Edge computing has been introduced as a promising technology for real-time video streaming systems. However, due to the lack of automatic incentives and the limitation of resources, traditional edge computing performs poorly in nowadays scenarios. To handle these two challenges, we propose a framework ofJointIncentive design andResourceAllocation (JIRA) for edge-based real-time video streaming systems. Technically, to ensure the trust and automatic distribution of incentives, we develop a novel smart contract based incentive mechanism and implement a prototype. Meanwhile, we propose an efficient online algorithm, i.e., JIRA, which dynamically adjusts compression ratio, offloading decision, and resource allocation to achieve performance optimization for video streaming under long-term latency and resource constraints. Specifically, JIRA is based on Lyapunov optimization, which decomposes the challenging long-term decision problem into a series of real-time optimization problems. Then we propose a multi-cut Generalized Benders Decomposition based algorithm (MGA) to tackle the non-convexity of the decomposed problem. Through rigorous theoretical analysis, we prove the performance bound of JIRA. Extensive simulations demonstrate that the proposed schemes can achieve an efficient trade-off between accuracy performance and energy consumption.

Offloading computation based on mobile edge computing paradigms can augment the computational capabilities of resource-scarce mobile devices. However, the capacity limitations of edge servers constrain the performance improvement achieved through computation offloading. In this article, we consider a three-tier computation offloading schema with multiple users, edge servers, and cloud servers. Computation can be offloaded from mobile devices to edge servers or can be further offloaded to remote cloud servers if necessary. Since a number of mobile devices connected to edge servers will share a common wireless communication network, which may contain both uplink and downlink channels, the assignment of bandwidth assignment the channels also constrains the performance improvement of computation offloading. In this paper, the problems of how to determine the offloading strategy and of how to assign the bandwidth are jointly studied and formulated as a programming problem to minimize the average application response time. We analyze the joint problem and further transform it into a piecewise convex programming problem. We propose an efficient algorithm that can find the optimal solution. Extensive experiments demonstrate that our algorithm significantly outperforms previous algorithms. The experimental results also show that the performance of our algorithm is highly robust.

Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. However, TPAs may also be untrusted for forging the auditing results or colluding with cloud storage servers to deceive users. In this paper, we propose a novel Blockchain-based Public Auditing Outsourcing system without TPAs (BPAO), in which the computationally expensive operations in public auditing are outsourced through blockchain to the cloud servers without risking users' privacy. Our security analysis indicates that BPAO achieves soundness and robustness. The experimental results show that BPAO is computationally efficient for cloud storage user.

Network function virtualization (NFV) is a new network architecture concept that simplifies the deployment of network services and improves service management. However, it is challenging for a network service provider (NSP) to decide where to place virtual network functions (VNFs). Most previous studies have considered only single-chain services, wherein the VNFs for a request are executed in sequence. In contrast to previous approaches, we consider more general and practical situations in which the VNFs of a request can be executed in parallel and are represented as a forwarding graph. Our objective is to maximize the profits earned by providing network services while satisfying the delay requirements of requests. We formulate the VNF placement problem as an integer linear programming (ILP) problem. Due to the complexity of this problem, we propose a probability-based approach called PBP, in which the placements of the VNFs are determined based on their probabilities of contributing to the profit. Furthermore, we propose a heuristic reliability-aware algorithm to guarantee service reliability, in which each VNF of a request is assigned a backup that can be shared with other requests. Simulation experiments show that PBP achieves a much shorter computation time than previous algorithms while earning higher profit, and furthermore, our reliability-aware algorithm provides the same reliability as a previous algorithm while yielding much higher profit.

In edge computing, computation offloading of video stream tasks and collaboration processing among edge nodes is a huge challenge. The previous research mainly focuses on the selection of computing modes and resource allocation, but taking no joint consideration of computation offloading and collaborative processing of edge node groups. In order to jointly tackle these issues in edge computing, we propose an innovative distributed collaborative video stream processing framework for edge computing(DCVP), where the video tasks are assigned to mobile edge computing (MEC) nodes or edge groups based on the offloading decision. First, we design a method for the group formation, which matches video subtasks to appropriate edge groups. In addition, we present two offloading modes for video streaming tasks, e.g., offloading to MEC nodes or edge groups, to handle computationally intensive video tasks. Furthermore, we formulate the joint optimization problem for offloading decision and collaborative processing of video subtasks into a distributed optimization problem. Finally, we employ an alternating direction method of multipliers (ADMM)-based algorithm to solve the problem. Simulation results under multiple parameters show the proposed schemes outperform other typical schemes.

In this paper, we focus on the problem of offloading computation intensive tasks of mobile applications from resource-scarce mobile devices to the servers located at the edge networks in order to minimize the average response time of the applications. We consider a number of mobile devices connected by a shared communication channel to a server located in the edge network and therefore transmission collision occurs if more than one mobile device attempts to transmit data simultaneously. We first formulate the offloading problem as a mixed integer programming (MIP) problem. Since the problem is NP-hard, we design a heuristic algorithm that considers possible transmission collision over the shared channel and offloads efficiently tasks of a mobile application to the server. We demonstrated that our proposed algorithm outperforms previous offloading algorithms significantly in terms of the average response time. Furthermore, we showed that our proposed algorithm yields less energy consumption than previous algorithms in realistic system scenarios.

