Cost Of Cloud Services Research Articles

Makespan and Security-Aware Workflow Scheduling for Cloud Service Cost Minimization

Makespan and Security-Aware Workflow Scheduling for Cloud Service Cost Minimization

Cloud Configuration Optimization for Recurring Batch-Processing Applications

Recognizing the diversity of Big Data analytic jobs, cloud providers offer a wide range of VM instance types or even clusters to cater for different use cases. The choice of cloud configurations can have a significant impact on the response time and running cost of batch-processing applications, which may need to be re-run regularly with cloud-scale resources. However, identifying the best cloud configuration with a low search cost is quite challenging due to i) the large and high-dimensional configuration space, ii) the time-varying cloud service cost (e.g., AWS Spot instances), and iii) job response time variation even given the same configuration. To tackle these challenges, we design and implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accordia</i> , a system that enables Adaptive Cloud Configuration Optimization for Recurring Data-Intensive Applications. By leveraging recent algorithmic advances in Gaussian Process UCB techniques, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accordia</i> can unearth the cost-optimal configuration with a deadline constraint (i.e., maximum tolerated running time) under the time-varying cloud service cost. More importantly, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accordia</i> manages to achieve a theoretical performance guarantee, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sub-linearly increasing dynamic regret</i> of the job completion cost. Using extensive trace-driven simulations and empirical measurements of our Kubernetes-based implementation, we demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Accordia</i> can identify a near-cost-optimal configuration (i.e., within 10% of the optimum) after fewer than 20 runs from over 7000 candidate choices, which translates to a 2X-speedup and up to 17.9% cost-savings, when comparing to the state-of-the-art approach, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CherryPick</i> .

Service Cost Effective and Reliability Aware Job Scheduling Algorithm on Cloud Computing Systems

Nowadays, the cloud computing services encounter several cloud Quality of Service challenges, such as reliability, cost, and response time. The most common mechanism to improve cloud service reliability is fault-tolerant technique. However, this reliability enhancement technique inevitably results in multiple replications, which lead to high service cost. In recognition of these challenges, we build a cloud computing systems resources management architecture. Then, we analyze the cloud service execution reliability on the physical resources of a VM and used a CUDA-enabled parallel two-dimensional LSTM to predict the software faults of a cloud VM. Thirdly, we propose an effective primary/backup cloud service cost calculation approach. To overcome the cloud service response time constraint, we integrate a response time slack factor into this method. Fourthly, we formulate the cloud service reliability and cost aware job scheduling problem, which aims at minimizing the total cloud service cost and rejection rate, and improving the system reliability. Fifthly, a heuristic greedy reliability and cost aware job scheduling (RCJS) algorithm is proposed. Finally, a performance evaluation is conducted and the experimental results demonstrate that our proposed RCJS algorithm significantly outperforms optimal redundant VM placement (OPVMP), MIN-MIN algorithms in terms of average service cost and rejection rate.

T-Counter: Trustworthy and Efficient CPU Resource Measurement Using SGX in the Cloud

As cloud services have become popular, and their adoption is growing, consumers are becoming more concerned about the cost of cloud services. Cloud Service Providers (CSPs) generally use a pay-per-use billing scheme in the cloud services model: consumers use resources as they needed and are billed for their resource usage. However, CSPs are untrusted and privileged; they have full control of the entire operating system (OS) and may tamper with bills to cheat consumers. So, how to provide a trusted solution that can keep track of and verify the consumers’ resource usage has been a challenging problem. In this article, we propose a T-Counter framework based on Intel SGX. The T-Counter allows applications to construct a trusted solution to measure its CPU usage by itself in cloud computing. These constructed applications are instrumented with counters in basic blocks and added three components in trusted parts to count instructions and defend against malicious CSPs’ manipulations. We propose two algorithms which selectively instrument counters in the CFG. T-Counter is implemented as an extension of the LLVM framework and integrated with the SGX SDK. Theoretical analyses and evaluations show that T-Counter can effectively measure CPU usage and defend against malicious CSPs’ manipulations.

