IaaS Cloud Research Articles

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.

Data Placement Oriented Scheduling Algorithm for Scheduling Scientific Workflow in Cloud: A Budget-Aware Approach

Background: A large number of communities and enterprises deploy numerous scientific workflow applications on cloud service. Aims: The main aim of the cloud service provider is to execute the workflows with a minimal budget and makespan. Most of the existing techniques for budget and makespan are employed for the traditional platform of computing and are not applicable to cloud computing platforms with unique resource management methods and pricing strategies based on service. Methods: In this paper, we studied the joint optimization of cost and makespan of scheduling workflows in IaaS clouds, and proposed a novel workflow scheduling scheme. Also, data placement is included in the proposed algorithm. Results: In this scheme, DPO-HEFT (Data Placement Oriented HEFT) algorithm is developed which closely integrates the data placement mechanism with the list scheduling heuristic HEFT. Extensive experiments using the real-world and synthetic workflow demonstrate the efficacy of our scheme. Conclusion: Our scheme can achieve significantly better cost and makespan trade-off fronts with remarkably higher hypervolume and can run up to hundreds times faster than the state-of-the-art algorithms.

A survey on preemptible IaaS cloud instances: challenges, issues, opportunities, and advantages

Despite the popularity and widespread usage of cloud computing, the cost of resources is one of the most important issues in Infrastructure as a Service (IaaS) clouds. Therefore, dynamic pricing models presented by some IaaS service providers, offers considerable price savings on spot instances or low-priority virtual machines. This significant discount has increased the popularity of using such resources among users. However, some of the most important Quality of Service (QoS) metrics such as reliability and availability are eliminated by obtaining this discount. For instance, the availability criterion is influenced by issues such as the user’s bid, supply, and demand rate of that specific instance, etc. In this paper, an extensive survey has been conducted on the issue of cloud preemptible instances, and the challenges in this context are studied. Furthermore, we point out the challenges that have not yet been investigated and define future directions in this research area.

A Reinforcement Learning-Based Mixed Job Scheduler Scheme for Grid or IaaS Cloud

Job scheduling is a necessary prerequisite for performance optimization and resource management in the cloud computing system. Focusing on accurate scaled cloud computing environment and efficient job scheduling under Virtual Machine (VM) resource and Server Level Agreement (SLA) constraints, we introduce the architecture of cloud computing platform and optimization job scheduling scheme in this study. The system model is comprised of clearly defined separate constituent parts, including portal, job scheduler, and resources pool. By analyzing the execution process of user jobs, we designed a novel job scheduling scheme based on reinforcement learning to minimize the makespan and Average Waiting Time (AWT) under the VM resource and deadline constraints, and employ parallel multi-age parallel technologies to balance the exploration and exploitation in learning process and accelerate the convergence of Q -learning algorithm. Both simulation and real cloud platform experiment results demonstrate the efficiency of the proposed job scheduling scheme.

Quantitative Fault-Tolerance for Reliable Workflows on Heterogeneous IaaS Clouds

Reliability requirement is one of the most important quality of services (QoS) and should be satisfied for a reliable workflow in cloud computing. Primary-backup replication is an important software fault-tolerant technique used to satisfy reliability requirement. Recent works studied quantitative fault-tolerant scheduling to reduce execution cost by minimizing the number of replicas while satisfying the reliability requirement of a workflow on heterogeneous infrastructure as a service (IaaS) clouds. However, a minimum number of replicas does not necessarily lead to the minimum execution cost and shortest schedule length in a heterogeneous IaaS cloud. In this study, we propose the quantitative fault-tolerant scheduling algorithms QFEC and QFEC+ with minimum execution costs and QFSL and QFSL+ with shortest schedule lengths while satisfing the reliability requirements of workflows. Extensive experimental results show that (1) compared with the state-of-the-art algorithms, the proposed algorithms achieve less execution cost and shorter schedule length, although the number of replicas are not minimum; (2) QFEC and QFEC+ are designed to reduce execution cost, and QFEC+ is better than QFEC for all low-parallelism and high-parallelism workflows; and (3) QFSL and QFSL+ are designed to decrease schedule length, and QFSL+ is better than QFSL for all low-parallelism and high-parallelism workflows.

