Search

Cloud computing and health monitoring is increasingly being employed together as the healthcare equipment’s are giving proper monitoring and collection of patient health record is being transferred and collected using cloud computing services like SaaS which stands for storage as a service. The need of tech equipped. equipment’s are increasing day by day as equipment’s are highly technical and the software and configuration which need to keep equipment’s work progressively can easily be served by cloud services which named under infrastructure as a service and software as a service. Our paper is a literature survey for how the cloud web services are merging with health care and in how many ways it is beneficial for human being for adding the cloud web services in healthcare in terms of facility and industry utilization. The digitalization of everything gives the benefit of availability of each record about a disease or patient, this makes the data available for all and all experts can look and monitor without having limitation of physically present there. Also, there are some challenges in employment of cloud services they are also mentioned in this paper with possible solution.

Cloud computing is a model of distributed systems. This system allows users to access virtual resources including the processing power, storage, applications, etc. Storage as a Service (SaaS) is one of the cloud computing services. Cloud storage systems provide this service for the end-users, and deliver data availability and durability as well as global accessibility throughout the Internet. High data availability and scalability are very crucial criteria for the end-users in cloud storage systems. To achieve them, we need replication in these systems. However, the replication brings about asynchronization of data among replicas in different cloud data-centers. It reduces the performance of the cloud storage systems as well. Therefore, replication is one of the crucial challenges in cloud storage systems. These systems need to ensure that the data are synchronized among different replicas by implementing consistency policies. In this paper, we present the Strict Timed Causal Consistency (STCC) as a hybrid consistency model which can be considered as an extension to the cloud computing. This consistency model has two components: client-side, and server-side. At the client-side, this model supports monotonic read, monotonic write, read your write, and write follow read consistencies. At the server-side, it supports the Timed Causal Consistency (TCC) as well. Additionally, it is stronger than the client-centric and is more flexible than the data-centric approaches. In spite of partition tolerance, our proposed method guarantees the consistency and satisfies data availability. Cassandra is a NoSQL database with high scalability and availability. Cassandra comes with multiple consistency levels as a service such as ONE, ALL, QUORUM, etc. We have examined the proposed approach with respect to different consistency levels of Cassandra and Causal Consistency (CC). Yahoo Cloud Serving Benchmark (YCSB) consists of a number of workloads which are used to evaluate our proposed method. We have executed different workloads on the Cassandra clusters and with respect to which we have made a comparison between the performance of our proposed method and the four other different consistency levels in Cassandra. The experimental results based on the comparison between the proposed method and ONE, ALL, QUORUM, as well as the CC consistencies, on a Cassandra cluster with 24 nodes, testify that on average our approach has reduced the stale read rate by 24% on workload-A, and on workload-B by 25%. Also, the system throughput with respect to workload-A has increased by more than 20%. Besides, when we applied our proposed STCC on workload-B the system throughput increased by almost 35%.

Cloud computing is emerging as a very prominent and promising technology with its unique characteristics like low cost, scalability, elasticity, and on demand services. On the other hand, quantum computing is also gaining increasing research attention due to its enormous computational power. The future will soon be defined by the combined capabilities of both technologies. The challenges of cloud computing like threats to confidentiality, integrity, authentication, availability, and authorization are deteriorating day by day. The cloud Storage as a Service also suffers from such threats and challenges. Due to the rapid advancements in cryptanalytical methodologies and techniques, existing solutions to secure cloud storage based on traditional cryptography techniques will no longer be viable. Henceforth, this research work proposes a Quantum Memory for Cloud Data Storage (QMCDS) as a solution after performing a detailed analysis on the difficulties and limitations in cloud storage. The proposed solution is based on Qubits and the same is also discussed here. The proposed research work is an effort to pave way towards developing solutions by keeping quantum computing at center in the cloud quantum computing technology.

Recent advancements in the automotive industry have led to the design of smart vehicles with high capacity resources for communication, sensing, processing, and storage of data. In the near future, it is envisaged that these resources will be harnessed and utilized to provide cloud services such as storage as a service, computation as a service, and sensing as a service. This paradigm of computing termed vehicular cloud, presents a lot of opportunities for the deployment of delay-sensitive applications in vehicular environment. However, high mobility of vehicles and rapidly changing topology of vehicular networks introduce new challenges such as instability of resources which make the management of resources in vehicular cloud complicated. Therefore, vehicular cloud computing require robust and mobility-aware resource management solutions to be designed. In order to achieve this goal, researchers and engineers need to be abreast with the techniques of resource management in vehicular cloud. This article presents a detailed survey of resource management tasks in vehicular clouds. A thorough introduction to vehicular cloud is presented initially. Then we identify and examine all the resource management tasks that are carried out in vehicular cloud by classifying them into three different phases: pre-resource assignment phase, resource-assignment phase, and post resource assignment phase. Proposed solutions to resource management challenges in literature at each phase are reviewed in detail. Unlike the existing surveys on vehicular clouds, this study covers all aspects of resource management such as resource brokering, resource demand prediction, resource allocation, and resource scheduling. Following the extensive survey, we also propose frameworks for resource monitoring and resource brokering for vehicular cloud. Finally, open issues and research directions for vehicular cloud resource management are presented.

