Multi-access edge computing (MEC) is a promising architecture to provide low-latency applications for future Internet of Things (IoT)-based network systems. Together with the increasing scholarly attention on task offloading, the problem of servers’ resource allocation has been widely studied. The limited computational resources of edge servers (ESs) cannot meet the different demands of terminal entities (TEs). This makes it a challenge to efficiently schedule computational tasks on ESs. In this paper, we consider a MEC resource transaction market with multiple ESs and multiple TEs, which are interdependent and mutually influence each other. This paper aims to investigate the dynamic tasks allocation problem between TEs and ESs and to meet the optimal benefits for both parties in MEC system. However, this many-to-many interaction requires resolving several problems, including task allocation, TEs’ selection on ESs and conflicting interests of both parties. A bilateral game framework is applied to tackle the tasks allocation problem by modeling the problem as two noncooperative games: the supplier and customer side games. The existence and uniqueness of the Nash equilibrium in the aforementioned games are proved. Adistributedtaskoutsourcingalgorithm (DTOA) is designed to determine the equilibrium. Our simulation results have demonstrated the superior performance of DTOA in increasing the ESs’ profit and TEs’ payoffs, as well as flattening the peak and off-peak loads.

Combining a series of atomic services into a composite value-added service offers promise in meeting the increasingly diverse needs of users in the cloud market. However, without considering concurrency of composite service requests among multiple users, existing studies seldom focus on the competition relationship among users and thus lack an incentive mechanism to efficiently provide the request strategy of users. In this article, taking into consideration the multi-user competition and interaction with the cloud provider, we design a composite service reservation framework in which users’ requests can be rationally and efficiently multiplexed. Due to selfishness, each user tends to expect to maximize its own utility in terms of revenue, payoff, and performance of composite service. The optimization problem is modeled from the perspective of game theory and defined as a non-cooperative game. The existence of Nash equilibrium solution of the formulated game is proved by an equivalent variational inequality problem. An iterative proximate algorithm (IPA) is proposed to find a utility-balanced request strategy, which theoretically leads to a Nash equilibrium solution. A series of simulation experiments are conducted to verify our theoretical analysis. The experimental results show thatIPAconverges to a Nash equilibrium in an acceptable number of iterations. The stable request strategies can improve the utilities of users and the resource utilization of the cloud provider, as the proposed framework empowers users to transfer requests from peak periods into non-peak ones.

With the popularity of the Internet of Things (IoT) and large-scale deployment of sensors, data have exploded. Big data is utilized to extract useful knowledge and information and served as data services to consumers. While most of the current researches in the field of big data services focus on developing and improving algorithms for data mining and information extraction, and rarely studies “big data” from an economic perspective. This article thus studies the pricing and profit maximization problems in big data markets from an economic perspective. First, a method of quantifying the value of data is proposed to study the utility of raw data. Then, we build an economic model of the data market, which consists of three parties: 1) data vendor; 2) service provider; and 3) service users. The data vendor gathers various raw data and sells them to the service provider. The raw data are further processed into data services by a service provider, who provides service subscriptions to users to obtain profits. The interactions among them are formulated as a Stackelberg game to maximize the profits of all participators. The existence and uniqueness of equilibria pricing strategies are proved. Finally, numerical results show that the participators of the data market can achieve the maximum profit through the proposed pricing mechanism and economic model.