Resources In Cloud Infrastructure Research Articles

Deep Learning Modified Reinforcement Learning with Virtual Machine Consolidation for Energy-Efficient Resource Allocation in Cloud Computing

Cloud computing has attracted significant attention because of the growing service demands of businesses that outsource computationally intensive tasks to the data center. Meanwhile, the infrastructure of a data center is comprised of hardware resources that consume a great deal of energy and release harmful levels of carbon dioxide. Cloud data centers demand massive amounts of electrical power as modern applications and organizations grow. To prevent resource waste and promote energy efficiency, virtual machines (VMs) must be dispersed over numerous physical machines (PMs) in a data center in the cloud. The actual allocation of VMs to PMs can involve more complex decision-making processes, such as considering the resource utilization, load balancing, performance requirements, and constraints of the system. Advanced techniques, like intelligent placement algorithms or dynamic resource allocation, may be employed to optimize resource utilization and achieve efficient VM distribution across multiple PMs. Cloud service suppliers aim to lower operational expenses by reducing energy consumption while offering clients competitive services. Minimizing large-scale data center power usage while maintaining the quality of service (QoS), especially for social media-based cloud computing systems, is crucial. Consolidating VMs has been highlighted as a promising method for improving resource efficiency and saving energy in data centers. This research provides deep learning augmented reinforcement learning (RL)-based energy efficient and QoS-aware virtual machine consolidation (VMC) approach to meet the difficulties. The proposed deep learning modified reinforcement learning-virtual machine consolidation (DLMRL-VMC) model can motivate both cloud providers and customers to distribute cloud infrastructure resources to achieve high CPU utilization and good energy efficiency as measured by power usage effectiveness (PUE) and data center infrastructure efficiency (DCiE). The suggested model, DLMRL-VMC, offers a VM placement approach based on resource usage and dynamic energy consumption to determine the best-matched host and VM selection strategy, Average Utilization Migration Time (AUMT). Based on AUMT, deep learning modified reinforcement learning (DLMRL) will choose a VM with a low average CPU utilization and a short migration time. The DLMRL-VMC Energy-efficient, Resource Allocation strategy is evaluated on the trace of the CloudSim VM to attain good PUE and CPU utilization.

Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing

The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction.

INSTABILITY OF CLOUD INFRASTRUCTURE RESOURCES AND SERVICES

Increasing non-stationarity of cloud infrastructure resources and services leads to a significant decrease in its productivity. Therefore, the purpose of the article is to identify the reasons for non-stationarity of cloud infrastructure resources and services; finding ways to reduce the level of non-stationarity . As a result of the research, the following results were obtained . Sources leading to the specified non-stationarity are identified . Existing approaches to reducing non-stationarity are analyzed . An example of basic resource allocation using standard linear programming methods is given. Variants of application of these methods for dynamic redistribution of resources are shown. Conclusion. Allocation and dynamic redistribution of resources in the cloud infrastructure can be done using standard linear programming methods. But due to the significant non-stationarity of the cloud environment, the proposed approach will reduce the productivity of cloud resources. At the same time, with an increase in the number of variables and restrictions, the computational complexity of the proposed algorithm will grow exponentially . Therefore, it is necessary to look for other approaches for the distribution and redistribution of cloud resources in conditions of significant non-stationarity .

Diseño de Interfaz de Usuario Web de un Editor Visual para el Modelamiento de Infraestructura en la Nube

Cloud computing has evolved the way IT technicians manage infrastructure resources to support software applications. Leasing equipment and services from cloud infrastructure providers, such as Amazon Web Services, Microsoft Azure, etc., has replaced the traditional strategy of locally installing expensive equipment. Nowadays, IT technicians model the infrastructure they need by writing scripts, then running these scripts in the provider web platform. However, writing scripts becomes a complex task that involves interacting with the Command Line Interface and knowing the commands each provider imposes. In this context, replacing textual with visual interaction becomes a need. The purpose is to fesign the user interface (UI) of a visual web editor to model the cloud infrastructure resources. The User-Centered Design (UCD) methodology was applied to design the UI. As part of DCU, a study of the UIs of diagramming online tools was conducted to identify UI design patterns; those that expert designers use when designing visual editors.The results show that a set of 11 UI patterns for designing visual editor UIs was defined. In addition, a pattern language was built considering the relationships between patterns. By using the pattern language, the visual editor UI design was composed. As conclusions, the pattern language provided a logical way to compose the visual editor UI. In this sense, the proposed UI together with the pattern language can become a reference point for designing UIs in this domain. In future work, the effectiveness of the UI in reducing the complexity of defining the cloud infrastructure will be evaluated with user tests.