Nowadays, storing and retrieving information over the Cloud is critical for the survival and growth of organizations and people. In this context, the possibility to store a huge amount of data and files on remote third-party Cloud storage providers is becoming an even more concrete practice. Unfortunately, there is not any guarantee regarding data availability and reliability of such providers. In such a context, if the Cloud wants to use the storage as a service of another Cloud, it has only to trust it. A possible solution consists of using a Multi-Cloud Storage (MCS) system. However, how organizations should compose their own MCS system on geo-distribution basis is not trivial at all. In this paper, we specifically discuss how to optimize the overall system in terms of data storage and retrieval by testing and validating a MCS system composed of three major Cloud Storage providers; Dropbox, Google Drive and Copy. Experiments have proved that the choice of the Cloud storage providers where to store files depends on the data transfer performance according to the file chunk size. In addition, we demonstrated that not always the provider that offers the best performance in upload also offers the best performance in download.

International audience; Cloud federation enables service providers to collaborate to provide better services to customers. For cloud storage services, I/O performance and network latency are important to ensure end-to-end QoS. Therefore, effectively placing customer objects for a cloud that is a member of a federation is a real challenge. In order to optimize data placement, storage, migration and latency costs need to be considered. These costs are contradictory in some cases. In this paper, we modeled object placement as a multi-objective optimization problem. The proposed model takes into account parameters related to the local infrastructure, the federated environment, customer workloads and their SLAs. For resolving this problem, we propose CDP-NSGAII , a Constraint Data Placement matheuristic based on NSGAII with injection and repair functions. The objective of the injection function is to enhance the solutions' quality. It consists to calculate some solutions using an exact method then inject them into the initial population of NSGAII. The repair function was designed to ensure that the solutions obey the problem constraints in term of storage and so prevents from exploring large sets of infeasible solutions. It allows to reduce the execution time of NSGAII. Our experimental results show that, the injection function improves the HV of NSGAII and the exact method by up to 94% and 60% respectively while the repair function reduces the execution time by an average of 68%. CCS Concepts: • Federated cloud → Hybrid storage system; • Data placement → Cost optimization.

SAAS (Storage as a service) is referred as a significant cloud computing (CC) services that support cloud users (CU) to overcome the bottleneck on constrained assets and extent storage without updating their gadgets. To guarantee the protection & privacy of CU, the information is generally outsourced in encrypted format. Nevertheless, encrypted information might produce more storage waste in cloud and complicates the data exchange among authorized users. Even now, some problems exist with encrypted data and deduplication in cloud and storage management. Traditional deduplication methods focus on specific application scenarios, whereas in the cloud, reduplication will be completely controlled by servers or data owners. They might not flexibly fulfill the separate requests obtained from data owners, who rely on sensitivity level of data. In this manuscript, the proposed method flexibly provides the similar time through numerous Cloud service providers [CSPs]. The researchers assesse the execution with security examination, comparison &execution.

Recently, the demand for cloud computing increases rapidly due to its multiple benefits to the customers like scalability, efficiency, user friendly, 24x7 availability, and so on. Cloud becomes very popular due to its salient features which can incorporate various domains such as the Internet of Things (IoT), Big Data, Machine Learning for creating easy and flexible computation mechanisms. The concept of Enterprise Resource Planning (ERP) having the mechanism in doing the operations in various domains in the field of research. Generally, we define only these services but when the services came into mind whatever the cloud provides it is treated as a service such as a network as a service, storage as a service, data as a service, security as a service, Privacy as a service, etc., whatever the services offered by the cloud are undefined There are various clouds such as Public clouds, Private clouds, Hybrid clouds, and Community clouds.The concept of ERP technology is merged with the various services offered by the cloud such as SaaS, PaaS, and IaaS. The working mechanism of the cloud is quite different compared to normal applications over ERP applications. However, the cloud services definitions were based upon a person to person or organization to organization in terms of flexibility, application usage, portability, reliability, scalability, etc., the future of cloud computing technology is very huge in all the sectors like education, clinical, pharmacy, industries, etc.

