On-demand Resource Research Articles

A review on buckling and postbuckling of thin elastic beams

This paper provides a review of models and solutions for the buckling and postbuckling of beams available from the 50s of the last century to date. Beams with axially unrestrained (movable) ends and restrained (immovable) ends are covered. In each class, the formulation of the nonlinear buckling problem for the buckling loads and the postbuckling states is discussed and the underlying assumptions are highlighted. For relatively large-amplitude buckling of beams with movable ends, approximate analytical solutions up to the third order are presented and compared with the exact solutions expressed in terms of elliptic integrals. For beams with immovable ends, buckling involves midplane stretching that makes the nonlinear buckling problem takes the same form of the standard eigenvalue problem and, hence, exact solutions are affordable. This review combines the research outcomes on buckled beams from two scientific viewpoints: the structural dynamics and the nonlinear vibration viewpoints, respectively. Moreover, it presents in one place the formulation and the exact solutions for the buckling of beams to serve as an on-demand resource for researchers concerned with the buckling and postbuckling of beams.

Sharing economy platform firms and their resource orchestration approaches

Drawing upon key insights from the resource orchestration framework as a dynamic perspective on the resource-based view (RBV), we investigated the value creation dynamics found in sharing economy platform firms. By performing multiple-case analyses on platform firms operating in the sharing economy in China, we identified three main mechanisms by which sharing economy platform firms orchestrate their external resources (i.e., crowds of suppliers and consumers) to create value and gain a competitive advantage—constructing on-demand resource adaptation, building big-data-driven network effects, and enabling ecosystem resource coordination. We contribute to the emerging literature on the sharing economy while extending the RBV to the digital platform context, in which the value creation process is significantly shifted to beyond the boundaries of the firm.

Dynamic Cloud Resource Allocation Considering Demand Uncertainty

Cloud computing provisions scalable resources for high performance industrial applications. Cloud providers usually offer two types of usage plans: reserved and on-demand. Reserved plans offer cheaper resources for long-term contracts while on-demand plans are available for short or long periods but are more expensive. To satisfy incoming user demands with reasonable costs, cloud resources should be allocated efficiently. Most existing works focus on either cheaper solutions with reserved resources that may lead to under-provisioning or over-provisioning, or costly solutions with on-demand resources. Since inefficiency of allocating cloud resources can cause huge provisioning costs and fluctuation in cloud demand, resource allocation becomes a highly challenging problem. In this paper, we propose a hybrid method to allocate cloud resources according to the dynamic user demands. This method is developed as a two-phase algorithm that consists of reservation and dynamic provision phases. In this way, we minimize the total deployment cost by formulating each phase as an optimization problem while satisfying quality of service. Due to the uncertain nature of cloud demands, we develop a stochastic optimization approach by modeling user demands as random variables. Our algorithm is evaluated using different experiments and the results show its efficiency in dynamically allocating cloud resources.

Hybrid Resource Provisioning for Cloud Workflows with Malleable and Rigid Tasks

In cloud computing, reserved and on-demand instances are generally provided by service providers. Hybridization of the two alternatives can considerably save costs when renting resources from the cloud. However, it is a big challenge to determine the appropriate amount of reserved and on-demand resources in terms of users’ requirements. In this paper, the workflow scheduling problem with both reserved and on-demand instances is considered. The objective is to minimize the total rental cost under deadline constrains. The considered problem is mathematically modeled. A multiple sequence-based earliest finish time method is proposed to construct schedules for the workflows. Four different rules are used to generate initial task allocation sequences. Types and quantities of resources are determined by a free time block-based schedule construction mechanism. New sequences are generated by a variable neighborhood search method. Experimental and statistical analyses and results demonstrate that the proposed algorithm algorithm generates considerable cost savings when compared to the algorithms with only on-demand or reserved instances.