The authors address the problem of determining a replica placement scheme for sensor data in a cache network constructed by a number of cache nodes in edge networks of the Internet, with the purpose of minimising the total costs for data placement and access. They formulate the placement problem as a mixed integer programming (MIP) problem and then propose several heuristic approaches to solve the problem. They first relax the MIP problem to a linear programming (LP) problem and then round the fractional solution values of the LP problem to integers. Next, they employ a Lagrangian relaxation approach to find a solution for each data item without considering node capacity and then rearrange the placement at each node to satisfy the capacity constraint. For comparison, they also propose a greedy approach that determines a replica placement at a node by using the node local information. They show that the former two approaches lead to good performance that is only <8% worst than the theoretical lower bound.

A time division multiple access (TDMA) channel control approach is an efficient method for large-scale wireless sensor networks, especially when the network is congested. However, it is generally difficult to allocate the time slots to the sensors in a distributed way. A distributed approach, called Tree MAC Tree MAC, was proposed wherein the sensors in the network are constructed in a tree hierarchy with the sink as the root, and a time cycle is divided into frames and each frame into time slots. Tree MAC guarantees that in each time cycle a sensor has one time slot to transfer its data to the sink. However, the key disadvantage of Tree MAC is its inefficiency and low channel utilization. In this paper, we propose a new approach, called Pack MAC, based on Tree MAC that tries to utilize the unused time slots in order to improve the channel utilization. It has been shown that Pack MAC can be realized with a negligible overhead, but better channel utilization than Tree MAC. Furthermore, it has also been shown using simulation that in Pack MAC both the transmission delay and the network throughput can be improved significantly in comparison with Tree MAC.

Development of cloud computing and mobile wireless technologies has given rise to mobile cloud computing (MCC). The limitations of battery capacity and computing capability of mobile devices can be alleviated by offloading some tasks from mobile devices to the cloud. In this paper, we focus on the computation offloading problem in mobile cloud computing. In particular, for a given set of computational components which constitute a mobile application, we attempt to decide which components should be offloaded to the cloud such that the application can be completed at the minimal execution cost. We formulate the mobile computation offloading problem as an optimization problem. Then we propose two optimal offloading algorithms to solve the problem. The efficiency of the proposed algorithms is evaluated using numerical experiments.

Mobile ad hoc networking allows nodes to form temporary networks and communicate with each other possibly via multiple hops. By using a special node called the gateway, an ad hoc network can be connected to the Internet so that packets generated in the ad hoc network can be relayed to the Internet and vice versa. However, a problem may arise in which the network bandwidth is not used fairly among the nodes. That is, nodes near the gateway may overuse the bandwidth while nodes far away from the gateway scarcely share the bandwidth. Furthermore, if the destination node is located in the same ad hoc domain as the source, existing route selection methods result in high overhead or long routes. Addressing these problems, we propose an efficient tree construction algorithm that constructs a tree structure rooted at the gateway in order to improve the fairness of the bandwidth usage. We also devise a rerouting algorithm that dynamically finds new and better routes for the internal traffic within the same ad hoc domain.

A sensor network consists of a large number of distributed wireless sensors which are equipped with low power wireless transceivers. Network lifetime, scalability, and load balancing are important requirements for many data gathering sensor network applications. Network clustering is an effective approach for achieving these goals wherein sensors are grouped into multiple clusters. Multihop data transmission may provide efficient energy conservation further. A sensor in a cluster may work as a cluster head for data gathering, aggregation, and report to the base station (BS). These tasks can also be performed by distinct sensors in the same cluster. In this paper, we clarify some characteristics of multi-hop data transmission. Furthermore, we propose a new data gathering approach for single-hop transmission wherein both the data gathering and the aggregation are performed by the same sensor in a cluster but the report to the BS may be done by a different sensor. The simulation results show that the proposed algorithm achieves better performance than other existing algorithms.

We propose several heuristic algorithms that reconfigure logical topologies in wide-area wavelength-routed optical networks. Our reconfiguration algorithms attempt to keep the network availability as much as possible during the reconfiguration process. For this purpose, a lightpath is taken as the minimum unit of reconfiguration, which is an all-optical wavelength-division multiplexed (WDM) channel. The proposed algorithms are evaluated by using an NFSNFT-like network model with 16 nodes and 25 links. The results show that very simple algorithms provide very small computational complexity but poor performance, i.e., bad network availability, and that an efficient algorithm provides reasonable computational complexity and very good performance. More complex algorithms may improve performance somewhat further but have unrealistically large computational complexity.

We propose an efficient heuristic algorithm that sets up and releases lightpaths for connection requests dynamically. We partition the routing and wavelength assignment (commonly known as RWA) problem into two subproblems and solves both of them using a well-known shortest path routing algorithm. For solving the routing subproblem, an auxiliary graph is created whereby the nodes and links in the original network are transformed to the edges and vertices, respectively, and the availability of each wavelength on the input and output links of a node as well as the number of available wavelength converters are taken into account in determining the weights of edges. Furthermore, for solving the wavelength assignment subproblem, an auxiliary graph is also utilized and the cost for wavelength conversion is taken into consideration in the edge weight function. A distinguished feature of our algorithm is that it employs more accurate network information on the availability of both the wavelengths and the wavelength converters than the existing algorithms in deciding the routing and the wavelength assignment. Simulation results show that our algorithm performs much better than previously proposed algorithms with comparable computation time, especially when the number of wavelengths is large while the number of converters at each node is limited.

We propose a distributed channel allocation algorithm based on a threshold scheme, called D-CAT, for cellular mobile networks. The D-CAT algorithm employs two thresholds: (i) a heavy threshold used for determining whether a cell is heavy, i.e., overloaded, and for triggering the channel allocation algorithm; and (ii) a target threshold used for indicating the target number of free channels that a heavy cell intends to acquire. The D-CAT algorithm determines the optimal number of free channels as well as the cell(s) from where a heavy cell should import to satisfy its channel demand.