Fault tolerance and QoS scheduling using CAN in mobile social cloud computing

The performance of mobile devices including smart phones and laptops is steadily rising as prices plummet sharply. So, mobile devices are changing from being a mere interface for requesting services to becoming computing resources for providing and sharing services due to immeasurably improved performance. With the increasing number of mobile device users, the utilization rate of SNS (Social Networking Service) is also soaring. Applying SNS to the existing computing environment enables members of social network to share computing services without further authentication. To use mobile device as a computing resource, temporary network disconnection caused by user mobility and various HW/SW faults causing service disruption should be considered. Also these issues must be resolved to support mobile users and to provide user requirements for services. Accordingly, we propose fault tolerance and QoS (Quality of Services) scheduling using CAN (Content Addressable Network) in Mobile Social Cloud Computing (MSCC). MSCC is a computing environment that integrates social network-based cloud computing and mobile devices. In the computing environment, a mobile user can, through mobile devices, become a member of a social network through real world relationships. Essentially, members of a social network share cloud service or data with other members without further authentication by using their mobile device. We use CAN as the underlying MSCC to logically manage the locations of mobile devices. Fault tolerance and QoS scheduling consists of four sub-scheduling algorithms: malicious-user filtering, cloud service delivery, QoS provisioning, and replication and load-balancing. Under the proposed scheduling, a mobile device is used as a resource for providing cloud services, faults caused from user mobility or other reasons are tolerated and user requirements for QoS are considered. We simulate scheduling both with and without CAN. The simulation results show that our proposed scheduling algorithm enhances cloud service execution time, finish time and reliability and reduces the cloud service error rate.     

Design a cloud storage platform for pervasive computing environments

An increasing number of personal electronic handheld devices (e.g., SmartPhone, netbook, MID and etc.), which make up the personal pervasive computing environments, are playing an important role in our daily lives. Data storage and sharing is difficult for these devices due to the data inflation and the natural limitations of mobile devices, such as the limited storage space and the limited computing capability. Since the emerging cloud storage solutions can provide reliable and unlimited storage, they satisfy to the requirement of pervasive computing very well. Thus we designed a new cloud storage platform which includes a series of shadow storage services to address these new data management challenges in pervasive computing environments, which called as “SmartBox”. In SmartBox, each device is associated its shadow storage with a unique account, and the shadow storage acts as backup center as well as personal repository when the device is connected. To facilitate file navigation, all datasets in shadow storage are organized based on file attributes which support the users to seek files by semantic queries. We implemented a prototype of SmartBox focusing on pervasive environments being made up of Internet accessible devices. Experimental results with the deployments confirm the efficacy of shadow storage services in SmartBox.     

Proxy based seamless connection management method in mobile cloud computing

Nowadays, complex smartphone applications are developed that support gaming, navigation, video editing, augmented reality, and speech recognition which require considerable computational power and battery lifetime. The cloud computing provides a brand new opportunity for the development of mobile applications. Mobile Hosts (MHs) are provided with data storage and processing services on a cloud computing platform rather than on the MHs. To provide seamless connection and reliable cloud service, we are focused on communication. When the connection to cloud server is increased explosively, each MH connection quality has to be declined. It causes several problems: network delay, retransmission, and so on. In this paper, we propose proxy based architecture to improve link performance for each MH in mobile cloud computing. By proposed proxy, the MH need not keep connection of the cloud server because it just connected one of proxy in the same subnet. And we propose the optimal access network discovery algorithm to optimize bandwidth usage. When the MH changes its point of attachment, proposed discovery algorithm helps to connect the optimal access network for cloud service. By experiment result and analysis, the proposed connection management method has better performance than the 802.11 access method.     

