to store and retrieve true Pareto front. A grid mechanism is used to improve the non-dominated VMs in the archive. A mechanism is also used for the maintenance of an archive. The proposed algorithm mimics the leadership and hunting behavior of gray wolves (GWs) in multi-objective search space. The proposed algorithm was tested on nine well-known bi-objective and tri-objective benchmark functions to verify the compatibility of the work done. LMOGWO was then compared with simple multi-objective gray wolf optimization (MOGWO) and multi-objective particle swarm optimization (MOPSO). Two scenarios were considered for simulations to check the adaptivity of the proposed algorithm. The proposed LMOGWO outperformed MOGWO and MOPSO for University of Florida 1 (UF1), UF5, UF7 and UF8 for Scenario 1. However, MOGWO and MOPSO performed better than LMOGWO for UF2. For Scenario 2, LMOGWO outperformed the other two algorithms for UF5, UF8 and UF9. However, MOGWO performed well for UF2 and UF4. The results of MOPSO were also better than the proposed algorithm for UF4. Moreover, the PM utilization rate (%) was minimized by 30% with LMOGWO, 11% with MOGWO and 10% with MOPSO.

Current cloud challenges in Germany: the perspective of cloud service providers

Cloud computing has a significant impact on information and communication technology (ICT) and is one of the most important technological drivers of the digitalization of enterprises. However, due to the increasing dissemination of cloud services and the growing number of cloud service providers (CSPs), the uncertainty and risks for user companies in adopting cloud services have also increased. In this paper, we address those aspects from the perspective of the CSPs. We identified relevant literature and studies and conducted interviews with business experts from 16 German CSPs. In our results, we present current customer requirements and barriers to using cloud services from a provider’s viewpoint and identify the actions of and obstacles for CSPs in meeting the needs and constraints of the customers. Finally, we identify current and future challenges for CSPs in dealing with customer requirements and barriers by addressing their root causes. One of the main challenges from the CSPs’ perspective is addressing customers appropriately and building relationships of trust. This also “forces” changes in the sales processes. In this process, the essential challenges can be identified as an increase in complexity and a simultaneous simplification of specific sales activities. Therefore, the necessity arises for the continuous support of business relationships through value-adding and additional services. However, this results in another challenge for the CSPs – Namely, to find the right balance between standardization and meeting customer-specific requirements. In our paper, we show that the perspective of the CSPs is rarely discussed in the literature. Nevertheless, understanding the perceptions of the providers and their actions and measures is essential for future research activities in the field of cloud service selection. Comparing the customers’ perspectives and viewpoints with the CSPs’ actions will enhance the development of a holistic selection approach for future cloud projects. Therefore, our paper’s contribution to research is also the identification of this missing integration.

Risk-based framework for SLA violation abatement from the cloud service provider’s perspective

The constant increase in the growth of the cloud market creates new challenges for cloud service providers. One such challenge is the need to avoid possible service level agreement (SLA) violations and their consequences through good SLA management. Researchers have proposed various frameworks and have made significant advances in managing SLAs from the perspective of both cloud users and providers. However, none of these approaches guides the service provider on the necessary steps to take for SLA violation abatement; that is, the prediction of possible SLA violations, the process to follow when the system identifies the threat of SLA violation, and the recommended action to take to avoid SLA violation. In this paper, we approach this process of SLA violation detection and abatement from a risk management perspective. We propose a Risk Management-based Framework for SLA violation abatement (RMF-SLA) following the formation of an SLA which comprises SLA monitoring, violation prediction and decision recommendation. Through experiments, we validate and demonstrate the suitability of the proposed framework for assisting cloud providers to minimize possible service violations and penalties.

VDC-Analyst: Design and verification of virtual desktop cloud resource allocations

One of the significant challenges for Cloud Service Providers (CSPs) hosting “virtual desktop cloud” (VDC) infrastructures is to deliver a satisfactory quality of experience (QoE) to the user. In order to maximize the user QoE without expensive resource overprovisioning, there is a need to design and verify resource allocation schemes for a comprehensive set of VDC configurations. In this paper, we present “VDC-Analyst”, a novel tool that can capture critical quality metrics such as Net Utility and Service Response Time, which can be used to quantify VDC platform readiness. This tool allows CSPs, researchers and educators to design and verify various resource allocation schemes using both simulation and emulation in two modes: “Run Simulation” and “Run Experiment”, respectively. The Run Simulation mode allows users to test and visualize resource provisioning and placement schemes on a simulation framework. Run Experiment mode allows testing on a real software-defined network testbed using emulated virtual desktop application traffic to create a realistic environment. Results from using our tool demonstrate that a significant increase in perceived user QoE can be achieved by using a combination of the following techniques incorporated in the tool: (i) optimizing Net Utility through a “Cost-Aware Utility-Maximal Resource Allocation Algorithm”, (ii) estimating values for Service Response Time using a “Multi-stage Queuing Model”, and (iii) appropriate load balancing through software-defined networking adaptations in the VDC testbed.