Cloud Consumers Research Articles

Cloud service models, business continuity and disaster recovery plans, and responsibilities

Purpose The purpose of this article is to identify the role of cloud computing services in business continuity and disaster recovery plans and delineate responsibilities for their execution. In recent times, there has been a huge upsurge in the usage of cloud service models such as infrastructure-as-a-service, platform-as-a-service, software-as-a-service and disaster recovery-as-a-service. However, in case of an emergency event or during contract negotiations, a question might arise as to who should be accountable and responsible for the content and execution of recovery plans. The main stakeholders in this scenario are cloud service providers and cloud consumers. Design/methodology/approach After a review of academic articles, standards, guidelines and vendor documentation, a proposal for assigning accountability and responsibility for business continuity and disaster recovery plans is presented, based on the RACI (responsible, accountable, consulted and informed) matrix. In this regard, a critical information infrastructure protection plan, a disaster recovery plan, an information systems contingency plan and a business continuity plan have been elaborated on in the article. Findings RACI matrices are presented for three general cloud service models and for three DRaaS models (managed, assisted and self-service). Accountability and responsibilities depend on the deployed cloud service model and the roles of cloud service providers and cloud consumers. Originality/value The proposed model for accountability and responsibility assignment provides a guideline for the allocation of responsibilities to roles not only during recovery but also during contract negotiations between cloud service providers and cloud consumers. By delving into business continuity and disaster recovery processes and activities, similar yet nuanced RACI matrices should be developed, as presented in this paper. They need to be customised for the specific context.

Cloud Security Using Fine-Grained Efficient Information Flow Tracking

This study provides a comprehensive review and comparative analysis of existing Information Flow Tracking (IFT) tools which underscores the imperative for mitigating data leakage in complex cloud systems. Traditional methods impose significant overhead on Cloud Service Providers (CSPs) and management activities, prompting the exploration of alternatives such as IFT. By augmenting consumer data subsets with security tags and deploying a network of monitors, IFT facilitates the detection and prevention of data leaks among cloud tenants. The research here has focused on preventing misuse, such as the exfiltration and/or extrusion of sensitive data in the cloud as well as the role of anonymization. The CloudMonitor framework was envisioned and developed to study and design mechanisms for transparent and efficient IFT (eIFT). The framework enables the experimentation, analysis, and validation of innovative methods for providing greater control to cloud service consumers (CSCs) over their data. Moreover, eIFT enables enhanced visibility to assess data conveyances by third-party services toward avoiding security risks (e.g., data exfiltration). Our implementation and validation of the framework uses both a centralized and dynamic IFT approach to achieve these goals. We measured the balance between dynamism and granularity of the data being tracked versus efficiency. To establish a security and performance baseline for better defense in depth, this work focuses primarily on unique Dynamic IFT tracking capabilities using e.g., Infrastructure as a Service (IaaS). Consumers and service providers can negotiate specific security enforcement standards using our framework. Thus, this study orchestrates and assesses, using a series of real-world experiments, how distinct monitoring capabilities combine to provide a comparatively higher level of security. Input/output performance was evaluated for execution time and resource utilization using several experiments. The results show that the performance is unaffected by the magnitude of the input/output data that is tracked. In other words, as the volume of data increases, we notice that the execution time grows linearly. However, this increase occurs at a rate that is notably slower than what would be anticipated in a strictly proportional relationship. The system achieves an average CPU and memory consumption overhead profile of 8% and 37% while completing less than one second for all of the validation test runs. The results establish a performance efficiency baseline for a better measure and understanding of the cost of preserving confidentiality, integrity, and availability (CIA) for cloud Consumers and Providers (C&P). Consumers can scrutinize the benefits (i.e., security) and tradeoffs (memory usage, bandwidth, CPU usage, and throughput) and the cost of ensuring CIA can be established, monitored, and controlled. This work provides the primary use-cases, formula for enforcing the rules of data isolation, data tracking policy framework, and the basis for managing confidential data flow and data leak prevention using the CloudMonitor framework.

