Mobile cloudcomputing (MCC) has drawn significant research attention as the popularity and capability of mobile devices have been improved in recent years. In this paper, we propose a prototype MCC offloading system ...
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Mobile cloudcomputing (MCC) has drawn significant research attention as the popularity and capability of mobile devices have been improved in recent years. In this paper, we propose a prototype MCC offloading system that considers multiple cloud resources such as mobile ad-hoc network, cloudlet and public clouds to provide an adaptive MCC service. We propose a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and which potential cloud resources as the offloading location based on the device context. We also conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm can select suitable wireless medium and cloud resources based on different context of the mobile devices, and achieve significant performance improvement.
cloudcomputing is an utility computing paradigm that allows users to flexibly acquire virtualized computing resources in a pay-as-you-go model. To realize the benefits of using cloud, users need to first select the s...
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ISBN:
(纸本)9781479936311
cloudcomputing is an utility computing paradigm that allows users to flexibly acquire virtualized computing resources in a pay-as-you-go model. To realize the benefits of using cloud, users need to first select the suitable cloud services that can satisfy their applications' functional and non-functional requirements. However, this is a difficult task due to large number of available services, users' unclear requirements, and performance variations in cloud. In this paper, we propose a system that evaluates trust of clouds according to users' fuzzy Quality of Service (QoS) requirements and services' dynamic performances to facilitate service selection. We demonstrate the effectiveness and efficiency of our system through simulations and case studies.
Mobile and cloudcomputing are converging as the prominent technologies that are leading the change to the post personal computing (PC) era. Computational offloading and data binding are the core techniques that foste...
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ISBN:
(纸本)9781479944248
Mobile and cloudcomputing are converging as the prominent technologies that are leading the change to the post personal computing (PC) era. Computational offloading and data binding are the core techniques that foster to elastically augment the capabilities of low-power devices, such as smartphones. Mobile applications may be bonded to cloud resources by following a task delegation or code offloading criteria. In a delegation model, a handset can utilize the cloud in a service-oriented manner to delegate asynchronously a resource-intensive mobile task by direct invocation of the service. In contrast, in an offloading model, a mobile application is partitioned and analyzed so that the most computational expensive operations at code level can be identified and offloaded to a remote cloud-based surrogate. We compared in this paper, the mobile cloudcomputing models for offloading and delegation. We utilized our own frameworks for computational offloading and data binding in the analysis. While in principle, offloading and delegation are viable methods to augment the capabilities of the mobile devices with cloud power, they enrich the mobile applications from different perspectives at diverse computational scales.
Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are su...
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Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.
Amid the widespread use of blockchain technology, the escalating frequency of cyber attacks exploiting its inherent security challenges underscores the critical necessity for a robust and adaptable security risk asses...
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Amid the widespread use of blockchain technology, the escalating frequency of cyber attacks exploiting its inherent security challenges underscores the critical necessity for a robust and adaptable security risk assessment approach. The distinctive attributes and intricate internal structure of blockchain not only attract malicious actors but also elevate the risk of ill-informed architectural design decisions, potentially introducing security vulnerabilities. This study addresses this imperative by conducting a systematic literature review, classifying publications that elucidate secure architectural design approaches, and categorising those that delineate methods for assessing security risks associated with blockchain and smart contracts. The findings reveal four prevalent approaches supporting secure architectural design—decision models, taxonomies, design patterns, and guidelines—alongside contributions in blockchain risk assessment encompassing risk identification, analysis, and evaluation methods. Furthermore, the study identifies unresolved architectural design challenges and proposes future research directions in this evolving landscape.
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