There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

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Context Aware Computing for The Internet of Things: A Survey

Service Oriented Architecture Concepts Technology And Design

Benchmarking the performance of public cloud providers is a common research topic. Previous work has already extensively evaluated the performance of different cloud platforms for different use cases, and under different constraints and experiment setups. In this article, we present a principled, large-scale literature review to collect and codify existing research regarding the predictability of performance in public Infrastructure-as-a-Service (IaaS) clouds. We formulate 15 hypotheses relating to the nature of performance variations in IaaS systems, to the factors of influence of performance variations, and how to compare different instance types. In a second step, we conduct extensive real-life experimentation on four cloud providers to empirically validate those hypotheses. We show that there are substantial differences between providers. Hardware heterogeneity is today less prevalent than reported in earlier research, while multitenancy has a dramatic impact on performance and predictability, but only for some cloud providers. We were unable to discover a clear impact of the time of the day or the day of the week on cloud performance.

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Patterns in the Chaos—A Study of Performance Variation and Predictability in Public IaaS Clouds

Challenges and recommended technologies for the industrial internet of things: A comprehensive review

The algorithm for scheduling resources under clouding computing environment is different from that under traditional distributed computing environment because of the high scalability and heterogeneity of computing resources in cloud computing environment. In this paper, a resource-scheduling algorithm based on dynamic load balance is presented. The different data-processing power of nodes in cloud is considered in this algorithm, as well as different data-transferring power and transfer delay between nodes in cloud. The algorithm selects the "best" node to fulfill the task in order to improve the efficiency of cloud computing and minimize the average response time of tasks. And the simulation results show that the algorithm distinctly reduces the average response time of tasks.

A load-balance based resource-scheduling algorithm under cloud computing environment

Cloud computing has emerged as a new paradigm, which is the long-held dream of computing as utility, customer get it on an on-demand model. When discuss about performance requirement and QoS (Quality of Service) of service, it is hard to tackle these problem in a round consideration. In this work we propose an SLA-aware (Service Level Agreement aware) framework ¨C cloud service provider (CSP) for cloud service (cloud infrastructure) delivery, and making allowance for the benefit of stakeholder of cloud service provider and service consumer. By using our proposed system and hierarchy SLA monitoring model we reached our win-win objective which is maximum revenue of cloud provider and minimum SLA violation.

Policy-Based SLA-Aware Cloud Service Provision Framework

Automated Web Service composition is an important way for integrating information flexibly and quickly However, existing composition approaches are insufficient for satisfying users' requirements In this paper, we firstly discuss the limitations of the existing web service composition approaches and then introduce the separation model to separate the representation of web service type definitions from instance definitions in order to organize the expected thousands of web services well At last we construct an automated web service composition system, which allows for fast and flexible composition of web services This system AFlow consists of two subsystems: AI Planning subsystem and Service Matchmaking subsystem The AI Planning subsystem receives the user's request to form an abstract workflow and transfer it to the Service Matchmaking subsystem The Service Matchmaking subsystem selects the most suitable service instances according to the abstract workflow via the matchmaking engine and then generates an executable workflow to execute

AFlow: An Automated Web Services composition system based on the AI planning and workflow

The fast and constant development of economic and computer network technology urges the building of enterprise integration platform, and quick delivery of new web services. These problems can be solved by applying technologies like Enterprise Service Bus (ESB) and automated web services composition. In this paper, we present an ESB based automated web service composition prototype. Our prototype D-Composer consists of two subsystems: AI planning subsystem and workflow binding subsystem. The former subsystem takes users’ requirement as input, uses an AI planning engine to construct an abstract workflow, and transforms it to the workflow binding subsystem through ESB. The latter one binds appropriate service instances to the abstract workflow via a matchmaking engine, then generates a BPEL workflow, and executes it.

Automated Web Service Composition System on Enterprise Service Bus

The traditional information retrieving method, which is based on keyword, cannot meet the needs of users of online recruitment. We proposed an efficient algorithm that is based on an automatically modeling of user demands. We use vector to present job and resume and the core part is the Genetic Algorithm. The GA algorithm is used to learn the recruitment records of a job and then establish the user's demand model such that the job-resume matching model is achieved.

Xinhuai Tang

Papers

A load-balance based resource-scheduling algorithm under cloud computing environment

Policy-Based SLA-Aware Cloud Service Provision Framework

AFlow: An Automated Web Services composition system based on the AI planning and workflow

Automated Web Service Composition System on Enterprise Service Bus

A Resume Recommendation Model for Online Recruitment