Software quality in the clouds: a cloud-based solution

Cloud computing, an on-demand computation model that consists of large data-centers (Clouds) managed by cloud providers, offers storage and computation needs for cloud users based on service level agreements (SLAs). Services in cloud computing are offered at relatively low cost. The model, therefore, forms a great target for many applications, such as startup businesses and e-commerce applications. The area of cloud computing has grown rapidly in the last few years; yet, it still faces some obstacles. For example, there is a lack of mechanisms that guarantee for cloud users the quality that they are actually getting, compared to the quality of service that is specified in SLAs. Another example is the concern of security, privacy and trust, since users lose control over their data and programs once they are sent to cloud providers. In this paper, we introduce a new architecture that aids the design and implementation of attestation services. The services monitor cloud-based applications to ensure software quality, such as security, privacy, trust and usability of cloud-based applications. Our approach is a user-centric approach through which users have more control on their own data/applications. Further, the proposed approach is a cloud-based approach where the powers of the clouds are utilized. Simulation results show that many services can be designed based on our architecture, with limited performance overhead.     

A limited-trust capacity model for mitigating threats of internal malicious services in cloud computing

Hidden persistent malware in guest virtual machine instances are among the most common internal threats in cloud computing, affecting the security of both cloud customers and providers. With the growing sophistication of modern malware, traditional methods are becoming increasingly ineffective for tackling cloud security problems. Moreover, given the pay-per-use model of clouds, consumption of resources by these malwares and malicious services can cause huge losses to both the cloud provider and customer. Thus, it is important to develop mechanisms that can limit the scale of malicious attacks in order to minimize their resources consumption. Trust management is a fundamental technique for assessing and increasing the reliability and security of cloud services. Unfortunately, majority of existing mechanisms for trust management in clouds have limitations that prevent them from being fully effective. In this paper, we propose a novel limited-trust capacity model to mitigate the threats of internal malicious software and services in cloud computing using concepts from flow networks to reduce the scale of malicious software or services. Our limited-trust capacity model can be utilized in the following two ways: (1) to manage the trust relationship among the guest services and to evaluate the threats of unknown malicious services, and (2) to minimize risk associated with renting cloud services and limiting the resource drain caused by malicious guest services. Finally, experimental results show that our limited-trust capacity model can effectively restrict the scale of malicious services and significantly mitigate the threats of internal attacks.     

Trust-Enhanced Cloud Service Selection Model Based on QoS Analysis

Cloud computing technology plays a very important role in many areas, such as in the construction and development of the smart city. Meanwhile, numerous cloud services appear on the cloud-based platform. Therefore how to how to select trustworthy cloud services remains a significant problem in such platforms, and extensively investigated owing to the ever-growing needs of users. However, trust relationship in social network has not been taken into account in existing methods of cloud service selection and recommendation. In this paper, we propose a cloud service selection model based on the trust-enhanced similarity. Firstly, the direct, indirect, and hybrid trust degrees are measured based on the interaction frequencies among users. Secondly, we estimate the overall similarity by combining the experience usability measured based on Jaccard’s Coefficient and the numerical distance computed by Pearson Correlation Coefficient. Then through using the trust degree to modify the basic similarity, we obtain a trust-enhanced similarity. Finally, we utilize the trust-enhanced similarity to find similar trusted neighbors and predict the missing QoS values as the basis of cloud service selection and recommendation. The experimental results show that our approach is able to obtain optimal results via adjusting parameters and exhibits high effectiveness. The cloud services ranking by our model also have better QoS properties than other methods in the comparison experiments.     

Classified power capping by network distribution trees for green computing

Power management is becoming very important in data centers. To apply power management in cloud computing, Green Computing has been proposed and considered. Cloud computing is one of the new promising techniques, that are appealing to many big companies. In fact, due to its dynamic structure and property in online services, cloud computing differs from current data centers in terms of power management. To better manage the power consumption of web services in cloud computing with dynamic user locations and behaviors, we propose a power budgeting design based on the logical level, using distribution trees. By setting multiple trees or forest, we can differentiate and analyze the effect of workload types and Service Level Agreements (SLAs, e.g. response time) in terms of power characteristics. Based on these, we introduce classified power capping for different services as the control reference to maximize power saving when there are mixed workloads.     

