Keke Gai

Phase-Change Memory Optimization for Green Cloud with Genetic Algorithm

Green cloud is an emerging new technology in the computing world in which memory is a critical component. Phase-change memory (PCM) is one of the most promising alternative techniques to the dynamic random access memory (DRAM) that faces the scalability wall. Recent research has been focusing on the multi-level cell (MLC) of PCM. By precisely arranging multiple levels of resistance inside a PCM cell, more than one bit of data can be stored in one single PCM cell. However, the MLC PCM suffers from the degradation of performance compared to the single-level cell(SLC) PCM, due to the longer memory access time. In this paper, we present a genetic-based optimization algorithm for chip multiprocessor (CMP) equipped with PCM memory in green clouds. The proposed genetic-based algorithm not only schedules and assigns tasks to cores in the CMP system, but also provides a PCM MLC configuration that balances the PCM memory performance as well as the efficiency. The experimental results show that our genetic-based algorithm can significantly reduce the maximum memory usage by 76.8 percent comparing with the uniform SLC configuration, and improve the efficiency of memory usage by 127 percent comparing with the uniform 4 bits/cell MLC configuration. Moreover, the performance of the system is also improved by 24.5 percent comparing with the uniform 4 bits/cell MLC configuration in terms of total execution time.

Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing

Employing mobile cloud computing (MCC) to enable mobile users to acquire benefits of cloud computing by an environmental friendly method is an efficient strategy for meeting current industrial demands. However, the restrictions of wireless bandwidth and device capacity have brought various obstacles, such as extra energy waste and latency delay, when deploying MCC. Addressing this issue, we propose a dynamic energy-aware cloudlet-based mobile cloud computing model (DECM) focusing on solving the additional energy consumptions during the wireless communications by leveraging dynamic cloudlets (DCL)-based model. In this paper, we examine our model by a simulation of practical scenario and provide solid results for the evaluations. The main contributions of this paper are twofold. First, this paper is the first exploration in solving energy waste problems within the dynamic networking environment. Second, the proposed model provides future research with a guideline and theoretical supports. HighlightsThe first attempt for extending the functionality of cloudlets.Achieve energy-aware performances in the dynamic networking environment.Provide future research in the field with the theoretical support and exploring directions.The model may be migrated and applied in multiple industries.

A novel pre-cache schema for high performance Android system

As a mobile operating system framework, Android plays a significant role in supporting mobile apps. However, current Android application model is not efficient by using current two common approaches, including Activity+XML Layout Files (AXLF) and HTML+WebKit (HWK) models. In this paper, we propose a novel middleware service solution that overcomes the drawbacks with using the pre-cache approach, PrecAche Technology of Android System (PATAS). The proposed method uses HTML to design the application interface and separately store the Page Framework (PF) and Page Data (PD). We create a new middleware of web pages, Version Flags, to indicate whether PF and PD are expired. Our experimental results represent that the proposed approach can improve the execution efficiency as well as reduce the networking costs, which can be broadly used in cloud-based distributed systems. Propose a novel model for high-performance Heterogeneous Android systems.Use middleware-based pre-cache technology.Use middleware-based approach to save networking traffics.Generate Version Flags, which is a proposed new component of web pages.

Privacy-Preserving Data Encryption Strategy for Big Data in Mobile Cloud Computing

Privacy has become a considerable issue when the applications of big data are dramatically growing in cloud computing. The benefits of the implementation for these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The execution time of the data encryption is one of the serious issues during the data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level companioning with privacy concerns. In this paper, we concentrate on privacy and propose a novel data encryption approach, which is called Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data and use privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements. The performance of D2ES has been evaluated in our experiments, which provides the proof of the privacy enhancement.

A survey on FinTech

Abstract As a new term in the financial industry, FinTech has become a popular term that describes novel technologies adopted by the financial service institutions. This term covers a large scope of techniques, from data security to financial service deliveries. An accurate and up-to-date awareness of FinTech has an urgent demand for both academics and professionals. This work aims to produce a survey of FinTech by collecting and reviewing contemporary achievements, by which a theoretical data-driven FinTech framework is proposed. Five technical aspects are summarized and involved, which include security and privacy, data techniques, hardware and infrastructure, applications and management, and service models. The main findings of this work are fundamentals of forming active FinTech solutions.