Estimating the Resource Demand in Power-Aware Clusters by Regressing a Linearly Dependent Relation

Large-scale clusters are often built with over-provisioned service resources, so as to satisfy the huge demand raised by enormous users in cloud environments. By estimating the resource demand of workloads, an on-demand resource provisioning method can be realized in these clusters, thus improving the energy efficiency. However, to guarantee Quality of Service (QoS), the resource demand of workload should be accurately estimated so as to provide suitable resources. Many statistical approaches estimate actual resource demand based on some workload features. But the relations between actual resource demand and workload features are generally obscure, and it's a big challenge to gain an accurate estimation under an obscure relation. In this paper, by considering a cluster as a queueing system, we construct a linearly dependent relation between resource demand and multiple feature combinations. The linearly dependent relation is inconstant due to its variable coefficients. Then, to ascertain specific relations which match actual situations, we design a Basic Linear regression (BL) algorithm. BL can obtain the optimal values for these coefficients, thus determining the inconstant relation to specific ones. Finally, we propose a Constructed Linear regression (CL) approach to estimate actual resource demands. CL forms a two-layer neural network by using several processes of BL as the neurons. To evaluate CL, we realize an On-Demand Resource Provisioning (ODRP) method in a typical power-aware cluster. Several evaluation metrics are proposed for conducting extensive experiments. The experimental results show that CL is effective to make accurate estimations.

A Robust Strategic Theory based Load Balancing and Resource Allocation in Cloud Environment

An on demand elastic service is cloud computing. At particular time, based on clients requirements, software, information, shared resources and other devices are provided in this. For supporting cost effective usage of computing resources, it is designed using distributed computing and virtualization advances and it enhances resource scalability. Based on requirement, business outcomes can scale up or down its resources. On-demand resource allocation challenges are created by customer demand management. To minimize energy, Dynamic Particle Swarm Optimization (DPSO) model is developed in available research works, where CPU utilization are regulated while operating at maximum frequency. Geographically distributed resources like computers, storage etc. are owned by self interested organizations or agents are available in large-scale computing systems of cloud computing. In their own benefit, this resource allocation algorithm can be manipulated by these agents and severe degradation in performance are produced due to its selfish behaviour and its efficiency is also very poor. To solve this kind of problem, a strategy is designed for developing a resource allocation protocols with load balancer in first phase of this research work. In this agents are forced to follow the rules and tell truth. In heterogeneous distributed systems, to solve load balancing problem, a truthful strategy is designed using this strategic theory. Optimal allocation algorithm based on Improved Elephant Herd Optimization (IEHO) is proposed where a truthful payment scheme is admitted by output function which satisfies voluntary participation. Good performance is exhibited by proposed approach as indicated in experimentation results.

A survey on virtualization techniques in Mobile edge computing

In the present scenario, the field of Information Technology(IT) is moving from physical storage to cloud storage as "cloud" providers deliver on-demand resources over the Internet. MEC's key idea is to provide an IT infrastructure system and cloud computing services at the mobile network's edge, within the RAN and close to mobile users. MEC expands the idea of cloud computing by taking the benefits of the cloud closer to consumers in the form of a network edge, resulting in less latency from end to end. It is a decentralized computing infrastructure where some applications use signal processing, storage, control and computing are distributed between the data source and the cloud in the most effective and logical way. Virtualization is the main cloud infrastructure technology used in MEC. Virtualization is accomplished by virtualizing the software or hardware resource layer. Virtualization in MEC can be done by the hypervisor, Virtual machine, Docker Container or by Kubernetes. Hypervisors and VMs are the technologies used earlier. Docker is the technology we use nowadays, and Kubernetes is the future of Virtualization. In the face of large-scale and highly scalable needs, the cloud computing infrastructure is hard to fulfil in a short time, and the conventional virtual machine-based cloud host absorbs a lot of device resources on its own hence in this paper, we will address Docker as new container technology and introduce you to how this technology has solved previous problems in Virtualization, including the creation and deployment of large applications. The purpose of this paper is to provide a detailed survey of related MEC research and technological developments where specifically relevant research and future directions are illustrated.

