Scalable Services Research Articles

Lightweight and Privacy-Preserving Delegatable Proofs of Storage with Data Dynamics in Cloud Storage

Cloud storage has been in widespread use nowadays, which alleviates users' burden of local data storage. Meanwhile, how to ensure the security and integrity of the outsourced data stored in a cloud storage server has also attracted enormous attention from researchers. Proofs of storage (POS) is the main technique introduced to address this problem. Publicly verifiable POS allowing a third party to verify the data integrity on behalf of the data owner significantly improves the scalability of cloud service. However, most of existing publicly verifiable POS schemes are extremely slow to compute authentication tags for all data blocks due to many expensive group exponentiation operations, even much slower than typical network uploading speed, and thus it becomes the bottleneck of the setup phase of the POS scheme. In this article, we propose a new variant formulation called “Delegatable Proofs of Storage (DPOS)”. Then, we construct a lightweight privacy-preserving DPOS scheme, which on one side is as efficient as private POS schemes, and on the other side can support third party auditor and can switch auditors at anytime, close to the functionalities of publicly verifiable POS schemes. Compared to traditional publicly verifiable POS schemes, we speed up the tag generation process by at least several hundred times, without sacrificing efficiency in any other aspect. In addition, we extend our scheme to support fully dynamic operations with high efficiency, reducing the computation of any data update to O(log n) and simultaneously only requiring constant communication costs. We prove that our scheme is sound and privacy preserving against auditor in the standard model. Experimental results verify the efficient performance of our scheme.

Towards an Elastic Fog‐Computing Framework for IoT Big Data Analytics Applications

IoT applications have been being moved to the cloud during the last decade in order to reduce operating costs and provide more scalable services to users. However, IoT latency‐sensitive big data streaming systems (e.g., smart home application) is not suitable with the cloud and needs another model to fit in. Fog computing, aiming at bringing computation, communication, and storage resources from “cloud to ground” closest to smart end‐devices, seems to be a complementary appropriate proposal for such type of application. Although there are various research efforts and solutions for deploying and conducting elasticity of IoT big data analytics applications on the cloud, similar work on fog computing is not many. This article firstly introduces AutoFog, a fog‐computing framework, which provides holistic deployment and an elasticity solution for fog‐based IoT big data analytics applications including a novel mechanism for elasticity provision. Secondly, the article also points out requirements that a framework of IoT big data analytics application on fog environment should support. Finally, through a realistic smart home use case, extensive experiments were conducted to validate typical aspects of our proposed framework.

Enabling Efficient and Scalable Service Search in IoT With Topic Modeling: An Evaluation

Service search in IoT's large-scale, heterogeneous and multi-domain services space is a challenging task. It can take the time that may not be acceptable for many IoT applications and requires resources that may not be available in many IoT devices. A categorisation of these services into their application domains can reduce the search space and offer an efficient and scalable service search. Recently, in many fields, such as short text messages and categorisation of IoT service specifications, generative probabilistic models, like Topic modelling, are being used. Generally, IoT service descriptions are short and sparse. Existing work on IoT services categorisation is based on Latent Dirichlet Allocation (LDA), but it does not perform well in short and sparse texts. Also, IoT services categorisation has few specific issues, which are not well addressed by existing short texts-specific topic modelling approaches. In this paper, we identify these issues and quantitatively and qualitatively evaluate how well a set of selected short texts-specific topic modelling approaches perform as IoT service categorisers against these issues. The results show that these approaches do not perform well in a corpus of noisy APIs descriptions and heterogeneous service descriptions. Also, they do not support domain identification of services, which is essential in domain-based service search. We conclude that integrating an appropriate and comprehensive knowledge base (i.e., domain ontology) could minimise noise and address IoT's APIs and service descriptions' heterogeneity. More importantly, it can identify the domains of those APIs and services.

