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Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC., Comment: 36 pages, submitted to ACM CSUR for Review

We propose a novel CP-Net based composition approach to qualitatively select an optimal set of consumers for an IaaS provider. The IaaS provider's and consumers' qualitative preferences are captured using CP-Nets. We propose a CP-Net composability model using the semantic congruence property of a qualitative composition. A greedy-based and a heuristic-based consumer selection approaches are proposed that effectively reduce the search space of candidate consumers in the composition. Experimental results prove the feasibility of the proposed composition approach., Comment: published in The 19th International Conference on Web Information Systems Engineering (WISE) 2018

There are two primary subscription models for IaaS cloud services: a) pay-as-you and b) reservation. Reservation-based subscriptions are typically offered for a long-term period such as 1 to 3 years. Long-term subscriptions are typically cost-efficient than short-term subscriptions for consumers who need services for a long-term period. Large organizations such as airline companies, banks, and research institutes tend to utilize IaaS services on a long-term basis for economic reasons. The performance of IaaS services is a key criterion to consider when selecting a service for a long-term. Selecting a service that may exhibit poor performance in the future may cause a significant loss of revenue for a business organization. Most IaaS providers, however, are reluctant to provide detailed information about their long-term service performance. This research aims at developing a long-term IaaS cloud service selection framework where IaaS providers reveal limited performance information about their services. First, we propose a novel framework to find the closest match of IaaS cloud service according to a consumer's long-term QoS requirements. The proposed framework leverages free short-term trials to discover the unknown QoS performance information. A temporal skyline-based filtering method is proposed to select candidate services for short-term trials. A novel cooperative long-term QoS prediction approach is introduced that utilizes past trial experiences of similar consumers using a workload replay technique. We propose a new trial workload generation model that estimates a provider's long-term performance in the absence of past trial experiences. The confidence of the prediction is measured based on the trial experience of the consumer. Next, we propose a new long-term IaaS cloud service selection framework that utilizes a consumer's trial experience and the performance fingerprints of IaaS cloud services for the long-term selection. We design a novel equivalence partitioning-based trial strategy to discover the unknown QoS performance variability of IaaS cloud services. A trial experience transformation method is proposed to estimate the long-term performance of an IaaS cloud service. Next, we introduce a signature-based IaaS cloud service selection framework that leverages a new significance-based trial scheme and a signature technique to discover a service's long-term performance. Next, we propose a novel event-based change detection approach to manage changes in IaaS performance signatures. A new anomaly-based event detection technique is proposed to detect changes in long-term IaaS performance behavior over time. We then propose an IaaS performance noise model to identify noise and actual changes in IaaS performance accurately. A novel categorical signature-based approach is proposed to detect the long-term performance changes using the proposed performance noise model. Finally, we introduce a signature change detection framework that leverages a sliding window-based approach and a Signal-to-Noise ratio-based approach to detect long-term changes in IaaS performance signatures. We have conducted a set of experiments based on real-world datasets to evaluate the proposed frameworks. The proposed long-term selection framework achieved almost 92% ranking accuracy. The signature-based IaaS cloud service selection framework achieved 96% ranking accuracy. The proposed changed detection frameworks achieved up to 90% change detection accuracy.

We propose a novel framework to detect changes in the performance behavior of an IaaS service. The proposed framework leverages the concept of the IaaS signature to represent an IaaS service's long-term performance behavior. A new type of performance signature called categorical IaaS signature is introduced to represent the performance behavior more accurately. A novel performance noise model is proposed to accurately identify IaaS performance noise and accurate changes in the performance behavior of an IaaS service. A set of experiments based on real-world datasets is carried out to evaluate the effectiveness of the proposed framework., Comment: 15 pages, accepted and to be published in 19th International Conference on Service Oriented Computing. arXiv admin note: text overlap with arXiv:2007.11705