Quantum-inspired binary chaotic salp swarm algorithm (QBCSSA)-based dynamic task scheduling for multiprocessor cloud computing systems

Scheduling in multiprocessor computing systems is experiencing prolific challenges in datacenters due to the alarmingly growing need for dynamic on-demand resource provisioning. This problem has become a challenge for the cloud broker due to the involvement of the numerous conflicting performance metrics such as minimization of makespan, energy consumption and load balancing, and maximization of resource utilization. These challenges are to be alleviated by the practical assignments of tasks onto VMs in a way to disperse loads among VMs with high utilization of resources uniformly. In this research, authors propose a quantum-inspired binary chaotic salp swarm algorithm for scheduling the tasks in multiprocessor computing systems by considering the above conflicting objectives. The principles of quantum computing are amalgamated with the BCSSA with the aim to intensify the exploration capability. Besides, a load balancing approach is incorporated with the algorithm for uniformly dispersing the loads. This algorithm considers a multi-objective fitness function to evaluate the fitness of the particles in the problem space. The performance of the proposed algorithm is validated and analyzed through extensive experimental results using the synthetic as well as the benchmark datasets in both homogeneous and heterogeneous environments. It is evident that the proposed work shows considerable improvements over Bird Swarm Optimization, Modified Particle Swarm Optimization, JAYA, standard SSA, and GAYA (a hybrid approach) with the considered objectives.

A Utility Game Driven QoS Optimization for Cloud Services

Cloud services request lower cost compared to traditional software of self-purchased infrastructure due to the characteristics of on-demand resource provisioning and pay-as-you-go mode. Current enterprises compact their business software as services into cloud platform to users. In the cloud services market, service providers attempt to make more profits from their services, while users hope to choose low-cost services with high-quality. The conflict of interests between users and service providers is an important challenge for the booming cloud service market. This article characterizes this application problem formally based on a utility game model of service providers and users. In the model, QoS is considered as the basis for determining the utilities of both parties from an economic point of view. By analyzing the behaviors of users and service providers, we introduce the concept of reputation cost for the first time in the model and find a QoS solution that balances the utilities of users and service providers in service transactions. In such a balance, any change in either party's strategy will result in a loss of utility. And then a QoS optimization method is designed to obtain a near-optimal QoS solution for a tradeoff between user satisfaction and provider profit. Extensive simulation experiments are conducted to substantiate the effectiveness of our method. The results are applicable to win-win service applications between service providers and users.

Energy, network, and application-aware virtual machine placement model in SDN-enabled large scale cloud data centers

Cloud computing has been considered a core model of elastic on-demand resource allocation using a pay-as-you go model. One of the big challenges of this environment is to provide high quality service (QoS) through efficient and stringent management of cloud data center resources. With the increasing demand for cloud based services, the traffic volume inside cloud data centers (DC) has been increased exponentially. Accordingly, and to provide high QoS, a proper scheduling mechanism has to be followed by the cloud service provider. Furthermore, accurate scheduling is necessary for advancing the problem of energy consumption and resource utilization. In this paper, we propose an optimal resource allocation and consolidation virtual machine (VM) placement model for multi-tier applications in modern large cloud DCs. The proposed model targets to optimize the DCs’ energy and communication cost that influence the overall cloud performance through Software Defined Networking (SDN) control features. To solve the formulated multi-objective optimization problem, a novel adaptive genetic algorithm is proposed. The experimental results validate the efficacy of the proposed model through extensive simulations using synthetic and real workload traces. These results show that the proposed model jointly optimizes cloud QoS as well as energy consumption.

