A Risk Assessment Framework for Cloud Computing

Djemame, Karim; Armstrong, Django; Guitart, Jordi; Macías, Mario

doi:10.1109/tcc.2014.2344653

Cited by 63 publications

(42 citation statements)

References 23 publications

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“…A sample of four such academic papers are in Brender and Markov (), Fan, Chiang, and Kao (), Fang and Chen (), Zhang, Wuwong, Li, and Zhang (). (c) There are those from the IEEE Xplore databases when the keyword CCRM is given, such as Buyya, Yeo, and Venugopal (), Djemame, Armstrong, Guitart, and Macias (), El Kefel and Mohamed (), Medhioub, Hamdi, and Kim (), Tanimoto, Hiramoto, Iwashita, Sato, and Kanai (). (d) Last but not least, the key term of “with Digital Simulation” is added to the latter keyword.…”

Section: Literature Survey Discussion and Further Reviewsmentioning

confidence: 99%

CLOUD computing risk management for server‐farm repair rates, consumer load cycle, server‐farm repair crew count, and additional servers

Sahinoǧlu

2018

WIREs Computational Stats

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A discrete event simulator, CLOURAM: CLOUD Risk Assessor and Manager, algorithmically estimates the risk indices in modern-day CLOUD computing scenarios with tangible risk management targets that are favorable to the intractably tedious, theoretical Markov solutions or hand calculations overly limited in scope. The goal is to improve the operational quality of CLOUD by optimizing the number of servers for capacity addition and optimizing the final repair crew count. We too optimize the server unit repair rates, and the consumer load cycle by curbing the demand using Linear Programming (LP)-based optimization with the proper objective functions and constraints. Small and large CLOUD systems are simulated with cost and benefit comparisons. The 2-state (UP and DN) or 3-State (UP, DN, and DER) units statistically fail and recover with Negative Exponential or Weibull densities. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Reliability, Survivability, and Quality Control Algorithms and Computational Methods > Networks and Security Statistical Models > Simulation Models Algorithms and Computational Methods > Linear Programming 1 | INTRODUCTIONThe fundamental idea of CLOUD computing is an internet-based, pragmatic, business-savvy data processing solution ( Figure A1). The model's central idea for on-demand access to a shared pool of resources is to utilize these just-in-time resources instead of depending on local servers or devices to run applications or provide data access. These networks cannot be effectively created without reliable and secure software and infrastructure to actively generate risk-free applications. CLOUD computing, a paradigm of a business model using services provided through the Internet of Things (IOT) has progressed as an alternative to the traditional in-house IT computing services A quantitative assessment of the Quality of Service (QoS) in such enterprises is very much needed. CLOUD computing's QoS can be difficult to measure, not only qualitatively but, most importantly, quantitatively, that is, index-based. However, as users turn to CLOUD computing services for their commercial operations, there is a growing concern from security, privacy, and reliability perspectives. Algorithmic, discrete event-simulated, cost-benefit analyses are reviewed to estimate the reliability indices of small or large service-based CLOUD systems to mimic real-life scenarios (Sahinoglu & Cueva-Parra, 2011).The federal government has approved use of commercial CLOUD products (Leavitt, 2009). Operating in a CLOUD environment, there is an expected level of service to be provided by the environment. This is called the service capacity. This could be Virtual Machine (VM), bandwidth, computing speed, or storage in GB or TB (Terabyte = 2 40 bytes), which the

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Section: Literature Survey Discussion and Further Reviewsmentioning

confidence: 99%

CLOUD computing risk management for server‐farm repair rates, consumer load cycle, server‐farm repair crew count, and additional servers

Sahinoǧlu

2018

WIREs Computational Stats

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“…To quantify this risk which is due to data leakage attack across cloud tenants, we adopt the definition of risk by ISO 27005 as: "The potential that a given threat will exploit vulnerability of an asset or group of assets and thereby cause harm to the organization". The quantitative risk is the product of the likelihood of the attack and its consequences or impact [15]. The likelihood of an attack depends on the vulnerabilities of the system and the attacker threat.…”

Section: Cloud Vulnerability and Riskmentioning

confidence: 99%

Risk-Aware Management of Virtual Resources in Access Controlled Service-Oriented Cloud Datacenters

Almutairi

Sarfraz

Ghafoor

2018

IEEE Trans. Cloud Comput.

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For economic benefits and efficient management of resources, organizations are increasingly moving towards the paradigm of "cloud computing" by which they are allowed on-demand delivery of hardware, software and data as services. However, there are many security challenges which are particularly exacerbated by the multitenancy and virtualization features of cloud computing that allow sharing of resources among potentially untrusted tenants in access controlled cloud datacenters. This can result in increased risk of data leakage. To address this risk vulnerability, we propose an efficient risk-aware virtual resource assignment mechanism for clouds multitenant environment. In particular, we introduce the notion of sensitivity in datacenters and the objective is to minimize the risk of data leakage. In addition, the risk should not exceed in high sensitivity datacenters in comparison to low sensitivity datacenters. We present three assignment heuristics and compare their relative performance.

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“…On the one hand, this system should be responsive to the big volume of the data and, on the other hand, it should have low cost. The issue of lowering the energy costs has been investigated in different ways in the system [24]. We therefore propose a method based on the cloud computing that its cost is low as the services are provided over time.…”

Section: Workflow Management Systemmentioning

confidence: 99%

“…We initially estimate the system failure rate based on the Poisson distribution like [24]. Then, we must balance the systems loads with the migration of workflows in the proposed framework.…”

Section: Workflow Management Model Based On Cloud Computingmentioning

confidence: 99%