A regression tree predictive model for virtual machine startup time in IaaS clouds

Cloud computing provides effective ways to rapidly provision computing resources over the Internet. For a better management of resource provisioning, the system requires to predict service-level agreements (SLAs) such as virtual machine (VM) startup times under various conditions of computing resources. The VM startup time is an important SLA parameter, which can impact other SLA parameters such as service initiation time and VM scale out times. By predicting VM startup times, Cloud providers can improve Cloud users’ expectations. Various quality of service (QoS) parameters have been considered in different resource allocation frameworks. Also, there are several efforts addressing QoS prediction in cloud environments. However, little research has considered VM startup time as a QoS parameter. In this paper, we propose a regression tree model for predicting average, minimum, and maximum VM startup times. To test the efficiency of our model, we implemented the model in an OpenStack test environment. The test results show that our model predicts VM startup times with an average accuracy of 91.81%.

Scheduling multiple scientific workflows using containers on IaaS cloud

With the adaptable paradigm of cloud computing and obtainable of data accumulated from largely high-powered scientific devices, workflows have turn into an occurring aim to execute considerable scientific advances at an enhanced speed. Occurring Workflow as a Service (WaaS) frameworks provide scientists an effortless, simply accessible and cost-efficient manner of using their applications from anywhere and at anytime in the cloud. They are multitenant platforms and are developed to handle the execution of heterogeneous workflows continuous workload. To fulfill this, they utilize the compute, network and storage services provided by Infrastructure as a Service (IaaS) vendors. Therefore, at any considerable particular moment, a WaaS framework should be proficient of effectively schedule these continuous workload of workflows with various features and quality of service (QoS). Therefore, we propose a strategy of scheduling and resource provisioning planned particularly for WaaS platforms. The algorithm is dynamic and scalable to adjust to improve in the workload and platform. It supports containers to deal the inefficiency of resource utilization and targets to reduce the overall execution cost of infrastructure resources as fulfilling each single workflow deadline constraint. To our information, this approach that explicitly deals VM sharing in the subject of WaaS by devising the utilization of containers in the heuristics of scheduling and resource provisioning. Our experimental results shows its responsiveness to the uncertainties of the environment, its potential to achieve deadlines, and its cost-effectiveness when compared to other recent algorithms.

Constructing Assessment Indicators for Enterprises Employing Cloud IaaS

Rapid advances in cloud computing have seen infrastructure as a service (IaaS) become an essential service paradigm for enterprises. However, the assessment indicators of cloud-computing services are incomplete. This research is based on an updated information system success model. Perceived value is utilized to form the research structure, and this is modified according to resource dependence theory and transaction cost theory. These assessment indicators were examined by experts using the Delphi technique. The results demonstrate that data backup, high availability, confidentiality, and overall value are the most important factors for cloud-computing services.

Expelliarmus: Semantic-centric virtual machine image management in IaaS Clouds

Infrastructure-as-a-service (IaaS) Clouds concurrently accommodate diverse sets of user requests, requiring an efficient strategy for storing and retrieving virtual machine images (VMIs) at a large scale. The VMI storage management requires dealing with multiple VMIs, typically in the magnitude of gigabytes, which entails VMI sprawl issues hindering the elastic resource management and provisioning. Unfortunately, existing techniques to facilitate VMI management overlook VMI semantics (i.e at the level of base image and software packages), with either restricted possibility to identify and extract reusable functionalities or with higher VMI publishing and retrieval overheads. In this paper, we propose Expelliarmus, a novel VMI management system that helps to minimize VMI storage, publishing and retrieval overheads. To achieve this goal, Expelliarmus incorporates three complementary features. First, it models VMIs as semantic graphs to facilitate their similarity computation. Second, it provides a semantically-aware VMI decomposition and base image selection to extract and store non-redundant base image and software packages. Third, it assembles VMIs based on the required software packages upon user request. We evaluate Expelliarmus through a representative set of synthetic Cloud VMIs on a real test-bed. Experimental results show that our semantic-centric approach is able to optimize the repository size by 2.3−22 times compared to state-of-the-art systems (e.g. IBM’s Mirage and Hemera) with significant VMI publishing and slight retrieval performance improvement.