Among the data center services that Lepida ScpA offers to the Public Administrations (PAs) of Emilia-Romagna Region, there is the Storage As A Service; in particular, a growing number of PAs is asking for NAS to use for file systems joined to their domain, that we erogate over shared hardware. However, on the storage in our possession only a storage administrator user has the permissions to manage the quotas on the shares; but a storage administrator, on the other hand, would see all the shares of the different PAs that have been allocated on that physical storage. We therefore decided to develop a layer capable of decoupling the users who could access the storage from the permissions necessary for managing the quotas, in order to allow each individual PA to manage independently the quota trees on their own shares; in this paper we describe the solution we have devised and implemented.

The rapid growth of social networks and cloud services has led to the enhancement of online users in which the people can transform and access information. With the anonymous increase of social networking websites with additional applications is a huge gain of profits with the growth of online users. Consequently, a greater number of users are using social networks where the users are using online networks. Since the storage of data can be increased exponentially, the storage of these data can be complex and difficult. As a solution, the cloud provides storage as a service to store massive data. Since additional personal information is stored on social networking sites, this violates privacy concerns. However, researchers have been addressing various security issues in cloud-based social networks that violate confidentiality and privacy. The evolutions of cloud computing (CC) models enable various types of users to store a large quantity of data in a flexible and scalable manner. Despite several advantages of using cloud as a service (CaaS) to store the data, various security-related issues have been identified that take down the performance of CaaS. These could face several challenges in both hardware and software over the internet including privacy, integrity, availability, authorization, and confidentiality of the data over the cloud. Subsequently, weak network traffic encryption across the network may also lead to the influence of confidential information. Data access can be a major security-related issue in CC. Data integrity must be maintained across the network to avoid data insertion, modification, and fabrication so that data might not be accessed by other users. Data authorization maintained across an environment helps to the secure transmission of information. As part of recent technologic advancements, various overwhelming dedicated connection caskets have been deployed everywhere. These advancements may lead to connecting with high-end mobile devices and wireless communication technologies to make available with social networks. There is a need to protect the users from various security concerns including trust and autonomy, which demand control over cloud applications, data, and services away from central nodes. Therefore, edge computing can be an innovative pattern that has control over CC applications and services at the edge of the network. In this chapter, we provide a framework that examines the security and privacy issues in the cloud-based social networks.

Digital Data or information is multiplying in a tremendous volume nowadays. Due to storage capacity restriction in local devices we use in our daily life, this concern was getting a great importance for on demand new innovation to eliminate the restriction. The development of Storage as a Service (STaaS), a cloud-based distributed storage framework fulfilled all the necessities of consumers. It can handle a huge amount of data by its scalable computing power and resources. Because of the expanding measure of data, a few important security issues arise for information security, privacy, confidentiality, trustworthiness and authentication. These issues the significant barrier in the adoption of cloud computing as STaaS. As indicated by a survey by EMC, practically 75% of current digital data is the copied duplicates that hold up a lot of extra storage space in the cloud. So, efficient uses of storage are also important. In this paper, we proposed an approach that is storage efficient and eliminates the issue of data security for distributed cloud-based storage (STaaS) with the use of data compression and cryptography procedures. The transferred data to the cloud will be compacted and encoded with compression and Encryption algorithm and afterwards, the output data will be chunked and stored onto the distributed storage which will make the transferred data hard to get any access even for the cloud service providers without data owner's permission. If data compression and encryption are executed simultaneously, at that point it requires less processing time and more speed.

In modern power systems, shiftable loads contribute to the flexibility needed to increase robustness and ensure security. Thermal loads are among the most promising candidates for providing such service due to the large thermal storage time constants. This study demonstrates the use of variable-speed refrigeration (VSR) technology, based on brushless DC motors, for the fast-frequency response. First, the authors derive a detailed dynamic model of a single-phase VSR unit suitable for time-domain and small-signal stability analysis in low-inertia systems. For analysing dynamic interactions with the grid, they consider the aggregated response of multiple devices. However, the high computational cost involved in analysing large-scale systems leads to the need for reduced-order models. Thus, a set of reduced-order models is derived through transfer function fitting of data obtained from time-domain simulations of the detailed model. The modelling requirements and the accuracy versus computational complexity trade-off are discussed. Finally, the time-domain performance and frequency-domain analyses reveal substantial equivalence between the full- and suitable reduced-order models, allowing the application of simplified models in large-scale system studies., IET Smart Grid, 3 (6)