Trust management for internet of things using cloud computing and security in smart cities

Many consumers participate in the smart city via smart portable gadgets such as wearables, personal gadgets, mobile devices, or sensor systems. In the edge computation systems of IoT in the smart city, the fundamental difficulty of the sensor is to pick reliable participants. Since not all smart IoT gadgets are dedicated, certain intelligent IoT gadgets might destroy the networks or services deliberately and degrade the customer experience. A trust-based internet of things (TM-IoT) cloud computing method is proposed in this research. The problem is solved by choosing trustworthy partners to enhance the quality services of the IoT edging network in the Smart architectures. A smart device choice recommendation method based on the changing networks was developed. It applied the evolutionary concept of games to examine the reliability and durability of the technique of trust management presented in this article. The reliability and durability of the trustworthiness-managing system, the Lyapunov concept was applied.A real scenario for personal-health-control systems and air-qualitymonitoring and assessment in a smart city setting confirmed the efficiency of the confidence-management mechanism. Experiments have demonstrated that the methodology for trust administration suggested in this research plays a major part in promoting multi-intelligent gadget collaboration in the IoT edge computer system with an efficiency of 97%. It resists harmful threads against service suppliers more consistently and is ideal for the smart world's massive IoT edge computer system.

     

vcluster: a framework for auto scalable virtual cluster system in heterogeneous clouds

Cloud computing is an emerging technology and is being widely considered for resource utilization in various research areas. One of the main advantages of cloud computing is its flexibility in computing resource allocations. Many computing cycles can be ready in very short time and can be smoothly reallocated between tasks. Because of this, there are many private companies entering the new business of reselling their idle computing cycles. Research institutes have also started building their own cloud systems for their various research purposes. In this paper, we introduce a framework for virtual cluster system called vcluster which is capable of utilizing computing resources from heterogeneous clouds and provides a uniform view in computing resource management. vcluster is an IaaS (Infrastructure as a Service) based cloud resource management system. It distributes batch jobs to multiple clouds depending on the status of queue and system pool. The main design philosophy behind vcluster is cloud and batch system agnostic and it is achieved through plugins. This feature mitigates the complexity of integrating heterogeneous clouds. In the pilot system development, we use FermiCloud and Amazon EC2, which are a private and a public cloud system, respectively. In this paper, we also discuss the features and functionalities that must be considered in virtual cluster systems.     

eCatch: Enabling collaborative fisheries management with technology

Modernizing data systems to inform collaborative management is critical to adaptively managing fisheries in an era of climate change. In 2006, The Nature Conservancy of California purchased 13 federal groundfish permits in California with the objective of managing the fishing and reporting activities in a manner that protected sensitive habitats and species. At that time, collecting data for this fishery was done with paper logbooks. This made queries and visualization that could inform management decisions towards our objective impossible in a timely manner. To solve this problem, we built successive software prototypes that leveraged location-aware mobile devices, cloud-based computing, and visualization and query of geographic data over the web. The resulting software, eCatch, enabled avoidance of sensitive species and habitats and quantitative reporting on performance metrics related to those activities. What started as a technology solution to a problem of timely scientific monitoring revealed collateral benefits of collaboration with the fishing industry and markets that support sustainable activities.     

Distributed Network,Wireless and Cloud Computing Enabled 3-D Ultrasound; a New Medical Technology Paradigm

Medical technologies are indispensable to modern medicine. However, they have become exceedingly expensive and complex and are not available to the economically disadvantaged majority of the world population in underdeveloped as well as developed parts of the world. For example, according to the World Health Organization about two thirds of the world population does not have access to medical imaging. In this paper we introduce a new medical technology paradigm centered on wireless technology and cloud computing that was designed to overcome the problems of increasing health technology costs. We demonstrate the value of the concept with an example; the design of a wireless, distributed network and central (cloud) computing enabled three-dimensional (3-D) ultrasound system. Specifically, we demonstrate the feasibility of producing a 3-D high end ultrasound scan at a central computing facility using the raw data acquired at the remote patient site with an inexpensive low end ultrasound transducer designed for 2-D, through a mobile device and wireless connection link between them. Producing high-end 3D ultrasound images with simple low-end transducers reduces the cost of imaging by orders of magnitude. It also removes the requirement of having a highly trained imaging expert at the patient site, since the need for hand-eye coordination and the ability to reconstruct a 3-D mental image from 2-D scans, which is a necessity for high quality ultrasound imaging, is eliminated. This could enable relatively untrained medical workers in developing nations to administer imaging and a more accurate diagnosis, effectively saving the lives of people.     

