Cloud Administrators Research Articles

A trust management framework for clouds

In today’s cloud computing platforms, more and more users are now working or collaborating in the multi-cloud environment, in which collaborators, clouds, computing nodes may belong to different institutions or organizations. Those different organizations might have their policies. Security is still a big concern in cloud computing. To help cloud vendors and customers to detect and prevent from being affected by potential attacks, we propose a trust management framework. We consider link/flow’s level trust, node’s level trust, and task/mission’s level trust.We introduced a new security metric trustability (trust–reliability) and a new algorithm to calculate it. Trustability measures how much a system can be trusted under a specific attack vector. Trustability can be used to explore the design space of resource configuration in order be able to choose the right trade-off between trustability and cost of redundancy. We show that our trust management framework can guide the administrators and customers to make decisions. For example, based on the real-time trust information, cloud administrators can migrate tasks from suspect nodes to trustworthy nodes, dynamically allocate a resource, and manage the trade-off between the degree of redundancy and the cost of the resource.

A Study of Resource Allocation Techniques In Cloud Computing

The process of allocation and reallocation of resources is the key to accommodating unpredictable demands and improving the return on investment from the infrastructure supporting the cloud. A cloud environment consists of multiple customers requesting for resources in a dynamic environment with possible constraints. In cloud computing allocation of resources for every client with high gain and the profit is the major task for the cloud administrator. Cloud administrator has the problem for allocating the same resources with many users. So while allocating the resources prioritisation of tasks is very important. The literature concerns seven scheduling algorithms and six allocation techniques as well as the parameter measures for effectiveness of scheduling. Methodical analysis of this research helps the researchers to find the importance of a scheduling algorithm for particular allocation technique for increasing profit and return on investment in cloud.

Risk-based availability modelling and reputation management on fault tolerant cloud computing systems

This paper exhibits a risk-based philosophy in cloud computing to estimate ideal quality and maintenance which maximise the high availability of cloud administration infrastructure. The procedure is confined into two stages: 1) accessibility displaying the cloud; 2) hazard based assessment. The projected procedure is practical to the healthcare scheme using Markov chain process. The proposed system predicts, mitigate and eliminate the risk-based inspection and Markov chain process to efficiently identify the repair and failures of each virtual machine and reduce the energy consumption rate. The result of our proposed method will definitely bound optimum solutions and specifies the conquest of an efficient risk-based availability modelling on minimal cost and energy consumption. Finally, the proposed model is verified by the Nagios and OPTIMIS toolkit. The result obtained in our proposed model definitely bounds optimum solutions and triumph of an efficient risk analysis of failures on minimal cost and energy consumption.

Scheming an Proficient Auto Scaling Technique for Minimizing Response Time in Load Balancing on Amazon AWS Cloud

Cloud computing is one of the developing innovation in the field of data improvement. Because of its huge application, for example, virtualization and dynamic asset portion, the cloud computing ends up being the most encouraging innovation. To give the solid support of the customers with Quality of Service (QOS), the load balancing component with auto scaling methods would be vital in cloud computing conditions. With the end goal to keep the framework from overloading and slamming, a productive calculation must be given by the application and approaching client activity in the cloud computing condition. The proposed Auto Load Distribution (ALD) calculation have been assessed and contrasted and some of existing load balancing algorithm and its effectiveness is vastly improved than the past calculations. To accomplish the previously mentioned issues, we tried our undertakings in Amazon EC2 stage, one of open cloud administrations which give correct qualities. Results have been investigated and limiting the reaction time greater by utilization of auto scaling system through our proposed ALD algorithm.

