Optimized Deep Learning Schemes for Secured Resource Allocation and Task Scheduling in Cloud Computing - A Survey

Scheduling involves allocating shared resources gradually so that tasks can be completed within a predetermined time frame. In Task Scheduling (TS) and Resource Allocation (RA), the phrase is used independently for tasks and resources. Scheduling is widely used for Cloud Computing (CC), computer science, and operational management. Effective scheduling ensures that systems operate efficiently, decisions are made effectively, resources are used efficiently, costs are kept to a minimum, and productivity is increased. High energy consumption, lower CPU utilization, time consumption, and low robustness are the most frequent problems in TS and RA in CC. In this survey, RA and TS based on deep learning (DL) and machine learning (ML) were discussed. Additionally, look into the methods employed by DL-based RA and TS-based CC. Additionally, the benefits, drawbacks, advantages, disadvantages, and merits are explored. The work's primary contribution is an analysis and assessment of DL-based RA and TS methodologies that pinpoint problems with cloud computing

Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks

Cooperative video caching and transcoding in mobile edge computing (MEC) networks is a new paradigm for future wireless networks, e.g., 5G and 5G beyond, to reduce scarce and expensive backhaul resource usage by prefetching video files within radio access networks (RANs). Integration of this technique with other advent technologies, such as wireless network virtualization and multicarrier non-orthogonal multiple access (MC-NOMA), provides more flexible video delivery opportunities, which leads to enhancements both for the network's revenue and for the end-users' service experience. In this regard, we propose a two-phase RAF for a parallel cooperative joint multi-bitrate video caching and transcoding in heterogeneous virtualized MEC networks. In the cache placement phase, we propose novel proactive delivery-aware cache placement strategies (DACPSs) by jointly allocating physical and radio resources based on network stochastic information to exploit flexible delivery opportunities. Then, for the delivery phase, we propose a delivery policy based on the user requests and network channel conditions. The optimization problems corresponding to both phases aim to maximize the total revenue of network slices, i.e., virtual networks. Both problems are non-convex and suffer from high-computational complexities. For each phase, we show how the problem can be solved efficiently. We also propose a low-complexity RAF in which the complexity of the delivery algorithm is significantly reduced. A Delivery-aware cache refreshment strategy (DACRS) in the delivery phase is also proposed to tackle the dynamically changes of network stochastic information. Extensive numerical assessments demonstrate a performance improvement of up to 30% for our proposed DACPSs and DACRS over traditional approaches.Comment: 53 pages, 24 figure