Currently, the data to be explored and exploited by computing systems increases at an exponential rate. The massive amount of data or so-called “Big Data” put pressure on existing technologies for providing scalable, fast and efficient support. Recent applications and the current user support from multi-domain computing, assisted in migrating from data-centric to knowledge-centric computing. However, it remains a challenge to optimally store and place or migrate such huge data sets across data centers (DCs). In particular, due to the frequent change of application and DC behaviour (i.e., resources or latencies), data access or usage patterns need to be analyzed as well. Primarily, the main objective is to find a better data storage location that improves the overall data placement cost as well as the application performance (such as throughput). In this survey paper, we are providing a state of the art overview of Cloud-centric Big Data placement together with the data storage methodologies. It is an attempt to highlight the actual correlation between these two in terms of better supporting Big Data management. Our focus is on management aspects which are seen under the prism of non-functional properties. In the end, the readers can appreciate the deep analysis of respective technologies related to the management of Big Data and be guided towards their selection in the context of satisfying their non-functional application requirements. Furthermore, challenges are supplied highlighting the current gaps in Big Data management marking down the way it needs to evolve in the near future.

In today's complex Internet platform, online users need help to protect their online identity. Only sometimes, websites are very transparent about how user data will be collected, stored and processed by them. Sometimes Internet entities collect more online user information than required. These entities often share user identity-related data with third parties without consent. Existing traditional identity schemes need to be improved to stop and counter new ways of digital identity theft and fraud. Blockchain is a promising technology to strengthen the preservation of online users' digital identity due to its decentralised nature and robust data security features. In this paper, we proposed and implemented a generic blockchain- IoT-based self-sovereign identity management framework called ChainDiscipline. We have demonstrated the framework's oper- ability and functionality by implementing healthcare and smart home data management-based use cases.

The rapid evolution of Cloud-based services and the growing interest in deep learning (DL)-based applications is putting increasing pressure on hyperscalers and general purpose hardware designers to provide more efficient and scalable systems. Cloud-based infrastructures must consist of more energy efficient components. The evolution must take place from the core of the infrastructure (i.e., data centers (DCs)) to the edges (Edge computing) to adequately support new/future applications. Adaptability/elasticity is one of the features required to increase the performance-to-power ratios. Hardware-based mechanisms have been proposed to support system reconfiguration mostly at the processing elements level, while fewer studies have been carried out regarding scalable, modular interconnected sub-systems. In this paper, we propose a scalable Software Defined Network-on-Chip (SDNoC)-based architecture. Our solution can easily be adapted to support devices ranging from low-power computing nodes placed at the edge of the Cloud to high-performance many-core processors in the Cloud DCs, by leveraging on a modular design approach. The proposed design merges the benefits of hierarchical network-on-chip (NoC) topologies (via fusing the ring and the 2D-mesh topology), with those brought by dynamic reconfiguration (i.e., adaptation). Our proposed interconnect allows for creating different types of virtualised topologies aiming at serving different communication requirements and thus providing better resource partitioning (virtual tiles) for concurrent tasks. To further allow the software layer controlling and monitoring of the NoC subsystem, a few customised instructions supporting a data-driven program execution model (PXM) are added to the processing element’s instruction set architecture (ISA). In general, the data-driven programming and execution models are suitable for supporting the DL applications. We also introduce a mechanism to map a high-level programming language embedding concurrent execution models into the basic functionalities offered by our SDNoC for easing the programming of the proposed system. In the reported experiments, we compared our lightweight reconfigurable architecture to a conventional flattened 2D-mesh interconnection subsystem. Results show that our design provides an increment of the data traffic throughput of $9.5$% and a reduction of $2.2\times$ of the average packet latency, compared to the flattened 2D-mesh topology connecting the same number of processing elements (PEs) (up to 1024 cores). Similarly, power and resource (on FPGA devices) consumption is also low, confirming good scalability of the proposed architecture.

In high-performance computing (HPC) platform, resource usage pattern changes over time which makes the resource monitoring a challenge. Maintaining the performance goals within a good power range is very critical where servers suffer from under utilisation, failure or degraded hardware support. Better forecast of the workload can reduce the energy cost by predicting the future workload more accurately. Identifying the usage pattern is also vital for efficient capacity planning because prediction can also be augmented with an effective resource allocation strategy to manage resource distribution goals. However, a single prediction model does not fit for all. In this paper, we compare forecast performance of the state-of-the-art ARMA class (integrated ARMA (ARIMA), seasonal integrated ARMA (SARIMA) and fractionally integrated ARMA (ARFIMA)) with the singular spectrum analysis (SSA) method using CPU, RAM and Network traces collected from Wikimedia grid. We found that the most simple model of ARMA class (ARIMA) had outperformed other complex ARMA class models while forecasting the bursty pattern of Networks. ARIMA model provides the best forecast for the Network data while SSA is found to be the best method for CPU and RAM. We also show that with proper model fitting, we can achieve high forecasting precision as low as 0.00586% for RAM and maximum error around 5% for Network without having complete information about the underlying system hardware and the running applications type.