Vehicular networks have emerged in recent years offering novel medical services to vehicular users. In-vehicle equipment such as On-Board Units (OBUs), Internet of Things (IoT) devices and sensors are used to supervise the health of on-board users and create an increasing amount of medical information. This information should be processed and then transmitted to medical units (e.g. hospitals) with the lowest possible processing and communication delays. Furthermore, it should be organized considering well-defined standards, in order to be easily reusable from third-party medical systems. Thus, the medical staff will be able to remotely provide immediate medical support to vehicular patients. In this paper, an interoperable Storage as a Service (STaaS) scheme which delivers medical data through a 5G wireless network architecture is described. The Health Level 7 (HL7) standard is applied for the manipulation of the collected medical data, ensuring the interoperability of the proposed scheme with third-party systems that use the same standard. The Web Ontology Language (OWL) is used to produce and manipulate the relative ontological descriptions about the collected data.

The Internet of Things (IoT) and the 5G app need many services to be real, as they have very limited processing and storage resources, and their cloud computing connection is a relevant factor and great interest for researchers from all over the world. Latency, big data, storage, and power processing problems near high-end devices led to the appearance of fog computing. Fog computing is the interconnection between IoT devices and cloud computing and can bring processing and storage capacity to IoT devices, making it possible for new technologies and applications to address these flaws today. This paper introduces the concept of fog computing, its challenges, theoretical context, implementation and implementation of fog computing, and the application of a genetic algorithm for the distribution of resources for IoT devices using integrated systems platforms and comparing their results with those obtained by a high-performance server. The Fog-Sys system was developed with the main objective of simulating, receiving, verifying, and storing data from IoT devices to be transferred to the cloud computing, and it works like a fog computing to provide the storage as a service (StaaS) service. Also using a fuzzy genetic algorithm (FGA) to try to reduce the energy consumed. The results showed that implementing this service in the built-in system devices can be a good alternative to reduce one of these problems, in this case, storing data and reducing the energy consumed, which today affects IoT devices.

Wide-ranging edge cloud data centers are a vital part of the solution for the problems caused by enormous growth in the IT industry for high computational power by advanced service applications. Majority of IoT applications switched to the Cloud and this stimulated the emergence of Edge technology to better manage the computing applications, data, resource and services. Consequently, with the massive client size and enormous applications trying to benefit from the cloud service, it makes it a challenging task for the edge cloud data centers to work in a power saving mode. In this paper, we propose a virtual machine consolidation method to switch the idle physical servers into hibernation mode, resulting in reduced power usage. We know that edge cloud data centers offer storage as a service, in this study we address the issues pertaining to storage units in the data centers. A unique classification approach is adopted to ensure load is balanced accordingly during allocation and our main contribution is on the VM migration technique. The VM migration is aimed at consolidating the VMs based on the workload to reduced number of physical machines to mitigate the energy consumption and promoting green computing. Therefore, we name the approach as Workload Aware Virtual Machine Consolidation Method (WAVMCM). We validate the proposed method with a competitive analysis of experimental results gathered from comparing it with Artificial Intelligence based probabilistic algorithm like Simulated Annealing, Genetic Algorithm and a case of no migration. Experimental results demonstrate that the proposed WAVMCM reduces 9% active servers saving 15% of power consumption when compared to genetic algorithm based method.

Currently, Storage-as-a-Service (StaaS) clouds offer multiple data storage and access pricing options which usually consist of hot and cold tiers. The cold tier storage option offers a lower storage price while the hot tier storage option offers a lower access price. Cloud users need to choose an optimal tier to store their data objects economically based on the frequency of accesses to their data objects. Besides, StaaS cloud users can transfer data objects between these two tiers to save cost according to the varying frequency of accesses to their data objects. Therefore, in order to make optimal transferring decisions, future access curves are needed to be predicted. However, for cloud users, it is difficult to precisely predict future access frequencies for their data objects. In this paper, we propose an online algorithm to guide StaaS cloud users in making decisions on whether and when to transfer their data objects between cold and hot tiers for achieving cost optimizations, while users do not need to have any prior knowledge of future access frequencies. We prove theoretically that the proposed online algorithm can achieve guaranteed competitive ratios for data objects stored in a two-tier StaaS cloud. Finally, through extensive experiments, we validate the effectiveness of our proposed online algorithm and show that it can save costs significantly compared with always keeping data objects in one tier or always transferring data objects from one tier to the other when their access frequencies begin to vary.