Dynamic resource allocation in cloud infrastructure using ant lion‐based auto‐regression model

SummaryCloud computing provides a quick, simple, and gainful means for configuring and assigning the resources for a web‐based appliance, like medical records systems, smart grid applications, and security management infrastructures. The optimization of resource allocation in the cloud is a major task for meeting customer demands and maximizing profit. This paper devises an optimization method for allocating resources in cloud infrastructures. The main contribution is to provide a framework for allocating the available resource to the deserving tasks. Here, the dynamic resource allocation is performed to allocate the resources dynamically as requested by users without affecting the system performance. Here, the ant lion‐based auto‐regressive optimization (ALAO) strategy is employed to allocate the cloud resources. ALAO is designed by integrating Ant Lion Optimizer (ALO) in auto‐regression model for further enhancing the allocation of resources, and a new fitness function is adapted, which considers certain parameters, such as cost, speed, and load. The results proved that the proposed ALAO algorithm attained enhanced performance with a maximal profit of 0.153, a minimum load of 0.028, and a minimal task assignment cost of 0.094.

Does cloud computing improve team performance and employees' creativity?

PurposeThe present investigation goals to empirically test the role of expert cloud on team performance and employee creativity. Here, the expert cloud comprises cloud application, cloud management, cloud infrastructure and cloud resources. The present study aims to identify important and key criteria and examine the relationships among them. In other words, the purpose of this study to find out the impact and relationship between cloud application, cloud management, cloud infrastructure and cloud resources and team performance and employee creativity.Design/methodology/approachToday, human societies’ rapid growth and the environmental changes that surround us every day are clearly visible. They highly affect our activities. In today’s highly complex organizations, people alone cannot handle all the issues that have arisen. As organizational managers are faced with diverse cultures in the governance of organizations, the need to use work teams with different abilities and specializations to achieve the goals of organizations leads managers to use teamwork and focus on employee creativity. On the other hand, the expert cloud makes it possible for human societies like universities, firms, industries, institutes, businesses and colleges to pool and share their human resources’ skills, knowledge and experiences to meet the competitive era’s demands. Therefore, the current investigation examines the impact of expert cloud on team performance and employee creativity. The research information is collected using an online questionnaire. The data collected is analyzed using AMOS and SMART PLS software.FindingsAll of the formulated hypotheses are supported. The results have shown that cloud application, cloud management, cloud infrastructure and cloud resources positively and significantly affect team performance and employee creativity.Practical implicationsManagers must be conscious of the vital role that the professional cloud plays in team performance and the innovation of workers. This paper would also make executives more conscious of the powerful tools in the field of cloud computing. Firms can use the outcomes of this paper investigation to improve team performance and employee creativity.Originality/valueIt is considered one of the initial efforts to demonstrate the impacts of expert cloud on team performance and employee creativity. This study’s value relies on that practitioners and academics may have supporting evidence on the role played by novel technology such as an expert cloud.

A latency-aware max-min algorithm for resource allocation in cloud

Cloud computing is an emerging distributed computing paradigm. However, it requires certain initiatives that need to be tailored for the cloud environment such as the provision of an on-the-fly mechanism for providing resource availability based on the rapidly changing demands of the customers. Although, resource allocation is an important problem and has been widely studied, there are certain criteria that need to be considered. These criteria include meeting user’s quality of service (QoS) requirements. High QoS can be guaranteed only if resources are allocated in an optimal manner. This paper proposes a latency-aware max-min algorithm (LAM) for allocation of resources in cloud infrastructures. The proposed algorithm was designed to address challenges associated with resource allocation such as variations in user demands and on-demand access to unlimited resources. It is capable of allocating resources in a cloud-based environment with the target of enhancing infrastructure-level performance and maximization of profits with the optimum allocation of resources. A priority value is also associated with each user, which is calculated by analytic hierarchy process (AHP). The results validate the superiority for LAM due to better performance in comparison to other state-of-the-art algorithms with flexibility in resource allocation for fluctuating resource demand patterns.

MCAF: Developing an Annotation-Based Offloading Framework for Mobile Cloud Computing

Offloading computation from mobile to remote cloud servers is a promising way to reduce energy consumption and improve the performance of mobile applications. However, a great challenge arises as automatic integration of powerful computing resources in remote cloud infrastructure and the portability of mobile devices. In this paper, we develop a Java annotation-based offloading framework, called MCAF, for android mobile devices. This framework is designed and committed to simplifying the development of android applications enabled with the offload capability. All the developers need to do is to import the SDK library of our MCAF and annotate the computation-intensive methods. MCAF can automatically extract the annotated source code and generate the code that will be run in the Cloud. Moreover, the codes of making the offloading decisions are automatically inserted into the original source code. We also conducted the real experiments to show the applicability of our MCAF.

