An Approach to Ad hoc Cloud Computing

We consider how underused computing resources within an enterprise may be harnessed to improve utilization and create an elastic computing infrastructure. Most current cloud provision involves a data center model, in which clusters of machines are dedicated to running cloud infrastructure software. We propose an additional model, the ad hoc cloud, in which infrastructure software is distributed over resources harvested from machines already in existence within an enterprise. In contrast to the data center cloud model, resource levels are not established a priori, nor are resources dedicated exclusively to the cloud while in use. A participating machine is not dedicated to the cloud, but has some other primary purpose such as running interactive processes for a particular user. We outline the major implementation challenges and one approach to tackling them

Directory-based incentive management services for ad-hoc mobile clouds

Mobile cloud computing is envisioned as a promising approach to augment the computational capabilities of mobile devices for emerging resource-intensive mobile applications. This augmentation is generally achieved through the capabilities of stationary resources in cloud data centers. However, these resources are mostly not free and sometimes not available. Mobile devices are becoming powerful day by day and can form a self-organizing mobile ad-hoc network of nearby devices and offer their resources as on-demand services to available nodes in the network. In the ad-hoc mobile cloud, devices can move after consuming or providing services to one another. During this process, the problem of incentives arises for a node to provide service to another device (or other devices) in the network, which ultimately decreases the motivation of the mobile device to form an ad-hoc mobile cloud. To solve this problem, we propose a directory-based architecture that keeps track of the retribution and reward valuations (in terms of energy saved and consumed) for devices even after they move from one ad-hoc environment to another. From simulation results, we infer that this framework increases the motivation for mobile devices to form a self-organizing proximate mobile cloud network and to share their resources in the network

A Redundancy-based Security Model for Smart Home

Recent developments in smart devices, Cloud Computing and Internet of Things (IoT) are introducing network of intelligent devices. These intelligent devices can be used to develop smart home network. The home appliance in a smart home forms an ad-hoc network. A smart home network architecture can be exploited by compromising the devices it is made up of. Various malicious activities can be performed through such exploitation. This paper presents a security approach to combat this. By using a collaborative and redundant security approach, the ad-hoc network of a smart home would be able to prevent malicious exploitation. The security approach discussed in this paper is a conceptual representation on the proposed security model for smart home networks

Spontaneous ad hoc mobile cloud computing network

Cloud computing helps users and companies to share computing resources instead of having local servers or personal devices to handle the applications. Smart devices are becoming one of the main information processing devices. Their computing features are reaching levels that let them create a mobile cloud computing network. But sometimes they are not able to create it and collaborate actively in the cloud because it is difficult for them to build easily a spontaneous network and configure its parameters. For this reason, in this paper, we are going to present the design and deployment of a spontaneous ad hoc mobile cloud computing network. In order to perform it, we have developed a trusted algorithm that is able to manage the activity of the nodes when they join and leave the network. The paper shows the network procedures and classes that have been designed. Our simulation results using Castalia show that our proposal presents a good efficiency and network performance even by using high number of nodes.Lacuesta, R.; Lloret, J.; Sendra, S.; Peñalver Herrero, ML. (2014). Spontaneous ad hoc mobile cloud computing network. Scientific World Journal. 2014:1-19. doi:10.1155/2014/232419S1192014Rodrigues, J. J. P. C., Zhou, L., Mendes, L. D. P., Lin, K., & Lloret, J. (2012). Distributed media-aware flow scheduling in cloud computing environment. Computer Communications, 35(15), 1819-1827. doi:10.1016/j.comcom.2012.03.004Feeney, L. M., Ahlgren, B., & Westerlund, A. (2001). Spontaneous networking: an application oriented approach to ad hoc networking. IEEE Communications Magazine, 39(6), 176-181. doi:10.1109/35.925687Fernando, N., Loke, S. W., & Rahayu, W. (2013). Mobile cloud computing: A survey. Future Generation Computer Systems, 29(1), 84-106. doi:10.1016/j.future.2012.05.023Lacuesta, R., Lloret, J., Garcia, M., & Peñalver, L. (2013). A Secure Protocol for Spontaneous Wireless Ad Hoc Networks Creation. IEEE Transactions on Parallel and Distributed Systems, 24(4), 629-641. doi:10.1109/tpds.2012.168Lacuesta, R., Lloret, J., Garcia, M., & Peñalver, L. (2011). Two secure and energy-saving spontaneous ad-hoc protocol for wireless mesh client networks. Journal of Network and Computer Applications, 34(2), 492-505. doi:10.1016/j.jnca.2010.03.024Lacuesta, R., Lloret, J., Garcia, M., & Peñalver, L. (2010). A Spontaneous Ad Hoc Network to Share WWW Access. EURASIP Journal on Wireless Communications and Networking, 2010(1). doi:10.1155/2010/232083Lacuesta, R., Palacios-Navarro, G., Cetina, C., Peñalver, L., & Lloret, J. (2012). Internet of things: where to be is to trust. EURASIP Journal on Wireless Communications and Networking, 2012(1). doi:10.1186/1687-1499-2012-203Capkun, S., Buttyan, L., & Hubaux, J. (2003). Self-organized public-key management for mobile ad hoc networks. IEEE Transactions on Mobile Computing, 2(1), 52-64. doi:10.1109/tmc.2003.1195151Goodman, J., & Chandrakasan, A. (2000). An Energy Efficient Reconfigurable Public-Key Cryptography Processor Architecture. Lecture Notes in Computer Science, 175-190. doi:10.1007/3-540-44499-8_13Mayrhofer, R., Ortner, F., Ferscha, A., & Hechinger, M. (2003). Securing Passive Objects in Mobile Ad-Hoc Peer-to-Peer Networks. Electronic Notes in Theoretical Computer Science, 85(3), 105-121. doi:10.1016/s1571-0661(04)80687-xMendes, L. D. P., Rodrigues, J. J. P. C., Lloret, J., & Sendra, S. (2014). Cross-Layer Dynamic Admission Control for Cloud-Based Multimedia Sensor Networks. IEEE Systems Journal, 8(1), 235-246. doi:10.1109/jsyst.2013.2260653Dutta, R., & B, A. (2014). Protection of data in unsecured public cloud environment with open, vulnerable networks using threshold-based secret sharing. Network Protocols and Algorithms, 6(1), 58. doi:10.5296/npa.v6i1.486