Internet‐of‐Things‐Infrastructure‐as‐a‐Service: The democratization of access to public Internet‐of‐Things Infrastructure

SummaryWe propose a new cloud service model called Internet‐of‐Things (IoT)‐Infrastructure‐as‐a‐Service (IoT‐IaaS). Under an IoT‐IaaS, an IaaS cloud service provider can go beyond offering IoT data streams and offer access to IoT infrastructure to users, the same way data center infrastructure is shared in the IaaS model. We developed a reference design for the proposed IoT‐IaaS cloud service model, built from open source components. We demonstrate the proposed reference design in the form of a virtualized‐IoT (vIoT) testbed. The vIoT testbed uses the OpenStack cloud management system and Raspberry Pi embedded computers (standing in for IoT devices) configured as Nova‐compute nodes and LXD containers. We also developed a simple mechanism for shared access to high data rate sensors (a camera) and low data rate sensors (temperature‐humidity) by multiple tenants. We tested the performance of container launch times, memory access (with and without ZRAM optimization kernel module enabled), CPU, and file I/O for applications running inside Linux containers and compared it with the same performance when running on the Raspberry Pi's host OS. We conclude that there is no significant performance penalty for executing the edge component of an IoT application inside a Linux container, rather than directly on the device's host OS.

The enhancement of logging system accuracy for infrastructure as a service cloud

We introduce the novel technical results of the enhanced logging system for customer virtual machines (VMs) in an Infrastructure as a Service (IaaS) cloud. The main contribution is that the enhanced system can work with a better system's accuracy and speed, with the simplicity of the design and implementation. We measure the accuracy of the unenhanced logging system, then find a quick solution to enhance the system based on the results of the measurement. To measure and enhance the unenhanced system, we increase the main memory and CPU cores of the VMs then collect the accuracy results from each increment configuration. We analyze the results and propose to use the taskset tool to enhance the accuracy of the system. Found three main findings include: firstly, the accuracy of the enhanced system is about 20% on maximum better than the unenhanced one; the enhanced system accuracy becomes 100%; lastly, the enhanced system can detect a file with the smaller file size as almost 12% smaller. The findings can be a basis to design the logging systems in an IaaS cloud, to decrease hardware and energy investment. To the best of our knowledge, the contribution and findings are not in the literature.

A Cost Efficient Service Broker Policy for Data Center Allocation in IaaS Cloud Model

Cloud computing provides distributed computing resources such as servers, storage and applications to the end users through data centers. The data centers are geographically located at different locations. The client applications or requests being serviced by cloud service providers on “pay per use”. So, different pricing models are adapted to compute the cost and revenue of the data centers. The cost of VM is computed depending on its placement in the data centers through broker policies. So, the broker policies have a significant role in evaluating the cost of the VM which directly impacts on revenue of the service provider. In the computing competition, the VM cost should be minimized by which service demand will be maximized. Moreover, the response time and processing time of the data centers need to be minimized to posses commendatory Quality of Service. In this work, we propose a new service broker policy to minimize the total cost. The total cost considers the VM cost and the data transfer cost. The proposed mechanism also reduces the response time and data processing time of the data centers. The proposed policy is simulated in Cloud Analyst. We examine the performance of the proposed mechanism with ten different scenarios. Finally, we compare performance results with respect to VM cost, data transfer cost, total cost, processing time and response time of data centers with the existing policies and observe better than these.

Reliability aware Resource Scheduling based on Fuzzy Cuckoo Search (FCS) Technique for IaaS Cloud

Resource scheduling assigns the precise and accurate tasks to CPU, network, and storage. The aim behind this is the optimum usage of resources. However, well-organized scheduling is needed for both cloud providers and cloud users. Several resource scheduling algorithms have been discussed in the literature, but there are little emphases to reliability aware resource scheduling. In this research article, an innovative technique is proposed that is known as the Fuzzy Cuckoo Search (FCS) technique based on the fuzzy theory and cuckoo search algorithm to solve real-time optimization problematic issues. The FCS technique is used to address the reliability aware resource scheduling problems in IaaS Cloud. An experiment has been carried out on the CloudSim simulator and results of FCS techniques are compared with the Genetic Algorithm (GA), Honey Bee (HB) and Particle Swarm Optimization (PSO) scheduling algorithms. Finally, computational results demonstrate that the FCS technique is produced 39.21% better optimal solutions than the best solutions obtained by the comparison algorithms in terms of failure rate. It specifies that the FCS technique is more appropriate for reliability aware resource scheduling for IaaS Cloud.

Monitoring IaaS Cloud for Healthcare Systems

Healthcare functionality is enriched by cloud services which offers a perspective for broad integration and interoperability. Cloud-based facilities support healthcare systems to remain connected to remote access devices to various tasks and information. The healthcare actors should have an understanding of the risks and benefits associated with the usage of Cloud Computing resources utilization. Also, they must launch an appropriate contract-based relationship between the Cloud Service Providers and the actors of healthcare systems by means of Service Level Agreements (SLAs). The variation in both demand and supply within the healthcare information affects the use of information technology. Hence, monitoring resources can play an important role in accommodating the healthcare data. To deal with the aforementioned problems; reinforcement learning mechanisms along with the metrics has been used and experimented with the various dynamics of workload to deliver services with quality assurance.