A Review of Cloud Security Issues and Challenges

The advancement of information technology today depends on cloud computing. Every cloud consumer continues to search for the best services available, particularly in terms of security. The absence of uniform standards in the security architecture of the cloud, especially within the cloud computing community, has become an ongoing problem. Most cloud service providers automatically follow the best security practises and actively protect the operation of their systems. Enterprises are required to independently judge the security of their data, apps, and the cloud’s workload. The complexity of security concerns is increasing as the digital environment develops. Virtualization, multi-tenancy, security controls, rules, and risk profiling are all concepts in cloud security. DoS, service injection, user-to-root attacks, man-in-the-middle attacks, data loss leakage andloss, identity theft, and backdoor channel attacks are a few of the dangers associated with cloud computing. Cloud platforms, data outsourcing, data storage standardisation and security, data backup, and data recovery are all examples of cloud security services. Data storage, computation, untrusted computing, and virtualization security challenges are all a part of the cloud.

Competing on price and guarantee compensation: Heeding cloud consumer's quality perception

As frequent service failures raise user concerns, guarantee compensation has become a competitive instrument for cloud service providers (CSPs) in addition to price. This study proposes a game-theoretical model where two CSPs compete on both price and compensation. We consider two roles of compensation: (1) remedying user losses and (2) helping users to form quality perception. The results indicate that the higher-quality CSP should always guarantee higher compensation but may not always charge a higher price. We further investigate the signaling policies and find that the lower-quality CSP is more likely to signal a higher-than-actual quality.

Analytical hierarchy process model for managing cloud security

PurposeThis paper aims to explore key management actions for implementing security on the cloud, which is a critical issue as many organizations are moving business processes and data on it. The cloud is a flexible, low cost and highly available technology, but it comes with increased complexity in maintaining the cloud consumer’s security. In this research, a model was built to assist strategic decision-makers in choosing from a diverse range of actions that can be taken to manage cloud security.Design/methodology/approachPublished research from 2010 to 2022 was reviewed to identify alternatives to management actions pertaining to cloud security. Analytical hierarchical process (AHP) was applied to rate the most important action(s). For this, the alternatives, along with selection criteria, were summarized through thematic analysis. To gauge the relative importance of the alternatives, a questionnaire was distributed among cloud security practitioners to poll their opinion. AHP was then applied to the aggregated survey responses.FindingsIt was found that the respondents gave the highest importance to aligning information security with business needs. Building a cloud-specific risk management framework was rated second, while the actions: enforce and monitor contractual obligations, and update organizational structure, were rated third and fourth, respectively.Research limitations/implicationsThe research takes a general view without catering to specialized industry-based scenarios.Originality/valueThis paper highlights the role of management actions when implementing cloud security. It presents an AHP-based multi-criteria decision-making model that can be used by strategic decision-makers in selecting the optimum mode of action. Finally, the criteria used in the AHP model highlight how each alternative contributes to cloud security.

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

<span>The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy.</span>

Implementasi Keamanan di Cloud Storage Menggunakan Algoritma Key Public Berdasarkan Block Cipher

Cloud computing is a new era of the modern world. It is an emerging paradigm and has become one of the hottest research to date, because of its ability to reduce the costs associated with computing. The main issues associated with cloud computing are data privacy, security and authenticity. The aim of the researcher is to find out how the model of cloud data storage and data security of cloud computing systems. In this research, researchers propose an efficient method for providing data storage. Security in cloud computing uses a new public key algorithm based on a linear block cipher. This algorithm has been proven by previous studies such as Data encryption, E-commerce, Digital signature. In research, researchers apply the same algorithm, namely cloud computing. In this algorithm several important security services are included such as key generation, encryption and decryption provided in the cloud computing system. The scope of this research is to solve security problems in cloud provisioning and cloud consumers by using new cryptographic methods.

Comparative Study of Top Cloud Providers on basis of Service Availability and Cost

The term "cloud computing" refers to a place where we can store our important data and use pay-as-you-go computing and networking services without a physical environment. Today's cloud computing provides us with robust computing and storage, high availability and security, rapid accessibility and adaption, ensured scalability and interoperability, and cost and time efficiency. Users in a cloud environment assume that there are endless resources accessible, and they only pay for the resources they really utilize. The number of cloud service providers is growing quickly and they are adding new capabilities as technology advances. However, a cloud consumer may find it challenging to identify the best service provider for their needs. Three cloud service providers—Amazon Web Services, Google and Microsoft Azure—are explored in this study, and a comparison of these cloud service providers is provided. On the basis of service availability, price, pricing structure, data security, operating system, Windows support, free trial, and geographies, the cloud service providers were compared.