I/O access frequency-aware cache method on KVM/QEMU

Together with the rapid development of IT technology, cloud computing has been considered as the next generation’s computing infrastructure. One of the essential part of cloud computing is the virtual machine technology that enables to reduce the data center cost with better resource utilization. Especially, virtual desktop infrastructure (VDI) is receiving explosive attentions from IT markets because of its advantages of easier software management, greater data protection, and lower cost. However, sharing physical resources in VDI to consolidate multiple guest virtual machines (VMs) on a host has a tradeoff that can lead to significant I/O degradation. Optimizing I/O virtualization overhead is a challenging task because it needs to scrutinize multiple software layers between guest VMs and host where those VMs are executing. In this paper, we present a hypervisor-level cache, called hyperCache, which is possible to provide a shortcut in KVM/QEMU. It intercepts I/O requests in the hypervisor and analyses their I/O access patterns to select data retaining high access frequency. Also, it has a capability of maintaining the appropriate cache memory size by utilizing the cache block map. Our experimental results demonstrate that our method improves I/O bandwidth by up to 4.7x over the existing QEMU.     

CloudDMSS: robust Hadoop-based multimedia streaming service architecture for a cloud computing environment

The delivery of scalable, rich multimedia applications and services on the Internet requires sophisticated technologies for transcoding, distributing, and streaming content. Cloud computing provides an infrastructure for such technologies, but specific challenges still remain in the areas of task management, load balancing, and fault tolerance. To address these issues, we propose a cloud-based distributed multimedia streaming service (CloudDMSS), which is designed to run on all major cloud computing services. CloudDMSS is highly adapted to the structure and policies of Hadoop, thus it has additional capacities for transcoding, task distribution, load balancing, and content replication and distribution. To satisfy the design requirements of our service architecture, we propose four important algorithms: content replication, system recovery for Hadoop distributed multimedia streaming, management for cloud multimedia management, and streaming resource-based connection (SRC) for streaming job distribution. To evaluate the proposed system, we conducted several different performance tests on a local testbed: transcoding, streaming job distribution using SRC, streaming service deployment and robustness to data node and task failures. In addition, we performed three different tests in an actual cloud computing environment, Cloudit 2.0: transcoding, streaming job distribution using SRC, and streaming service deployment.     

Use of cloud computing in biomedicine

Nowadays, biomedicine is characterised by a growing need for processing of large amounts of data in real time. This leads to new requirements for information and communication technologies (ICT). Cloud computing offers a solution to these requirements and provides many advantages, such as cost savings, elasticity and scalability of using ICT. The aim of this paper is to explore the concept of cloud computing and the related use of this concept in the area of biomedicine. Authors offer a comprehensive analysis of the implementation of the cloud computing approach in biomedical research, decomposed into infrastructure, platform and service layer, and a recommendation for processing large amounts of data in biomedicine. Firstly, the paper describes the appropriate forms and technological solutions of cloud computing. Secondly, the high-end computing paradigm of cloud computing aspects is analysed. Finally, the potential and current use of applications in scientific research of this technology in biomedicine is discussed.     