Secure cyber incident analytics framework using Monte Carlo simulations for financial cybersecurity insurance in cloud computing

The remarkable increasing demands of mitigating losses from cyber incidents for financial firms have been driving the rapid development of the Cybersecurity Insurance (CI). The implementations of CI have covered a variety of aspects in cyber incidents, from hacking to frauds. However, CI is still at its exploring stage so that there are a number of dimensions that are uncovered by the current applications. The cyber attack on critical infrastructure is one of the serious issues that prevents the expansions of CI. This paper addresses CI implementations focusing on cloud‐based service offerings and proposes a secure cyber incident analytics framework using big data, named as Cost‐Aware Hierarchical Cyber Incident Analytics (CA‐HCIA). The approach is designed for matching different cyber risk scenarios, which uses repository data. We use Monte Carlo simulations for extracting the incident features based on the training datasets. The main algorithms in CA‐HCIA include Monte Carlo Cyber Feature Extraction (MC2FE) and Optimal Cost Balance (OCA) Algorithms. Our experimental evaluation has provided the theoretical proof of the adoptability and feasibility. Results show that our proposal improves the cost of existing techniques in 7.98% and 15.39%. Copyright

Maintainable Mobile Model Using Pre-Cache Technology for High Performance Android System

As a mobile operating system framework, Android plays a significant role in supporting mobile apps. Using cloud-based approaches have further enabled the implementations of mobile apps. However, current Android application model is not efficient by using current common approaches. In this paper, we proposed a new mobile app model using precache technology to overcome the current existing obstacles. The results of experiments show that our model can reduce network traffic of Android apps efficiently. At the same time, using our proposed model can reduce the data redundancy and improve maintainability of Android apps.

In-memory big data analytics under space constraints using dynamic programming

The emergence of persistent memories has powered the data processing with the in-memory environment and in-memory data analytics have become an advance of high-performance data processing. Recent explorations of using in-memory technologies address the improvement of the memory performance from re-designing file systems. Most current approaches mitigate data exchanges between buffers and disks by migrating workload to memories. However, this type of solutions will be encountering the restriction of the memory size with the rapid growth of the application volume. This paper focuses on the issue caused by the large amount of data processing within in-memory systems and proposes a novel approach that is designed to dynamically determine whether the data processing should be accomplished in the memory. The proposed approach is called Smart In-Memory Data Analytics Manager (SIM-DAM) model, which utilizes a dynamic working manner of the file system, as well as fully uses hardware mappings. The experimental results obtained from our laboratory evaluations represent that the throughputs of SIM-DAM can achieve a high-level performance with different input data sizes without the constraints of the memories spaces.

Cost-aware optimal data allocations for multiple dimensional heterogeneous memories using dynamic programming in big data

Abstract Multiple constraints in SPMs are considered a problem that can be solved in a nondeterministic polynomial time. In this paper, we propose a novel approach solving the data allocations in multiple dimensional constraints. For supporting the approach, we develop a novel algorithm that is designed to solve the data allocations under multiple constraints in a polynomial time. Our proposed approach is a novel scheme of minimizing the total costs when executing SPM under multiple dimensional constraints. Our experimental evaluations have proved the adaptation of the proposed model that could be an efficient approach of solving data allocation problems for SPMs.

Novel Differential Schema for High Performance Big Data Telehealth Systems Using Pre-cache

With the rapid development of the mobile computing and wireless network technology, the mobile telehealth system has been used more and more widely. An important issue of the telehealth system is that too many network traffics are generated while transferring data from the sensors to servers. To solve this problem, we propose a differential algorithm for reducing the network traffics of the telehealth system. The accuracy of sensors is often too high under some conditions and normally the captured data by sensors are little changed. Our algorithm provides an optimal tradeoff solution addressing both network traffics and data accuracy. The experimental results represent that the proposed approach can reduce network traffic effectively based on the acceptable data accuracy with choosing appropriate parameter.