STorage as a service (STaaS) allows its subscribers the ability to access their stored data with the use of internet enabled digital devices at anywhere, anyplace and anytime. The easy accessibility of cloud storage with digital devices is one of the major benefits of cloud computing but this benefit can also be exploited by cybercriminals to perform various forms of malicious usages. During forensic investigation, forensic examiners are expected to provided evidence in relation to the malicious usages but the physical inaccessibility to the digital artifacts on the cloud servers, the difficulty in retrieving evidential artifacts from various cloud storage services and the difficulty in obtaining forensic logs from the concerned cloud service providers among other factors make it difficult to perform forensic investigations. This paper provided step by step experimental guidelines to extract digital artifacts from Google Chrome and Internet Explorer from Windows 10 personal computer using iDrive cloud storage as a case study. The study used Nirsoft forensic tool to locate the relevant forensic artifacts and an integrated conceptual digital forensic framework was adopted to carry out the investigation. This study increases the knowledge of client forensics using web browser analysis during cloud storage forensic investigation.

Storage as a service has become an important paradigm in cloud computing for its great flexibility and economic savings. However, the development is hampered by data privacy concerns: data owners no longer physically possess the storage of their data. In this work, we study the issue of privacy-preserving set-valued data publishing. Existing data privacy-preserving techniques (such as encryption, suppression, generalization) are not applicable in many real scenes, since they would incur large overhead for data query or high information loss. Motivated by this observation, we present a suite of new techniques that make privacy-aware set-valued data publishing feasible on hybrid cloud. On data publishing phase, we propose a data partition technique, named extended quasi-identifier-partitioning (EQI-partitioning), which disassociates record terms that participate in identifying combinations. This way the cloud server cannot associate with high probability a record with rare term combinations. We prove the privacy guarantee of our mechanism. On data querying phase, we adopt interactive differential privacy strategy to resist privacy breaches from statistical queries. We finally evaluate its performance using real-life data sets on our cloud test-bed. Our extensive experiments demonstrate the validity and practicality of the proposed scheme.

The researchers have been focusing on embedding security from the early phases of software development lifecycle. They have researched and innovated a field of Security Engineering where security concerns are embedded during requirement, design, and testing phases of software development. Efforts were made in developing methods, methodologies, and tools to handle security issues. Various methods are present in the literature for eliciting, analyzing and prioritizing the security requirements. During the design phase based on prioritized requirements, environment parameters and attribute a suitable security algorithm mainly cryptography algorithms are identified. Then a question arises how to test the effectiveness of chosen algorithm? Therefore, as an answer to the issue in this paper, a process for Security Testing is presented that evaluates the selected security algorithms. Evaluation is done by generating the test scenarios for functionalities using sequence diagram representing the threats at vulnerable points. Then, checking the mitigation of potential threats at identified vulnerable points. A security index is generated which shows the effectiveness of deployed/ chosen security algorithm. The process ends with the generation of a test report depicting the testing summary. For a clear understanding of the process, the proposal is illustrated with a case study of the cloud storage as a service model.

StaaS (Storage as a Service) has been envisioned as the next generation architecture of IT enterprise. Enterprise, individual end users move more and more important information to outsourced data centers, where the data may be misappropriated in the malicious model. Moreover, users hesitate to store data in the cloud storage because of network fluctuation. Security and availability become the main concern of cloud storage. This paper, to address these issues, presents an architecture of High Available cloud Storage Gateway(HASG). In our design, data file is divided into implicit, redundant blocks by file fragment algorithm. Part of these blocks, which are stored on different designated cloud servers, can be used to reconstruct original file. Additionally, dynamic service provider selection algorithm and redundant block mechanism for file updating is proposed to effectuate cost and bandwidth savings and accelerate data accessing. Experimental results illustrates that our design enhanced security of cloud storage and improved data access efficiency.

Storage as a Service (StaaS) is a vital component of cloud computing by offering the vision of a virtually infinite pool of storage resources. It supports a variety of cloud-based data store classes in terms of availability, scalability, ACID (Atomicity, Consistency, Isolation, Durability) properties, data models, and price options. Application providers deploy these storage classes across different cloud-based data stores not only to tackle the challenges arising from reliance on a single cloud-based data store but also to obtain higher availability, lower response time, and more cost efficiency. Hence, in this article, we first discuss the key advantages and challenges of data-intensive applications deployed within and across cloud-based data stores. Then, we provide a comprehensive taxonomy that covers key aspects of cloud-based data store: data model, data dispersion, data consistency, data transaction service, and data management cost. Finally, we map various cloud-based data stores projects to our proposed taxonomy to validate the taxonomy and identify areas for future research.

