Availability Of Cloud Services Research Articles

Minimized False Alarm Predictive Threshold for Cloud Service Providers

Background: Cloud Security is a strong hindrance which discourages organisations to move toward cloud despite huge benefits. Distributed denial of service attacks operated via distributed systems compromise availability of cloud services which cause limited resources for authentic users and high expense for cloud service users and business owners. Objective: Techniques to identify distributed denial of service attacks with minimized false positives are highly required to ensure availability of cloud services to genuine users. Scarcity of solution which can detect denial of service attacks with minimum false positives and reduced detection delay has motivated us to compare classification algorithms for detection of distributed denial of service attacks with minimum false positive rate. Methods: Classification of incoming requests and outgoing responses using machine learning algorithms is a quite effective way of detection and prevention. We designed a performance tuned support vector machine algorithm with features of F-hold cross validation strategy. Results: F-hold crosses validation strategy, which can detect denial of service packets with 99.89% accuracy. Conclusion: This system ensures economic sustainability for business owners and limited resources mitigation for authenticated and valid cloud users.

Modelling cloud service latency and availability using a deep learning strategy

Low latency and high availability in cloud services give users satisfactory response time and guarantee stability to request they make to services that are hosted in the cloud, thus increasing the usability and reliability of cloud services. On the other hand, high latencies and poor availability will cost businesses their customers due customers dissatisfaction, thus losing customers to competitors. This situation noticeable in e-commerce businesses where real-time response and decision making within seconds are critical for business service delivery. Therefore, latency and availability are important parameters in the Service Level Agreement for cloud users when choosing Cloud Services Providers (CSPs). But the challenge for businesses is that they do not have their in–house mechanism that can accurately predict the required latency and availability for their requirements. Companies only rely on the CSPs tools for estimating resource requirements, which is biased towards the CSPs business model. In this paper, we developed a deep learning algorithm that predicts the latency and availability of cloud services using real-time live data from three CSPs. We designed and implemented experiments on Amazon Web Services, Alibaba Cloud and Tencent Cloud in Beijing University of Posts and Telecommunications to run compute instances across the United States, Europe and Asia Pacific regions. In each cloud platform, five servers were used that resulted in 30,815,100 invocations of http and ping operations for 6 weeks. The algorithm used the data on hourly, daily and weekly basis as historical network data to predict latency and availability. We used MATLABs deep learning toolbox for the implementation of our algorithm and the results showed that the prediction is usually above 90% accurate as compared with the data obtained. The results also revealed that latency performance depends on the locations of users and the availability depends on number of availability zones used.

Adaptive Neuro Fuzzy Interference and PNN Memory Based Grey Wolf Optimization Algorithm for Optimal Load Balancing

In recent years, cloud computing provides a spectacular platform for numerous users with persistent and alternative varying requirements. In the cloud environment, security and service availability are the two most significant factors during the data encryption process. For providing optimal service availability, it is necessary to establish a load balancing technique that is capable of balancing the request from diverse nodes present in the cloud. This paper aims in establishing a dynamic load balancing technique using the APMG approach. Here in this paper, we integrated adaptive neuro-fuzzy interference system-polynomial neural network as well as memory-based grey wolf optimization algorithm for optimal load balancing. The memory-based grey wolf optimization algorithm is employed to enhance the precision of ANFIS-PNN and to maximize the locations of the membership functions respectively. Also, two significant factors namely the turnaround time and CPU utilization involved in optimal load balancing scheme are evaluated. Finally, the performance evaluation of the proposed MG-ANFIS based dynamic load balancing approach is compared with various other load balancing approaches to determine the system performances.

A New Adaptive Fault Tolerant Framework in the Cloud

The of robustness in cloud computing is becoming mainstream due to unprecedented growth in the cloud. In order to enhance cloud service reliability, various fault tolerance techniques have been suggested in the literature. Many cloud service reliability and availability issues are arising due to the failure of virtual machines (VMs). Therefore, this study contributes a new Threshold-Based Adaptive Fault Tolerance (TBAFT) approach in the cloud by amalgamating proactive and reactive approaches. Furthermore, in this paper, VM migration and replication are being utilized in the proactive approach, whereas retry is used as a reactive approach. Additionally, experimental validation is performed by comparing three benchmark fault tolerance approaches by incorporating the first fit and least full host selection policies. The obtained experimental results of the proposed approach are presented in terms of different metrics such as failure rate, throughput, migration, and execution time using CloudSim simulator.