A reversible and secure patient information hiding system for IoT driven e-health

Internet of things (IoT) coupled with mobile cloud computing has made a paradigm shift in the service sector. IoT-assisted mobile cloud based e-healthcare services are making giant strides and are likely to change the conventional ways of healthcare service delivery. Though numerous approaches for preventing unauthorized access to information exchanged between a mobile phone and cloud platform do exist, but there is no security mechanism to prevent unauthorized access by the cloud administrators. With an aim to ensure security of client data such as Electronic Patient Records (EPR), we propose a novel high-capacity and reversible data hiding approach for securely embedding EPR within the medical images using Optimal Pixel Repetition (OPR). OPR converts every pixel of the input image to a 2 × 2 block to facilitate reversibility by ensuring all the pixels in a 2 × 2 block to have different values. Since a 2 × 2 block is comprised of 4-pixel elements, which could be arranged in sixteen possible ways; we generate a lookup table corresponding to sixteen possible positions of pixels. EPR hiding in each block is achieved by permuting the pixels of a block according to the four-bit word of secret data, resulting in a histogram invariant stego image. The histogram invariance improves the robustness of the proposed scheme to statistical attacks. A stego image is said to hide embedded data securely, when it provides better imperceptivity for an appreciably high payload. Thus, while using information embedding approach for securing client data on a mobile-cloud platform, high imperceptivity is a desirable feature. Experimental results show that average PSNR obtained is 42 dB for payload 1.25 bpp by our scheme, showing its effectiveness for preventing unauthorized access to client’s sensitive data.

Security Issues in Cloud Computing and Existing Solutions- a Survey

Cloud computing is turning into an outstanding fluff word these days. It is an appealing innovation for long-haul benefits through cost diminishment and change in business results. It additionally tackles numerous issues of stereotyped processing, including taking care of more volume of burdens, introducing programming updates, and, utilizing overabundance figuring cycles. Regardless, the contemporary advancement has new troubles, for instance, data security, information inborn, and trans-code data storing. Cloud computing is consumed and compared to the modern transformation. Cloud administrations are conveyed from server farms, for example, Amazon, Google, Microsoft et cetera, all through the world. The quick blast in the territory of "Cloud computing" moreover expands outrageous wellbeing concerns. This paper review cloud structure and the monstrous evaluation of Cloud computing with the essential consideration on holes and security issues. We perceive the best security dangers and their present arrangements.

Implementation of key-cryptosystem for efficient data sharing in cloud environment

The distributed storage based on data recovery administration is a promising innovative way that mounts market actively sooner rather than later. However, in spite of the various studies which have been done in terms of secure information recovery over encoded information in cloud administrations, the majority of these studies concentrated on the strict security given to information in an outside space. Those methodologies oblige dynamite expenses to unify on cloud administration supplier that can be an important hindrance to accomplish productive information recovery in distributed storage. In this paper, a proficient information recovery plan utilizing property based encryption was proposed. The proposed plan is most appropriate for distributed storage frameworks with the gigantic measure of infor-mation. Affluent expressiveness as access control and quick pursuits with straightforward examinations of looking elements were given. Additionally, the plan ensures information security and client protection among information recovery process.

TNGuard: Securing IoT Oriented Tenant Networks Based on SDN

In the paradigm of infrastructure-as-a-service cloud computing involving an Internet of Things network, customers outsource their infrastructure to the cloud. An outsourced infrastructure is a virtual infrastructure that mimics the physical infrastructure of the precloud era; it is therefore referred to as a tenant network (TN) in this paper. This practice draws upon the notion of TN abstraction , which specifies how TNs should be managed. However, current virtual software-defined network (SDN) technology uses an SDN hypervisor to attain TNs, where the cloud administrator is given much-more-than-necessary privileges; thus, not only could violation of the security principle of least privilege occur, but the threat of a malicious or innocent-but-compromised administrator may be present. Motivated by this need, we propose the specification of TN abstraction, including its functions and security requirements. Then, we present a platform-independent concretization of this abstraction called TNGuard , which is an SDN-based architecture that protects the TNs while removing unnecessary privileges from the cloud administrator. In order to show that TNGuard concretizes the TN abstraction, we present an instantiation of TNGuard on the Xen virtualization platform with the Ryu controller. Experimental results show that the resulting system is practical, incurring a small performance overhead.