With the introduction of Cloud computing, a new era of computing has begun. Cloud has the ability to provide flexible, cost effective pay-as-you-go service. In the modern day computing, outsourcing of data/multimedia into the cloud has become an effective trend as cloud provides storage as a service, platform/software as a service, infrastructure as a service etc. Seamless exchange of data /multimedia is made possible ensuring the data available anytime, anywhere. Even though cloud based services offer many advantages, data owners are still hesitant to keep their data with the third party. Confidentiality, Integrity, Privacy and Non-repudiation are the major concerns of the outsourced data. To secure the data exchange between users and the cloud, many traditional security approaches are proposed. In this paper, a hybrid encryption technique to secure the images is proposed. The scheme uses Elliptic Curve Cryptography to generate the secret key, which in turn used for DES and AES algorithms.

Nowadays, shared network infrastructures are being designed for supporting various services that may be subject to diverse failures and disruptions due to the malfunctioning of network component(s) or human-caused disasters. As the delivery of these services may be subject to multiple correlated failures and disruptions, the present chapter develops a reliable model for multi-objective design problems. This model finds applicability in content distribution, storage as a service, computing as a service and their possible combinations (including virtual machine service) as well as resource installation, dimensioning, and allocation for virtual networks. This chapter further elaborates on the motivation underlying the development of such a mixed-integer optimization model from the operational perspective compared to current practices. Indeed, for such problems, the objective function includes in addition to the installation and the transport/delivery cost, terms such as sizing, production, and supply costs. These optimization problems are usually modelled and solved independently though, as proposed in this chapter, it is more realistic and effective to model and solve them simultaneously because related decisions are interdependent. Next, using this generic model, we demonstrate the gain that could be achieved by resilience scheme(s) involving cooperation between the network infrastructure and the service level against conventional protection schemes upon independent and conditionally dependent service-level failure events.

Now-a-days, cloud computing proved its impor-tance where it is being used by small and big organizations. The importance of cloud computing is due to the various services provided by the cloud. One of these services is storage as a service (SaaS) which allows users to store their data in the cloud databases. The drawback of this service is the security challenge since a third party manages the data. The users need to feel safe to store their data in the cloud. Consequently, we need for models that will enhance the data security. The image steganography is a way to protect data from unauthorized access. Image steganography allows users to conceal secret data in a cover image. In this paper, we review and compare some of the recent works proposed to protect cloud data using image steganography. The first comparison of models based on the algorithms they used, advantages and drawbacks. The second comparison of the models based on the aims of steganography: quality where the model produces a stego-image with high quality, security where the secret data is difficult to detect and capacity where the model allows to hide large amounts of data.

Service Level Agreements (SLA) are employed to ensure the performance of Cloud solutions. When a component fails, the importance of logs increases significantly. All departments may turn to logs to determine the cause of the issue and find the party at fault. The party at fault may be motivated to tamper with the logs to hide their role. We argue that the critical nature of Cloud logs calls for immutability and verification mechanism without the presence of a single trusted party. This paper proposes such a mechanism by describing a blockchain-based log storage system, called Logchain, which can be integrated with existing private and public blockchain solutions. Logchain uses the immutability feature of blockchain to provide a tamper-resistance platform for log storage. Additionally, we propose a hierarchical structure to address blockchains' scalability issues. To validate the mechanism, we integrate Logchain into Ethereum and IBM Blockchain. We show that the solution is scalable and perform the analysis of the cost of ownership to help a reader select an implementation that would address their needs. The Logchain's scalability improvement on a blockchain is achieved without any alteration of blockchains' fundamental architecture. As shown in this work, it can function on private and public blockchains and, therefore, can be a suitable alternative for organizations that need a secure, immutable log storage platform., Comment: Accepted for publication in IEEE Transactions on Services Computing

We are living in the age of technology where every field is blended with technology either its healthcare or education or entertainment and immeasurably more. For these, IT organizations play a vital role. IT organizations are going up so high either they are small or big. Small IT organizations are running very smoothly and efficiently. This is becoming possible because of Cloud-Computing and its splendid services. Different cloud-service-providers are available all over the globe. So, it is becoming necessary for a client to make the right choice in accordance with their requirement. So, this paper differentiate cloud-service-providers are compared and on the basis of their virtual machine types, their storage as a service, and their availability zones.

Cloud Computing is an emerging technology which aims to provide efficient access to remote and distributed resources with the help of cloud services providers such as Amazon Googles Application, Microsoft Azure. Storage as a Service (STaaS) is among the most important and popular kind of services being investigated in the Cloud computing community to serve both private users and business enterprise with remote and scalable data storage that can be charged per customer usage. Providing safety, privacy and availability for the data that is hosted in cloud are the major concerns for users and businesses. This survey presents the data storage security concerns that arise due to the nature of cloud computing like multi-tenancy and virtualization, in addition to the details of the recent solutions presented in the literature to overcome the security issues in cloud storage. Cloud computing, Data Security, Storage as a Service

Advances in Information Technology field have replaced local storage of data with cloud storage which has become the crux of the day. As the number of people adopting storage as a service is increasing, security of personal and private information stored on cloud remains a major concern. Different approaches to achieve security have been used like encryption, file division, access control security (ACS), authentication, authorization and many more.