Optimized extreme learning machine for detecting DDoS attacks in cloud computing

Distributed denial of service (DDoS) attack is a serious security threat to cloud computing that affects the availability of cloud services. Therefore, defending against these attacks becomes imperative. In this paper, we present a DDoS attack detection system based on an improved Self-adaptive evolutionary extreme learning machine (SaE-ELM). SaE-ELM model is improved by incorporating two more features. Firstly, it can adapt the best suitable crossover operator. Secondly, it can automatically determine the appropriate number of hidden layer neurons. These features improve the learning and classification capabilities of the model. The proposed system is evaluated using four datasets namely, NSL-KDD, ISCX IDS 2012, UNSW-NB15, and CICIDS 2017. It achieves the detection accuracy of 86.80%, 98.90%, 89.17%, and 99.99% with NSL-KDD, ISCX IDS 2012, UNSW-NB15, and CICIDS 2017 datasets, respectively. The experiments show that the performance of the proposed attack detection system is better than the system based on original SaE-ELM and state-of-the-art techniques. However, it shows a longer training time than SaE-ELM based system.

SaaSRec+: a new context-aware recommendation method for SaaS services.

Cloud computing is an attractive model that provides users with a variety of services. Thus, the number of cloud services on the market is growing rapidly. Therefore, choosing the proper cloud service is an important challenge. Another major challenge is the availability of diverse cloud services with similar performance, which makes it difficult for users to choose the cloud service that suits their needs. Therefore, the existing service selection approaches is not able to solve the problem, and cloud service recommendation has become an essential and important need. In this paper, we present a new way for context-aware cloud service recommendation. Our proposed method seeks to solve the weakness in user clustering, which itself is due to reasons such as 1) lack of full use of contextual information such as cloud service placement, and 2) inaccurate method of determining the similarity of two vectors. The evaluation conducted by the WSDream dataset indicates a reduction in the cloud service recommendation process error rate. The volume of data used in the evaluation of this paper is 5 times that of the basic method. Also, according to the T-test, the service recommendation performance in the proposed method is significant.

Validation of Architectural Requirements for Tackling Cloud Computing Barriers: Cloud Provider Perspective

Abstract Although researchers and organisations have recognised cloud computing obstacles in the prior cloud adoption stage, few efforts have been made to investigate and address cloud barriers in depth during cloud implementation projects. The current technical cloud barriers faced by governments, such as a lack of cloud service availability and flexibility, a lack of application of service-level agreements (SLAs), and deficiencies in data security and privacy, require a proper strategic solution to elevate the role of enterprise architecture. This research designed and measured architectural requirements to overcome common technical cloud barriers. The architectural requirements were guided through The Open Group Architecture Framework (TOGAF) Architecture Development Method (ADM). The research employed a qualitative approach through three case studies to address the role of cloud providers. The findings of this research provide validation and overall support of the proposed value propositions with 12 identification-confirmed, newly emerged and designed architectural requirements, each of which can successfully contribute to overcoming barriers to government cloud projects. The elicited architectural requirements presented in the matrix are focused on the most important cloud constructs with different levels of emphasis.

A DDoS attack detection and defense scheme using time-series analysis for SDN

Software defined networking (SDN) has emerged as the integral part of cloud services since it provides flexible management capabilities to monitor and to analyze the network traffic with the help of programmable entities. Although, such functionalities play a significant role in terms of protecting the availability of cloud services against the security threats, SDN still has some vulnerabilities such as the distributed denial of service (DDoS) attacks. The DDoS attackers use spurious packets similar to normal ones and endanger the service continuity of SDN. Although conventional packet-based intrusion detection systems have broad databases to detect DDoS attacks, they are impotent of detection when the attack traffic is sheltered by the normal network traffic. The idea is therefore, to come up with a new countermeasure by observing and distinguishing the instant changes in network. In this work, we propose a DDoS attack detection and defense scheme using time-series analysis for SDN. The proposed scheme employs a model based on the upcoming traffic feature forecasting and the chaos theory together with the exponential filter and the dynamic threshold method to detect instant changes in the network. The experimental result shows that our algorithm has high detection rate and low false alarm.

