Threats jeopardize some basic security requirements in a cloud. These threats generally constitute privacy breach, data leakage and unauthorized data access at different cloud layers. This paper presents a novel multilevel classification model of different security attacks across different cloud services at each layer. It also identifies attack types and risk levels associated with different cloud services at these layers. The risks are ranked as low, medium and high. The intensity of these risk levels depends upon the position of cloud layers. The attacks get more severe for lower layers where infrastructure and platform are involved. The intensity of these risk levels is also associated with security requirements of data encryption, multi-tenancy, data privacy, authentication and authorization for different cloud services. The multilevel classification model leads to the provision of dynamic security contract for each cloud layer that dynamically decides about security requirements for cloud consumer and provider

Mobile cloud computing and edge computing-based solutions provide means to offload tasks for resource-limited mobile devices. Mobile cloud computing provides remote cloud solutions while edge computing provides closer proximity-based solutions. Remote cloud solutions suffer from network latency and limited bandwidth challenges due to distance and dependency on the Internet. However, these challenges are addressed by edge-based solutions since the edge node is available in the same network. The use of Internet of Things-based solutions considering future Information Communication Technology infrastructure is on the rise resulting in the massive growth of digital equipment increasing the load at edge devices. Hence, some load balancing mechanism is required at the edge level to avoid resource congestion. The load balancing at the edge must consider the user’s preferences about edge resources such as personal computers or mobile devices. A user must declare which resources can be spared for other devices to avoid overprovisioning essential resources. We present Load Balancing for Resource Optimization (LBRO), a collaborative cloudlet platform to address load balancing challenges in edge computing considering users’ preferences. A comparative analysis of the proposed approach with the conventional edge-based approach yields that the proposed approach provides significantly improved results in terms of CPU, memory, and disk utilization.

Cross-Cloud Federation (CCF) is beneficial for heterogeneous Cloud Service Providers (CSPs) for leasing additional resources from each other. Despite the benefits of on-demand scalability and enhanced service footprints for better service quality, the adoption of CCF is however mainly hindered due to the lack of a comprehensive trust model. The basic aim of such a model should be to address the security and performance concerns of a home CSP on its foreign peers before placing its users’ data and applications in their premises. A transitivity of users’ trust on home CSP and home CSP's trust on its foreign CSPs marks the uniqueness of trust paradigm in CCF. Addressing the concerns of cloud-to-cloud trust paradigm is inevitable to achieve users’ trust in a federation. Various trust models have been proposed in literature for conventional and multi-cloud computing environments. They focus on user requirements but none on federation perspective. Their applicability to CCF for addressing the concerns of cloud-to-cloud trust paradigm requires further consideration. For this reason, we have first outlined the general characteristics of CCF as being dynamic, multi-level and heterogeneous. Afterwards, cloud-to-cloud trust paradigm is proposed based on a set of unique principles identified as (i) trust bi-directionality, (ii) trust composition, (iii) delegation control, and (iv) Resource awareness. An insightful review of Trust Management Systems (TMS) proposed in literature reveals their shortcomings in addressing the requirements of cloud-to-cloud trust paradigm. To overcome these shortcomings, we suggest that some challenges can be merely addressed by aligning the existing methods to the nature of CCF. The remaining challenges require entirely new mechanisms to be introduced. A demonstration of this concept is presented in the form of a requirement matrix suggesting how the characteristics and properties of both CCF and the TMS are influenced by each other. This requirement matrix reveals the potential avenues of research for a TMS aimed specifically for CCF.

Mobile devices (MDs) face resource scarcity challenges owing to limited energy and computational resources. Mobile cloud computing (MCC) offers a resource-rich environment to MDs for offloading compute-intensive tasks encountering resource scarcity challenges. However, users are unable to exploit its full potential owing to challenges of distance, limited bandwidth, and seamless connectivity between the remote cloud (RC) and MDs in the conventional MCC model. The cloudlet-based solution is widely used to address these challenges. The response of the cloudlet-based solution is faster than the conventional mobile cloud-computing model, rendering it suitable for the Internet of Things (IoT) and Smart Cities (SC). However, with the increase in devices and workloads, the cloudlet-based solution has to deal with resource-scarcity challenges, thus, forwarding the requests to remote clouds. This study has been carried out to provide an insight into existing cloudlet-based mobile augmentation (CtMA) approaches and highlights the underlying limitations for resource optimization. Furthermore, numerous performance parameters have been identified and their detailed comparative analysis has been used to quantify the efficiency of CtMA approaches.