Abstract Cloud computing is now a well-consolidated paradigm for on-demand services provisioning on a pay-as-you-go model. Elasticity, one of the major benefits required for this computing model, is the ability to add and remove resources “on the fly” to handle the load variation. Although many works in literature have surveyed cloud computing and its features, there is a lack of a detailed analysis about elasticity for the cloud. As an attempt to fill this gap, we propose this survey on cloud computing elasticity based on an adaptation of a classic systematic review. We address different aspects of elasticity, such as definitions, metrics and tools for measuring, evaluation of the elasticity, and existing solutions. Finally, we present some open issues and future directions. To the best of our knowledge, this is the first study on cloud computing elasticity using a systematic review approach.

Abstract Small cells are expected to increase network capacity, extend the macrocell coverage and to add edge-based intelligence. These advantages are achieved by overlaying macrocell networks with the small cells, resulting in a two-tier network. However, the average capacity of the network is reduced due to interference generated by the two-tier configuration. Conventional small cells are configured to have either an open or closed access scheme. Small cells with a hybrid access scheme or partially open access scheme are known for their flexibility and improved performance achieved through their interference-mitigating ability and adaptive resource allocation capabilities. In a hybrid access scheme, resource allocation is a vital issue in the design of small cells networks. In this paper, we propose an uplink resource allocation technique to enhance hybrid small cell capacity in orthogonal frequency division multiple access (OFDMA) two-tier cellular networks. In particular, we investigate a new possibility available for developing an optimal scheme for OFDMA hybrid access small cells. We formalize the problem as an optimization problem for an uplink resource allocation that partitions the bandwidth for both subscribed or unsubscribed users. Moreover, we propose a convex optimization model for the gradient of the capacity difference between closed access and hybrid access capacity. We present two schemes, firstly an optimal scheme and secondly a scheme that is the simple version of the optimal scheme. Numerical results show the effectiveness of the proposed bandwidth resource allocation, where the simple solution shows inferior performance than the optimal solution with a small margin while the optimal solution has a more mathematical complexity disadvantage due to the mathematical functions it employs. Numerical results also show the convergence and effectiveness of the proposed uplink bandwidth resource allocation scheme.

Abstract Nowadays, smartphones are used to download files such as movies, music, etc. These files present some flexibility in delivery time; we exploit this feature to reduce network overload peaks. If the network is overloaded, downloads are postponed, thus reducing the traffic load during rush hours. We propose an architecture that enables the network to determine the best time to trigger a download that is queued. This architecture is based on a specific server in the network and a mobile application. This contribution describes the proposed architecture, presents the developments that have been made, and analyzes the performance of this novel concept.

Abstract Autonomous systems provide network control without external monitoring. This can be a problem when the traffic is asymmetric with a higher portion being inbound, as networks are generally designed for symmetrical loads. This problem occurs frequently in networks within South American countries as the inbound and outbound portions of traffic are unbalanced by a factor greater than 5:1. Furthermore, national content is usually hosted in the USA, exacerbating the problem. In this study, networks in Brazil, Colombia, and Venezuela are analyzed and characterized. Decision-making techniques are proposed as a tentative external control method or, in some cases, as a proactive attempt to interfere in an internal decision. Our new adaptive algorithm outperformed manual human intervention, which has potential utility to assist long-term capacity planning of the interconnection between autonomous systems.