Towards Energy-Efficient, Fault-Tolerant, and Load-Balanced Mobile Cloud

Recent advances in mobile technologies have enabled a new computing paradigm in which large amounts of data are generated and accessed from mobile devices. However, running resource-intensive applications (e.g., video/image storage and processing or map-reduce type) on a single mobile device still remains off bounds since it requires large computation and storage capabilities. Computer scientists overcome this issue by exploiting the abundant computation and storage resources from traditional cloud to enhance the capabilities of end-user mobile devices. Nevertheless, the designs that rely on remote cloud services sometimes underlook the available resources (e.g., storage, communication, and processing) on mobile devices. In particular, when the remote cloud services are unavailable (due to service provider or network issues) these smart devices become unusable. For mobile devices deployed in an infrastructureless network where nodes can move, join, or leave the network dynamically, the challenges on energy-efficiency, reliability, and load-balance are still largely unexplored. This research investigates challenges and proposes solutions for deploying mobile application in such environments. In particular, we focus on a distributed data storage and data processing framework for mobile cloud. The proposed mobile cloud computing (MCC) framework provides data storage and data processing services to MCC applications such as video storage and processing or map-reduce type. These services ensure the mobile cloud is energy-efficient, fault-tolerant, and load-balanced by intelligently allocating and managing the stored data and processing tasks accounting for the limited resources on mobile devices. When considering the load-balance, the framework also incorporates the heterogeneous characteristics of mobile cloud in which nodes may have various energy, communication, and processing capabilities. All the designs are built on the k-out-of-n computing theoretical foundation. The novel formulations produce a reliability-compliant, energy-efficient data storage solution and a deadline-compliant, energy-efficient job scheduler. From the promising outcomes of this research, a future where mobile cloud offers real-time computation capabilities in complex environments such as disaster relief or warzone is certainly not far

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017