Cost Optimization for Big Data Workloads Based on Dynamic Scheduling and Cluster-Size Tuning

Analytical data processing has become the cornerstone of today's businesses success, and it is facilitated by Big Data platforms that offer virtually limitless scalability. However, minimizing the total cost of ownership (TCO) for the infrastructure can be challenging. We propose a novel method to build resilient clusters on cloud resources that are fine-tuned to the particular data processing task. The presented architecture follows the infrastructure-as-a-code paradigm so that the cluster can be dynamically configured and managed. It first identifies the optimal cluster size to perform a job in the required time. Then, by analyzing spot instance price history and using ARIMA models, it optimizes the schedule of the job execution to leverage the discounted prices of the cloud spot market. In particular, we evaluated savings opportunities when using Amazon EC2 spot instances comparing to on-demand resources. The performed experiments confirmed that the prediction module significantly improved the cost-effectiveness of the solution – up to 80% savings compared to the on-demand prices, and at the worst-case, 1% more cost than the absolute minimum. The production deployments of the architecture show that it is invaluable for minimizing the total cost of ownership of analytical data processing solutions. • Extended cloud architecture that dynamically selects and schedules resources. • Evaluated several spot price prediction models to forecast future prices. • Confirmed feasibility of short-term spot price prediction through real data from AWS. • Cost savings of up to 80% compared to on-demand and within 1% of the absolute minimum.

Multi-Agent Reinforcement Learning-Based Resource Management for End-to-End Network Slicing

To meet the explosive growth of mobile traffic, the 5G network is designed to be flexible and support multi-access edge computing (MEC), thereby improving the end-to-end quality of service (QoS). In particular, 5G network slicing, which allows a physical infrastructure to split into multiple logical networks, keeps the balance of network resource allocation among different service types with on-demand resource requests. However, achieving effective resource allocation across the end-to-end network is difficult due to the dynamic characteristics of slicing requests such as uncertain real-time resource demand and heterogeneous requirements. In this paper, we develop a reinforcement learning (RL)-based dynamic resource allocation framework for end-to-end network slicing with heterogeneous requirements in multi-layer MEC environments. We first design a hierarchical MEC architecture and formulate a resource allocation problem for the end-to-end network slicing as an optimization problem using the Markov decision process (MDP). Using proximal policy optimization (PPO), we develop independently-collaborative and jointly-collaborative dynamic resource allocation algorithms to maximize resource efficiency while satisfying the QoS of slices. Experimental results show that the proposed algorithms can recognize the characteristics of slice requests and coming resource demands and efficiently allocate resources with a high QoS satisfaction rate.

Smart Network Price Policy for ISP Based on Traffic Prediction

The explosion of traffic brings the challenges for Internet Service Providers (ISPs) to make a profit with the high cost of infrastructure and increased competition. This calls for economic mechanisms that can enable providers to allocate on-demand resources through the prediction of traffic volumes and adjust the price. In this paper, we analyze the network traffic pattern of mobile data and make an accurate prediction of traffic volumes through ARIMA and LSTM. Based on the analysis, we then suggest a scalable price strategy for ISPs to satisfy the various requirements of customers.

Optimized mobile cloud resource discovery architecture based on dynamic cognitive and intelligent technique

In current world, the Mobile Cloud Computing (MCC) will undergo a dramatic upgrade with integrating of mobile cloud resources in small regions to provide a better MCC service. This paper considers the MCC resource availability problem in integrated mobile cloud environment to optimization resources availability with a better effectiveness, accuracy, reliable, low latency and low complexity. The DCICRD architecture is proposed in this paper optimizes the resource discover process with better resource availability. The proposed DCICRD architecture runs various operations such Resource demand prediction to find the required level of resources, Cloudlet Resource Discovery process which discovers resources based on the requirement predicted using two states expand and shrink. The expand state is used to discover resources on-demand in resource scarcity situation and the shrink state is called when abundant resource available to the required level. Further, Resource reliability check is performed to identify the reliable resource with energy level and signal strength above the threshold level. Finally, Local Resource Information Management Table is used to store the local resource provider information and Central Resource Information Management Table is used to store all local resource information for handover of resource provider. The implementation and evaluation is conducted by comparing the HARD architecture. The comparison result shows that proposed DCICRD architecture is better than HARD architecture by optimizing the resource discovery process and produce reliable resources on demand as per the required level with a better effectiveness using expand and shrink state, accuracy by making resource available for required level, reliable by sending message for frequent checking of Resource with required energy level and signal strength, low latency by making resource available locally and reduce complexity by performing only on-demand resource discovery operation after initial startup. Thus, the proposed DCICRD architecture is better when compared to HARD architecture.