An anomaly-based framework for mitigating effects of DDoS attacks using a third party auditor in cloud computing environments

Today, the providers of cloud computing services are among the most prominent service suppliers worldwide. Availability of cloud services is one of the most important concerns of cloud service providers (CSPs) and cloud users (CUs). Distributed Denial of Service (DDoS) attacks are common types of security issues which affect cloud services and consequently, can lead to unavailability of the services. Therefore, reducing the effects of DDoS attacks helps CSPs to provide high quality services to CUs. In this paper, first, we propose an anomaly-based DDoS attack detection framework in cloud environment using a third party auditor (TPA). Second, we provide multiple basic assumptions and configurations of cloud environments for establishing simulation tests to evaluate our proposed framework. Then, we provide results of simulation tests to analyze the feasibility of our approach. Simulation results demonstrate that our method for detecting DDoS attacks in CSPs has following advantages: efficiency, because of the low overhead of computations on CSPs for attack detection; rapid, due to informing a CSP about an attack in a short course of time regarding the maximum valid response time which is defined in a service level agreement (SLA); and precision, through no false positive detection as well as a low rate of false negative detection which is < 2% of all scenarios of the simulation tests. Finally, we present a table to compare characteristics of our framework with other ones in the literature.

USING THE WEB-TECHNOLOGIES IN THE PROCESS OF PSYCHOLOGICAL AND PEDAGOGICAL TRAINING OF FUTURE PRIMARY SCHOOL TEACHERS

The article deals with the problems of professional and pedagogical education, in particular the question of using web-technologies in the process of psychological and pedagogical preparation of future primary school teachers. The author describes a number of topical problems that can be solved by introducing elements of a blended learning system using cloud technologies (providing access to various educational resources; intensifying the system of professional and pedagogical education; developing the student's creative and intellectual abilities by means of cloud technologies; communicative activities based on common educational and professional interests etc.). The article presents the results of an online questionnaire of students in the specialty "Primary education" to determine the degree of using the web-applications in the process of psychological and pedagogical training and their dimension of interest in using cloud technologies in future professional activity. The basic directions of improvement in psychological and pedagogical preparation of future primary school teachers with using web-technologies (combination of traditional and online learning; using the web-quests, Google-services, E-textbooks, multimedia presentations, etc. ; development of critical and creative thinking, cognitive motivation; optimization of independent work at the expense of mobility, flexibility and availability of cloud services; constant professional growth of academic in order to master high the level of digital literacy) were characterized. The ways of introduction the cloud services in the educational process of high school (Google Sites, Google Classroom, Google Docs, Google Charts, Canva, Piktochart, Scratch) were highlighted. The system of principles of effective organization the psychological and pedagogical training of future primary school teachers with help of web-technologies were considered.

Multi-device digital assistance

Increased availability of cloud services and ownership of multiple digital devices create unique opportunities for digital assistants to provide guidance across a range of tasks and scenarios.

Improved DDoS Attacks detection using Hybrid Statistical Model and sparse representation for Cloud Computing

While hosting various cloud based information technology facilities by handling various assets on the internet, Cloud service accessibility has remained one of the chief concerns of cloud service providers (CSP). Several security concerns associated to cloud computing service simulations, and cloud’s major qualities contribute towards its susceptibility of security threats related with cloud service availability, the liability of internet, and the dispense behavior of cloud computing. Distributed Denial of Service (DDoS) attacks is one of the main advanced threats that occur to be extremely problematic and stimulating to stand owing towards its dispersed behavior and resulted in cloud service interruption. Although there exist amount of interruption recognition resolutions anticipated by various investigation groups, there exists not at all such a faultless result that avoids the DDoS attack and cloud service providers (CSP) are presently consuming various detection resolutions by assuring that their product stays well protected. The features of DDoS attack consuming various forms with dissimilar scenarios make it problematic to identify. Inspecting and analyzing various surviving DDoS detecting methods contrary to several factors is accomplished by this paper. To enhance the system performance further, sparse based data optimization is proposed to remove the redundant data. This enhancement reduced the execution time of the system by0.2%.