GRP-HEFT: A Budget-Constrained Resource Provisioning Scheme for Workflow Scheduling in IaaS Clouds

In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (Multi-Objective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.

The Possibility of Resource Scheduling for a Collaborative Cloud Computing Using Failure Workflow Control in IAAS Cloud

The Possibility of Resource Scheduling for a Collaborative Cloud Computing Using Failure Workflow Control in IAAS Cloud

RETRACTED ARTICLE: Cloud management architecture to improve the resource allocation in cloud IAAS platform

Cloud computing (CC) is an attractive emerging technology due to offering services based on-demand by the process of virtualization. Since CC platform offers services based on-demand it has been widely used in the field of various emerging IT infrastructure. In cloud platform each application is run in individual virtual machine (VM) for execution of services within the host. Since cloud platform operates on on-demand service it need to cope with multiple application in single time hence it is necessary to adopt an effective approach for balancing memory utilization in cloud network. For effective utilization of available memory existing approaches uses probability distribution method for allocating resources in cloud platform but still there exists a lack of utilization of available memory in cloud platform. This paper aims to develop an effective approach for dynamic memory allocation in VM in cloud platform. For memory allocation among VM in cloud platform proposed approach uses cloud vertical elasticity manager (CVEM), memory reporter (MR), memory over subscription granter (MOG). The MOG uses a scheduler to allocate the memory in a dynamic way inside a host. Finally, we adopt host elasticity rule to balance the available memory to allocate dynamically the memory inside an available host in cloud.

Budget aware scheduling algorithm for workflow applications in IaaS clouds

Cloud computing, a novel and promising model of Service-oriented computing, provides a pay-per-use framework to solve large-scale scientific and business workflow applications. Workflow scheduling in cloud is challenging due to dynamic nature of the cloud, particularly, on demand provisioning, elasticity, heterogeneous resource types, static & dynamic pricing models and virtualization. An example of workflow scheduling is mapping workflow tasks to cloud computing resources. Additionally, these workflow applications have a runtime constraint—the most typical being the cost of the computation and the time that computation requires to complete. Therefore, the focus is on two criteria: makespan and cost. This paper presents an algorithm called NBWS (Normalization based Budget constraint Workflow Scheduling) which generates a workflow schedule which minimizes the schedule length while satisfying the budget constraint. The algorithm undergoes a process of min–max normalization tailed by computing expect reasonable budget $$ (erb) $$ for dispatching the workflow tasks into one of the virtual machines. To minimize the execution time, NBWS algorithm maps the workflow tasks to resources which are having the earliest finish time within the allocated budget. The experimental results demonstrate that NBWS outperforms current state-of-the-art heuristics with respect to budget constraint and minimizing the makespan.

Pricing cloud IaaS computing services

The economics of cloud computing has recently attracted increasing attention. In particular, a topic which is still under debate is how prices charged to customers for cloud resources are formed, since alternative pricing rules could be considered. Based on three pricing schemes inspired by those used by Amazon EC2, the main global cloud service provider, in the paper we address two main issues. First we present a methodology for the relevant parameters of the pricing rules to be determined in an optimal way, that is to maximise the provider’s revenue. Moreover, we discuss reasons for co-existence of three pricing rules, rather than fewer, to access the cloud. Our findings suggest that this may be due to a larger coverage of the potential demand, since customers applying for cloud services vary in their willingness to pay for the job, the time length of the service, the computational power requested etc. Furthermore, the pricing rule in the so-called, spot market, can provide the platform with useful information on the customers willingness to pay for cloud services. This is because in the spot market users offer a price for service, but pay less than that if their request is satisfied.

Analysis of Energy Consumption on IaaS Cloud Using Simulation Tool

The cloud-computing technique provides services to the customer globally. The large-scale datacenters arrangement is very complex due to demand for optimization of resources utilization and power consumption. Simulation tools provide an easy way to model complex research problems through GUI representation. Cloudreports is a GUI based simulation tool that generates reports and provides an API with extensions and user defined plugins. In the present paper using this simulation tool user may set the simulation scenario for various VM’s and resources consumption, and simulation environment for the provider, customer, SAN, network performance etc. The resource utilization and overall resource utilization reports by various data centers, customers are also generated. PUE and DCIE, is also computed for effective power usage within datacenters.