Workload management and server speed setting for cost‐performance ratio optimization

AbstractThe cost‐performance tradeoff is a fundamental issue in a data center for cloud computing, which is closely related to two key metrics that both cloud consumers and service providers care the most, that is, quality of service and cost of service. While there are different definitions of quality of service, the average response time is a common choice of performance metric. While there are various considerations in cost of service, the average power consumption is a common choice of cost metric. Hence, the cost‐performance tradeoff becomes the power‐performance tradeoff. In this article, we deal with the power‐performance tradeoff at the data center level. We study cost‐performance ratio optimization by using the techniques of workload management and server speed setting. In particular, we make the following tangible contributions. We solve three optimization problems, that is, (1) the workload management problem—to find a workload distribution, such that the cost‐performance ratio is minimized; (2) the server speed setting problem—to find a server speed setting, such that the cost‐performance ratio is minimized; (3) the workload management and server speed setting problem—to find a workload distribution and a server speed setting, such that the cost‐performance ratio is minimized. All the three optimization problems are analytically defined as multivariable optimization problems based on M/M/m queueing systems for multiple heterogeneous multiserver systems, together with two power consumption models, that is, the idle‐speed model and the constant‐speed model. Our approach makes it possible to quantitatively evaluate and optimize the cost‐performance ratio of a data center within a rigorously developed framework. Each multivariable optimization problem is transformed to a nonlinear system of equations. Due to the sophistication of these equations, they are solved algorithmically by a numerical procedure. Furthermore, we provide approximate, accurate, and analytical solutions to the first two problems. Performance data are demonstrated for each problem, and the accuracy of our approximate solutions are also discussed. To the best of the author's knowledge, this is the first paper which analytically and algorithmically minimizes the cost‐performance ratio of a data center with multiple heterogeneous multiserver systems using the techniques of workload management and server speed setting.

Comparison of Different Cloud Providers

Abstract: It is a better alternative for enterprises and ordinary people to use cloud service providers' services (IaaS, PaaS, SaaS, DaaS, and so on) and pay as you go. There are already a huge number of service providers on the market, and finding a good supplier for long-term needs is tough owing to the range of features and services. Amazon, Microsoft, and Google are the top three providers in terms of market share. The goal of this article is to introduce cloud consumers to the most prominent Cloud Service Providers (CSPs). We also compare and contrast these CSPs based on a variety of parameters linked to the services they supply. Before moving their business to the cloud, the analysis described in this article can assist people and companies in making key judgments on the benefits and costs of cloud technology. Keywords: Amazon, AWS, Google Cloud Platform, Microsoft Azure, GCP, CSP’s, Cloud Service Providers.

Agent-based multi-tier SLA negotiation for intercloud

The evolving intercloud enables idle resources to be traded among cloud providers to facilitate utilization optimization and to improve the cost-effectiveness of the service for cloud consumers. However, several challenges are raised for this multi-tier dynamic market, in which cloud providers not only compete for consumer requests but also cooperate with each other. To establish a healthier and more efficient intercloud ecosystem, in this paper a multi-tier agent-based fuzzy constraint-directed negotiation (AFCN) model for a fully distributed negotiation environment without a broker to coordinate the negotiation process is proposed. The novelty of AFCN is the use of a fuzzy membership function to represent imprecise preferences of the agent, which not only reveals the opponent’s behavior preference but can also specify the possibilities prescribing the extent to which the feasible solutions are suitable for the agent’s behavior. Moreover, this information can guide each tier of negotiation to generate a more favorable proposal. Thus, the multi-tier AFCN can improve the negotiation performance and the integrated solution capacity in the intercloud. The experimental results demonstrate that the proposed multi-tier AFCN model outperforms other agent negotiation models and demonstrates the efficiency and scalability of the intercloud in terms of the level of satisfaction, the ratio of successful negotiation, the average revenue of the cloud provider, and the buying price of the unit cloud resource.

AI assisted multi-objective cloud computing model for optimized task scheduling and enhanced QOS

In recent decades, cloud computing has gained popularity due to the extensive collection of autonomous systems with a flexible framework and diversified features. Various communities require a cloud computing paradigm to maximize their revenue due to its commercial reality. Scheduling of resources to the cloud consumers dramatically influences the cost-benefit of the service providers. Several kinds of research have already been made, focusing on task scheduling and resource utilization. Job shop scheduling is one of the strong NP-hard problem for the production of optimal scheduling strategies. Evolutionary algorithms such as genetic algorithm and tabu search have been emerged to perform optimal job scheduling in cloud computing environments, but that are confined to perform a single objective. Hence to meet the multiple objectives in cloud computing platforms, we proposed a novel artificial intelligence-based task scheduling strategy to facilitate minimum makespan, energy efficiency, reduced computational cost, and reliability. The proposed modified sheep flock heredity algorithm (MSFHA) facilitates the optimal task scheduling strategy by selecting the job schedules with the Longest job to the High-speed processor (LJHP), Smallest job to the High-speed processor (SJHP), and high-affinity values.