Optimizing Energy Efficiencies in Cloud Data Center Resources with Availability Constraints

Cloud infrastructure Resources hosted in Data Centers, support the effective execution of Cloud computing applications. Given the increased adoption of the Cloud Computing Applications and the Businesses getting to be Data-driven, there is a huge increase in the number of Data Centers and the Size and amount of resources hosted in these Data Centers. These Data Center resources consume a significant amount of energy and this continuous scaling of the resources is leading to increased power consumption and a large carbon footprint. Given our fragile eco-system, optimization of the Data Center resources for energy conservation and thus the carbon footprint is the primary area of our focus. Businesses also need to satisfy QoS guarantees on Availability to their customers. Optimization towards Energy efficiencies may compromise on the Availability and thus may warrant a trade-off, and a need for them to be considered together. Although there have been numerous studies towards Energy efficiencies, most of them have been focused on only energy. In this paper, we initially segregate Optimization activities towards the Data Center resources like Compute, Network, and Storage. We then study the different control parameters or approaches which will lead to meeting the objectives of Energy Efficiencies, Availability and Energy Efficiency constrained with Availability. Thus, this will support the selection of approaches for the optimization of energy while meeting the QoS Availability requirement.

On the Effectiveness of Tools to Support Infrastructure as Code: Model-Driven Versus Code-Centric

Infrastructure as Code (IaC) is an approach for infrastructure automation that is based on software development practices. The IaC approach supports code-centric tools that use scripts to specify the creation, updating and execution of cloud infrastructure resources. Since each cloud provider offers a different type of infrastructure, the definition of an infrastructure resource (e.g., a virtual machine) implies writing several lines of code that greatly depend on the target cloud provider. Model-driven tools, meanwhile, abstract the complexity of using IaC scripts through the high-level modeling of the cloud infrastructure. In a previous work, we presented an infrastructure modeling approach and tool (Argon) for cloud provisioning that leverages model-driven engineering and supports the IaC approach. The objective of the present work is to compare a model-driven tool (Argon) with a well-known code-centric tool (Ansible) in order to provide empirical evidence of their effectiveness when defining the cloud infrastructure, and the participants' perceptions when using these tools. We, therefore, conducted a family of three experiments involving 67 Computer Science students in order to compare Argon with Ansible as regards their effectiveness, efficiency, perceived ease of use, perceived usefulness, and intention to use. We used the AB/BA crossover design to configure the individual experiments and the linear mixed model to statistically analyze the data collected and subsequently obtain empirical findings. The results of the individual experiments and meta-analysis indicate that Argon is more effective as regards supporting the IaC approach in terms of defining the cloud infrastructure. The participants also perceived that Argon is easier to use and more useful for specifying the infrastructure resources. Our findings suggest that Argon accelerates the provisioning process by modeling the cloud infrastructure and automating the generation of scripts for different DevOps tools when compared to Ansible, which is a code-centric tool that is greatly used in practice.

Optimizing the Cloud Resources, Bandwidth and Deployment Costs in Multi-Providers Network Function Virtualization Environment

The introduction of network function virtualization (NFV) leads to a new business model in which the Telecommunication Service Provider needs to rent cloud resources to infrastructure provider (InP) at prices as low as possible. Lowest prices can be achieved if the cloud resources can be rented in advance by allocating long-term virtual machines (VM). This is in contrast with the short-term VMs that are rented on demand and have higher costs. For this reason, we propose a proactive solution in which the cloud resource rent is planned in advance based on peak traffic knowledge. We illustrate the problem of determining the cloud resources in cloud infrastructures managed by different InPs and so as to minimize the sum of cloud resource, bandwidth and deployment costs. We formulate an integer linear problem (ILP) and due to its complexity, we introduce an efficient heuristic approach allowing for a remarkable computational complexity reduction. We compare our solution to a reactive solution in which the cloud resources are rented on demand and dimensioned according to the current traffic. Though the proposed proactive solution needs more cloud and bandwidth resources due to its peak allocation, its total resources cost may be lower than the one achieved when a reactive solution is applied. That is a consequence of the higher cost of short-term VMs. For instance, when a reactive solution is applied with traffic variation times of ten minutes, our proactive solution allows for lower total costs when the long-term VM rent is lower than the short-term VM one by 33%.

