Nowadays, the development of Collaborative Edge Computing (CEC) has greatly accelerated deep neural network (DNN) inference by enabling edge service providers (ESPs) in the JointCloud federation to deploy computational resources and well-trained DNN models at the edge of the network for collaborative inference with mobile devices (MDs), thereby promoting the rapid advancement of intelligent applications. This raises the need for an effective DNN inference offloading mechanism between MDs and ESPs. However, existing schemes often lack market efficiency and fail to ensure desirable economic properties. To this end, we propose a truthful double auction mechanism for delay-aware DNN inference offloading (TDAD), which integrates a dynamic programming approach with an adaptive resource allocation and pricing strategy to maximize social welfare while ensuring truthfulness, budget balance, and individual rationality. Specifically, TDAD first employs a binary search-based delay-aware partitioning and offloading method to determine the minimum feasible resource profile for each MD, and then applies a dynamic programming-based double auction to match MDs' inference demands with ESPs' combinatorial resources, and compute the corresponding payments and rewards. Theoretical analysis proves that TDAD satisfies the desired economic properties, while experiments in realistic CEC environments validate its effectiveness and efficiency.

As mobile applications become increasingly computation-intensive, mobile devices (MDs) face growing limitations due to their constrained computational capabilities and battery life. Collaborative Edge Computing (CEC) has emerged as a promising solution to address these challenges by enabling multiple edge service providers (ESPs) to offer computation offloading services to MDs. As such, a CEC resource trading market is essential for efficient interactions between MDs and ESPs. However, jointly determining the offloading ratios, allocating combinatorial computation and communication resources, and designing appropriate pricing strategies in a dynamic market remains a significant challenge. To this end, we propose a truthful online double auction based resource allocation mechanism for partial computation offloading (TRAPO) that explicitly accounts for the stochastic nature of both MDs and ESPs. TRAPO first leverages spatial diversity to construct a set of bids for each MD by mapping their task requirements into resource demands through considering MDs' preferences and partial offloading. Next, we match resource-demanding MDs with resource-supplying ESPs based on adaptive valid price thresholds to maximize social welfare, and calculate the payments of MDs and the rewards of ESPs. Theoretical analyses demonstrate that TRAPO satisfies truthfulness, budget balance, individual rationality, and computational tractability. Simulation experiments further verify the effectiveness and efficiency of TRAPO.

Traditional computation offloading and resource allocation strategies encounter several issues that lead to poor service experience and resource wastage. The resource allocation scheme lacks the flexibility to adapt to the time-varying offloading demands of User Equipment (UEs). Furthermore, there is an imbalance between UEs seeking better service and Service Providers (SPs) aiming to minimize cost expenditures. In this paper, we propose a knowledge-defined networking-based Multi-Layer Computation Offloading and Resource Allocation strategy optimization (ML-CORA) architecture. Based on the ML-CORA, we design a Multi-Layer Local-Global Collaborative computation offloading and resource allocation strategy optimization (ML2GC) algorithm. The basic level of the ML2GC algorithm expresses and optimizes computation offloading demands from the perspective of UE (local), while the meta level optimizes the resource allocation strategy on demand from the perspective of the SP (global), achieving a collaborative multi-objective optimization for a win-win system between UEs and SPs. The two-layer structure of the ML2GC algorithm outputs continuous and discrete actions respectively, which improves the flexibility and efficiency of the algorithm while effectively balancing the interests of all parties and promoting efficient resource utilization. Simulation results based on the real-world dataset of Shanghai Telecom indicate that the ML2GC algorithm significantly improves both social welfare and resource utilization compared to baseline algorithms.

More and more application providers (APs) start to deploy their geo-distributed services in multiple cloud environments, such as JointCloud, federated clouds and InterCloud. Thus, massive cross-cloud traffic is generated from the services of APs, who need to pay Internet service providers (ISPs) for using their bandwidth. As such, an effective cross-cloud link bandwidth allocation and pricing mechanism is needed between APs and ISPs. Existing fixed-price scheme lacks market efficiency. Thus, we propose a truthful padding-based auction mechanism (TPAM) for cross-cloud bandwidth, which introduces the padding method and well-designed pricing strategy to ensure desirable properties. This mechanism is flexible enough to allow each AP to win the whole request, or win the specified proportional request, or lose and get nothing. Specifically, we first devise a linear-program-based method to calculate the padding vector for each candidate AP. Next, we design a padding-based method to determine the winning APs and match them with ISPs who offer the cheapest bandwidth. Finally, we design a critical-value-based pricing strategy and a marginal-cost-based pricing strategy for APs and ISPs to achieve truthfulness and budget balance. Theoretical analyses prove that TPAM achieves truthfulness, budget balance, individual rationality, asymptotic efficiency and computational tractability. Trace-driven simulation results also validate the effectiveness and efficiency of TPAM.

Mobile edge computation (MEC) has recently emerged as a promising computing paradigm for supporting latency-sensitive mobile applications. Due to the limited resources of the edge servers (ESs), efficient resource allocation mechanisms are key to realize the MEC paradigm. In such a resource allocation process, it is a significant challenge to guarantee truthfulness while enabling flexible task offloading and satisfying the locality constraint. To address such a challenge, we propose a truthful auction-based resource allocation mechanism with flexible task offloading (TARFO) in an MEC system. Specifically, we first design the minimum delay task graph partitioning algorithm, aiming at calculating the minimum completion time and the task offloading solutions under different resource profiles. Based on this algorithm, for each smart mobile device (SMD), we further determine the set of feasible non-dominated resource profiles and the corresponding task offloading solutions. We next propose an efficient primal-dual approximation winning bid selection algorithm to determine the set of the winning bids and a critical value based pricing algorithm to calculate the payments of the winning bids. Strict theoretical analysis demonstrates TARFO can ensure truthfulness, individual rationality, computational efficiency and a smaller approximation ratio. Simulation results verify the effectiveness and efficiency of TARFO.