A computation offloading method over big data for IoT-enabled cloud-edge computing

Benefiting from the real-time processing ability of edge computing, computing tasks requested by smart devices in the Internet of Things are offloaded to edge computing devices (ECDs) for implementation. However, ECDs are often overloaded or underloaded with disproportionate resource requests. In addition, during the process of task offloading, the transmitted information is vulnerable, which can result in data incompleteness. In view of this challenge, a blockchain-enabled computation offloading method, named BeCome, is proposed in this article. Blockchain technology is employed in edge computing to ensure data integrity. Then, the nondominated sorting genetic algorithm III is adopted to generate strategies for balanced resource allocation. Furthermore, simple additive weighting and multicriteria decision making are utilized to identify the optimal offloading strategy. Finally, performance evaluations of BeCome are given through simulation experiments.

BeCome: Blockchain-Enabled Computation Offloading for IoT in Mobile Edge Computing

Currently, edge computing (EC), emerging as a burgeoning paradigm, is powerful in handling real-time resource provision for Internet of Things (IoT) applications. However, due to the spatial distribution of geographically sparse IoT devices and the resource limitations of EC units (ECUs), the resource utilization of corresponding edge servers is relatively insufficient and the execution performance is ineffective to some extent. A privacy leakage, including personal information, location, media data, etc., during the transmission process from IoT devices to edge servers severely restricts the application of ECUs in IoT. To address these challenges, a two-phase offloading optimization strategy is put forward for joint optimization of offloading utility and privacy in EC enabled IoT. Technically, a utility-aware task offloading method, named UTO, is devised first to obtain the goal of maximizing the resource utilization of ECUs and minimizing the implementation time cost. Then a joint optimization method, named JOM, for utility and privacy tradeoffs is designed to balance the privacy preservation and execution performance. Eventually, the experimental evaluations are designed to illustrate the efficiency and reliability of UTO and JOM.

Joint Optimization of Offloading Utility and Privacy for Edge Computing Enabled IoT

An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks

Smart city is gradually forming a large scope of Internet of Things (IoT) networks with diffusely deployed IoT devices that produce quantities of services. Considering the large-scale and widely distributed features of IoT networks, edge computing is emerged as a powerful and suitable paradigm to provide computing abilities for the IoT devices at the edge of the networks. In edge computing, the IoT services could be placed on the edge computing units (ECUs) for execution, which provides low latency and eases the burden of bandwidth. However, it is still challenging to improve the overall ECU execution performance (i.e., the resource usage, the load balance levels, and the power consumption of ECUs) and meanwhile prevent privacy leakage of the IoT devices for service placement. To tackle this challenge, a trust-oriented IoT service placement method, abbreviated as TSP, is proposed for smart cities in edge computing. Technically, improving the strength Pareto evolutionary algorithm (SPEA2) is leveraged to acquire the balanced placement strategies for the tradeoffs among the execution performance metrics with privacy preservation. Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) and multicriteria decision-making (MCDM) techniques are employed to identify the optimal placement strategy among the obtained service placement strategies. Eventually, systematic experiments are conducted to verify the efficiency and reliability of TSP.

Trust-Oriented IoT Service Placement for Smart Cities in Edge Computing

Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training set, the prediction model can guarantee that the social welfare, allocation accuracy, and resource utilization in the feasible solution are very close to those of the optimal allocation solution. The experimental results show that the proposed scheme has good effect on resource allocation in cloud computing.

Machine Learning Based Resource Allocation of Cloud Computing in Auction

An online auction mechanism for time-varying multidimensional resource allocation in clouds

An online auction mechanism for cloud computing resource allocation and pricing based on user evaluation and cost

Cloud computing is characterized by strong computing and storage capabilities, and edge computing has the advantages of low latency and low power consumption. Many service providers have begun to combine the advantages of cloud and edge computing to provide better quality of service, but the heterogeneity of cloud and edge computing represents a challenge for service deployment and resource allocation. This paper proposes a framework for cloud-edge collaboration based on live video webcast services and transforms the resource allocation problem into a constrained integer programming (IP) model. Additionally, we introduce an auction mechanism to solve the problem of resource competition among the anchor users in live services. By solving the IP resource allocation problem and Vickrey–Clarke–Groves mechanism, we obtain the optimal resource allocation mechanism. Based on the dominant resource proportion and matching model, we design a resource allocation mechanism for the online environment. These mechanisms can be used for reservation and live webcast scenarios. Furthermore, we prove that the two mechanisms have individual rationality and truthfulness. Our approach is characterized by high social welfare, high resource utilization and a short execution time.

Strategy-proof mechanism for online resource allocation in cloud and edge collaboration

Time-varying resource allocation in which the resource requirements of a job can vary over time is a new challenge in cloud computing. Time-varying resource allocation can be combined with an auction mechanism to improve the social welfare of resource providers. However, existing research results are based on fixed resource requirements and consequently cannot be used in time-varying resource allocation. This paper proposes a creative integer programming model for time-varying resource allocation problems and designs a strategy-proof online auction mechanism that allows jobs to be scheduled in a preemptive-restart mode. The advantage of this approach is that it can respond to high-priority jobs in a timely manner while still executing low-priority jobs with the restart mode. For the resource allocation and scheduling algorithm, we propose dynamic priority based on the dominant resource proportion and valid active time to improve social welfare and resource utilization. Furthermore, we present a payment pricing algorithm based on critical value theory. Finally, we prove that our proposed mechanism is strategy-proof. Our approach is experimentally compared with existing algorithms in terms of execution time, social welfare, resource utilization and job completion ratio.

Ning Xie

Papers

Machine Learning Based Resource Allocation of Cloud Computing in Auction

An online auction mechanism for time-varying multidimensional resource allocation in clouds

An online auction mechanism for cloud computing resource allocation and pricing based on user evaluation and cost

Strategy-proof mechanism for online resource allocation in cloud and edge collaboration

Strategy-Proof Mechanism for Online Time-Varying Resource Allocation with Restart