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This paper reviews the background and related studies in the areas of cloud systems, intrusion detection and blockchain applications against cyber attacks. This work aims to discuss collaborative anomaly detection systems for discovering insider and outsider attacks from cloud centres, including the technologies of virtualisation and containerisation, along with trusting intrusion detection and cloud systems using blockchain. Moreover, the ability to detect such malicious attacks is critical for conducting necessary mitigation, at an early stage, to minimise the impact of disruption and restore cloud operations and their live migration processes. This paper presents an overview of cloud architecture and categorises potential state-of-the-art security events based on their occurrence at different cloud deployment models. Network Intrusion Detection Systems (NIDS) in the cloud, involving types of classification and common detection approaches, are also described. Collaborative NIDSs for cloud-based blockchain applications are also explained to demonstrate how blockchain can address challenges related to data privacy and trust management. A summary of the research challenges and future research directions in these fields is also explained.

Dynamic consolidation of virtual machines (VMs) is presented as a significant technique of energy conservation in cloud environments, which can eliminate the hotspot of overloaded hosts and switch the under loaded hosts to sleep mode through live migration of virtual machines. However, since the fact that migrating VM consumes a certain amount of extra resources, the process of reallocation can cause Service Level Agreement (SLA) violations. In this paper, a novel proactive framework which considers both predicted resource utilization and Performance-to-power Ratio (PPR) of heterogeneous hosts is proposed to perform dynamic VM consolidation to achieve balance of performance and energy. More precisely, a workload predictor is proposed based on the modified Weighted Moving Average (WMA) algorithm, representing the support for dynamic resource allocation; a cluster controller is proposed based on reinforcement learning for exploring the optimal matching relationship between resource requests and host at different PPR levels; a resource allocator is designed based on greedy strategy for achieving the trade-off between energy consumption and application performance across the cluster. Moreover, the framework is implemented based on distributed architecture and off-line learning pattern, which are able to not only scale up quickly but also improve the computing efficiency of the system. To validate the effectiveness of the proposed method, we have performed experimental evaluation on CloudSim with real-world workload traces of PlanetLab, and the simulation results demonstrate that it reduces the energy consumption up to 45.25% and effectively deals with high Quality of Service (QoS) requirements and heterogeneous distributed infrastructures in comparison with other competitive approaches.

Virtual machine (VM) re-deployment and migration has been proven to be a key technique for cloud data centers to implement resource optimization and load balance. Also, live VM migration aims to guarantee the continuity of the existing data flows. Though VM management has been well studied, it has so far mostly focused on optimizing resource utilization, while the important issue of users' quality of experience has been ignored. In mobile cloud computing, user distribution changes dynamically over time and it can significantly affect both the service latency and network resource utilization. In the traditional network, VM migration may cause a flash crowd of flow changing and a long service downtime due to VM's IP address changing. Software-defined networking is an emerging paradigm to logically centralize the network control plane and automate the configuration of individual network elements, which can be ubiquitously deployed in data centers and serve as an effective means for flow handling. In this paper, we design a user distribution-aware virtual machine re-deployment mechanism and propose a traffic-redirection virtual machine migration scheme to keep the active flows from being interrupted. We further provide simulations to show the effectiveness and superiority of the proposed approaches over existing schemes.

Accurate working set size estimation is important to increase the consolidation ratio of data centers and to improve the efficiency of live migration. Thus, it is important to come up with a technique that provides an accurate estimation of the working set size of virtual machines that can respond to changes in memory usage in real-time. In this paper, we study the problem of working set size estimation in virtual machines and come up with a method that allows us to better estimate the working set size of virtual machines in Linux. Toward that end, we design a finite state machine that can be used to accurately estimate the working set size and that is responsive to changes in workload. We then implement the algorithm on Linux using QEMU-KVM as our hypervisor. The system is tested using the sysbench benchmark for memory, CPU, and database workloads. The results indicate that our algorithm provides better results in terms of average working set size estimations and is competitive with existing techniques in terms of page faults.

The incoming era of the Fifth-Generation Fog Computing-supported Radio Access Networks (shortly, 5G FOGRANs) aims at exploiting computing/networking resource virtualization, in order to augment the limited resources of wireless devices through the seamless live migration of Virtual Machines (VMs) towards nearby Fog data centers. For this purpose, the bandwidths of the multiple Wireless Network Interface Cards (WNICs) of the wireless devices may be aggregated under the control of the emerging MultiPathTCP (MPTCP) protocol. However, due to fading and mobility-induced phenomena, the energy consumptions of current state-of-the-art VM migration techniques may still offset their expected benefits. Motivated by these considerations, in this paper, we analytically characterize, implement in software and numerically test the optimal minimum-energy Settable-Complexity Bandwidth Manager (SCBM) for the live migration of VMs over 5G FOGRAN MPTCP connections. The key features of the proposed SCBM are that: (i) its implementation complexity is settable on-line on the basis of the target energy consumption-vs.-implementation complexity tradeoff; (ii) it minimizes the network energy consumed by the wireless device for sustaining the migration process under hard constraints on the tolerated migration times and downtimes; and, (iii) by leveraging a suitably designed adaptive mechanism, it is capable to quickly react to (possibly, unpredicted) fading and/or mobility-induced abrupt changes of the wireless environment without requiring forecasting. The actual effectiveness of the proposed SCBM is supported by extensive energy-vs.-delay performance comparisons, that cover: (i) a number of heterogeneous 3G/4G/WiFi FOGRAN scenarios; (ii) synthetic and real-world workloads; and, (iii) MPTCP and SinglePathTCP (SPTCP) wireless connections., Comment: A shorter version of this paper has been submitted to IEEE Access