SDCon: Integrated Control Platform for Software-Defined Clouds

Cloud computing has been empowered with the introduction of Software-Defined Networking (SDN) which enabled dynamic controllability of cloud network infrastructure. Despite the increasing popularity of studies for joint resource optimization in the cloud environment with SDN technology, the realization is still limited for developing integrated management platform providing a simultaneous controllability of computing and networking infrastructures. In this paper, we propose SDCon , a practical platform developed on OpenStack and OpenDaylight to provide integrated manageability for both resources in cloud infrastructure. The platform can perform VM placement and migration, network flow scheduling and bandwidth allocation, real-time monitoring of computing and networking resources, and measuring power usage of the infrastructure with a single platform. We also propose a network topology aware VM placement algorithm for heterogeneous resource configuration (TOPO-Het) that consolidates the connected VMs into closely connected compute nodes to reduce the overall network traffic. The proposed algorithm is evaluated on SDCon and compared with the results from the state-of-the-art baselines. Results of the empirical evaluation with Wikipedia application show that the proposed algorithm can effectively improve the response time while reducing the total network traffic. It also shows the effectiveness of SDCon to manage both resources simultaneously.

Optimal allocation of cloud multi-tenant platform infrastructure resources

Infrastructure resources optimisation is a significant challenge in multi-tenant cloud environment. A system and associated algorithm and processes to enable a multi-tenant platform operator or administrator to make more optimal decisions with regards to the allocation of platform infrastructure resources (such as computational capabilities, data storage, etc.) among one or more tenants or accounts are provided. In particular, we construct a data 'signature' for a set of identified users, accounts, or tenants, where the signature contains data regarding the user, account, or tenant's 'consumption' of platform infrastructure resources. Later tenants 'signatures' are being used to accomplish cloud infrastructure resources optimisation in multi-tenant environment. An innovative algorithm for cloud infrastructure resources optimisation in multi-tenant environment is introduced in current paper.

Reinforcement learning based methodology for energy-efficient resource allocation in cloud data centers

Energy-efficient Cloud Infrastructure Resource Allocation Framework is getting popularity as it is paying effective attention to cloud data management with a view to achieve maximize revenue and minimize cost. This infrastructure can encourage for both cloud providers and users for allocating cloud infrastructure resources for fulfilling not only good energy efficiency measured in Power Usage Effectiveness (PUE) and data center Infrastructure Efficiency (DCiE) but also high CPU utilization. Therefore, in this paper we proposed a framework which can show effective performance for achieving the high data center energy efficiency and preventing Service Level Agreement (SLA) violation respectively with the aim of green cloud resources deployment. The framework accomplishes cloud infrastructure resource allocation on the basic of Reinforcement Learning mechanism and Fuzzy Logic for green solutions. The evaluation for Energy-efficient Resource Allocation is experimented on the traces of the PlanetLab virtualized environment for gaining good PUE and CPU utilization.

Cloud Infrastructure Resource Allocation for Big Data Applications

Increasing popular big data applications bring about invaluable information, but along with challenges to industrial community and academia. Cloud computing with unlimited resources seems to be the way out. However, this panacea cannot play its role if we do not arrange fine allocation for cloud infrastructure resources. In this paper, we present a multi-objective optimization algorithm to trade off the performance, availability, and cost of Big Data application running on Cloud. After analyzing and modeling the interlaced relations among these objectives, we design and implement our approach on experimental environment. Finally, three sets of experiments show that our approach can run about 20 percent faster than traditional optimization approaches, and can achieve about 15 percent higher performance than other heuristic algorithms, while saving 4 to 20 percent cost.

A Hybrid Approach for Virtual Machine Migration in Cloud Computing Environment

A lot of research has been done in the field of cloud computing in computing domain. For its effective performance, variety of algorithms has been proposed. The role of virtualization is significant and its performance is dependent on VM Migration and allocation. More of the energy is absorbed in cloud; therefore, the utilization of numerous algorithms is required for saving energy and efficiency enhancement in the proposed work. In the proposed work, green algorithm has been considered with meta heuristic algorithms, ABC (Artificial Bee colony .Every server has to perform different or same functions. A cloud computing infrastructure can be modelled as Primary Machineas a set of physical Servers/host PM1, PM2, PM3… PMn. The resources of cloud infrastructure can be used by the virtualization technology, which allows one to create several VMs on a physical server or host and therefore, lessens the hardware amount and enhances the resource utilization. The computing resource/node in cloud is used through the virtual machine. To address this problem, data centre resources have to be managed in resource -effective manner for driving Green Cloud computing that has been proposed in this work using Virtual machine concept with ABC and Neural Network optimization algorithm. The simulations have been carried out in CLOUDSIM environment and the parameters like SLA violations, Energy consumption and VM migrations along with their comparison with existing techniques will be performed.