SNUAGE: an efficient platform-as-a-service security framework for the cloud

In this paper we present SNUAGE, a platform-as-a-service security framework for building secure and scalable multi-layered services based on the cloud computing model. SNUAGE ensures the authenticity, integrity, and confidentiality of data communication over the network links by creating a set of security associations between the data-bound components on the presentation layer and their respective data sources on the data persistence layer. SNUAGE encapsulates the security procedures, policies, and mechanisms in these security associations at the service development stage to form a collection of isolated and protected security domains. The secure communication among the entities in one security domain is governed and controlled by a standalone security processor and policy attached to this domain. This results into: (1) a safer data delivery mechanism that prevents security vulnerabilities in one domain from spreading to the other domains and controls the inter-domain information flow to protect the privacy of network data, (2) a reusable security framework that can be employed in existing platform-as-a-service environments and across diverse cloud computing service models, and (3) an increase in productivity and delivery of reliable and secure cloud computing services supported by a transparent programming model that relieves application developers from the intricate details of security programming. Last but not least, SNUAGE contributes to a major enhancement in the energy consumption and performance of supported cloud services by providing a suitable execution container in its protected security domains for a wide suite of energy- and performance-efficient cryptographic constructs such as those adopted by policy-driven and content-based security protocols. An energy analysis of the system shows, via real energy measurements, major savings in energy consumption on the consumer devices as well as on the cloud servers. Moreover, a sample implementation of the presented security framework is developed using Java and deployed and tested in a real cloud computing infrastructure using the Google App Engine service platform. Performance benchmarks show that the proposed framework provides a significant throughput enhancement compared to traditional network security protocols such as the Secure Sockets Layer and the Transport Layer Security protocols.     

A resource-sharing model based on a repeated game in fog computing

With the rapid development of cloud computing techniques, the number of users is undergoing exponential growth. It is difficult for traditional data centers to perform many tasks in real time because of the limited bandwidth of resources. The concept of fog computing is proposed to support traditional cloud computing and to provide cloud services. In fog computing, the resource pool is composed of sporadic distributed resources that are more flexible and movable than a traditional data center. In this paper, we propose a fog computing structure and present a crowd-funding algorithm to integrate spare resources in the network. Furthermore, to encourage more resource owners to share their resources with the resource pool and to supervise the resource supporters as they actively perform their tasks, we propose an incentive mechanism in our algorithm. Simulation results show that our proposed incentive mechanism can effectively reduce the SLA violation rate and accelerate the completion of tasks.     

Developing an integrated cloud-based spatial-temporal system for monitoring phenology

Geospatial cloud computing offers computing infrastructure, software and data services that enable rapid integration of ecological data from various resources. The objectives of this study were to utilize readily-available and low-cost technology (e.g., GPS–enabled cameras, Cloud photo storage, Google Drive) to create a cloud-based spatial-temporal inventory of plant (including flowering phenology) and other relevant information. An interactive ArcGIS Online Map of Lake Issaqueena, SC with sampling locations of flowering plants allows users to obtain additional information (plant, soil, weather data) by selecting sampling locations or soil polygons. The contents of the map can be filtered using any of the attributes (e.g., growth form) in the data tables by selecting specific information. Plant information can be viewed at custom time intervals using the settings in ArcGIS Online. Spatial patterns (e.g., clustering) in the plant data can be viewed using the ArcGIS Online heat map view. The map can be easily queried and viewed on both computers and hand-held devices. Services from multiple cloud infrastructures can be integrated for use by various species monitoring programs, improving workflow and assessment capabilities.     

Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms

Cluster Computing - Due to the limitations associated with the processing capability of mobile devices in cloud environments, various tasks are offloaded to the cloud server. This has led to an...     

A Forensically Sound Adversary Model for Mobile Devices

In this paper, we propose an adversary model to facilitate forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given the ongoing and rapidly changing nature of mobile device technologies. An integral principle and significant constraint upon forensic practitioners is that of forensic soundness. Our adversary model specifically considers and integrates the constraints of forensic soundness on the adversary, in our case, a forensic practitioner. One construction of the adversary model is an evidence collection and analysis methodology for Android devices. Using the methodology with six popular cloud apps, we were successful in extracting various information of forensic interest in both the external and internal storage of the mobile device.