Cost Minimization of Cloud Services for On-Demand Video Streaming

Cloud Technology is adopted to process video streams because of the great features provided to video stream providers such as the high flexibility of using virtual machines and storage servers at low rates. Video stream providers prepare several formats of the same video to satisfy all users' devices' specifications. Video streams in the cloud are either transcoded or stored. However, storing all formats of videos is still costly. In this research, we develop an approach that optimizes cloud storage. Particularly, we propose a method that decides which video in which cloud storage should be stored to minimize the overall cost of cloud services. The results of the proposed approach are promising, it shows effectiveness when the number of frequently accessed video grow in a repository, and when the views of videos increases. The proposed method decreases the cost of using cloud services by up to 22%.

Cloud-based services and customer satisfaction in the small and medium-sized businesses (SMBs)

PurposeCloud-based technologies are reliably improving Information Technology (IT) environment incorporating changes and modifications to the present business structure. Cloud computing enables small and medium-sized businesses (SMBs) to organize and exchange pertinent data and information in real time. This study checks out the influence of cloud-based services (IT infrastructure, expenses of cloud services, cloud data security and IT knowledge of human resources) on customers' satisfaction in SMBs.Design/methodology/approachCloud computing offers a way to coordinate and share organizational and personal information and data. The adoption of cloud services is one of the most emerging technological advances in the current competitive business environment. Cloud-based services allow start-ups or SMBs to take advantage of technological advancements and respond more quickly to changing consumer demands. In the available literature, cloud computing has received a lot of attention. However, there is also a research gap in creating a paradigm that links the value development drivers in the electronic industry with the leveraging impact of cloud and intercloud computing resources for start-ups and SMBs. A hypothetical model was constructed based on a literature review, and the associations between the latent variables were investigated utilizing structural equations.FindingsFindings from the study confirmed the validity of the proposed model for customer satisfaction assessment. Besides, the results showed that customer satisfaction is affected by cloud-based services in SMBs. The results illustrated that IT infrastructure influences customer satisfaction significantly and positively, with a T-value of 2.42. Also, the results confirmed that the cost of cloud services with a T-value of 2.68 affects customer satisfaction significantly and positively. Cloud data security also impacts customer satisfaction with a T-value of 5.20. The results also showed that the IT knowledge of human resources affects customer satisfaction with a T-value of 3.01.Originality/valueThe main originality of this research is proposing a new conceptual model to assess the impact of cloud-based services on the satisfaction of the customers in SMBs.

Parallel random matrix particle swarm optimization scheduling algorithms with budget constraints on cloud computing systems

Nowadays, increasing number of Internet of Things and mobile Internet application services are migrated to cloud computing systems. One of the most important cloud challenges for this business is to optimize services cost. The efficient way to deal with this challenge is to improve the performance of resource management and task scheduling in cloud systems. However, this kind of service scheduling is a typical combinatorial optimization problem, and it is difficult to obtain the optimal solution. In this study, we first formalize this cloud services on virtual machines (VMs) with budget constraints scheduling problem. Then, we propose a random matrix particle swarm optimization scheduling algorithm (RMPSO), which uses the random integer matrix to represent its position and a feasible task scheduling scheme, to achieve the optimal total cost of cloud services. However, the drawback of this solution for large-scale systems is its high time complexity. Therefore, we propose two parallel RMPSO algorithms: CPU parallel algorithm (M-RMPSO) on multi-core system with shared memory and manycore GPU-accelerated strategy (G-RMPSO) to reduce its time complexity. Finally, the rigorous performance evaluation results clearly show that our proposed G-RMPSO outperforms M-RMPSO and existing FMPSO, HYBRID (MPSO+MCSO) in terms of cloud services total cost and algorithm execution time. Therefore, our proposed GPU-accelerated G-RMPSO algorithm is very suitable for cloud service scheduling.

Cloud solutions for GxP laboratories: considerations for data storage.