Investigations into elasticity in cloud computing

The pay-as-you-go model supported by existing cloud infrastructure providers is appealing to most application service providers to deliver their applications in the cloud. Within this context, elasticity of applications has become one of the most important features in cloud computing. This elasticity enables real-time acquisition/release of compute resources to meet application performance demands. In this thesis we investigate the problem of delivering cost-effective elasticity services for cloud applications. Traditionally, the application level elasticity addresses the question of how to scale applications up and down to meet their performance requirements, but does not adequately address issues relating to minimising the costs of using the service. With this current limitation in mind, we propose a scaling approach that makes use of cost-aware criteria to detect the bottlenecks within multi-tier cloud applications, and scale these applications only at bottleneck tiers to reduce the costs incurred by consuming cloud infrastructure resources. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness by studying the behaviour of an example electronic commerce site application. Furthermore, we consider the characteristics of the algorithm for implementing the business logic of cloud applications, and investigate the elasticity at the algorithm level: when dealing with large-scale data under resource and time constraints, the algorithm's output should be elastic with respect to the resource consumed. We propose a novel framework to guide the development of elastic algorithms that adapt to the available budget while guaranteeing the quality of output result, e.g. prediction accuracy for classification tasks, improves monotonically with the used budget.

Power-Efficient Provisioning for Online Virtual Network Requests in Cloud-Based Data Centers

A cloud computing paradigm enables users to access services, applications, and infrastructure resources by using thin clients anywhere and at any time. In this paradigm, multiple users can share cloud infrastructure resources. The application or service requests from a user can be abstracted as a virtual network (VN) request and can be submitted to the cloud-based data centers. How to map a VN onto the cloud infrastructure network is a challenging issue in cloud resource provisioning. Thus, efficient mapping techniques that intelligently use the resources of cloud infrastructure are important and necessary. Current research on VN mapping and design focuses on resource-efficient VN mapping or cost-efficient VN mapping. However, there is another important issue in cloud-based data centers that we must pay attention to, i.e., the amount of power or energy that is consumed by a data center. The power consumption in data centers can be a significant percentage of the total power consumption, and it not only leads to a higher data center operating cost but also contributes to carbon emissions and the greenhouse effect. In this paper, we propose a power-efficient resource provisioning technique in cloud-based data centers while complying with service level agreements. We first model a power-efficient VN provisioning problem as a mathematical optimization problem, with the objective of minimizing the power consumption by employing mixed-integer programming. We then propose a heuristic algorithm to efficiently solve this model since this optimization problem is NP-hard. We validate and evaluate our framework and algorithm by conducting extensive simulations on different cloud infrastructure networks under various scenarios. The simulation results show that our approach performs well.

Toward a telco cloud environment for service functions

Deploying service functions, SFs, is an essential action for a network provider. However, the action of creating, modifying and removing network SFs is traditionally very costly in time and effort, requiring the acquisition and placement of specialized hardware devices and their interconnection. Fortunately, the emergence of concepts like cloud computing, SDN, and ultimately NFV is expected to raise new possibilities for the management of SFs with a positive impact in terms of agility and cost. From a telco viewpoint these concepts can help to both reduce OPEX and open the door to new business opportunities. In this article, we identify how teleos can benefit from the abovementioned paradigms, and explore some of the aspects that still need to be addressed in the NFV domain. We focus on two major aspects: enabling telco infrastructures to adopt this new paradigm, and orchestrating and managing SFs toward telco-ready cloud infrastructures. The technologies we describe enable a telco to deploy and manage SFs in a distributed cloud infrastructure. In this context, the Cloud4NFV platform is presented. Special attention is given to the way SFs are modeled toward cloud infrastructure resources. In addition, we explore the ability to perform service function chaining as one of the fundamental features in the composition of SFs. Finally, we describe a proof of concept that demonstrates how a telco can benefit from the described technologies.

IaaS Cloud Architecture: From Virtualized Datacenters to Federated Cloud Infrastructures

As a key component in a modern datacenter, the cloud operating system is responsible for managing the physical and virtual infrastructure, orchestrating and commanding service provisioning and deployment, and providing federation capabilities for accessing and deploying virtual resources in remote cloud infrastructures.