An intrusion detection system for connected vehicles in smart cities

In the very near future, transportation will go through a transitional period that will shape the industry beyond recognition. Smart vehicles have played a significant role in the advancement of intelligent and connected transportation systems. Continuous vehicular cloud service availability in smart cities is becoming a crucial subscriber necessity which requires improvement in the vehicular service management architecture. Moreover, as smart cities continue to deploy diversified technologies to achieve assorted and high-performance cloud services, security issues with regards to communicating entities which share personal requester information still prevails. To mitigate these concerns, we introduce an automated secure continuous cloud service availability framework for smart connected vehicles that enables an intrusion detection mechanism against security attacks and provides services that meet users’ quality of service (QoS) and quality of experience (QoE) requirements. Continuous service availability is achieved by clustering smart vehicles into service-specific clusters. Cluster heads are selected for communication purposes with trusted third-party entities (TTPs) acting as mediators between service requesters and providers. The most optimal services are then delivered from the selected service providers to the requesters. Furthermore, intrusion detection is accomplished through a three-phase data traffic analysis, reduction, and classification technique used to identify positive trusted service requests against false requests that may occur during intrusion attacks. The solution adopts deep belief and decision tree machine learning mechanisms used for data reduction and classification purposes, respectively. The framework is validated through simulations to demonstrate the effectiveness of the solution in terms of intrusion attack detection. The proposed solution achieved an overall accuracy of 99.43% with 99.92% detection rate and 0.96% false positive and false negative rate of 1.53%.

A continuous diversified vehicular cloud service availability framework for smart cities

The intelligent and connected transportation system (ICTS) is a significant and mandatory component of the smart city architecture. Multimedia content sharing, vehicle power management, and road navigation are all examples of ICTS services. As smart cities continue to deploy different technologies to improve the performance and diversity of vehicular cloud services, one of the main issues that prevails is efficient and reliable service discovery and selection for smart vehicles. Furthermore, cloud service providers (SPs) are limited to the availability, variety and quality of services made available to vehicular cloud subscribers. Smart vehicles rely on a number of SPs to acquire the required services while moving. It therefore becomes challenging for vehicular cloud subscribers to acquire services that meet their quality of experience (QoE) preferences. This paper introduces a new service provision scheme to provide continuous availability of diversified cloud services targeting vehicular cloud users through a cluster-based trusted third party (TTP) framework. TTPs act as cloud service mediators between cloud service subscribers and providers. Vehicles that are considered to have similar patterns of movement and service acquisition characteristics are grouped into service-specific clusters. TTPs communicate with service providers and cluster heads to negotiate for services with high QoE characteristics. A location prediction method is adopted to determine a vehicle's future location and allow services to be negotiated for before the vehicle's arrival. We provide simulation results to show that our approach can adequately discover and deliver cloud services with increased QoE results, minimal overhead burden and reduced end-to-end latency.

Migration cost and profit oriented cloud federation formation: hedonic coalition game based approach

Cloud federation has paved the way for cloud service providers (CSP) to collaborate with other CSPs to serve users’ resource requests, which are prohibitively high for any single CSP during peak time. Moreover, to entice different CSPs to participate in federation, it is necessary to maximize the profit of all CSPs involved in the federation. Further, federation enables overloaded CSPs to distribute their load among other underloaded member CSPs of federation by migrating the virtual machines (VM). Migration of VM among member CSPs of federation, also enables to increase the reliability and availability of cloud services on occurrence of faults in the datacenters of CSPs. Thus it becomes important for CSPs to form a federation with other CSPs, in such a way that the migration cost of VMs between CSPs of the same federation is minimized and simultaneously profit of CSPs in federation is maximized. In this paper, we model the problem of forming federation among CSPs as a hedonic coalition game, with a utility function depending on profit and migration cost, with the objective of maximizing the former and minimizing the latter. We propose an algorithm to solve this hedonic game and compare its performance with other existing game-theory based cloud federation formation mechanisms.

Quantifying Cloud Performance and Dependability

In only a decade, cloud computing has emerged from a pursuit for a service-driven information and communication technology (ICT), becoming a significant fraction of the ICT market. Responding to the growth of the market, many alternative cloud services and their underlying systems are currently vying for the attention of cloud users and providers. To make informed choices between competing cloud service providers, permit the cost-benefit analysis of cloud-based systems, and enable system DevOps to evaluate and tune the performance of these complex ecosystems, appropriate performance metrics, benchmarks, tools, and methodologies are necessary. This requires re-examining old system properties and considering new system properties, possibly leading to the re-design of classic benchmarking metrics such as expressing performance as throughput and latency (response time). In this work, we address these requirements by focusing on four system properties: (i) elasticity of the cloud service, to accommodate large variations in the amount of service requested, (ii) performance isolation between the tenants of shared cloud systems and resulting performance variability , (iii) availability of cloud services and systems, and (iv) the operational risk of running a production system in a cloud environment. Focusing on key metrics for each of these properties, we review the state-of-the-art, then select or propose new metrics together with measurement approaches. We see the presented metrics as a foundation toward upcoming, future industry-standard cloud benchmarks.