Challenges for data storage during drug development have become increasingly complex as the pharmaceutical industry expands in an environment that requires on-demand availability of data and resources for users across the globe. While the efficiency and relative low cost of cloud services have become increasingly attractive, hesitancy toward the use of cloud services has decreased and there has been a significant shift toward real-world implementation. Within GxP laboratories, the considerations for cloud storage of data include data integrity and security, as well as access control and usage for users around the globe. In this review, challenges and considerations when using cloud storage options for the storage of laboratory-based GxP data are discussed and best practices are defined.

Выбор оптимального размещения данных при использовании облачной инфраструктуры

Benefits of using cloud technology are obvious, their application is expanding, as a result, it determines the steady growth of demand. Cloud computing has acquired particular relevance for large companies connected with Internet services, retailing, logistics that generate large volume of business and other information. The use of cloud technologies allows organizing the joint consumption of resources, solving the problems of storing and transferring significant amounts of data. Russian consumer cooperation refers to large territory distributed organizations actively forming their own digital ecosystem. The issue of data storing and processing for consumer coo-peration organizations is very relevant. At the same time, the prices of cloud service providers are significantly different and require solving the problem of minimizing the cost of storing and transferring significant amounts of data. The application of the linear programming method is considered to select the optimal data storage scheme for several cloud service providers having different technical and economic parameters of the package (maximum amount of storage, cost of allocated resources). Mathematical model includes the equation of costs for data storing and transferring and restrictions on the amount of storage, the amount of data and its safety. Software tool that allows to perform numerical calculations is selected Microsoft Excel in combination with the "search for solutions" add-on. In accordance with the mathematical model, the conditions for minimizing the amount of cloud storage costs and the necessary restrictions are established. Initial data are set for three data forming centers, storages of certain size for five cloud service providers and nominal price for information storage and transmission. Calculations of expenses are performed in several variants: without optimization, with the solution of the optimization problem, with price increase by cloud service providers. Results of the calculations confirm the necessity to solve the problem of minimizing the cost of cloud services for corporate clients. The presented model can be expanded for any cost conditions as well as for different areas of cloud applications.

Affinity-Aware VNF Placement in Mobile Edge Clouds via Leveraging GPUs

Mobile edge computing becomes a promising technology to mitigate the latency of various cloud services. In addition, network function virtualization (NFV) has been shown a great potential in reducing the operational cost of cloud services while enhancing the flexibility of virtual network function deployments, by implementing dedicated hardware network functions as pieces of software in generic servers. Recently, the GPU acceleration has been investigated to speed up flow processing in virtual network functions (VNFs), by leveraging the parallelism of GPUs. VNFs that need accelerations prefer to stay at cloudlets (locations) equipped with GPUs. However, little attention has been paid for the VNF placement that takes into account GPU-affinity in cloudlets of mobile edge clouds. In this paper, we consider the affinity-aware throughput maximization problem in a mobile edge cloud via leveraging the parallelism on GPUs for user requests with VNF requirements. We consider two types of affinities in the VNF placement: The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">soft-affinity</i> that allows VNFs to be executed by either CPUs or GPUs in cloudlets; and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">hard-affinity</i> that only allows VNFs to be placed to the GPUs of a specified set of cloudlets. We formulate two corresponding VNF placement problems in a mobile edge cloud. Specifically, we first propose an exact solution to the soft-affinity throughput maximization problem by formulating an Integer Linear Program (ILP). We then propose an efficient algorithm for the problem, by proposing a randomized algorithm with a provable approximation ratio for the hard-affinity-aware throughput maximization problem and extending the proposed approximation algorithm to the soft-affinity throughput maximization problem. Furthermore, assuming that user requests arrive into the mobile edge cloud one by one without the knowledge of future arrivals, we devise an online algorithm with a good competitive ratio for this dynamic hard-affinity-aware throughput maximization problem. Finally, we evaluate the performance of the proposed algorithms, through simulations and implementations in a real test-bed. Experimental results show that the performance of the proposed algorithms outperform their existing counterparts and achieve higher throughput.