Framing Outcomes and Programming Assessment for Digital Scholarship Services: A Logic Model Approach

Assessing digital scholarship services offered either through academic libraries or elsewhere on campuses is important for both program development and service refinement. Digital scholarship support is influenced by fluid campus priorities and limited resources, including staffing, service models, infrastructure, and partnership opportunities available at a university. Digital scholarship support is built upon deep, ongoing relationships, and there is an intrinsic need to balance these time-intensive collaborations with scalable service offerings. Therefore, typical library assessment methods do not adequately capture the sustained engagement and impacts to research support and collaboration that come from digital scholarship services. This article discusses the creation of a logic model as one approach to frame assessment of digital scholarship services in the university environment.

Blockchain-Based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing

Fog computing is a modern computing model which offers geographically dispersed end-users with the latency-aware and highly scalable services. It is comparatively safer than cloud computing, due to information being rapidly stored and evaluated closer to data sources on local fog nodes. The advent of Blockchain (BC) technology has become a remarkable, most revolutionary, and growing development in recent years. BT’s open platform stresses data protection and anonymity. It also guarantees data is protected and valid through the consensus process. BC is mainly used in money-related exchanges; now it will be used in many domains, including healthcare; This paper proposes efficient Blockchain-based secure healthcare services for disease prediction in fog computing. Diabetes and cardio diseases are considered for prediction. Initially, the patient health information is collected from Fog Nodes and stored on a Blockchain. The novel rule-based clustering algorithm is initially applied to cluster the patient health records. Finally, diabetic and cardio diseases are predicted using feature selection based adaptive neuro-fuzzy inference system (FS-ANFIS). To evaluate the performance of the proposed work, an extensive experiment and analysis were conducted on data from the real world healthcare. Purity and NMI metrics are used to analyze the performance of the rule based clustering and the accuracy is used for prediction performance. The experimental results show that the proposed work efficiently predicts the disease. The proposed work reaches more than 81% of prediction accuracy compared to the other neural network algorithms.

Community Empowerment in Health: An Exploratory Study in Rural Community of a District of West Bengal

Background: Community empowerment is the process and outcome where community itself is able to identify, prioritize health problems and address them. It has been considered as the key strategy for scalability and sustainability of health services.
 Objectives: To explore the status of community empowerment in health in rural areas in West Bengal, India and the interplay of different stakeholders.
 Methods: A cross-sectional, qualitative study was conducted in 2017 – 2018 among the people residing in rural areas of Birbhum district in West Bengal, India who utilized the public health system (lay informants), formal and informal leaders of the community, community level health workers and peripheral health staff (key informants). Three community blocks, two sub-centers from each block and one village from each sub-center were selected randomly. In-depth interviews were conducted among 36 lay and 36 key informants using Laverack’s nine dimension model of community empowerment. Framework analysis was done to summarize data.
 Results: Participation of people was restricted to awareness and utilization of existing health services. Unmet aspiration for greater participation was noted among a small section of the community. They were mostly fitted to the role of beneficiaries. Functioning of village level organization to promote communitization as envisaged in national health programmes was largely deficient. The community health workers acted as the most peripheral appendages of formal health system rather than the health activists to empower community regarding community’s health.
 Conclusion: Although, every national health programme advocated community empowerment, the current status and the process of empowerment in health is in nascent stage.

Identifier and Locator Mapping Service Research

The original TCP/IP architecture has faced some critical challenges from scalability, mobility, security and so on. It also hampered the development of some new network technologies such as multi-homing, traffic engineering. One of the most important reasons is IP address semantic overloading. Academy commonly considered that we should give a clean-slate design for the naming and addressing architecture of the future Internet. “Locator/ID Split” is one of the most important research areas. The scalable flat-labels based mapping service is the kernel of “Locator/ID Split”. HLIM uses hash-based routing to provide a deterministic mapping resolution for the edge network through distributed mapping servers. HLIM satisfies the scalability of the flat-labels mapping service, and can adapt mapping nodes’ dynamic join and exit.

Data Processing and Management in IoT and Wireless Sensor Network

The deployment of internet over larger scale may introduce huge challenges based on data processing. The enormous amount of IoT based data needs design-based solution for faster data processing and improving its extensibility and adaptability. Based on various IoT based data processing, servicing technologies may provide data-centric models for scalable services. This work concentrates on an extensive review towards the scalable realization and acquisition of data for process. This IoT based services are larger enough to be concentrated. This review gives an insight towards the data processing and management in IoT and sensor-based networking.