Enhanced Adaptive Learning Mechanism for Cloud Selection

Estimation of cloud services in a distributed computing environment is taking more interests. There is a wealth of developing cloud benefit assets that makes it difficult for the user to select the best administration related to own applications in an evolving multiple cloud environment, particularly for online processing applications. To make clients to choose their interested cloud adequately, we need a model which holds the cloud profits, and hence dynamic cloud benefit determination procedure named Dynamic Cloud Selection (DCS) is adapted. In this procedure of selected services, every cloud benefit business deals with some group of cloud administrations, and executes the DCS method. This paper studies the cloud selection and proposed a way to improve the cloud selection based on related measures. The measures are reliability, response time, throughput, availability, utilization, resilience, scalability, and elasticity. The system is contrived to enhance the cloud benefit choice powerfully and to restore the best administration result to the client. These measures are used to form best selection strategy. User memory requirement is also considered to improve the preferred task. Experimental results proved that using this new strategy, best cloud selection is made efficiently.

CoMe4ACloud: An end-to-end framework for autonomic Cloud systems

Autonomic Computing has largely contributed to the development of self-manageable Cloud services. It notably allows freeing Cloud administrators of the burden of manually managing varying-demand services, while still enforcing Service-Level Agreements (SLAs). All Cloud artifacts, regardless of the layer carrying them, share many common characteristics. Thus, it should be possible to specify, (re)configure and monitor any XaaS (Anything-as-a-Service) layer in an homogeneous way. To this end, the CoMe4ACloud approach proposes a generic model-based architecture for autonomic management of Cloud systems. We derive a generic unique Autonomic Manager (AM) capable of managing any Cloud service, regardless of the layer. This AM is based on a constraint solver which aims at finding the optimal configuration for the modeled XaaS, i.e. the best balance between costs and revenues while meeting the constraints established by the SLA. We evaluate our approach in two different ways. Firstly, we analyze qualitatively the impact of the AM behavior on the system configuration when a given series of events occurs. We show that the AM takes decisions in less than 10 s for several hundred nodes simulating virtual/physical machines. Secondly, we demonstrate the feasibility of the integration with real Cloud systems, such as Openstack, while still remaining generic. Finally, we discuss our approach according to the current state-of-the-art.

An Early Risk Detection and Management System for the Cloud with Log Parser

In a software-defined data center (SDDC), detecting potential risks from structured or unstructured data in real-time is challenging and proves to be of vital significance to deliver zero downtime services. It is essential for the cloud administrators to be aware of any operation or sequence of operations that could cause critical failures resulting in the loss of Business/Mission Critical Systems (BCS/MCS). This demands for a solution such as an Early Risk Detection and Management System (ERDMS). ERDMS provides insights on the operations that can put the system in peril, and recommends the suitable steps to reduce or eliminate the risks involved.In this work, we present our implementation of an Early Risk Detection and Management System (ERDMS) for the cloud using data analytics, association rule learning and machine learning techniques. The ERDMS continually monitors various system parameters by processing the sequence of operations performed on the system to detect potential risk(s) and recommend the probable solution(s). Initially, it parses the log bundles to learn rules, known as “association rules”, for risk detection using apriori algorithm. Each of these association rules consists of premise − sequence of operations and inference − potential risk(s). While constantly monitoring a system, if ERDMS detects a pattern it has learnt, it classifies the pattern into a set of potential risk(s) using decision tree algorithm. It computes a probability for each potential risk to gauge the impact; it also generates a summary of its learning. Once the potential risk(s) is detected, it searches the relevant sites to recommend a set of probable solution(s). Furthermore, it offers “auto-resolution”, where the recommended steps are automatically executed. Consequently, upon appropriate action, the system might not encounter the issue and will continue to work seamlessly.

Improving scheduling efficiency by probabilistic execution time model in cloud environments

Recently, cloud computing has become a promising paradigm for various kinds of large-scale applications. Due to the unpredictable characteristics of resource availability and workload intensity, execution latency still drastically impairs the performances of cloud applications. In this paper, we model the execution latency by a probabilistic distribution and propose a general task execution model which can be used in most of scenarios. By using the proposed execution time model, cloud administrators can easily refine their resource management or implement some fine-grained task scheduling policies for cloud applications in various cases. Massive experiments are conducted in a real-world cloud platform, and the results indicate the proposed model can be used in many existing scheduling policies for improving the efficiency of task execution.

