Heuristic Optimization, Job Scheduling, Job Allocation, Load Balancing, Multi-Tier Cloud Computing
Public Cloud , Data Protection , storage , Cloud Security , Security Model , Data storage , Data retrieval , retrieval
Cloud Security, Virtualization, Virtual Machine Image, Security Threats, Threat modeling First Section
Sanjay Sareen and Meenu Sareen, Guru Nanak Dev University, india
The growing need of sharing health information of patients between healthcare agencies, doctors, and other authorized persons in order to provide better care of patients has resulted in the cloud-based healthcare system. However, the sharing of health information on the cloud raises major security and privacy issues that need to address. This article introduces a framework that protects health information from unauthorized access and lets the patient as data owner decide who the authorized persons are, i.e., who the patient discloses her health information to. In this article, a framework is proposed that presents a new methodology using a combination of different techniques such as information granulation, information pseudonymization, and secret sharing scheme, allowing privacy-preserving primary and secondary use of the health records. In this model, the privacy of patient’s identity is protected so that users and providers of healthcare services do not need to trust the cloud service provider with privacy related issues. We designed and implemented different privacy preserving algorithms on the Amazon EC2 to evaluate the security and performance analysis of our proposed system. The security analysis showed that the framework is secure and protected against common intruder scenarios.
Pseudonymization; secret sharing scheme; cloud computing; secure data sharing; healthcare systems
Security Considerations For Edge computing
John M. Acken1 and Naresh K. Sehgal2, 1Portland State University, Portland, OR and 2Intel Corp, Santa Clara, CA
Present state of edge computing is an environment of different computing capabilities connecting via awide variety of communication paths. This situation creates both great operational capabilityopportunities and unimaginable security problems. This paper emphasizes that the traditionalapproaches to security of identifying a security threat and developing the technology and policies todefend against that threat are no longer adequate. The wide variety of security levels, computationalcapabilities, and communication channels requires a learning, responsive, varied, and individualizedapproach to information security. We describe a classification of the nature of transactions with respectto security based upon relationships, history, trust status, requested actions and resulting responsechoices. We propose that each element in the edge computing world utilizes a localized ability toestablish an adaptive learning trust model with each entity that communicates with the element
Edge Computing, Security, Adaptive learning, Trust model, Threats, Cloud Computing, InformationSecurity
A New Prediction Framework For Improving the Energy Efficiency in Cloud
Maryam Amiri and Leyli Mohammad-Khanli, University of Tabriz, Iran
Cloud computing relies on sharing pool of computing resources. It delivers services with availability and scalability obligations to users. On the other hand, green computing and energy efficiency are two main challenges of cloud. Therefore, resource should be allocated in a way that energy consumption is reduced and Quality of Service (QoS) dropping is avoided. Due to the point that storage is one of the biggest energy consumers, this paper focuses on the storage system in cloud. For improving energy efficiency of storage, the next access number of data blocks of disks is predicted. Based on predicted results, data blocks can be transmitted to the most appropriate disks and energy consumption is reduced. The goal of this paper is to represent a new method to predict the next access number of data blocks of disks. The proposed framework is composed of four components: fuzzy classifier, Markov chain, base prediction methods and neural network. Indeed, the main predictor component is neural network. After prediction of next state of data block, data can be distributed on the disks in a way that the number of disks is minimized due to energy consumption reduction and avoidance of Service Level Agreements (SLA) violation. The results of experiments show the high accuracy of the proposed method in comparison with the similar methods.
Cloud computing, Prediction framework, Energy efficiency, Neural Network, Fuzzy Classifier, Access number
Trust Modelling for Security of IoT Devices
Naresh K. Sehgal1, Shiv Shankar2 and John M. Acken3, 1Data Centre Group, Intel Corp, Santa Clara, CA, 2Chief Data Scientist, Maphalli, Bangalore, India and 3Portland State University, Portland, OR
IoT (Internet of Things), represents many kinds of devices in the field, connected to data-centers via various networks, submitting data, and allow themselves to be controlled. Connected cameras, TV, media players, access control systems, and wireless sensors are becoming pervasive. Their applications include Retail Solutions, Home, Transportation and Automotive, Industrial and Energy etc. This growth also represents security threat, as several hacker attacks been launched using these devices as agents. We explore the current environment and propose a quantitative and qualitative trust model, using a multi-dimensional exploration space, based on the hardware and software stack. This can be extended to any combination of IoT devices, and dynamically updated as the type of applications, deployment environment or any ingredients change.