Virtualized and Micro Services Provisioning in Space-Air-Ground Integrated Networks

In this article, we justify an online service provisioning (OSP) framework to provision virtualized and micro service (VMS) to users, which can achieve efficient, flexible, and scalable service provisioning. Compared to the onboard service provisioning framework, the system bottleneck shifts from the onboard computation limitation to the network capability and robustness in OSP, which the space-air-ground integrated network (SAGIN) can ably support. First, the motivations of OSP and VMS per the current mobile application industry are identified. Then we investigate the capabilities of the SAGIN to enable the OSP framework, where the evolution of the terrestrial network, on-demand deployment of an aerial network, and global coverage with a satellite network are reviewed. Afterward, we elaborate on three major techniques of VMS provisioning in the OSP framework: application decomposition, control protocol design, and microservice placement/replacement. Finally, we design and implement a case study to demonstrate the efficacy of provisioning VMS in OSP.

Sunflower Whale Optimization Algorithm for Resource Allocation Strategy in Cloud Computing Platform

Cloud computing environment supply the computing resources based on the demand of cloud user requirements. It builds the resource allocation model through distributed computing and virtualization to emphasize the scalability of cloud services. However, to manage the demand of user creates a complex issue in the on-demand resource allocation framework. Therefore, an effective optimization algorithm named Sunflower Whale Optimization Algorithm (SFWOA) is proposed to solve the issues in the resource allocation model. The concept of virtualization helps to execute the tasks based on the availability of resources and reduces the response time. The tasks are allocated to the virtual machine in a distributed manner to balance the workload in cloud. The proposed SFWOA uses the hunting strategy and the foraging behavior of humpback whale along with the peculiar behavior of sunflower to achieve the effective resource allocation. The performance enhancement of the proposed SFWOA is revealed through the performance measures such that the proposed method attained a maximum resource utilization of 0.942 using 20 virtual machines, maximum memory utilization of 0.215, and maximum CPU utilization of 0.269 using 15 virtual machines, and minimum skewness of 0.001 using 25 virtual machines.

Hierarchical Security Paradigm for IoT Multiaccess Edge Computing

The rise in embedded and IoT device usage comes with an increase in LTE usage as well. About 70% of an estimated 18 billion IoT devices will be using cellular LTE networks for efficient connections. This introduces several challenges, such as security, latency, scalability, and quality of service, for which reason edge computing or fog computing has been introduced. The edge is capable of offloading resources to the edge to reduce workload at the cloud. Several security challenges come with multiaccess edge computing (MEC), such as location-based attacks, the man- in-the-middle attacks, and sniffing. This article proposes a software-defined perimeter (SDP) framework to supplement MEC and provide added security. The SDP is capable of protecting the cloud from the edge by only authorizing authenticated users at the edge to access services in the cloud. The SDP is implemented within a mobile-edge LTE network. Delay analysis of the implementation is performed, followed by a Denial-of-Service (DoS) attack to demonstrate the resilience of the proposed SDP. Further analyses, such as CPU usage and port scanning were performed to verify the efficiency of the proposed SDP. This analysis is followed by concluding remarks with insight into the future of the SDP in MEC.

Cloud of Things (CoT): Cloud-Fog-IoT Task Offloading for Sustainable Internet of Things

With a high rise in the popularity of Internet of Things (IoT), mobile computing, and wearable devices, a huge amount of data is being generated. Running complex tasks such as that are machine learning-based with minimum energy consumption is a challenge. It requires complex algorithms to run locally such as on middleware fog within the proximity of the devices generating data, or globally in a cloud to analyze the acquired data and create robust and smart applications. However, it depends on the type of task execution policy applied at each level; local or global, to decide on energy and performance efficiency, since certain tasks are high in complexity. Hence, task execution will be hierarchically distributed among the IoT nodes, fog, and cloud. Given that, we present in this paper a three-tier IoT-fog-cloud model. We argue that with distributed task execution, we can achieve high scalability of IoT services, and manage the global energy consumption as well. As a proof-of-concept, we evaluate our three-tier architecture by taking into account computational tasks for various applications in IoT related to medical, multimedia, location-based, and text. We evaluate using real datasets, based on three scenarios: fog-only, cloud-only, and fog-cloud collaborative. Task execution policy (at fog/cloud) play a key role in efficiently processing a task (especially large tasks, such as in deep learning). Therefore, we take that into account and elaborate what types of policies suit what type of offloading environment (fog-only, cloud-only, or fog-cloud collaborative).

