Cloud computing has attracted many enterprises to migrate or deploy their applications into the cloud platform so as to reduce their operational costs. In this paper, we investigate the reasonable resource allocation for migrating enterprise applications to the cloud. We first formulate a generalisation form of utility maximisation model for resource allocation of enterprise applications migration. The generalisation-form optimisation problem is difficult to resolve due to its non-strict convexity and non-separation. We apply the successive approximation method to study the generalisation-form model and present a gradient-based resource allocation scheme to solve the approximation problems. Then, we analyse its performance through mathematical method and also illustrate with some numerical examples.

The Industrial Internet of Things (IIoT) has revolutionized industrial processes by facilitating the seamless connectivity and communication of devices and systems within industrial environments. As a critical component, edge computing provides the flexible data sensing and real-time processing services, which facilitates a comprehensive enhancement for IIoT systems. However, due to the tremendous increase in IIoT devices and the limited resources of edge servers, ensuring efficient resource sharing while prioritizing the benefits of both parties would be the driving force in edge-enabled IIoT systems. In this work, we propose a distributed resource sharing mechanism in edge-enabled IIoT systems. The objective of proposed mechanism is maximizing social welfare which optimizes the utility of IIoT device and cost of edge server simultaneously. In particular, we integrate the soft-defined network (SDN) technique into the threelayer IIoT framework to support agile resource management and formulate the resource sharing optimization models in multiple scenarios. Furthermore, we design the distributed alternating direction method of multipliers (ADMMs) algorithms which appropriately decompose the primal problem into multiple subproblems for IIoT devices and edge servers, and explore their interactive relationships of resource requesting and sharing using the characteristic of ADMM. Finally, extensive performance evaluations are conducted under multiple IIoT scenarios to manifest the effectiveness and reliability of proposed distributed mechanism in terms of convergence rate, social welfare and fault tolerance.

This paper studies the customers’ equilibrium and socially optimal balking behaviour in some single-server Markovian queues with double adaptive working vacations. Once the system becomes empty, the server takes a working vacation with a low service rate. If there are customer arrivals during this period, a regular busy period begins as soon as he finishes the vacation. Otherwise, he takes another working vacation with a much lower service rate than that in the first working vacation. After completing the second vacation, the server either stays idle or begins a busy period. We discuss two types of unobservable queues: the almost unobservable queues and the fully unobservable queues, respectively, and arriving customers can’t observe system occupancy in both cases. For each type of queues, we get both the customers’ equilibrium and socially optimal balking strategies and make numerical comparisons between them. We observe that their positive and stable equilibrium strategy and optimal strategy are unique, and especially, the customers’ actual arrival rates in equilibrium in vacation states are not necessarily smaller than that in busy state in the almost unobservable queues. Moreover, we also find that whether the system information should be revealed to customers depends on the potential demand arrivals.

Recently, more and more smart homes have become one of important parts of home infrastructure. However, most of the smart home applications are not interconnected and remain isolated. They use the cloud center as the control platform, which increases the risk of link congestion and data security. Thus, in the future, smart homes based on edge computing without using cloud center become an important research area. In this paper, we assume that all applications in a smart home environment are composed of edge nodes and users. In order to maximize the utility of users, we assume that all users and edge nodes are placed in a market and formulate a pricing resource allocation model with utility maximization. We apply the Lagrangian method to analyze the model, so an edge node (provider in the market) allocates its resources to a user (customer in the market) based on the prices of resources and the utility related to the preference of users. To obtain the optimal resource allocation, we propose a pricing-based resource allocation algorithm by using low-pass filtering scheme and conform that the proposed algorithm can achieve an optimum within reasonable convergence times through some numerical examples.