Cooperative trust relaying and privacy preservation via edge-crowdsourcing in social Internet of Things

Energy and performance-efficient task scheduling in heterogeneous virtualized cloud computing

A novel four-tier architecture for delay aware scheduling and load balancing in fog environment

Task scheduling approaches in fog computing: A systematic review

With the emergence of computation intensive applications, a huge number of end-user devices are in urgent need of computational capabilities. Up-to-now, conventional cloud-based solutions are challenged by the bottleneck of network bandwidth, high delay and low quality of experience (QoE). In this paper, we illustrate the concept of edge computing enabled Internet-of-Vehicles (EC-IoV), which leverages connected vehicles as the edge computing platform. Then, we design a QoE based node selection (QNS) strategy, through which users can choose a proper edge node from quantities of vehicles to achieve a satisfying QoE on the whole. Simulation results demonstrate the QoE enhancement of QNS when compared with baseline strategies.

QoE-based node selection strategy for edge computing enabled Internet-of-Vehicles (EC-IoV)

Summary
With the rapid development of cloud computing, the number of cloud users is growing exponentially. Data centers have come under great pressure, and the problem of power consumption has become increasingly prominent. However, many idle resources that are geographically distributed in the network can be used as resource providers for cloud tasks. These distributed resources may not be able to support the resource-intensive applications alone because of their limited capacity; however, the capacity will be considerably increased if they can cooperate with each other and share resources. Therefore, in this paper, a new resource-providing model called “crowd-funding” is proposed. In the crowd-funding model, idle resources can be collected to form a virtual resource pool for providing cloud services. Based on this model, a new task scheduling algorithm is proposed, RC-GA (genetic algorithm for task scheduling based on a resource crowd-funding model). For crowd-funding, the resources come from different heterogeneous devices, so the resource stability should be considered different. The scheduling targets of the RC-GA are designed to increase the stability of task execution and reduce power consumption at the same time. In addition, to reduce random errors in the evolution process, the roulette wheel selection operator of the genetic algorithm is improved. The experiment shows that the RC-GA can achieve good results.

A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model

Mobile cloud computing, which integrates the cloud computing techniques into the mobile environment, is regarded as one of the enabler technologies for 5G mobile wireless networks. There are many sporadic spare resources distributed within various devices in the networks, which can be used to support mobile cloud applications. However, these devices, with only a few spare resources, cannot support some resource-intensive mobile applications alone. If some of them cooperate with each other and share their resources, then they can support many applications. In this paper, we propose a resource cooperative provision mode referred to as "Crowd-funding", which is designed to aggregate the distributed devices together as the resource provider of mobile applications. Moreover, to facilitate high-efficiency resource management via dynamic resource allocation, different resource providers should be selected to form a stable resource coalition for different requirements. Thus, considering different requirements, we propose two different resource aggregation models for coalition formation. Finally, we may allocate the revenues based on their attributions according to the concept of the "Shapley value" to enable a more impartial revenue share among the cooperators. It is shown that a dynamic and flexible resource-management method can be developed based on the proposed Crowd-funding model, relying on the spare resources in the network.

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Crowd-Funding: A New Resource Cooperation Mode for Mobile Cloud Computing.

Data redundancy elimination (DRE) technology can efficiently reduce the redundant IP traffic in the network [1,2]. A detailed DRE process consists of many steps, such as the fingerprint selection, chunk matching, packet encoding, and packet decoding [3]. Fingerprint selection is one of the most important factors for DRE. In traditional DRE, the redundant chunk size is set to a fixed value. The fixed chunk size will affect the DRE performance seriously, so keeping the redundant chunk size variable is necessary [4]. Hence, multi-resolution chunking (MRC) has been proposed. However, MRC does not consider the overlap between the adjacent chunks, which inevitably brings some unnecessary overhead. Besides, chunk-matching is another key point for DRE. There are two popular chunk matching mechanisms, i.e., Chunk-Match and Max-Match. Chunk-Match may not bring much storage overhead, but the size of chunk-matching is limited, which results in some redundant bytes to be missed. By contrast, Max-Match can maximize the size of chunk-matching by extending the boundary of the identified redundant chunk towards its front or rear. Nevertheless, Max-Match brings high storage overhead because it needs to cache the whole packet for the maximizing match. Based on the assumption that consecutive popular chunks have a fair chance of appearing together, CombiHeader proposed a matching mechanism to maximize the chunk-matching size by using the chunk aggregation technique [4]. The disadvantage of CombiHeader’s mechanism is that the redundant chunk size must be a multiple of the size of a basal chunk. Thus, in order to overcome the problems, we propose a new DRE scheme named as redundancy-maximizing identification for data redundancy elimination (RMI-DRE). RMI-DRE includes two main parts: a new fingerprint selection algorithm (i.e. w -MAXP+) and a dynamic chunk matching mechanism. To illustrate RMI-DRE scheme in detail, four sub-tasks are presented, i.e., the setting of basal chunk size and upper bound, the generation of representative fingerprints, the chunk-matching to find redundant bytes as more as possible, and the encoding/decoding for redundant chunks. 1. How to set the basal chunk size and the upper bound? According to the distributions of matched chunk size in enterprise network and campus network in [4], we can find that more than 70% of matched chunks are shorter than 150 bytes while only less than 10% of matched chunks are near upon 1500 bytes. What is more, more than 60% of matched

RMI-DRE: a redundancy-maximizing identification scheme for data redundancy elimination

In multipath routing of wireless sensor network (WSN), greedy path selection is one of multipath routing schemes to choose optimal paths on demand to transmit traffic, but the greedy selection is always prone to cause path oscillation (frequent path changes) between each source-destination pair. To alleviate the side effect, we propose an adaptive path selection model (called WARAS) inspired by metabolism behaviors of Escherichia Coli (E. coli). The model consists of two main features. The first one is a redefined parameter called path-activity to indicate adaptation goodness of multipath traffic transmission to real-time network environments, which is inversely proportional to absolute difference between current path quality and best path quality. The second one is a novel attractor expression for each attractor of multi-attractor equations redesigned by us to concretely specify stochastic effect of noise on the path selection. Finally, by putting forward a path quality probe scheme in a multipath AODV protocol, we validate our model can perform better on reducing average network delay and path oscillation than the greedy path selection.

WARAS: An adaptive WSN multipath selection model inspired by metabolism behaviors of Escherichia Coli

With the rapid development of cloud computing and the exponential growth of cloud users, federated clouds are becoming increasingly prevalent based on the idea of resource cooperation. In this paper, we consider a new resource cooperation model called “Crowd-funding”, which is aimed at integrating and uniformly managing geographically distributed resource-limited resource owners to achieve a more effective use of resources. The resource owners are rational and maximize their own interest when contributing resources, so a reasonable pricing scheme can incentivize more resource owners to join the Crowd-funding system and increase their service level. Therefore, we propose a dynamic pricing scheme based on a repeated game between the “Crowd-funding” system and the resource owners. The simulation results show that our resource pricing scheme can achieve more effective and longer-lasting incentivizing effects for resource owners.

Nan Zhang

Papers

A genetic algorithm‐based task scheduling for cloud resource crowd‐funding model

Crowd-Funding: A New Resource Cooperation Mode for Mobile Cloud Computing.

RMI-DRE: a redundancy-maximizing identification scheme for data redundancy elimination

WARAS: An adaptive WSN multipath selection model inspired by metabolism behaviors of Escherichia Coli

A Dynamic Resource Pricing Scheme for a Crowd-Funding Cloud Environment