Design and Research of Access Control System for Virtual Cloud Desktop

Virtual cloud computing is a typical application of cloud computing. Based on physical server and desktop virtualization technology, hardware, software, network and data are allocated with virtualization means to provide flexible and scalable service platform for computer applications, which can make more effective use of IT resources, greatly reduce the maintenance and management costs of equipment and application environment, and provide a share Open and efficient experimental computer teaching environment. This paper introduces the relevant technologies of virtual cloud desktop, analyzes the overall architecture of virtual desktop platform and its application in teaching management, and provides ideas and methods for computer experiment teaching innovation management.

IOT based Industry Automation Using Raspberry PI

Industries have been automated with machines that allow for fully automated tasks without or with little manual intervention. There are many challenges to IoT and Industrial Automation for example Data and service security, Trust, data integrity, information privacy, scalability and interoperability Automation Domain Constrains. This paper combines the concept of Raspberry Pi Industrial workstation and Industrial Automation using IoT. Here we propose efficient industry automation system that allows user to efficiently control industry appliances/machines over the internet.

SMOaaS: a Scalable Matrix Operation as a Service model in Cloud

Matrix operations are fundamental to a wide range of scientific applications such as Graph Theory, Linear Equation System, Image Processing, Geometric Optics, and Probability Analysis. As the workload in these applications has increased, the sizes of matrices involved have also significantly increased. Parallel execution of matrix operations in existing cluster-based systems performs effectively for relatively small matrices but significantly suffers as matrices become larger due to limited resources. Cloud Computing offers scalable resources to handle this limitation; however, the benefits of having access to almost-infinite scalable resources in the Cloud also come with challenges of ensuring time and resource-efficient matrix operations. To the best of our knowledge, there is no specific Cloud service that optimizes the efficiency of matrix operations on Cloud infrastructure. To address this gap and offer convenient service of matrix operations, the paper proposes a novel scalable service framework called Scalable Matrix Operation as a Service. Our framework uses Dynamic Matrix Partition techniques, based on matrix operation and sizes, to achieve efficient work distribution, and scales based on demand to achieve time and resource-efficient operations. The framework also embraces the basic features of security, fault tolerance, and reliability. Experimental results show that the adopted dynamic partitioning technique ensures faster and better performance when compared to the existing static partitioning technique.

Game-Theoretic Actor–Critic-Based Intrusion Response Scheme (GTAC-IRS) for Wireless SDN-Based IoT Networks

In the era of the Internet of Things (IoT), reinforcement learning (RL)-based techniques are promising candidates to handle the intrusion response through the interaction between the IoT device and its environment. Given a large number of devices in the IoT network, wireless software-defined networking (W-SDN) is widely agreed to be introduced to facilitate network management, such as launching a new intrusion response scheme (IRS) on massive IoT devices. To guarantee the scalability and security of IoT services on the extended devices in the W-SDN-based network, a distributed RL-based IRS is proposed in this article, called game-theoretic actor–critic-based IRS (GTAC-IRS). GTAC-IRS employs a game-theoretic-based response selection matrix, aiming at reducing training time and facilitating the convergence of the response scheme. GTAC-IRS constructs a well-designed state representation of observed environment status, a low-dimension response matrix, and a simplified response selection policy to lower the complexity of the algorithms. Simulation results reveal that benefiting from local environment observation, GTAC-IRS achieves effective intrusion response without sophisticated feature engineering. Instead of the “warm-start” training adopted in conventional RL-based IRS, the low-dimension response matrix in GTAC-IRS can significantly improve the convergence speed. Thus, GTAC-IRS outperforms other popular IRSs in terms of the response time under the circumstance of the node’s behavior changing or malicious nodes ratio changing.