Server consolidation is a useful solution aiming at cost-efficiency and high resource utilization of data centers and clusters. Nowadays, as the data-intensive and I/O intensive applications are widely used, more attention is paid to the local storage-based clouds which can offer much better I/O performance at relatively low price compared with the shared storage. However, it will obviously increase the migration cost (e.g., energy and time). Meanwhile, there is few suitable resource demand estimation method for local storage-based clouds at present, which plays an important role in system's migration efficiency. And we find out that in this specific storage architecture, almost all the existing server consolidation algorithms do not have a suitable resource demand estimation method and a live migration scheme. To solve this problems, this paper designs and implements Combining Three (C3), a cloud architecture for local storage, C3 significant modules: prediction, consolidation, and migration. It was proved in statistical analysis that ARIMA may be the most suitable prediction model for the server workload, which motivates us to propose the resource estimation predictor. Also, we improve the existing consolidation method by adjusting the sorting index and fit degree during migration. Then, we propose a live migration scheme for local storage environment as the third module of C3. We conduct extensive experiments using real-world traces from Google to validate the effectiveness and superiority of our proposed algorithm.

The design of good host overload/underload detection and virtual machine (VM) placement algorithms plays a vital role in assuring the smoothness of VM live migration. The presence of the dynamic environment that leads to a changing load on the VMs motivates us to propose a Markov prediction model to forecast the future load state of the host. We propose a host load detection algorithm to find the future overutilized/underutilized hosts state to avoid immediate VMs migration. Moreover, we propose a VM placement algorithm to determine the set of candidates hosts to receive the migrated VMs in a way to reduce their VM migrations in near future. We evaluate our proposed algorithms through CloudSim simulation on different types of PlanetLab real and random workloads. The experimental results show that our proposed algorithms have a significant reduction in terms of service-level agreement violation, the number of VM migrations, and other metrics than the other competitive algorithms.

Fog computing, an extension of cloud computing services to the edge of the network to decrease latency and network congestion, is a relatively recent research trend. Although both cloud and fog offer similar resources and services, the latter is characterized by low latency with a wider spread and geographically distributed nodes to support mobility and real-time interaction. In this paper, we describe the fog computing architecture and review its different services and applications. We then discuss security and privacy issues in fog computing, focusing on service and resource availability. Virtualization is a vital technology in both fog and cloud computing that enables virtual machines (VMs) to coexist in a physical server (host) to share resources. These VMs could be subject to malicious attacks or the physical server hosting it could experience system failure, both of which result in unavailability of services and resources. Therefore, a conceptual smart pre-copy live migration approach is presented for VM migration. Using this approach, we can estimate the downtime after each iteration to determine whether to proceed to the stop-and-copy stage during a system failure or an attack on a fog computing node. This will minimize both the downtime and the migration time to guarantee resource and service availability to the end users of fog computing. Last, future research directions are outlined. Refereed/Peer-reviewed

Being a key computing element in cloud data center, virtual machines should be able to migrate from one location to another to meet the requirements of the cloud users and the defined policies of the cloud computing system. The mobility is an important issue when a virtual machine migrates across IP subnets. This paper focuses on the mobility management in cloud computing systems, and proposes a mobility-oriented cloud data center network architecture based on the identity/locator decoupling method of the mobility-driven networks. In cloud data center network, a mobile node refers to a virtual machine, and the mobility behavior mainly refers to virtual machine migration. In the proposed architecture, a virtual machine could implement live migration between IP subnets without service interruption. The evaluation shows that the proposed scheme can solve mobility issues effectively in virtual machine migration among IP subnets.

With the increasing demand for openness, flexibility, and monetization, the Network Function Virtualization (NFV) of mobile network functions has become the embracing factor for most mobile network operators. Early reported field deployments of virtualized Evolved Packet Core (EPC) - the core network (CN) component of 4G LTE and 5G non-standalone mobile networks - reflect this growing trend. To best meet the requirements of power management, load balancing, and fault tolerance in the cloud environment, the need for live migration of these virtualized components cannot be shunned. Virtualization platforms of interest include both Virtual Machines (VMs) and Containers, with the latter option offering more lightweight characteristics. This paper's first contribution is the proposal of a framework that enables migration of containerised virtual EPC components using an open-source migration solution which does not fully support the mobile network protocol stack yet. The second contribution is an experimental-based comprehensive analysis of live migration in two virtualization technologies - VM and Container - with the additional scrutinization on the container migration approach. The presented experimental comparison accounts for several system parameters and configurations: flavor (image) size, network characteristics, processor hardware architecture model, and the CPU load of the backhaul network components. The comparison reveals that the live migration completion time and also the end-user service interruption time of the virtualized EPC components is reduced approximately by 70% in the container platform when using the proposed framework. This work was supported in part by the NSF under Grant CNS-1405405, Grant CNS-1409849, Grant ACI-1541461, and Grant CNS-1531039T; and in part by the EU Commission through the 5GROWTH Project under Grant 856709.