Edge Computing, Security, Adaptive learning, Trust model, Threats, Cloud Computing, Information Security
QOS-Driven Job Scheduling: Multi-Tier Dependency Considerations
Husam Suleiman and Otman Basir, Department of Electrical and Computer Engineering, University of Waterloo, Canada
For a cloud service provider, delivering optimal system performance while fulfilling Quality of Service (QoS) obligations is critical for maintaining a viably profitable business. This goal is often hard to attain given the irregular nature of cloud computing jobs. These jobs expect high QoS on an on-demand fashion, that is on random arrival. To optimize the response to such client demands, cloud service providers organize the cloud computing environment as a multi-tier architecture. Each tier executes its designated tasks and passes the job to the next tier; in a fashion similar, but not identical, to the traditional job-shop environments. An optimization process must take place to schedule the appropriate tasks of the job on the resources of the tier, so as to meet the QoS expectations of the job. Existing approaches employ scheduling strategies that consider the performance optimization at the individual resource level and produce optimal single-tier driven schedules. Due to the sequential nature of the multi-tier environment, the impact of such schedules on the performance of other resources and tiers tend to be ignored, resulting in a less than optimal performance when measured at the multi-tier level.
In this paper, we propose a multi-tier-oriented job scheduling and allocation technique. The scheduling and allocation process is formulated as a problem of assigning jobs to the resource queues of the cloud computing environment, where each resource of the environment employs a queue to hold the jobs assigned to it. The scheduling problem is NP-hard, as such a biologically inspired genetic algorithm is proposed. The computing resources across all tiers of the environment are virtualized in one resource by means of a single queue virtualization. A chromosome that mimics the sequencing and allocation of the tasks in the proposed
virtual queue is proposed. System performance is optimized at this chromosome level. Chromosome manipulation rules are enforced to ensure task dependencies are met. The paper reports experimental results to demonstrate the performance of the proposed technique under various conditions and in comparison with
other commonly used techniques.
Cloud Computing, Task Scheduling and Allocation, QoS Optimization, Load Balancing, Genetic Algorithms
Virtual Enterprise Architecture Supply Chain (VEASC) Model On Cloud Computing: A Simulation-Based Study Through OPNET Modelling
Tlamelo Phetlhu, Department of Commerce and Law, University of Zululand, KwaZulu Natal, South Africa
The virtual enterprise architecture supply chain (VEASC) model has been studied in this research employing OPNET modelling and simulations. VEASC requires a synchronous framework of integrated applications and databases, coordination, collaborations, and communications for ensuring high accuracy and responsiveness in a supply chain (SC). The traditional models of electronic data interchange (EDI) and out-of-application methods for messaging and collaborations are not suitable to achieve the full benefits of VEASC because multiple human interventions may be required. In this research, a cloud-based SC application and its distributed databases contributed by multiple supplier and buyer organisations are modelled and simulated on OPNET. The application modelled on the cloud is based on a commercial software called INTEND. The simulation results revealed continuous flow of all the phases of the SC application because the reports reflected continuous interactions between the agents involved and the cloud distributed databases. This model is a good enabler of the VEASC model.
Supply chain, enterprise architecture, integration, collaboration, cloud computing, OPNET, simulations
SECURITY ISSUES IN CLOUD-BASED BUSINESSES
Mohamad Ibrahim AL Ladan, Rafik Hariri University, LEBANON
Cloud-based Business is a Business running and relying on Cloud computing IT paradigm. Cloud computing is an emerging technology paradigm that transfers current technological and computing concepts into utility-like solutions similar to electricity and communication systems. It provides the full scalability, reliability, computing resources configurability and outsourcing, resource sharing, external data warehousing, and high performance and relatively low cost feasible solutions and services as compared to dedicated infrastructures. Cloud-based Businesses store, access, use, and manage their data and software applications over the internet on a set of servers in the cloud without the need to have them stored/installed locally on their local devices. The cloud technology is used daily by many businesses/people around the world from using web based email services to executing heavy complex business transactions. Like any other emerging technology, Cloud computing comes with a baggage of some pros and cons. It is very useful in business development as it brings amazing results in a timely manner; however, it comes with increasing security and privacy concerns and issues. In this paper we will investigate, analyse, classify, and discuss the new security concerns and issues introduced by cloud computing. In addition, we present some security requirements that address and may alleviate these concerns and issues.
Cloud-based Business security issues and concerns; Cloud computing security issues and concerns. Cloud computing security requirements
A MapReduce based Algorithm for Data Migration in a Private Cloud Environment
Anurag Kumar Pandey, Ruppa K. Thulasiram and A. Thavaneswaran, University of Manitoba, Winnipeg, Canada
Use of cloud computing has grown quickly and is now a technology that serves the compute, storage and data management needs of individuals, businesses, governments and other organizations. Large data centers are created to serve large number of clients' workload from various walks of life. When a resource in a data center reaches its end-of-life, instead of investing in upgrading, it is possibly the time to decommission such a resource and migrate workloads to other resources in the data center. Data migration between different cloud servers of a given private cloud is risky due to the possibility of data loss. We have designed a MapReduce based algorithm and have introduced few metrics to test and
evaluate our proposed framework. We show that our algorithm for data migration works efficiently for text, image, audio and video files with minimum data loss and scale well for large data as well.
Cloud Computing, Private Cloud, Data Migration, MapReduce, Data Loss, Cost