Implementasi Service Oriented Architecture (SOA) pada Sistem Pencatatan Keuangan untuk UMKM

One of the indications of a UMKM's success is financial statements. UMKM can use SAK EMKM to prepare financial reports. Many UMKM, on the other hand, have yet to incorporate SAK EMKM. UMKM can use information technology to improve its financial records. With the quantity and scope of UMKM, as well as the demand for information technology, flexible systems must be established on a variety of platforms in order to be sustainable. As a result, SOA must be used in the financial recording system for UMKM. As a result of these issues, a SOA-based Financial Recording System was developed. SOA allows the system to be more adaptable and reusable. The system built consists of a service provider and a cloud consumer. Owners of UMKM can use the Android application to keep financial records and generate reports from service providers. According to the results of the Black Box approach, every service offered at the service provider, as well as the cloud consumer's function, has been performing as predicted. And, according to the proprietor of UMKM, the system has been performing admirably.

Critical Retrospection of Security Implication in Cloud Computing and Its Forensic Applications

Using cloud computing, businesses can adopt IT without incurring a significant upfront cost. The Internet has numerous benefits, but model security remains a concern, which affects cloud embracing negatively. The security challenge gets too difficult underneath data center, while additional dimensions such as model design, multitenancy, elasticity, and the layers dependency stack have been added to the problem scope. We present a thorough examination of the cloud security challenge in this article. We looked at the issue from the standpoints of network infrastructure: cloud-provided features, cloud consumers, and cloud service delivery methods. Based on this research, we developed an in-depth characterization of the data protection challenge and recommended security solutions that should address the critical aspects of the issue. Cloud computing is an exploding field of research that relies on distributing computing power instead of using dedicated computers or smart devices. Most of the growth in this sector is attributed to the indispensability of electronic and digital gadgets and the shift from a traditional IT subscription model to a unique cloud model. Cloud computing offered a significant danger and difficulty for information system projects, but it also provided them with several possibilities to improve their data processing. It has also been noted that cloud users and consumers do not yet have the necessary forensic skills to detect illegal activity in the cloud. Although the cloud offers potential technological and economic advantages, consumers have been reluctant to adopt it primarily due to security concerns and the difficulty of conducting an appropriate investigation into the cloud. Some study has been done in this area, and strategies for conducting forensic investigations have been proposed. In this research paper, we begin by analyzing the intrusion detection progress made by other academics, and then we analyze and evaluate our conclusions in order to assess the potential difficulties that cloud forensics face based on these findings.

Cloud Security: Challenges and Solutions

Cloud computing is the delivery of shared computing services—including servers, storage, databases, networking, software, analytics, and intelligence— over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. To simplify, Cloud computing is on-demand delivery of IT resources. Many organizations are stuck in the conundrum of whether to cloudify or not to cloudify, mainly due to concerns related to the security of enterprise sensitive data. Removing this barrier is the pre-requisite to fully unleash the tremendous potential of cloud computing. The revolutionary principle of ‘Shared Resources’ is the cause of concern from Information Security point of view. Confidentiality, Integrity, Availability, Authenticity, and Privacy are essential concerns for both Cloud providers and consumers as well. Security concerns have given rise to an active area of research due to the many security threats that many organizations have faced at present. This seminar report provides a concise study on data security and privacy protection issues associated with cloud computing. Then this report discusses some current solutions and finally describes some measures for top identified threats in data security and privacy protection issues in cloud.

A Review Paper on Secure Virtualization for Cloud Computing

Cloud technology acceptability and development are being endangered by unresolved cybersecurity issues that impact both the cloud operator and the cloud consumer. In this article, researchers show how virtualized may help secure public cloud by ensuring the integrity of the both guest virtual machines and cloud network equipment.. In specifically, we present the Advanced-Cloud-Protection-System (ACPS), a new construction targeted at ensuring improved security for cloud resources. ACPS may be used on a variety of cloud systems to efficiently while being anonymous to virtualization and cloud users, verify the validity of guest and network resources. Security breaches may be responded to nearby by ACPS, as well as notified to a higher security-management-layer. Two existing open source systems, Eucalyptus and Open ECP, have completely implemented a prototype of our ACPS concept. The prototype is put to the test in terms of efficiency and performance. In specifically, (a) the efficacy of our prototype is shown by testing it against known assaults in the nonfiction; (b) the ACPS prototype's performance is evaluated under various kinds of workload. The results indicate that our approach is resistant to assaults and that the upstairs imposed is minimal when associated to the landscapes available.

