Designed the Cloud Services Brokerage for Mobile Ubiquitous Computing (CSB–UCC)

There is rapid adoption of cloud computing recently by enterprises to deliver product and services to clients and customers. At the same time, mobile computing technologies are being improved. Thus, some enterprises have discovered the need to combine these two macro fields (i.e., cloud computing and mobile computing) to deliver next generation applications. This is known as Mobile Cloud Computing (MCC). Cloud services providers especially from the commodity cloud suppliers are guaranteeing high services availability for anytime access. The importance of cloud computing for companies and consumers, as detailed in Chapter 2, is enormous. Some of the advantages of cloud computing include: cost management as companies can cut down on their internal IT budget, improved maintenance culture as the task of infrastructure manageability is delegated to the cloud service provider, and soft-real services accessibility.

Some services provisioning models are: Software-as-a-Service (SaaS) - services as applications, Platform- as-a-Service (PaaS) - services for development, testing, interfaces, etc. and Infrastructure-as-a-Service (IaaS) - services that support virtualization such as network, servers, etc.

However, cloud computing alone cannot provide the needed requirements for services delivery to consumers since providers need to ensure anywhere access. This is where the mobile cloud computing comes in handy. With the MCC, the anytime access capability of the cloud can be complemented by the anywhere access feature of mobile computing. But what is more crucial is the recent attitude of services consumers. Most mobile users own multiple devices such as smartphones, tablets, body sensors, and smartwatches. The consumers then expect to have application, services, and data consistency across these devices. As well, users have several cloud services subscriptions and there is the need to facilitate these divergent services to be consumed in a single dimensional workflow. Supporting n–devices to access multi–cloud services is termed Ubiquitous cloud computing (UCC) or the personal cloud.

Currently, there is not enough research on the personal cloud. There has been some studies on Mobile Back-end-as-a-Service (MBaaS) which seek to ensure efficient server–side design for mobile services support. While MBaaS is not directly linked to the personal cloud, the initial designs seek to enable third party services integration into mobile specific applications. This includes the design of back-end layers that can facilitate user authentication from third parties and so on. This again leads to the open gap between research on the personal cloud and the existing up and coming solutions.

This means when adopted, users can be facilitated to access cross-cloud platform services on their n–devices. In Table 5.1, the key features that distinguish the CSB–UCC from the other existing solutions are highlighted.

Table 5.1: Features of the CSB–UCC Feature Explanation

Authentication The CSB–UCC supports hybrid authentication mechanism (i.e. either by social networking or proprietary personal login) based on OAuth 2.0 and password/username usage. This is achieved by proposing a graph technique for mapping users credentials across different cloud services. In this case, two account types are maintained on the brokerage service that checks registered accounts and authorized accounts. Registered accounts are the credentials that are created with the service providers and authorized accounts may be other credentials own by the user which are stored on the broker. The idea of providing the hybrid authentication technique is to help users better manage their credentials.

Aggregation As of now, the existing frameworks that have been proposed either in- tegrate multiple devices to a single cloud provider (e.g. Dropbox) or aggregate multiple cloud sources to a single user. The CSB–UCC is not aggregating the data on the broker as seen in some frameworks such as RackSpace; rather, the aim is to ensure services selection on the bro- ker and delivering the integrated multi–cloud services directly to the n–devices of the consumer.

Audit The CSB–UCC is deigned to employ provenance techniques to ensure transparent audit trail. Existing services at best allows users to know who made changes to information but are not able to prevent actions that may not be required by other users; especially regarding group sharing scenarios. The proposed provenance technique in the CSB–UCC enables users to perform actions based on contextual data and access level. Context is defined in this work as time and location.

Ubiquitous Device Support

The CSB–UCC is designed to support n–devices such as sensors and smartphones. Most of the services mentioned earlier support only smart- phones. However, supporting ubiquitous computing will require consid- eration for sensors. This is what puts the proposed framework in a pole position for future adoption.

Table 5.1 – continued from previous page Features Explanation

Agility One of the major issues with enterprises is the ability to accommodate infrastructural changes. Besides, though an enterprise will be offering a single service now (e.g., IaaS), there are chances that the service offering can be expanded in the future. Unlike the previously highlighted services in Table 2.3, the CSB–UCC supports integration with IaaS, SaaS, and PaaS. The Section 3 explains how this is achieved with DropBox, MEGA, Amazon S3, Maqetta, and so on.

Cost The CSB–UCC can be adopted as a centralized broker or a distributed broker. This is good for managing both small scale and large scale services. Since test results confirm the capacity of each architecture, enterprises can adopt say the centralized architecture to support about 3000 users who may own up to 4 devices with soft real–time need. This is also a good way to mange the cost of buying more virtual servers that may be required for a large scale distributed service.