Service composition for end-users

RESTful services are becoming a popular technology for providing and consuming cloud services. The idea of cloud computing is based on on-demand services and their agile usage. This implies that also personal service compositions and workflows should be supported. Some approaches for RESTful service compositions have been proposed. In practice, such compositions typically present mashup applications, which are composed in an ad-hoc manner. In addition, such approaches and tools are mainly targeted for programmers rather than end-users. In this paper, a user-driven approach for reusable RESTful service compositions is presented. Such compositions can be executed once or they can be configured to be executed repeatedly, for example, to get newest updates from a service once a week

Cognitive virtual ad hoc mobile cloud-based networking architecture

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThis thesis proposed cognitive techniques and intelligent algorithms that offered adaptive and advanced facilities to cloud-based networking by using Virtual Ad Hoc Mobile Cloud Computing Networks architecture (VAMCCNs). This is presented as a working case to address their global network challenges and to add cognitive support to the network design and implementation for better meeting traffic management and application requirements in mission objectives. The thesis concentrates on three main contributions. Firstly, an adaptive model, namely: a Heterogeneous Mobile Cloud Computing Network (HMCCN), was proposed to integrate different cloud networks architectures into one workflow. The cognitive data offloading task and the routing decision methods were applied using two different approaches: Fuzzy Analytic Hierarchy system (FAH) as a first approach and cognitive Software Defined Network (SDN) model as a second centralised approach. Experimental results show improvement in network reliability and throughputs, minimised in both nodes’ energy consumption and network latency with efficient intelligent data load balance and network resources allocation with best cloud model selection. Secondly, based on a virtual Ad Hoc cloud network with a realistic Random Waypoint Motion (RWM) model, an innovative cognitive routing algorithm was presented to improve efficient and reliable route selection among multiple possible routes. Routing protocols based on conventional, Fuzzy logic used important parameters with two data collections and decisions techniques and a new adaptive Intelligent Hybrid Fuzzy-Neural routing protocol (IHFN) that included prior knowledge to the network of the underlying motion and energy parameters were all proposed and compared. Results with the new hybrid algorithm shown a significant improvement to solve the network end-to-end performance degradation problem. The new hybrid protocol improved network throughput with an average of 20% higher than traditional Ad Hoc On-Demand Distance Vector (AODV) Routing protocol, improved the usage of network resources and reduced the maintenance process in adynamic topologies network. Finally, based on datasets collected from a realistic motion RWM model in a virtual Ad Hoc cloud network, the performance behaviour of six selected deep learning algorithms to predict the next steps of positions, speed and residual battery energy values of these mobile nodes have been evaluated and compared. This work goes further by presenting two algorithm's training techniques to predict the next 300-time steps of position, speed, and energy. Results and dissuasion show the differences concerning prediction accuracy between using the single node dataset model or Multiple node's dataset model

Cloud Instance Selection Using Parallel K-Means and AHP

Managing cloud spend and qualities when selecting cloud instances is cited as one of the timely research challenges in cloud computing. Cloud service consumers are often confronted by too many options and selection is challenging. This is because instance provision can be difficult to comprehend for an average technical user and tactics of cloud provider are far from being transparent biasing the selection. This paper proposes a novel cloud instance selection framework for finding the optimal IaaS purchase strategy for a VARD application in Amazon EC2. Analytical Hierarchy Process (AHP) and parallel K-Means Clustering algorithm are used and combined in Cloud Instance Selection environments. It allows cloud users to get the recommendation about cloud instance types and job submission periods based on requirements such as CPU, RAM, and resource utilisation. The system leverages AHP to select cloud instance type. Besides, AHP results are used by the parallel K-Means clustering model to find the best execution time for a given day according to the user's requirements. Finally, we provide an example to demonstrate the applicability of the approach. Experiments indicate that our approach achieves better results than ad-hoc and cost-driven approaches