SELCLOUD: a hybrid multi-criteria decision-making model for selection of cloud services

With the growing demand and commercial availability of cloud services, the need for comparison of their functionality against different prices and performance has arisen. A relevant and fair comparison is still challenging due to diverse deployment options and dissimilar features of different services. This paper addresses a hybrid multi-criteria decision-making model involving the selection of cloud services among the available alternatives. The proposed methodology assigns various ranks to cloud services based on the quantified quality-of-service parameters using a novel extended Grey Technique for Order of Preference by Similarity to Ideal Solution integrated with analytical hierarchical process. Further, we analyse the proposed cloud service selection method in terms of sensitivity analysis, adequacy under change in alternatives, adequacy to support group decision-making, and handling of uncertainty. This analysis helps both researchers and practitioners for analysing more fruitful approaches for cloud service selection.

A Framework to Secure Medical Image Storage in Cloud Computing Environment

Nowadays, modern healthcare providers create massive medical images every day because of the recent progress in imaging tools. This is generally due to the increasing number of patients demanding medical services. This has resulted in a continuous demand of a large storage space. Unfortunately, healthcare domains still use local data centers for storing medical data and managing business processes. This has significant negative impacts on operating costs associated with licensing fees and maintenance. To overcome these challenges, healthcare organizations are interested in adopting cloud storage rather than on-premise hosted solutions. This is mainly justified by the scalability, cost savings and availability of cloud services. The primary objective of this model is to outsource data and delegate IT computations to an external party. The latter delivers needed storage systems via the Internet to fulfill client's demands. Even though this model provides significant cost advantages, using cloud storage raises security challenges. To this aim, this article describes several solutions which were proposed to ensure data protection. The existing implementations suffer from many limitations. The authors propose a framework to secure the storage of medical images over cloud computing. In this regard, they use multi-region segmentation and watermarking techniques to maintain both confidentiality and integrity. In addition, they rely on an ABAC model to ensure access control to cloud storage. This solution mainly includes four functions, i.e., (1) split data for privacy protection, (2) authentication for medical dataset accessing, (3) integrity checking, and (4) access control to enforce security measures. Hence, the proposal is an appropriate solution to meet privacy requirements.

Energy-Efficient Fault-Tolerant Scheduling Algorithm for Real-Time Tasks in Cloud-Based 5G Networks

Green computing has become a hot issue for both academia and industry. The fifth-generation (5G) mobile networks put forward a high request for energy efficiency and low latency. The cloud radio access network provides efficient resource use, high performance, and high availability for 5G systems. However, hardware and software faults of cloud systems may lead to failure in providing real-time services. Developing fault tolerance technique can efficiently enhance the reliability and availability of real-time cloud services. The core idea of fault-tolerant scheduling algorithm is introducing redundancy to ensure that the tasks can be finished in the case of permanent or transient system failure. Nevertheless, the redundancy incurs extra overhead for cloud systems, which results in considerable energy consumption. In this paper, we focus on the problem of how to reduce the energy consumption when providing fault tolerance. We first propose a novel primary-backup-based fault-tolerant scheduling architecture for real-time tasks in the cloud environment. Based on the architecture, we present an energy-efficient fault-tolerant scheduling algorithm for real-time tasks (EFTR). EFTR adopts a proactive strategy to increase the system processing capacity and employs a rearrangement mechanism to improve the resource utilization. Simulation experiments are conducted on the CloudSim platform to evaluate the feasibility and effectiveness of EFTR. Compared with the existing fault-tolerant scheduling algorithms, EFTR shows excellent performance in energy conservation and task schedulability.

Deep learning framework for early detection of intrusion in Virtual Environment

Today's business enterprise adapts cloud-based services as its architectural design. Intelligence technique incorporated into the architecture gives massive tangible and intangible benefits in terms of performance and reliability. Such cloud-based business architecture faces many threats towards its availability. DDoS attack is the most prominent threat as its impact is more in the virtual resource-based cloud infrastructure. Therefore, there is a need for a business intelligence-based framework to detect early the attack by monitoring the virtual network traffic. The proposed intelligence framework uses a deep learning framework, continuous discriminative-deep belief network (CD-DBN). CD-DBN dynamically captures attack patterns from the network data, analyses the data and detects the intrusion to the cloud. The observed result shows that the earlier detection approach guarantees the availability of cloud services to the legitimate users and enhances the cloud resource usage.