Hybrid electro search with genetic algorithm for task scheduling in cloud computing

Cloud computing is on-demand Internet-based computing, which is a highly scalable service adopted by different working and non-working classes of people around the globe. Task scheduling one of the critical applications used by end-users and cloud service providers. The significant challenging in the task scheduler is to find an optimal resource for the given input task. In this paper, we proposed Hybrid Electro Search with a genetic algorithm (HESGA) to improve the behavior of task scheduling by considering parameters such as makespan, load balancing, utilization of resources, and cost of the multi-cloud. The proposed method combined the advantage of a genetic algorithm and an electro search algorithm. The genetic algorithm provides the best local optimal solutions, whereas the Electro search algorithm provides the best global optima solutions. The proposed algorithm outperforms than existing scheduling algorithms such as Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA), GA, ES, and ACO.

An Overview of Content Poisoning in NDN: Attacks, Countermeasures, and Direction

With a huge demand for replicated content on the Internet, a new networking paradigm called information-centric networking (ICN) has been introduced for efficient content dissemination. In ICN, named content is distributed over the network cache and it is accessed by name instead of a location identifier. These aspects allow users to retrieve content from any of the nodes having replicas, and consequently 1) network resources are more efficiently utilized by avoiding redundant transmission and 2) more scalable services are provided by distributing server loads. However, in-network caching in ICN brings about a new type of security issues, called content poisoning attacks, where fabricated content is located in the network cache and interferes with the normal behavior of the system. In this paper, we look into the problems of content poisoning in ICN and discuss security architectures against them. In particular, we reconsider the state-of-the-art schemes from the perspective of feasibility, and propose a practical security architecture.

To Eliminate the Threat of a Single Point of Failure in the SDN by using the Multiple Controllers

The Software Defined Network (SDN) provides an innovative paradigm for networking, which improve the programmability and flexibility of the network. Due to the separation between the control and data plane, all the control logic transfer to the controller. In SDN, the controller, which provides a global view of the whole network. That is why it acts as the “Network Brain” of the network. Because the controller has the capability to configure or reconfigure the forwarding devices by customizing their policies in a dynamic manner. Thus, the controller provides a centralized logical view of the entire network. Therefore, all manipulation and implementation in the network are control by the single controller in the SDN, which increases the maximum chance of a single point of failure (SPOF) in the network. As a consequence, it collapses the entire network. Therefore, a fault tolerance mechanism is required which reduce single point of failure in the network by using multiple controllers. As a significance, this mechanism also increases the scalability, reliability, and high availability of services in the network. The three different roles of multiple controllers are equal, master and slave exist in the SDN. In the simulation, the Ryu SDN controller and Mininet tool are utilized. During the simulation to analysis, what is happen when a single point of failure (SPOF) occur in the network and how to use the different roles of the multiple controllers (such as equal, master and slave) which reduces the threat of single point of failure in SDN network.

RETRACTED ARTICLE: Analysis of security issues of cloud-based web applications

The Cloud computing is a powerful tool to optimize the cost in terms of hardware, controllable, utility to sharing the data, due to abovementioned features most of the organization switching their applications and services on the cloud. Cloud services offer secure and scalable services, but there is always some security problem when data have transmitted from a central storage server to a different cloud, personal and private data commitment increase risk of data confidentiality, integrity, availability, and authentication before one choose a vendor in the cloud or choose the cloud and move services in the cloud. This paper aims to resolve issues and provide the countermeasures relating to security issues in clouds based web applications. The vulnerability scores, its impact on confidentiality, integrity, availability, access complexity, and risk on assets calculated, and it observed most of the vulnerabilities identified during the scanning are related to security due to mis-configurations of web servers. The results analyzed for the vulnerability scores, vulnerabilities impact on confidentiality, integrity, availability, access complexity, and risk are analyzed description, and risk assessment. The countermeasures for each vulnerability based on the experimental results discussed.