Sep 2026· International Symposium ELMAR· pp. 121-126· 0 citations· 25 references
Abstract
The growing adoption of Internet of Things (IoT) devices generates massive data volumes that increasingly strain centralized cloud infrastructures, particularly for latency-sensitive and privacy-aware applications. Hybrid cloud architectures mitigate these limitations by enabling private clouds to dynamically leverage public cloud resources. However, workloads in such environments often include jobs with heterogeneous privacy requirements, making security-aware scheduling essential for maintaining Quality of Service (QoS). This paper investigates scheduling techniques for applications with chained tasks in hybrid cloud systems. Privacy-sensitive jobs (private jobs ) are restricted to private cloud resources, whereas less sensitive jobs (cloud jobs) may execute on either private or public infrastructures. To improve the responsiveness of private jobs, we propose an imprecise-computation–based scheduling approach for cloud jobs and evaluate its effectiveness through simulation under diverse workload conditions.
The Zero-Knowledge random range proof based scheduling data protection algorithm (ZKRRP-SDPA) is introduced, which employs an enhanced zero-knowledge range proof for real-valued ranges together with digital signatures, guaranteeing the security of node attribute data against any adversary except the node itself.
A typical cloud resource management strategy is based on established policies and infrastructure-level metrics, but it may be inadequate to deal with latency sensitivity and privacy concerns in distributed application environments. The paper presents here an Adaptive NLP-Driven Cloud - Edge Framework combining transfor...
R. Guru, T. Vinothini, V. M. Jemin et al.· 2026 International Conferenc...· 0 citations
An extensive critical review of serverless functions in cloud–edge environments reveals that cloud–edge serverless systems need accountable placement, state-aware workflows, reproducible benchmarking, trustworthy orchestration, and carbon-aware lifecycle control, which can be achieved only by going beyond latency and e...
Abdullah Abbasi, D. Hakro, Asad Ullah et al.· Future Internet· 0 citations
The exponential growth of the Internet of Things (IoT) has created new opportunities to provide diverse data to applications in critical domains. By leveraging IoT data, applications can achieve real-time situation awareness. Cloud computing supports the acquisition, processing, and dissemination of IoT data. But cloud...
Kanaka Sai Jagarlamudi, Jahedul Anowar, Koushikur Islam et al.· IEEE Access· 0 citations
Proliferation of cloud-based latency-sensitive workloads requires infrastructures tuned to their workload-specific latency constraints. Today, they shape the cloud from a generalized computing platform to diverse workload-specific cloud environments. As the demand for latency-sensitive workloads increases, cloud servic...
Tharindu B. Hewage, Shashikant Ilager, Maria Rodriguez Read et al.· 0 citations
Modern cloud platforms provide diverse compute services, including virtual machines, Function-as-a-Service, and Query-as-a-Service, each offering unique trade-offs in performance, elasticity, and cost. While these services collectively cover the diverse needs of OLAP workloads, existing systems typically rely on a sing...
Wen-Bo Li, Hao-Qiong Bian, Chao Zhang et al.· Proceedings of the ACM on Ma...· 0 citations
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