This paper presents a complete overview of the storage landscape and cloud computing. It explains the major advantages and disadvantages of storing data online using cloud storage, how this works, and the basic concepts involved in cloud computing, while also demonstrating the layers of its architecture with reference to the Infrastructure as a Service (IaaS) that contains the cloud storage architecture. The paper further discusses the concept of Storage as a Service (StaaS), which lets users or clients utilize cloud storage to save data by providing them space without them having to use physical storage.

Storage as a Service (STaaS) is in a very advanced stage and 80-90% of the users trust and use public cloud storage. However, enterprises and communities are still skeptical and are concerned with data safety and theft, and are investing to build their own private storage system, where cost of storage space is not the most important concern. This limits resource (compute and network) scale at the peak on demand in hybrid/multi-cloud environment as one cannot keep buying and upgrading computing hardware resources to support peak workloads in private cloud [9]. There are commercial solutions offered by few public cloud service providers/brokers to maintain the data in a private cloud in a hybrid environment, but this may have vendor/broker lock-in and portability/migration issues. In this paper, we explore OpenStack cloud for private Storage as a Service in hybrid/multi-cloud environment and demonstrate with our framework (middleware), which is built upon OpenStack [16], an open source cloud and by leveraging existing OpenStack functionalities.

Uses of Internet of things (IoT) devices and sensors have increased to a great extent in recent years. In order to facilitate computational and storage needs of low powered end devices for time-sensitive applications such as data analytic, which use IoT devices and sensors to get their work done, a new programming paradigm “Fog computing” has come into existence. Storage and processing capabilities of Fog Computing are similar to that of cloud computing. In addition to this, it facilitates in bringing these services closer to the edge of the network near to low powered end devices. Keeping in view the drawbacks of cloud computing, this article presents a comprehensive review on storage-as-a-service at Fog layer. It elaborates the scope and contribution of keeping storage closer to edge of the network. The article also classifies the current scenario in secure storage as a service near the edge of the network. Further, it discusses about the benefits of implementing storage at Fog layer.

Cloud computing has gained tremendous popularity in recent years. By outsourcing computation and storage requirements to public providers and paying for the services used, customers can relish upon the advantages of this new paradigm. Cloud computing provides with a comparably lower-cost, scalable, a location-independent platform for managing clients' data. Compared to a traditional model of computing, which uses dedicated in-house infrastructure, cloud computing provides unprecedented benefits regarding cost and reliability. Cloud storage is a new cost-effective paradigm that aims at providing high availability, reliability, massive scalability and data sharing. However, outsourcing data to a cloud service provider introduces new challenges from the perspectives of data correctness and security. Over the years, many data integrity schemes have been proposed for protecting outsourced data. This paper aims to enhance the understanding of security issues associated with cloud storage and highlights the importance of data integrity schemes for outsourced data. In this paper, we have presented a taxonomy of existing data integrity schemes use for cloud storage. A comparative analysis of existing schemes is also provided along with a detailed discussion on possible security attacks and their mitigations. Additionally, we have discussed design challenges such as computational efficiency, storage efficiency, communication efficiency, and reduced I/O in these schemes. Furthermore, we have highlighted future trends and open issues, for future research in cloud storage security.

Researchers envisioned the Storage as a Service (StaaS) as an effective solution\ud to the distributed management of digital data, since it provides an inexpensive\ud and reliable online storage which is accessible by different types of computer\ud devices (e.g., mobile and desktop). However, the proliferation of cloud storage\ud services has fuelled concerns over privacy and security (e.g., dissemination\ud of malware and unauthorised access of private information). Together with\ud the growing of StaaS proposals, cloud storage is attracting the attention\ud of research, although cooperative storage cloud forensic is relatively underexplored as this is a fairly new concept. Using Symform as a case study,\ud we seek to determine the data remnants from the use of cooperative cloud\ud storage services. In particular, we consider both mobile devices and personal\ud computers running various popular operating systems, namely Windows 8.1,\ud Mac OS X Mavericks 10.9.5, Ubuntu 14.04 LTS, iOS 7.1.2, and Android\ud KitKat 4.4. Potential artefacts recovered during the research include data\ud relating to the installation and uninstallation of the cloud applications, login to and log-off from symform account using the client application, file\ud synchronisation (download, upload, and delete), as well as their timestamp\ud information. This research contributes to an in-depth understanding of\ud the types of terrestrial artefacts that are likely to remain after the use of\ud cooperative storage cloud on client devices.

