Multi-cloud System Research Articles

Enhanced Cloud Computing Security based on Single to Multi Cloud System

Enhanced Cloud Computing Security based on Single to Multi Cloud System

Secure transmission of medical images in multi-cloud e-healthcare applications using data hiding scheme

In recent years, medical image transmission using a multi-cloud system has played a significant role in e-Healthcare infrastructure. It allows medical practitioners to easily store, retrieve, and share patients’ medical information across multiple stakeholders. However, multi-cloud image transmission may be vulnerable to multiple security breaches, such as authentication, confidentiality, and security issues. Motivated by these issues, this paper proposes a data-hiding scheme for secure medical image transmission in a multi-cloud environment. The proposed scheme ensures imperceptible robustness and watermark security at a low computational cost. Here, the medical image is divided into a number of shares using Neighbor Mean Interpolation (NMI). To achieve confidentiality, Electronic Patient Healthcare Record (EPHR) is encrypted using Double Scan Pixel Position Shuffling (DSPPS) approach. Then, the encrypted EPHR is divided into shares and embedded in the cover medical image shares. Finally, a minimum of 50% of watermarked image shares are utilized to retrieve the original medical image and encrypted EPHR, consequently reducing multi-cloud latency and computational burden. Experimental results show that the proposed scheme shows high imperceptibility, robustness, and watermark security at a low computational cost. Comparative analysis with some of the recent popular data hiding schemes shows that the proposed scheme has improved imperceptibility and robustness by 10%–15% (approximately) with higher watermark security at a low computational cost.

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.

Models of the information structure of multicloud computer systems with data replication

In the article, a conceptual model of the information structure of a multicloud system in the form of an IDEF0 diagram is proposed. Its decomposition for a native multi-cloud of the DBaaS type with identifications of the input and output parameters, mechanisms, and restrictions is developed. Based on these information models, a mathematical model of a generalized type of multi-cloud with data replication has been developed, which takes into account the geo-location of both the resources of cloud service providers and clusters of end users. Figs.: 2. Refs.: 15 titles.
 Keywords: information structure; multicloud systems; native DBaaS; mathematical model.

Energy Aware Genetic Algorithm for Independent Task Scheduling in Heterogeneous Multi-Cloud Environment

Cloud datacentres contain a vast number of processors. The rapid expansion of cloud computing is resulting in massive energy usage and carbon emissions which has reported a substantial increase day by day. Consequently, the cloud service providers are looking for eco-friendly solutions. The energy consumption can be evaluated with an energy model, which identifies that, server energy consumption scales linearly with resource (cloud) utilization. This research provides an alternate solution to task scheduling problem which designs an optimized task schedule to minimize the makespan and energy consumptions in cloud datacenters. The proposed method is based on the principle of Genetic Algorithm (GA). In the context of task-scheduling using GA, chromosomal representation is considered as a schedule of set of independent tasks mapped with available cloud or machine in the proposed methodology. A fitness function is taken to optimize the overall execution time or makespan. Energy consumption is evaluated based on minimum makespan value. The proposed technique also tested upon synthesized and benchmark dataset which outperforms the conventional cloud task scheduling algorithms like Min-Min, Max-Min, and suffrage heuristics in heterogeneous multi-cloud system.

Enhanced encryption for light weight data in a multi-cloud system

Cloud storage is a cloud deployment that allows the setup of in-house data storage facilities by organizations. Cloud computing's popularity allows mobile devices to store/retrieve personal data from anywhere at any time. Consequently, the problem of data security in the mobile cloud is becoming increasingly serious and prevents further mobile cloud development. In order to improve cloud security, substantial studies have been conducted. However, most of them are not applicable for mobile cloud since mobile devices particularly have limited computing resources and power. This article proposes an enhanced Attribute-based encryption scheme for lightweight distributed cloud data. Multiple cloud computing provides storage assistance in a single heterogeneous environment. The proposed method ensures confidentiality, secure data storage, and file sharing through the multi-cloud environment. In comparison to traditional schemes, quantitative results suggest the feasibility of the proposed mechanism.

Dew Computing and Asymmetric Security Framework for Big Data File Sharing

Due to the advancement of technology, devices generate huge working files at a rapid rate. These data, which are of considerable scale and are created very fast, can be called big data. Keeping such files in one storage device is impossible. Therefore, a large file size is recommended for storage in a cloud storage service. Although this concept is a solution to solve the storage problem, it still faces challenges in terms of reliability and security. The main issues are the unreliability of single cloud storage when its service is down, and the risk of insider attack from the storage service. Therefore, this paper proposes a file sharing scheme that increases both the reliability of the file fragments using a multi-cloud storage system and decreases the risk from insider attack. The dew computing concept is used as a contributor to the file-sharing scheme. The working file is split into fragments. Each fragment is deployed to cloud storage services, one fragment per one cloud provider manner. The dew server controls users’ access and monitors the availability of fragments. Finally, we verify the proposed scheme in aspects of the downloading performance, and security.

