Search

The advantages of cloud computing attract a large number of enterprises to deploy their applications into the cloud, thereby reducing their own operating costs. This paper considers deploying inelastic applications into the cloud and proposes an optimal resource allocation model. The deployment functions for inelastic applications are nonconvex (e.g., sigmoidal), then the resource allocation model becomes a hard nonconvex optimization problem. The traditional gradient-based resource allocation algorithm cannot effectively achieve the global optimum. Therefore, this paper applies particle swarm optimization (PSO) method to design a resource allocation scheme. This scheme can not only effectively solve the resource allocation problem of deploying inelastic enterprise applications into the cloud, but also solve the hard problem of deploying multi-class applications into the cloud when the enterprise can support both elastic and inelastic applications. We also compare the performance of the proposed PSO-based resource allocation scheme with some other methods and illustrate some numerical examples to verify the effectiveness and superiority of the proposed resource allocation scheme. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the last years peer-to-peer (P2P) networks have become an important and prevalent architecture in the Internet and have been commonly applied into many scenarios such as big data storage and management, cloud computing, vehicular networks and social networking. The overall performance of P2P networks mainly depends on cooperation between peers, so as to encourage each peer to contribute its resource to others and reduce free-riding behaviors. Then each peer has to face a dilemma decision that how much of its available bandwidth it will allocate to uploads for others and how much it will reserve for its own downloads. In this paper we consider bandwidth allocation in P2P networks where both upstream and downstream flows traverse one common access link with finite capacity, and formulate the utility maximization model for bandwidth allocation. The model is difficult to resolve since it is an interplay problem between upload and download decisions of each peer. In order to achieve the optimal bandwidth allocation, we present a heuristic scheme using Particle Swarm Optimization (PSO), and discuss the performance with numerical examples in different network scenarios. Simulation results show that the scheme can achieve the global optimum within reasonable iterations. [ABSTRACT FROM AUTHOR]

Multipath networks allow that each source-destination pair can have several different paths for data transmission, thus they improve the performance of increasingly bandwidth-hungry applications and well cater for traffic load balancing and bandwidth usage efficiency. This paper investigates fair resource allocation for users in multipath networks and formulates it as a multipath network utility maximisation problem with several fairness concepts. By applying the Lagrangian method, sub-problems for users and paths are derived from the resource allocation model and interpreted from an economic point of view. In order to solve the model, a novel rate-based flow control algorithm is proposed for achieving optimal resource allocation, which depends only on local information. In the presence of round-trip delays, sufficient conditions are obtained for local stability of the delayed algorithm. As for the end-to-end implementation in Internet, a window-based flow control mechanism is presented since it is more convenient to implement than rate-based flow control. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cloud computing has been widely used in various industries in recent years. However, when migrating enterprise applications into the cloud, enterprise users face a problem with minimizing migration time and cloud resource providers face a dilemma of resource allocation problem, with the objective of maximizing the migration utility of enterprise users while minimizing the cost of cloud resource providers. In order to achieve them, this paper considered cloud migration objectives including cloud migration time, cloud migration utility, and cloud data center cost, and proposed a resource allocation model for enterprise applications migration into the cloud. The model is divided into two stages: the bandwidth allocation for enterprise applications migration to the cloud and the physical resource allocation of cloud resource providers for enterprise applications deployment into the cloud. In the first stage, we aim to minimize the cloud migration time for enterprise applications, and propose a scheme of bandwidth allocation for each component of applications. In the second stage, we present the resource allocation of cloud resource providers and propose a gradient-based algorithm which can achieve optimal resource allocation. Finally, we give some numerical simulation results to illustrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]