Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

Xu, Xiaolong; Zhao, Xuan; Ruan, Feng; Zhang, Jie; Tian, Wei; Dou, Wanchun; Liu, Alex X.

doi:10.1155/2017/2376484

Cited by 34 publications

(17 citation statements)

References 32 publications

(91 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The routers could enhance the performance of computation and storage, which could be fully utilized as the computing nodes. In the big data era, there are different performance requirements for the IoT applications, especially for the real-time applications; thus, such applications choose the edge computing nodes as a priority to host [9,10]. In the fog environment, the users could access and utilize the computation, storage, and network resources, like the way the customers use the cloud resources, and the virtualized technology is also applicative to provision the on-demand resources dynamically [11].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Resource Allocation for Load Balancing in Fog Environment

Qing

et al. 2018

Wireless Communications and Mobile Computing

Self Cite

155

View full text Add to dashboard Cite

Fog computing is emerging as a powerful and popular computing paradigm to perform IoT (Internet of Things) applications, which is an extension to the cloud computing paradigm to make it possible to execute the IoT applications in the network of edge. The IoT applications could choose fog or cloud computing nodes for responding to the resource requirements, and load balancing is one of the key factors to achieve resource efficiency and avoid bottlenecks, overload, and low load. However, it is still a challenge to realize the load balance for the computing nodes in the fog environment during the execution of IoT applications. In view of this challenge, a dynamic resource allocation method, named DRAM, for load balancing in fog environment is proposed in this paper. Technically, a system framework for fog computing and the load-balance analysis for various types of computing nodes are presented first. Then, a corresponding resource allocation method in the fog environment is designed through static resource allocation and dynamic service migration to achieve the load balance for the fog computing systems. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of DRAM.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamic Resource Allocation for Load Balancing in Fog Environment

Qing

et al. 2018

Wireless Communications and Mobile Computing

Self Cite

155

View full text Add to dashboard Cite

show abstract

“…The Service Level Agreement (SLA) of today's utility computing is centered around with multiple resources (denoted as multi‐resource), such as computation, storage, Internet traffic, bandwidth, and the availability of these resources. However, few large cloud systems consider all the multi‐resource as they map the workloads in their data center networks, most of them merely consider one or two kind of resources . For example, Amazon provides the CPU and memory resources for the users/tenants, but there is no bandwidth guarantee in network transmission; it does not provide bandwidth guarantee among the virtual machines (VMs) .…”

Section: Introductionmentioning

confidence: 99%

“…However, few large cloud systems consider all the multi-resource as they map the workloads in their data center networks, most of them merely consider one or two kind of resources. [4][5][6][7] For example, Amazon provides the CPU and memory resources for the users/tenants, but there is no bandwidth guarantee in network transmission 8 ; it does not provide bandwidth guarantee among the virtual machines (VMs). 9,10 Thus, how to efficiently mapping the workloads to the data center networks with multi-resource guarantee is a challenging issue.…”

Section: Introductionmentioning

confidence: 99%

Multi‐resource workload mapping with minimum cost in cloud environment

Tan

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

Summary Workload mapping in cloud environment refers to map multiple workloads provided by the cloud users/tenants to the substrate network provided by the cloud providers, which is a NP‐hard problem. The workload is a service demand made to the cloud, which is modeled as a logical network consists of virtual nodes and virtual links. Substrate network is a physical network consists of physical nodes that are inter‐connected via communication links. Devising heuristic methods has become the mainstream of workload mapping problem, which can obtain a feasible solution, but the quality of the solution is not guaranteed. Pointing to this issue, this paper takes the mapping cost of the workloads as the solving objective, and models the workload mapping as a constraint optimization problem. Based on the constraint optimization model, we devise two algorithms to solve the problem. These algorithms can not only find the feasible solution, but also ensure the solution is optimal. Lastly, we have demonstrated the optimality of the proposed algorithms through theoretical proof and evaluated the performance of them through simulation experiment.

show abstract

“…Fog computing provides computing, storage, and networking service between mobile users and traditional Cloud platform, which is complementary to Cloud. The fog computing introduces the middle layer between the cloud and the mobile users, extending the cloudbased network architecture [3][4][5][6]. A basic fog framework is shown in Figure 1, each mobile user is connected to one of the fog nodes.…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment

Peng

Leung

Zheng

et al. 2018

Wireless Communications and Mobile Computing

108

View full text Add to dashboard Cite

Fog computing, as the supplement of cloud computing, can provide low-latency services between mobile users and the cloud. However, fog devices may encounter security challenges as a result of the fog nodes being close to the end users and having limited computing ability. Traditional network attacks may destroy the system of fog nodes. Intrusion detection system (IDS) is a proactive security protection technology and can be used in the fog environment. Although IDS in tradition network has been well investigated, unfortunately directly using them in the fog environment may be inappropriate. Fog nodes produce massive amounts of data at all times, and, thus, enabling an IDS system over big data in the fog environment is of paramount importance. In this study, we propose an IDS system based on decision tree. Firstly, we propose a preprocessing algorithm to digitize the strings in the given dataset and then normalize the whole data, to ensure the quality of the input data so as to improve the efficiency of detection. Secondly, we use decision tree method for our IDS system, and then we compare this method with Naïve Bayesian method as well as KNN method. Both the 10% dataset and the full dataset are tested. Our proposed method not only completely detects four kinds of attacks but also enables the detection of twenty-two kinds of attacks. The experimental results show that our IDS system is effective and precise. Above all, our IDS system can be used in fog computing environment over big data.

show abstract

Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

Cited by 34 publications

References 32 publications

Dynamic Resource Allocation for Load Balancing in Fog Environment

Dynamic Resource Allocation for Load Balancing in Fog Environment

Multi‐resource workload mapping with minimum cost in cloud environment

Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment

Contact Info

Product

Resources

About