Increasing popular big data applications bring about invaluable information, but along with challenges to industrial community and academia. Cloud computing with unlimited resources seems to be the way out. However, this panacea cannot play its role if we do not arrange fine allocation for cloud infrastructure resources. In this paper, we present a multi-objective optimization algorithm to trade off the performance, availability, and cost of Big Data application running on Cloud. After analyzing and modeling the interlaced relations among these objectives, we design and implement our approach on experimental environment. Finally, three sets of experiments show that our approach can run about 20 percent faster than traditional optimization approaches, and can achieve about 15 percent higher performance than other heuristic algorithms, while saving 4 to 20 percent cost.

Cloud Infrastructure Resource Allocation for Big Data Applications

While enjoying the convenience brought by various kinds of IoT devices, our private data are leaked. Smartphones, as the typical pivot of IoT devices, use various kinds of apps, which collect our private data. In fact, private data leakage, as a potential hazard, is caused by the current design trend in industry, which is bigger and bigger. However, few people notice the side-effect, while we seem never bored by pursuing “smart” devices. In this article, we introduce the behaviors of data collection, and illustrate the motivations and reasons behind them. Thankfully, cloud computing with sufficient resources and exquisite services is a perfect way out. We introduce a mobile–cloud framework to provide fine-grained permission authorization service for IoT devices, and show its performance by experimental results. Nevertheless, there are still lots of issues to be solved for the perfect IoT-cloud architecture. We list and analyze some obstacles and trends in the field of IoT–cloud.

Who Moved My Data? Privacy Protection in Smartphones

Mobile vehicular cloud has become popular with the rapid development of cloud computing and mobile computing. Nested loops are usually the most critical part in multimedia and high performance Digital Signal Processing (DSP) systems which are widely used in vehicular applications and systems. In order to further explore the parallelism in nested loops, we study how to maximize the system performance with considering the energy reduction for applications on Chip Multiprocessor (CMP) architectures. We propose an algorithm Energy-Aware Loop Parallelism Maximization (EALPM) to maximize the system performance with the consideration of energy reduction for applications with multidimensional nested loops. Our experiment shows that using the EALPM algorithm significantly improves both performance and energy consumption on average in comparision to other algorithms.

Loop Parallelism Maximization for Multimedia Data Processing in Mobile Vehicular Clouds

Cloud Computing has emerged as a popular technology that support computing on demand services by allowing users to follow the pay-per-use-on-demand model. Minimizing energy consumption in cloud systems has many benefits that enable green computing. Energy aware task scheduling in cloud to the users by service cloud providers has non negligible influences on optimal resources utilization and thereby on the cost benefit. The traditional algorithms for task scheduling are not well enough for cloud computing. In such environment, tasks should be efficiently scheduled such a way that the makespan is reduced. In this paper, we proposed a biologically inspired scheduling scheme, which is a based on a modified version of the ant colony optimization that aims at reducing the makespan time while ensuring load balancing among resources in order to enable green computing. Experiments of the proposed scheme in various scenario have been conducted in order to elaborate the impact of proposed models in the reduction of makespan. The obtained results demonstrate the effectiveness of the proposal in regards to the compared algorithms.

Efficient and Scalable ACO-Based Task Scheduling for Green Cloud Computing Environment

In this mobile period, users enjoy various functions and convenience from mobile devices, but the coarse-grained data access permission of current mobile operating system brings about serious risk of privacy leakage. In other words, users mobile data are over-collected by apps. This study focuses on a new framework to provide fine-grained data access mechanism for mobile systems. With the help of cloud storage, we separate mobile data by types, contents, and privacy levels. We design and implement the DASS system to provide fine-grained data access management, while keep it transparent to users. Experimental results show that our system can protect users&#x2019; privacy greatly while keep the extra cost within a reasonable range.

DASS: A Web-Based Fine-Grained Data Access System for Smartphones

Heterogeneous cloud provides a feasible and attractive kind of infrastructure to the cloud applications which want to obtain high performance, availability or profit. Meanwhile, the cloud providers consider heterogeneous cloud as an ideal form to manage their computing resource since it can improve the utilization of their computing resources and extend the computing power of an individual provider. In this paper, we analyze the structure of heterogeneous cloud, and propose a framework of multi-objective constrained resource management, including dynamic profit-driven provisioning, availability-aware placement and power-saved consolidation. The result of our experiments has demonstrated the feasibility and effectiveness of the proposed framework.

RaHeC: A Mechanism of Resource Management for Heterogeneous Clouds

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RaHeC: A Mechanism of Resource Management for Heterogeneous Clouds