Improving scheduling efficiency by probabilistic execution time model in cloud environments

Recently, cloud computing has become a promising paradigm for various kinds of large-scale applications. Due to the unpredictable characteristics of resource availability and workload intensity, execution latency still drastically impairs the performances of cloud applications. In this paper, we model the execution latency by a probabilistic distribution and propose a general task execution model which can be used in most of scenarios. By using the proposed execution time model, cloud administrators can easily refine their resource management or implement some fine-grained task scheduling policies for cloud applications in various cases. Massive experiments are conducted in a real-world cloud platform, and the results indicate the proposed model can be used in many existing scheduling policies for improving the efficiency of task execution.

An auditing language for preventing correlated failures in the cloud

Today's cloud services extensively rely on replication techniques to ensure availability and reliability. In complex datacenter network architectures, however, seemingly independent replica servers may inadvertently share deep dependencies (e.g., aggregation switches). Such unexpected common dependencies may potentially result in correlated failures across the entire replication deployments, invalidating the efforts. Although existing cloud management and diagnosis tools have been able to offer post-failure forensics, they, nevertheless, typically lead to quite prolonged failure recovery time in the cloud-scale systems. In this paper, we propose a novel language framework, named RepAudit, that manages to prevent correlated failure risks before service outages occur, by allowing cloud administrators to proactively audit the replication deployments of interest. In particular, RepAudit consists of three new components: 1) a declarative domain-specific language, RAL, for cloud administrators to write auditing programs expressing diverse auditing tasks; 2) a high-performance RAL auditing engine that generates the auditing results by accurately and efficiently analyzing the underlying structures of the target replication deployments; and 3) an RAL-code generator that can automatically produce complex RAL programs based on easily written specifications. Our evaluation result shows that RepAudit uses 80x less lines of code than state-of-the-art efforts in expressing the auditing task of determining the top-20 critical correlated-failure root causes. To the best of our knowledge, RepAudit is the first effort capable of simultaneously offering expressive, accurate and efficient correlated failure auditing to the cloud-scale replication systems.

Ensuring Security to Data on Cloud using Cryptography

As the information created by the ventures that should be put away and used (e.g. messages, individual wellbeing records, photograph collections, charge reports, budgetary exchanges and so on) is quickly expanding information proprietors are inspired to outsourced their nearby complex information administration frameworks into the cloud for its extraordinary adaptability and monetary reserve funds. Distributed storage permits shoppers and organizations to utilize the application without establishment and access their own records at any PC with web access. In distributed storage, the information will be put away given by cloud administration suppliers (CSPs). Cloud administration suppliers must have a reasonable approach to ensure their customers information. In any case, in information security assurance and information recovery control is most testing exploration work in distributed computing. This paper basically concentrates on center secured distributed storage administrations i.e by encrypting the information it can store the data in securely.

Mathematical Ontology for Infectious Virtual Machines in IaaS Cloud Environment

Background/Objectives: Cloud virtualization is a creative and one of the altogether accomplished improvements in the current figuring environment. It provides a virtualized environment based on Service Level Agreement (SLA) to cloud. Methods/Statistical Analysis: Infrastructures of Physical machines are rapidly replacing by Virtual machines (VMs) for their abilities to emulate hardware environments, share hardware resources, and utilization of a variety of operating systems. Accordingly, giving security to the cloud virtual machines and clients’ information is one of the essential difficulties to data frameworks. This manuscript describes about the mathematical ontology based upon the attacked VMs and infectious VMs which predicts the trustworthiness of the IaaS virtual platform. Findings: The proposed work will diminish the dangers to the VMs in the cloud surrounding independent of the client’s security and applications approach. Application/Improvements: It will fundamentally guarantee the level of the security of VMs in a cloud situation which assists the cloud administration suppliers to take the fast choices and about the up-degree of the counter assault estimations.