Toward a domain‐specific language for scientific workflow‐based applications on multicloud system

SummaryThe cloud computing paradigm has emerged as the backbone of modern price‐aware scalable computing systems. Many cloud service models are competing to become the leading doorway to access the computational power of cloud providers. Recently, a novel service model, called function‐as‐a‐service (FaaS), has been proposed, which enables users to exploit the cloud computational scalability, left out the configuration and management of huge computing infrastructures. This article discloses Fly, a domain‐specific language, which aims at reconciling cloud and high‐performance computing paradigms adopting a multicloud strategy by providing a powerful, effective, and pricing‐efficient tool for developing scalable workflow‐based scientific applications by exploiting different and at the same time FaaS cloud providers as computational backends in a transparent fashion. We present several improvements of the Fly language, as well as a new enhanced version of a source‐to‐source compiler, which currently supports Symmetric Multiprocessing, Amazon AWS, and Microsoft Azure backends and translation of functions in Java, JavaScript, and Python programming languages. Furthermore, we discuss a performance evaluation of Fly on a popular benchmark for distributed computing frameworks, along with a collection of case studies with an analysis of their performance results and costs.

Replica-aware task scheduling and load balanced cache placement for delay reduction in multi-cloud environment

With the development of content-sharing and collaborative computing services such as online social networks, scientific workflow, there are huge amounts of data generated. To process this tremendous amount of data, multi-cloud system that integrates multiple clouds together to provide a unified service in a collaborative manner has been introduced. However, task scheduling in such heterogeneous multi-cloud environment is very challenging. To reduce response delay caused by cross-data centers file access, we proposed a replica-aware task scheduling algorithm based on data replication. For speeding up data access in multi-cloud cooperative caches, we presented a load balanced cache placement algorithm based on Bayesian networks. In our scheduling algorithm, combined transferring computation with transferring data, resource matching is accomplished according to node locality. Only non-local unassigned and failed map tasks’ input data are replicated and transferred in advance to target nodes to expedite task execution. In our cache placement method, based on Bayesian networks the next execute task is predicted. In accordance with caching profit and recycling cost, cache prefetching files are selected. For each prefetching file, according to load balancing, target placement node is determined. Extensive experimental results show that the performance of our proposed replica-aware task scheduling algorithm is better than benchmark scheduling algorithms in terms of node locality ratio and job response time, and our load balanced cache placement algorithm outperforms the baseline caching algorithms in performance of prefetching hit ratio and execution time saving ratio.

High Availability: A Approach with DNS and Reverse Proxy in Multi-Cloud

In spite of Cloud Computing services seem to be very attractive as an alternative to traditional on-premise data centers, there is still some concern about the providers availability. This article describes a solution that implements high availability Multi-Cloud through the distribution of DNS access and the use of reverse proxy. The results obtained, by means of simulations indicate that, in principle, one can obtain an arbitrary Multi-Cloud availability, by using Clouds of lower availability. Specifically, it was shown that, from two Clouds of 99.43% and 99.49% availability one can obtain a 99.9971% Multi-Cloud system. It is also shown that, in this particular case, the Multi-Cloud system costs 34% less than the solution with a single Cloud.

SECross: securing cross cloud boundary

Multi-cloud system may be cost-efficient and practical to integrate resources of multiple clouds. However, different clouds are usually managed by different organisations with different security policies and management platforms. When some components of a multi-cloud system are compromised, attackers can potentially have a high privilege that impacts the rest of system. We analysed the threats to overall system when some components of the multi-cloud system in a public cloud are compromised. We developed a model we call SECross for fine-grain database access policy for SECross components, and the method for users to access computing machines. We analysed how SECross resists various potential attacks when any of SECross components are compromised.

Dynamic scheduling strategy with efficient node availability prediction for handling divisible loads in multi-cloud systems

With large resource capacity, clouds have become a primary infrastructure for users to store big amount of data and perform large-scale computations. With the increasing demands and diversity of applications’ requirements, users are facing a fundamental problem of management of resources reserved from clouds. Particularly, the problem of load scheduling is the most important since it directly affects the performance of the system. Designing an efficient scheduling strategy for minimizing the total processing time of loads is challenging since it has to consider many intrinsic characteristics of the system such as the availability and heterogeneity of computing nodes, network topology and capacity. In this paper, we propose a novel architecture of a multi-cloud system that can satisfy complex requirements of users’ applications. Based on this architecture, we propose a dynamic scheduling strategy (DSS) that integrates the Divisible Load Theory and node availability prediction techniques to achieve high performance. We conduct intensive simulations to evaluate the performance of the proposed scheduling strategy. The results show that the proposed scheduling strategy outperforms the baseline schemes by reducing the total processing time of loads up to 44.60%. The results also provide useful insights on the applicability of the proposed approach in realistic scenarios.