Research on trust issues in cloud computing

Although cloud computing has some eminent advantages such as low cost, resource elasticity, yet the highly dynamic, distributed and opaque nature of cloud services makes it a meaningful challenge to establish and manage trust between cloud service providers and consumers. Based on this background, this paper first describes the common concepts and classifications of trust issues and trust models, then generalize trust issues in cloud computing based on the research of existing work, next enumerates typical trust evaluation models from several different perspectives in cloud computing. Finally, conclusions including several potential trends are proposed for the future work.

Reducing cloud infrastructure costs through task management

The transition of more and more companies from their own computing infrastructure to the clouds is due to a decrease in the cost of maintaining it, the broadest scalability, and thepresence of a large number of tools for automating activities. Accordingly, cloud providers provide an increasing number of different computing resourcesand tools for working in the clouds. In turn, this gives rise to the problem of the rational choice of the types of cloud services in accordance with the peculiarities of the tasks to be solved. One of the most popular areas of effort for cloud consumers is to reduce rental costs. The main base of this direction is theuse of spot resources. The article proposes a method for reducing the cost of renting computing resources in the cloud by dynamically managing the placement of computational tasks, which takes into account the possible underutilization of planned resources, the forecast of the appearance of spot resources and their cost. For each task, a state vector is generated that takes into account the duration of the task and the required deadline. Accordingly, for a suitable set of computing resources, an availability forecast vectorsareformed at a given time interval, counting from the current moment in time. The technique proposes to calculate at each discrete moment of time the most rational option for placing the task on one of the resources and the delay in starting the task on it. The placementoption and launch delays are determined by minimizing the rental cost function over the time interval using a genetic algorithm. One of the fea-tures of using spot resources is the auction mechanism for their provision by a cloud provider. This means that if there are more pref-erable rental prices from any consumer, then the provider can warn you about the disconnection of the resource and make this dis-connection after the announced time. To minimize the consequences of such a shutdown, the technique involves preliminary prepara-tion of tasks by dividing them into substages with the ability to quickly save the current results in memory and then restartfrom the point of stop. In addition, to increase the likelihood that the task will not be interrupted, a price forecast for the types of resources used is used and a slightly higher price is offered for the auction of the cloud provider, compared to the forecast. Using the example of using the Elastic Cloud Computing (EC2) environment of the cloud provider AWS, the effectiveness of the proposed method is shown.

Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser

Cloud computing has been a dominant computing paradigm for many years. It provides applications with computing, storage, and networking capabilities. Furthermore, it enhances the scalability and quality of service (QoS) of applications and offers the better utilization of resources. Recently, these advantages of cloud computing have deteriorated in quality. Cloud services have been affected in terms of latency and QoS due to the high streams of data produced by many Internet of Things (IoT) devices, smart machines, and other computing devices joining the network, which in turn affects network capabilities. Content delivery networks (CDNs) previously provided a partial solution for content retrieval, availability, and resource download time. CDNs rely on the geographic distribution of cloud servers to provide better content reachability. CDNs are perceived as a network layer near cloud data centers. Recently, CDNs began to perceive the same degradations of QoS due to the same factors. Fog computing fills the gap between cloud services and consumers by bringing cloud capabilities close to end devices. Fog computing is perceived as another network layer near end devices. The adoption of the CDN model in fog computing is a promising approach to providing better QoS and latency for cloud services. Therefore, a fog-based CDN framework capable of reducing the load time of web services was proposed in this paper. To evaluate our proposed framework and provide a complete set of tools for its use, a fog-based browser was developed. We showed that our proposed fog-based CDN framework improved the load time of web pages compared to the results attained through the use of the traditional CDN. Different experiments were conducted with a simple network topology against six websites with different content sizes along with a different number of fog nodes at different network distances. The results of these experiments show that with a fog-based CDN framework offloading autonomy, latency can be reduced by 85% and enhance the user experience of websites.

Dynamic Resource Allocation Method for Load Balance Scheduling Over Cloud Data Center Networks

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.