Resource Usage Cost Optimization in Cloud Computing Using Machine Learning

Cloud computing is gaining popularity among small and medium-sized enterprises. The cost of cloud resources plays a significant role for these companies and this is why cloud resource optimization has become a very important issue. Numerous methods have been proposed to optimize cloud computing resources according to actual demand and to reduce the cost of cloud services. Such approaches mostly focus on a single factor (i.e., compute power) optimization, but this can yield unsatisfactory results in real-world cloud workloads which are multi-factor, dynamic and irregular. This article presents a novel approach which uses anomaly detection, machine learning and particle swarm optimization to achieve a cost-optimal cloud resource configuration. It is a complete solution which works in a closed loop without the need for external supervision or initialization, builds knowledge about the usage patterns of the system being optimized and filters out anomalous situations on the fly. Our solution can adapt to changes in both system load and the cloud provider’s pricing plan. It was tested in Microsoft’s cloud environment Azure using data collected from a real-life system. Experiments demonstrate that over a period of 10 months, a cost reduction of 85 percent was achieved.

I-BOT: Interference-Based Orchestration of Tasks for Dynamic Unmanaged Edge Computing.

The increasing cost of cloud services and the need for decentralization of servers has led to a rise of interest in edge computing. In recent years, edge computing has become a popular choice for latency-sensitive applications like facial recognition and augmented reality because it is closer to the end users compared to the cloud. However, the presence of multiple edge servers adversely affects the reliability due to difficulty in maintenance of heterogeneous servers. In this thesis, we first evaluate the performance of various server configuration models in edge computing using EdgeCloudSim, a popular simulator for edge computing. The performance is evaluated in terms of service time and percentage of failed tasks for an Augmented Reality application. We evaluated the performance of the following edge computing models, Exclusive: Mobile only, Edge only, Cloud only; and Hybrid: Edge & Cloud hybrid with load-balancing on the Edge, and Mobile & Edge hybrid. We analyzed the impact of variation of different parameters such as WAN bandwidth, cost of cloud resources, heterogeneity of edge servers, etc., on the performance of the edge computing mod- els. We show that due to variation in the above parameters, the exclusive models are not sufficient for computational requirements and there is a need for hybrid edge computing models. Next, we introduce a novel edge computing model called unmanaged edge computing and propose an orchestration scheme in this scenario. Although infrastructure providers are working toward creating managed edge networks, personal devices such as laptops, desktops, and tablets, which are widely available and are underutilized, can also be used as potential edge devices. We call such devices Unmanaged Edge Devices (UEDs). Scheduling application tasks on such an unmanaged edge system is not straightforward because of three fundamental reasons—heterogeneity in the computational capacity of the UEDs, uncertainty in the availability of the UEDs (due to the devices leaving the system), and interference among multiple tasks sharing a UED. In this work, we present I-BOT, an interference-based orchestration scheme for latency sensitive tasks on an Unmanaged Edge Platform (UEP). It minimizes the completion time of applications and is bandwidth efficient. I-BOT brings forth three innovations. First, it profiles and predicts the interference patterns of the tasks to make scheduling decisions. Second, it uses a feedback mechanism to adjust for changes in the computational capacity of the UEDs and a prediction mechanism to handle their sporadic exits, both of which are fundamental characteristics of a UEP. Third, it accounts for input dependence of tasks in its scheduling decision (such as, two tasks requiring the same input data). To demonstrate the effectiveness of I-BOT, we run real-world unit experiments on UEDs to collect data to drive our simulations. We then run end-to-end simulations with applications representing autonomous driv- ing, composed of multiple tasks. We compare to two basic baselines (random and round-robin) and two state-of-the-arts, Lavea [SEC-2017] and Petrel [MSN-2018] for scheduling these applications on varying-sized UEPs. Compared to these baselines, I-BOT significantly reduces the average service time of application tasks. This reduction is more pronounced in dynamic heterogeneous environments, which would be the case in a UEP.