A dynamic load scheduling in IaaS cloud using binary JAYA algorithm

Datacenters receive dynamic workloads with disparate specifications to be scheduled on virtual machines (VMs). These unpredictable, alarmingly growing workloads with varying resource specifications may bring down the servers of datacenters into an imbalanced state. Thus, resulting in low resource utilization and high energy consumption among the servers. To cater to the need of fluctuating on-demand resource provisioning, it is essential to scale up the ability and capacityof existing infrastructure through virtualization. Moreover, due to the involvement of conflicting scheduling constraints, load scheduling in cloud computing fall under NP-hard problem. An effective scheduling mechanism in amalgamation with a load balancing strategy based on binary JAYA is implemented to alleviate the challenges as mentioned above. This technique not only improves resource utilization but also brings down the degree of energy consumption and makespan while keeping the whole system balanced. At first, it focuses on evoking a load balancing procedure to uniformly disperse the loads among VMs based on the compatibility between the tasks and VMs and secondly, JAYA algorithm is executed to find the best possible mapping of tasks onto VMs. In order to appraise the efficacy of the proposed algorithm, the scheduling of independent and non-preemptive tasks is simulated in CloudSim using a benchmark parallel workload by NASA-iPSC. Experiments are conducted in both homogenous and heterogeneous environments. The proposed algorithm is statistically validated using the Friedman test, followed by a Holm’s test. The proposed approach is compared over other algorithms such as Round Robin (RR), binary Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The simulation results show a notable amelioration over other mentioned algorithms by an increase of resource utilization with 18.47% (GA), 12.65% (BPSO) and 4.18% (GA), 2.51% (BPSO) and a reduction of makespan by 6.47% (GA), 4.35% (BPSO) and 4.17% (GA), 2.20% (BPSO) with an increasing number of tasks and VMs in two different test cases respectively.

An Efficient Hierarchical Representation Approach of Remote Sensing Application Modeling Based on Distributed Environment

In Earth science, information science, space science, and other disciplines, scientists use the land surface parameter inversion method in their work, applying this to the atmosphere, vegetation, soil, drought, and so on. Multidisciplinary experts sometimes collaborate on a particular application. However, these remote sensing models do not have a unified method of description and management and cannot effectively achieve the sharing of models and data resources. It is also hard to meet user demand for global data and models in the current state, especially in the face of global problems and long-term series problems. In this paper, we examine the scientific questions of the computability and scalability of remote sensing models. This paper adopts a data dependency approach to describe a remote sensing model and implements a hierarchical unified description and management method using modelling based on four layers: a data-processing view, an atomic model view, an on-demand resource package view, and a workflow view. We choose three typical remote sensing models for disaster monitoring as use cases and describe the practical application process of the proposed method. The results demonstrate the advantages and powerful capabilities of this efficient method.

Sunflower Whale Optimization Algorithm for Resource Allocation Strategy in Cloud Computing Platform

Cloud computing environment supply the computing resources based on the demand of cloud user requirements. It builds the resource allocation model through distributed computing and virtualization to emphasize the scalability of cloud services. However, to manage the demand of user creates a complex issue in the on-demand resource allocation framework. Therefore, an effective optimization algorithm named Sunflower Whale Optimization Algorithm (SFWOA) is proposed to solve the issues in the resource allocation model. The concept of virtualization helps to execute the tasks based on the availability of resources and reduces the response time. The tasks are allocated to the virtual machine in a distributed manner to balance the workload in cloud. The proposed SFWOA uses the hunting strategy and the foraging behavior of humpback whale along with the peculiar behavior of sunflower to achieve the effective resource allocation. The performance enhancement of the proposed SFWOA is revealed through the performance measures such that the proposed method attained a maximum resource utilization of 0.942 using 20 virtual machines, maximum memory utilization of 0.215, and maximum CPU utilization of 0.269 using 15 virtual machines, and minimum skewness of 0.001 using 25 virtual machines.

Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks

In this paper, we investigate multi-dimensional resource management for unmanned aerial vehicles (UAVs) assisted vehicular networks. To efficiently provide on-demand resource access, the macro eNodeB and UAV, both mounted with multi-access edge computing (MEC) servers, cooperatively make association decisions and allocate proper amounts of resources to vehicles. Since there is no central controller, we formulate the resource allocation at the MEC servers as a distributive optimization problem to maximize the number of offloaded tasks while satisfying their heterogeneous quality-of-service (QoS) requirements, and then solve it with a multi-agent deep deterministic policy gradient (MADDPG)-based method. Through centrally training the MADDPG model offline, the MEC servers, acting as learning agents, then can rapidly make vehicle association and resource allocation decisions during the online execution stage. From our simulation results, the MADDPG-based method can converge within 200 training episodes, comparable to the single-agent DDPG (SADDPG)-based one. Moreover, the proposed MADDPG-based resource management scheme can achieve higher delay/QoS satisfaction ratios than the SADDPG-based and random schemes.

Optimizing the Procurement of IaaS Reservation Contracts via Workload Predicting and Integer Programming

Cloud-based web applications are proliferating fast. Owing to the elastic capacity and diverse pricing schemes, cloud Infrastructure-as-a-Service (IaaS) offers great opportunity for web application providers to optimize resource cost. However, such optimization activities are confronting the challenges posed by the uncertainty of future demand and the increasing reservation contracts. This work investigates the problem of how to minimize IaaS rental cost associated with hosting web applications, while meeting the demand in the future business cycle. First, an integer liner program model is developed to optimize reservation-contract procurement, in which reserved and on-demand resources are planned for multiple provisioning stages as well as a long-term plan, e.g., twelve stages in an annual plan. Then, a Long Short-Term Memory (LSTM) based algorithm is designed to predict the workload in the future business cycle. In addition, the approaches for determining virtual instance capacity and the baseline workload of planning time slot are also presented. Finally, the experimental prediction results show the LSTM-based algorithm gains an advantage over several popular models, such as the Holter–Winters, the Seasonal Autoregressive Integrated Moving Average (SARIMA), and the Support Vector Regression (SVR). The simulations of resource planning show that the provisioning scheme based on our reservation-optimization model obtains significant cost savings than other typical provisioning schemes, while satisfying the demands.

Cloud-based elastic architecture for distributed video encoding: Evaluating H.265, VP9, and AV1

Areas with social and business impact such as entertainment, healthcare, surveillance, and e-learning would benefit from improvements in video coding and transcoding services. New codecs, such as AV1, are being developed to deal with new demands for high video resolutions with bandwidth constraints and quality requirements. However, these new codecs have high computational requirements and new strategies are needed to accelerate their processing.Cloud computing offers interesting features such as on-demand resource allocation, multitenancy, elasticity, and resiliency among others. Deploying video coding and transcoding services on these infrastructures is suitable because it allows the adaptation of the resources to the workload, offers high availability, and provides ubiquitous access.This work proposes a cloud-based distributed architecture, tuned for video coding, that relies on an elastic pool of workers and media servers to provide fault-tolerance. A distributed application is deployed on the top of the architecture to split the video encoding process of a video in several jobs that can be dynamically assigned among the elastic pool of workers. The proposed solution is analyzed in terms of scalability, resource usage, and job distribution varying the number of workers for three well-known video codecs: H.265, VP9, and AV1. Moreover, the quality of the encoded videos has been evaluated for different bit rates and number of frames per job using full reference metrics like: PSNR, MS-SSIM, and VIF. Results show that our solution achieve similar quality and bitrate compared with full video coding while improving the total encoding time, which can decrease more than 90% depending on the encoder and the number of workers.