Robust CHARM: an efficient data hosting scheme for cloud data storage system

Cloud computing is the recent evolving arena, which offers more benefits to cloud service providers and online users, compared to the traditional architecture. In this paper, an efficient data-hosting scheme with high availability for implementing over heterogeneous multi-cloud system is proposed. The proposed ROBUST CHARM (RCH) scheme is designed with data hosting, storage mode switching, speed mode and workload indicator modules. These modules process data with the support of heuristic and storage mode transition algorithm. The algorithm is proved efficient in identifying the apt cloud to store data. Storage transition is adopted by considering the cost and data access pattern of the data stored during any requirements. This evaluation enables efficient usage of cloud resources with high availability. The experimental results show that the proposed scheme works effectively without affecting the performance compared to the existing systems. The ability to utilize heterogeneous multi-cloud storage with the benefit of cost effectiveness is an added advantage to the cloud users.

Performance analysis of multi-institutional data sharing in the Clouds4Coordination system

Cloud computing is used extensively in Architecture/ Engineering/ Construction projects for storing data and running simulations on building models (e.g. energy efficiency/environmental impact). With the emergence of multi-Clouds it has become possible to link such systems and create a distributed cloud environment. A multi-Cloud environment enables each organisation involved in a collaborative project to maintain its own computational infrastructure/ system (with the associated data), and not have to migrate to a single cloud environment. Such infrastructure becomes efficacious when multiple individuals and organisations work collaboratively, enabling each individual/ organisation to select a computational infrastructure that most closely matches its requirements. We describe the “Clouds-for-Coordination” system, and provide a use case to demonstrate how such a system can be used in practice. A performance analysis is carried out to demonstrate how effective such a multi-Cloud system can be, reporting “aggregated-time-to-complete” metric over a number of different scenarios.

MC-GenomeKey: a multicloud system for the detection and annotation of genomic variants

BackgroundNext Generation Genome sequencing techniques became affordable for massive sequencing efforts devoted to clinical characterization of human diseases. However, the cost of providing cloud-based data analysis of the mounting datasets remains a concerning bottleneck for providing cost-effective clinical services. To address this computational problem, it is important to optimize the variant analysis workflow and the used analysis tools to reduce the overall computational processing time, and concomitantly reduce the processing cost. Furthermore, it is important to capitalize on the use of the recent development in the cloud computing market, which have witnessed more providers competing in terms of products and prices.ResultsIn this paper, we present a new package called MC-GenomeKey (Multi-Cloud GenomeKey) that efficiently executes the variant analysis workflow for detecting and annotating mutations using cloud resources from different commercial cloud providers. Our package supports Amazon, Google, and Azure clouds, as well as, any other cloud platform based on OpenStack. Our package allows different scenarios of execution with different levels of sophistication, up to the one where a workflow can be executed using a cluster whose nodes come from different clouds. MC-GenomeKey also supports scenarios to exploit the spot instance model of Amazon in combination with the use of other cloud platforms to provide significant cost reduction. To the best of our knowledge, this is the first solution that optimizes the execution of the workflow using computational resources from different cloud providers.ConclusionsMC-GenomeKey provides an efficient multicloud based solution to detect and annotate mutations. The package can run in different commercial cloud platforms, which enables the user to seize the best offers. The package also provides a reliable means to make use of the low-cost spot instance model of Amazon, as it provides an efficient solution to the sudden termination of spot machines as a result of a sudden price increase. The package has a web-interface and it is available for free for academic use.

Multi-criteria scheduling of Bag-of-Tasks applications on heterogeneous interlinked clouds with simulated annealing

Cloud computing has spurred the creation of a multitude of services that use the cloud to deliver their products on-demand. Behind it, stand multiple “Cloud Providers” that in the past few years have created data-centers, spread around the world, creating a mesh of distributed resources that can meet high availability and quality of service requirements. The growing number of cloud clients demand reliability, performance and better cost-to-performance ratios. Recently, scientific research has focused on the optimization of interlinked cloud systems, an aim which requires strategies for allocation of resources and distribution of computing tasks between them, while also considering their cost along with any factors that may differentiate them. In this study, we have evaluated the use of simulated annealing and thermodynamic simulated annealing in the scheduling of a dynamic multi-cloud system with virtual machines of heterogeneous performance serving Bag-of-Tasks applications. The scheduling heuristics applied, consider multiple criteria when scheduling said applications and try to optimize both for performance and cost, while also taking into account the heterogeneity of the virtual machines. Simulation results indicate that the use of these heuristics can have a significant impact in performance while maintaining a good cost-performance trade-off.