Since the use of network-based storage as a cloud computing service [Storage as a Service (StaaS)] becomes ubiquitous, users who store data in the cloud are exposed to risk and potentially severe consequences. Therefore, individuals should carefully consider security and privacy issues when using StaaS. Drawing upon the principle of self-preservation and the theory of protection motivation, this study develops a research model explaining the mechanisms leading to the development of a protection strategy for individuals’ safe use of StaaS and empirically tests the proposed model. The results show that the natural propensity to trust, an individual trait, influences an individual’s need for privacy. This, in turn, along with perception of privacy risk, significantly affects individuals’ perception of the need to protect themselves. Three coping appraisal factors, preventive measures, self-efficacy, and willingness to incur cost of prevention, together with the individual perceived need to protect se...

This work presents the concept of Fog Computing, its theoretical contextualisation and related works, and develops the FogSys system with the main objective of simulating, receiving, validating and storing data from IoT devices to be transferred to Cloud Computing, serving as a Fog Computing to provide the StaaS (Storage as a Service) service. The results showed that the implementation of this service in devices of embedded systems can be a good alternative to reduce the problems, in this case, the storage of data, which are faced currently by IoT devices.

Cloud computing offers many services among one is Storage as a service. Using this service user can outsource his data and whenever required he can download or he can share with others. Using cloud computing PHR owner can share his documents, but due to security challenges in cloud computing the health records of owner must be encrypted before outsourcing. In order to provide security for owner health records ABE (attribute-based encryption) is best suitable for other encryption techniques. Using ABE the PHR owner can define access control over his encrypted cloud data. But using ABE dynamic polices over encrypted cloud data is big challenge. In this paper for achieving grant and revoke privileges over encrypted data Timestamp server is introduced.

In recent years, cloud computing has gained lots of importance and is being used in almost all applications in terms of various services. One of the most widely used service is storage as a service. Even though the stored data can be accessed from anytime and at any place, security of such data remains a prime concern of storage server as well as data owner. It may possible that the stored data can be altered or deleted. Therefore, it is essential to verify the correctness of data (auditing) and an agent termed as Third Party Auditor (TPA) can be utilised to do so. Existing auditing approaches have their own strengths and weakness. Hence, it is essential to propose auditing scheme which eliminates limitations of existing auditing mechanisms. Here we are proposing public auditing scheme which supports data dynamics as well as preserves privacy. Data owner, TPA, and cloud server are integral part of any auditing mechanism. Data in the form of various blocks are encoded, hashed, concatenated and then signature is calculated on it. This scheme also supports data dynamics in terms of addition, modification and deletion of data. TPA reads encoded data from cloud server and perform hashing, merging and signature calculation for checking correctness of data. In this paper, we have proposed efficient privacy preserving and dynamic public auditing by utilizing Merkle Hash Tree (MHT) for indexing of encoded data. It allows updating of data dynamically while preserving data integrity. It supports data dynamics operations like insert, modify and deletion. Several users can request for storage correctness simultaneously and it will be efficiently handled in the proposed scheme. It also minimizes the communication and computing cost. The proposed auditing scheme is experimented and results are evaluated considering various block size and file size parameters.

Logs contain critical information about the quality of the rendered services on the Cloud and can be used as digital evidence. Hence, we argue that the critical nature of logs calls for immutability and verification mechanism without the presence of a single trusted party. In this paper, we propose a blockchain-based log system, called Logchain, which can be integrated with existing private and public blockchains. To validate the mechanism, we create Logchain as a Service (LCaaS) by integrating it with Ethereum public blockchain network. We show that the solution is scalable (being able to process 100 log files per second) and fast (being able to "seal" a log file in 23 seconds, on average).

Technological advancements in the area of cloud computing has resulted in massive and large scale ubiquitous environment. This environment provides an on demand access to configurable shared resources. Sharing of resources and service level management can be easily managed and maintained over the internet. Cloud computing relies on sharing of resources to achieve coherence. Cloud computing is significant because of the pay-as-you-go pricing. Cloud computing provides infrastructure, hardware, software and storage as a service. Thus it provides a holistic compute power to the user. Research shows that large amount of energy is consumed by the data centre. Thus there is a need to conserve energy through optimal resource allocation policy. This paper provides an overview of various methodologies incorporated to handle energy consumption. It also provides an insight to use appropriate techniques efficiently for better utilization of resources