The proposed CSB–UCC is in use in several real world systems. Since the system promises high scalability, it is adopted by ZenFri Inc. Canada in the deployment of the Clandestine Anomaly augmented reality game. This project is described by the New Media Manitoba as the biggest game property in the history of Manitoba. The project is part of MITACS partnership.

5.1.2

Sensor Data Sharing

Today, users are seen with several ubiquitous devices such as smartphones, tablets, smartwatches, body cameras, and sensors. Some sensors can facilitate the detection of bio–hazards that otherwise cannot be detected by other personalized devices. For instance, gamma rays are electromagnetic radiation with a very high frequency that can be biologically hazardous. Most workers in the mining, manufacturing, security, and other industries find themselves in such hazardous environments and governments are trying to contain this issue. While traditionally, high gamma radiation detection sensors have been manufactured to be carried by users, they are not good access point for actual dosage readings. With the recent advancement in mobile technology, the dissertation proposes a mobile hosting architecture to enable mobile-to-sensor communication following the edge–based technique. This means the sensor can detect the radiation and send readings to a smartphone device of the user. All other near–by mobile devices (which are authorized) will receive the notification to alert the people in the hazard zone. A crucial obstacle to overcome is latency reduction and efficient request routing in the mobile–to–sensor eco–system.

In this regard, the CSB–UCC is adopted by the Environmental Instruments Canada Inc. to facilitate the dissemination of sensor data sharing in the n–devices economy. The high dosage readings by the sensor can be transmitted to the nearby mobile so that notifications can be fired up or the data can be sent to the cloud back–end. The proposed work is tested and the results show that detected radiations are sent in soft real-time to the mobile devices.

Furthermore, we can advance on the mobile provisioning architecture by developing a mobile hosting and sensor ecosystem for high radiation detection (e.g., gamma rays). In recent time where workers in different enterprises are exposed to hazardous conditions, the risk can be minimized through sensor technologies. While sensors have limitations on data reporting and proactive action taking, we can combine them with mobile systems to ensure efficient message delivery.

In this dissertation, the sensor can detect high radiation and when the data is sent to the smartphone, the latter can vibrate, push notification, send email or SMS, and make quicker dosage counts which can aid the user to move away from a hazardous zone. The proposed system takes into account the ability to disseminate information to all near-by mobile devices (if there are several users in an enclosed high radiation area). A bigger challenge is how to reduce communication latency and ensure faster information dissemination between the sensor and the mobile. This is the reason the edge–based connection is proposed in an attempt to determine the optimal path between the adjacent mobile hosts. To ensure mobile–to–mobile/sensor service communication, this work therefore proposes an edge–based connection in an attempt to determine the optimal path between the adjacent mobile hosts. Different modes of mobile-to-mobile service communication are designed by adapting the services flow patterns that include sequential, parallelism, loop, and choice approaches. The work is evaluated to determine the best approach for achieving low–latency communication, efficient job re–assignment, and error management when communications between the mobile host and the consumer fails.

Since currently mobile devices are not equipped to determine gamma radiations, we have to rely on specific sensors that can determine the radiation (e.g., CT007 ) in this work. Preliminary evaluations focus on the determination of the best approach for achieving low-latency communication, efficient job re-assignment, and error management when communications between the mobile and the sensor fails.

The work is evaluated to determine the best approach for achieving low–latency communication and efficient job re–assignment within the sensor–mobile ecosystem. The preliminary evaluations show that the proposed consideration for the optimal RTT + PT is better than the existing approaches that evaluate latency solely on the optimal RTT (where RTT is the request response time and PT is the processing time of a mobile). Also, the results show that the parallelism flow pattern is better than the other two which are the sequential and choice flow patterns. In summary, the dissertation makes the following contributions regarding the use of the CSB–UCC to support sensor data management:

• Proposed mobile hosting architecture for group data sharing.

• It is observed that optimal time for a response is not dependent on optimal distance between the adjacent mobile nodes but factors such as the processing load on the host, and request travel time. • Failed communications can be re-routed to the adjacent node that has the next better optimal request-

response time.

• In most of the scenarios, the parallelism flow pattern is better at latency minimization.

In presenting the work on the sensor technology, I will like to acknowledge the efforts of the following individuals who helped with programming, experimentation, data collection, and manufacturing: Sihn Pham (Graduate student at the Department of Computer Science), Kai Kaletsch (Environmental Instruments Canada Inc.), and Prof. Ralph Deters (Department of Computer Science, University of Saskatchewan)