A framework for cloud forensics evidence collection and analysis using security information and event management

A primary feature of cloud computing is the provision of a variety of transparent services with efficient resource utilization. However, there are concerns with cloud computing in terms of the user's data privacy and security, especially in evidence collection for forensics analysis, because the tangible resources and hardware are out of reach for users who own the data. This paper presents a framework using security information and event management SIEM, to address the issue of efficient evidence collection for crime investigation, such as that for cloud forensics, with respect to the cloud service provider. Indeed, evidence could be shared with cloud users when required. SIEM can be considered as a major player in terms of evidence collection in a virtualized environment. The proposed mechanism using SIEM focuses on passive attacks and provides a solution from the cloud administrator or service provider's point of view. The proposed framework can help in performing detailed cloud forensics in terms of efficient evidence building for crime investigations. Copyright © 2016 John Wiley & Sons, Ltd.

A SECURE AUTHENTICATION SCHEME FOR CLOUD COMPUTING BASED ON UNIQUE IDENTITY BASED APPROACH

In critical edge social orders, the quantity of users has significantly ascended as of late. In this paper, a proficient validation scheme for appropriated cloud computing services is projected. The future scheme gives safety measures and accommodation to user to get to numerous mobile cloud computing services beginning different administration suppliers . The security quality of the proposed scheme depends on Diffie hellmann. In expansion, the scheme underpins common validation, key trade, user ambiguity, and user untraceability. From framework execution perspective, confirm tables are not necessary for the trusted Authentication server and cloud computing administration suppliers while receiving the projected method. In result, this system lessens the use of memory spaces on these comparing administration suppliers. In one user verification assembly, just the focused on cloud administration supplier needs to correspond with the supervision requestor. The trusted Authentication server as the protected key merchant for appropriated cloud administration suppliers. In the future method, the trusted Authentication server is not included in individual user confirmation development. With this outline, our method diminishes verification preparing time obligatory by correspondence and calculation connecting cloud administration suppliers and conventional trusted outsider administration.

A Protocol for Preventing Insider Attacks in Untrusted Infrastructure-as-a-Service Clouds

Recent technical advances in utility computing have allowed small and medium sized businesses to move their applications to the cloud, to benefit from features such as auto-scaling and pay-as-you-go facilities. Before clouds are widely adopted, there is a need to address privacy concerns of customer data outsourced to these platforms. In this paper, we present a practical approach for protecting the confidentiality and integrity of client data and computation from insider attacks such as cloud clients as well as from the Infrastructure-as-a-Service (IaaS) based cloud system administrator himself. We demonstrate a scenario of how the origin integrity and authenticity of health-care multimedia content processed on the cloud can be verified using digital watermarking in an isolated environment without revealing the watermark details to the cloud administrator. Finally to verify that our protocol does not compromise confidentiality and integrity of the client data and computation or degrade performance, we have tested a prototype system using two different approaches. Formal verification using ProVerif tool shows that cryptographic operations and protocol communication cannot be compromised using a realistic attacker model. Performance analysis of our implementation demonstrates that it adds negligible overhead.

Data Confidentiality and Security in Cloud Computing Using KIST Algorithm

Now days quickly expanded utilization of cloud computing in the numerous association and IT commercial industries and furnishes latest software solution with cost effective. So the cloud computing give us numerous of advantages with minimum cost and of information openness through Internet. The guaranteeing security risk of the cloud computing is the part in the distributed computing environment, the advancing, the evolving quintessence is cloud computing, that is gainful in cost effective parts, such as, ability inflexible computing, down the time period to advertise and in adequate computing power. By utilizing the complete capacity of cloud computing, information are transmitted, prepared and put away on the outside cloud administration suppliers. The truth of the matter is that, the proprietor of the information is feeling to a great degree unconfident to find their information external to their own particular control. Security and Secrecy of information put away in the cloud are key setbacks in the range of of Cloud computing. Security and Secrecy are the key problem for cloud storage. This paper proposes a KIST encryption calculation to focus on the security and Secrecy problem in cloud storage furthermore packed figure content information to ensure the information put away in the cloud. Keywords-Cloud storage, Security, Confidentiality, Cryptography, KIST Algorithm, Encryption, Decryption, Fraud Detection.