The concept of cloud computing continues to spread widely, as it has been accepted recently. Cloud many unique advantages which can be utilized facilitate workflow execution. Instance-intensive cost-constrained workflows are with a large number instances (i.e. instance intensive) bounded by certain budget for execution cost constrained) on platform workflows). However, there are, so far, no dedicated scheduling algorithms instance-intensive workflows. This paper presents novel...

Equipped with easy-to-access micro computation access points, the fog computing architecture provides low-latency and ubiquitously available offloading services to many simple cheap Internet of Things devices limited energy resources. One obstacle, however, is how seamlessly hand over mobile IoT among different points when in action so that service not interrupted -- especially for time-sensitive applications. In this article, we propose Follow Me Fog (FMF), a framework supporting new...

Directional distribution analysis has long served as a fundamental functionality in abstracting dispersion and orientation of spatial datasets. Spatial datasets that describe sensitive information individuals such health status home addresses must be used shared cautiously to protect individuals' privacy. There is an inherent tension between the need accurate directional result requirement location Plenty excellent privacy protection approaches geo-indistinguishability can provide...

Nowadays, large-scale Cloud-based applications have put forward higher demand for storage ability of data centres. Data in the Cloud need to be stored with high efficiency and cost effectiveness while meeting requirement reliability. While current systems, typical 3-replicas replication strategies are applied reliability, this paper we propose a novel cost-effective dynamic strategy which facilitates an incremental method reduce meet reliability at same time. This works very well especially...

Nowadays, smart environments (e.g., home, city) are built heavily relying on Cloud computing for the coordination and collaboration among objects. is typically centralized but objects ubiquitously distributed, thus, data transmission latency (i.e., end-to-end delay or response time) between a critical issue especially to applications that have strict requirements. To address concern, new Fog paradigm recently proposed by industry, while detailed platform yet be developed. The key idea bring...

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy data intensive applications without infrastructure investment, where large application sets can be stored in the cloud. Based on pay-as-you-go model, strategies benchmarking approaches have been developed for cost-effectively storing volume generated However, they are either insufficiently cost-effective or impractical used at runtime. In this paper, toward achieving minimum cost benchmark,...

Cloud computing is a promising distributed platform for big data applications, e.g., scientific since excessive resources can be obtained from cloud services processing and storing both existing generated application datasets. However, when tasks process stored in centers, the inevitable movements will cause huge bandwidth cost execution delay. In this paper, we construct tripartite graph based model to formulate replica placement problem propose genetic algorithm strategy applications...

The prosperity of Internet Things (IoT) and the success rich Cloud services have expedited emergence a new computing paradigm called Fog computing, which promotes processing data at proximity their sources. Complementary to Cloud, promises offer many appealing features, such as low latency, cost, high multitenancy, scalability, consolidate IoT ecosystem. Although concept has been widely adopted in areas, comprehensive realization yet adequately researched. To address all these issues, this...

Online social networks are plagued by fake information. In particu- lar, using massive accounts (also called Sybils), an attacker can disrupt the security and privacy of benign users spreading spam, malware, disinformation. Existing Sybil detection methods rely on rich content, behavior, and/or graphs generated Sybils. The key limitation these is that they incur significant delays in catching Sybils, i.e., Sybils may have already performed many malicious activities when being detected. this...

Today, artificial intelligence and deep neural networks have been successfully used in many applications that fundamentally changed people’s lives areas. However, very limited research has done the meteorology area, where meteorological forecasts still rely on simulations via extensive computing resources. In this paper, we propose an approach to using network forecast future temperature according past values. Specifically, design a convolutional recurrent (CRNN) model is composed of...

Data outsourcing to cloud has been a common IT practice nowadays due its significant benefits. Meanwhile, security and privacy concerns are critical obstacles hinder the further adoption of cloud. Although data encryption can mitigate problem, it reduces functionality query processing, e.g., disabling SQL queries. Several schemes have proposed enable one-dimensional on encrypted data, but multi-dimensional range not well addressed. In this paper, we propose secure scalable scheme that...

This research explores the application of machine learning (ML) in domain electrochromic (EC) technology, focusing specifically on liquid-state devices (ECDs). Unlike traditional solid-state ECDs, liquid offer a simpler structure, reducing manufacturing variables and potentially improving prediction accuracy with minimal input data. Two types ECDs were developed using solutions ammonium metatungstate-iron(II) chloride sulfate, resulting 20 different varying concentration gradients....

Many scientific workflows are data intensive where a large volume of intermediate is generated during their execution. Some valuable need to be stored for sharing or reuse. Traditionally, they selectively according the system storage capacity, determined manually. As doing science on cloud has become popular nowadays, more can in based pay-for-use model. In this paper, we build an Intermediate Dependency Graph (IDG) from provenances workflows. Based IDG, develop novel strategy that reduce...

SUMMARY Many scientific workflows are data intensive where large volumes of intermediate generated during their execution. Some valuable need to be stored for sharing or reuse. Traditionally, they selectively according the system storage capacity, determined manually. As doing science in cloud has become popular nowadays, more can based on a pay‐for‐use model. In this paper, we build an dependency graph (IDG) from provenance workflows. With IDG, deleted regenerated, and as such develop novel...

In current Cloud computing environments, management of data reliability has become a challenge. For data-intensive scientific applications, storing in the with typical 3-replica replication strategy for managing would incur huge storage cost. To address this issue, paper we present novel cost-effective mechanism named PRCR, which proactively checks availability replicas maintaining reliability. Our simulation indicates that, comparing strategy, PRCR can reduce space consumption by one-third...