Model-Based Extraction of Knowledge about the Effect of Cloud Application Context on Application Service Cost and Quality of Service

With the increased usage of cloud computing in production environments, both for scientific workflows and industrial applications, the focus of application providers shifts towards service cost optimisation. One of the ways to achieve minimised service execution cost is to optimise the placement of the service in the resource pool of the cloud data centres. An increasing number of research approaches is focusing on using machine learning algorithms to deal with dynamic cloud workloads by allocating resources to services in an adaptive way. Many of such solutions are intended for cloud infrastructure providers and deal only with specific types of cloud services. In this paper, we present a model-based approach aimed at the providers of applications hosted in the cloud, which is applicable in early phases of the service lifecycle and can be used for any cloud application service. Using several machine learning methods, we create models to predict cloud service cost and response times of two cloud applications. We also explore how to extract knowledge about the effect that the cloud application context has on both service cost and quality of service so that the gained knowledge can be used in the service placement decision process. The experimental results demonstrate the ability of providing relevant information about the impact of cloud application context parameters on service cost and quality of service. The results also indicate the relevance of our approach for applications in preproduction phase since application providers can gain useful insights regarding service placement decision without acquiring extensive training datasets.

MODELLING THE EFFICIENCY OF THE CLOUD COMPUTING IMPLEMENTATION AT ENTERPRISES

The article describes the main characteristics, types and properties of cloud computing. The most widespread cloud technologies in Ukraine are analyzed. It is identified that the largest share among users of cloud technologies in Ukraine currently belong to large holdings, IT companies, commercial enterprises and banks, but other sectors of business are also involved in the development of these services. The aim of the article is to develop the methodology for evaluating the efficiency of cloud technologies implementation at enterprises and its experimental verification. The economic component of the cloud computing implementation at enterprises (expenditures and revenues of both cloud technology owners and users) is considered. The efficiency of using cloud computing at enterprises is proved. It is found that organizations usually do not use the power of their personal data centers to a full extent. This leads to idle equipment, extra cost on maintenance and servicing of hardware, amortization, staff salaries and etc. The feasibility of transition of enterprises to cloud computing in such situations has been proved, which considerably reduce the costs of the enterprise due to the absence of need for hardware and necessary staff to support the operation of information systems. Usability of the methodology of total cost of ownership in evaluating the effectiveness of using services for the enterprise has been proved. The proposed methodology compares the main costs of using personal data centers and the cost of using cloud computing. It is experimentally proven that in most cases, the cost of maintaining personal data center (PDC) is higher than the cost of cloud services. It is also proved that the efficiency of cloud technology operation depends on the internal structure and organization of computing processes inside the systems, as well as on external factors such as the size of enterprises-clients, industries, costs for the organization of data centers, etc. Cloud computing is an advanced technology which has future prospects and is cost-effective for both enterprise users and provider organizations.

Resource optimization of container orchestration: a case study in multi-cloud microservices-based applications

An approach to optimize the deployment of microservices-based applications using containers in multi-cloud architectures is presented. The optimization objectives are three: cloud service cost, network latency among microservices, and time to start a new microservice when a provider becomes unavailable. The decision variables are: the scale level of the microservices; their allocation in the virtual machines; the provider and virtual machine type selection; and the number of virtual machines. The experiments compare the optimization results between a Greedy First-Fit and a Non-dominated Sorting Genetic Algorithm II (NSGA-II). NSGA-II with a two-point crossover operator and three mutation operators obtained an overall improvement of 300% in regard to the greedy algorithm.

