Cloud services are prominent within the private, public and commercial domains. Many of these services are expected to be always on and have a critical nature; therefore, security and resilience are increasingly important aspects. In order to remain resilient, a cloud needs to possess the ability to react not only to known threats, but also to new challenges that target cloud infrastructures. In this paper we introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection components of our cloud resilience architecture. More specifically, we exhibit the applicability of novelty detection under the one-class support Vector Machine (SVM) formulation at the hypervisor level, through the utilisation of features gathered at the system and network levels of a cloud node. We demonstrate that our scheme can reach a high detection accuracy of over $90$ percent whilst detecting various types of malware and DoS attacks. Furthermore, we evaluate the merits of considering not only system-level data, but also network-level data depending on the attack type. Finally, the paper shows that our approach to detection using dedicated monitoring components per VM is particularly applicable to cloud scenarios and leads to a flexible detection system capable of detecting new malware strains with no prior knowledge of their functionality or their underlying instructions.

HTTP adaptive streaming (HAS) is the most recent attempt regarding video quality adaptation. It enables cheap and easy to implement streaming technology without the need for a dedicated infrastructure. By using a combination of TCP and HTTP it has the advantage of reusing all the existing technologies designed for ordinary web. Equally important is that HAS traffic passes through firewalls and works well when NAT is deployed. The rate adaptation controller of HAS, commonly called adaptive bitrate selection (ABR), is currently receiving a lot of attention from both industry and academia. However, most of the research efforts concentrate on a specific aspect or a particular methodology without considering the overall context. This paper presents a comprehensive survey of the most significant research activities in the area of client-side HTTP-based adaptive video streaming. It starts by decomposing the ABR module into three subcomponents, namely: resource estimation function, chunk request scheduling, and adaptation module. Each subcomponent encapsulates a particular function that is vital to the operation of an ABR scheme. A review of each of the subcomponents and how they interact with each other is presented. Furthermore, those external factors that are known to have a direct impact on the performance of an ABR module, such as content nature, CDN, and context, are discussed. In conclusion, this paper provides an extensive reference for further research in the field.

Network services underpin operator revenues, and value-added services provide income beyond core (voice and data) infrastructure capability. Today, operators face multiple challenges: a need to innovate and offer a wider choice of value-added services, whilst increasing network scale, bandwidth and flexibility. They must also reduce operational costs, and deploy services far faster - in minutes rather than days or weeks. In the recent years, the network community, motivated by the aforementioned challenges, has developed production network architectures and seeded technologies, like Software Defined Networking, Application-based Network Operations and Network Function Virtualization. These technologies enhance the highly desired properties for elasticity, agility and cost-effectiveness in the operator environment. A key requirement to fully exploit the benefits of these new architectures and technologies is a fundamental shift in management and control of resources, and the ability to orchestrate the network infrastructure: coordinate the instantiation of high-level network services across different technological domains and automate service deployment and re-optimization. This paper surveys existing standardization efforts for the orchestration - automation, coordination, and management - of complex set of network and function resources (both physical and virtual), and highlights the various enabling technologies, strengths and weaknesses, adoption challenges for operators, and areas where further research is required

In essence, an information-centric network (ICN) is one which supports a content request/reply model. One proposed benefit of this is improved mobility. This can refer to provider, consumer or content mobility. Despite this, little specific research has looked into the effectiveness of ICN in this regard. This paper presents a survey of some of the key ICN technologies, alongside their individual approaches to mobility. Through this, we highlight some of the promising benefits of ICN, before discussing important future research questions that must be answered

A content-centric network is one which supports host-to-content routing, rather than the host-to-host routing of the existing Internet. This paper investigates the potential of caching data at the router-level in content-centric networks. To achieve this, two measurement sets are combined to gain an understanding of the potential caching benefits of deploying content-centric protocols over the current Internet topology. The first set of measurements is a study of the BitTorrent network, which provides detailed traces of content request patterns. This is then combined with CAIDA's ITDK Internet traces to replay the content requests over a real-world topology. Using this data, simulations are performed to measure how effective content-centric networking would have been if it were available to these consumers/providers. We find that larger cache sizes (10,000 packets) can create significant reductions in packet path lengths. On average, 2.02 hops are saved through caching (a 20% reduction), whilst also allowing 11% of data requests to be maintained within the requester's AS. Importantly, we also show that these benefits extend significantly beyond that of edge caching by allowing transit ASes to also reduce traffic.