The study on firm acceptance of cloud service introduction from the innovation diffusion theory

This study combines the technology acceptance model and innovation diffusion theory and probes the acceptance of cloud computing services introduced in firms through the use of pre-decision, in-decision and post-decision stages. Based on this framework, the researcher attempts to identify whether the different stages of cloud service introduction will be influenced by cost, security, service continuity/completeness, perceived usefulness, perceived ease of use and compatibility. This study treats the information technology industry, in which there is an urgent demand for information applications, as the subject. Using a literature review and expert interviews, a questionnaire scale is constructed, and the researcher then conducts a questionnaire survey. According to the research findings, obtained using the process-oriented innovation diffusion theory and the technology acceptance model, cloud service cost, security and service continuity/completeness will influence the stages of cloud service introduction in the information technology industry.

An Invocation Cost Optimization Method for Web Services in Cloud Environment

The advent of cloud computing technology has enabled users to invoke various web services in a “pay-as-you-go” manner. However, due to the flexible pricing model of web services in cloud environment, a cloud user’ service invocation cost may be influenced by many factors (e.g., service invocation time), which brings a great challenge for cloud users’ cost-effective web service invocation. In view of this challenge, in this paper, we first investigate the multiple factors that influence the invocation cost of a cloud service, for example, user’s job size, service invocation time, and service quality level; and afterwards, a novel Cloud Service Cost Optimization Method named CS-COM is put forward, by considering the above multiple impact factors. Finally, a set of experiments are designed, deployed, and tested to validate the feasibility of our proposal in terms of cost optimization. The experiment results show that our proposed CS-COM method outperforms other related methods.

An Efficient Resource Allocation Framework for Cloud Federations

With the evolution of Internet technology, cloud computing techniques are being used for many applications in our daily life. In particular, most large enterprises intend to help business groups reduce management cost and achieve higher benefits via cloud computing technology. In order to provide uninterrupted services to the clients and reduce the monetary cost of cloud services, the issue of retrieving borrowed resources efficiently has become increasingly important. In this study, we focus on the computation resource allocation in the cloud based environment. An efficient resource allocation mechanism conforming to cross-Identity Provider (IdP) architecture is proposed. We introduce an inter-trust relationship among clouds to allow a cloud to borrow available resources from external clouds via a constructed cloud federation. Solid simulation experiments are demonstrated to show the practicability and feasibility of our proposed mechanism in the cloud federation environment.DOI: http://dx.doi.org/10.5755/j01.itc.44.1.6875

A Revenue Maximization Approach for Provisioning Services in Clouds

With the increased reliability, security, and reduced cost of cloud services, more and more users are attracted to having their jobs and applications outsourced into IAAS data centers. For a cloud provider, deciding how to provision services to clients is far from trivial. The objective of this decision is maximizing the provider’s revenue, while fulfilling its IAAS resource constraints. The above problem is defined as IAAS cloud provider revenue maximization (ICPRM) problem in this paper. We formulate a service provision approach to help a cloud provider to determine which combination of clients to admit and in what Quality-of-Service (QoS) levels and to maximize provider’s revenue given its available resources. We show that the overall problem is a nondeterministic polynomial- (NP-) hard one and develop metaheuristic solutions based on the genetic algorithm to achieve revenue maximization. The experimental simulations and numerical results show that the proposed approach is both effective and efficient in solving ICPRM problems.

클라우드 서비스 중개를 위한 수리과학 모형연구

Cloud computing is fast becoming the wave of the future for both home and business computing. Because of this growing acceptance, we can expect an explosion of diverse cloud service providers in the coming years. However, the cloud is not a single entity, rather it is a set of many disconnected islands of application (SaaS), infrastructure (IaaS), and different platform (PaaS) services. Cloud brokering mechanisms are essential to transform the heterogeneous cloud market into a commodity-like service. Cloud service brokers (CSBs) are the new form of business entities to help to aggregate the scattered set of cloud services and make conveniently available to their diverse users. CSBs can reserve a certain percentage of their clients’ (users’) demand and satisfy the remaining portion in an on-demand basis. In doing so, they need to minimize cost of both reserved and on-demand instances as well as the distance of a link between the cloud service provider (CSP) and the user. The study proposes a reservation approach with a mixed integer model to optimizes the cloud service cost and quality.