ABSTRACT As mobile edge computing (MEC) expands, efficient resource allocation and job scheduling become increasingly important. Existing techniques are frequently unable to offer acceptable quality of service (QoS), owing to inflexible scheduling algorithms and insufficient consideration of complex task and resource metrics. To overcome these constraints, this work proposes a novel adaptive vector autoregressive moving average with exogenous variables (VARMAx)‐based bioinspired resource scheduling model designed specifically for mobile edge deployment. The proposed approach applies the resilient concepts of flower pollination optimisation (FPO) to map tasks to virtual machines (VMs), a technique that is sensitive to a wide variety of task variables such as makespan, deadline and CPU needs. Simultaneously, VM characteristics such as million instructions per second (MIPS), amount of cores, random access memory (RAM), availability and bandwidth are all taken into account, resulting in a more nuanced and adaptive scheduling process. Furthermore, a VARMAx model is included for task pre‐emption, which assists in the recalibration of future VM capabilities, hence improving overall scheduling efficiency, particularly in real‐time deployments. The suggested model outperforms existing techniques. Our results show an 8.3% reduction in makespan, a 4.5% improvement in deadline hit ratio, an 8.5% increase in energy efficiency, and a 10.4% increase in throughput. The huge improvements highlight the model's adaptability and efficacy, resulting in important advances in the field of QoS‐aware task scheduling for MEC. This work represents a significant advancement in the field of effective resource scheduling, with the potential to guide future research and development efforts in mobile edge deployments.

This paper develops analytical models for analog and RF parameters of trigate junctionless field effect transistor (TGJLFET) incorporating substrate voltage effects. Analog parameters like, transconductance, output conductance and transconductance generation factor are modeled using continuous drain current model of substrate biased TGJLFET. The charge partitioning scheme by Ward-Dutton is used to obtain the charges on device terminals. Further, these charge models are used to derive gate-to-source/drain/substrate capacitances and cut-off frequency. The paper demonstrates that applied substrate voltage can effectively enhance the analog and RF characteristics of TGJLFETs. The models are compared and verified with 3D TCAD simulation results.

This paper presents a 3-D TCAD simulation based study of double-Fin multi-channel (DFMc) junctionless field effect transistor (JLFET) with substrate bias effects in subthreshold-regime. DFMc-JLFETs provide many advantages over single-Fin JLFETs like, improved current drive capability and short-channel effects. However, the subthreshold current also increases with the on-state current in DFMc-JLFETs. This paper shows that negative substrate bias can efficiently improve the subthreshold characteristics of DFMc-JLFETs. Moreover, the effects of trench height and width, Fin height and width on device characteristics are also analyzed.

We present unikernels, a new approach to deploying cloud services via applications written in high-level source code. Unikernels are single-purpose appliances that are compile-time specialised into standalone kernels, and sealed against modification when deployed to a cloud platform. In return they offer significant reduction in image sizes, improved efficiency and security, and should reduce operational costs. Our Mirage prototype compiles OCaml code into unikernels that run on commodity clouds and offer an order of magnitude reduction in code size without significant performance penalty. The architecture combines static type-safety with a single address-space layout that can be made immutable via a hypervisor extension. Mirage contributes a suite of type-safe protocol libraries, and our results demonstrate that the hypervisor is a platform that overcomes the hardware compatibility issues that have made past library operating systems impractical to deploy in the real-world.

OS virtualization and cloud computing have radically changed the way Internet services are deployed: enterprises share third-party datacenters, deploying existing applications with minimal changes. Recent measurements reveal a lack of traffic isolation capabilities within the datacenter with network performance exhibiting high variability. We advocate addressing this problem by allowing applications to express their own forwarding logic using OpenFlow to achieve application specific optimal performance. We present an OpenFlow implementation within the Mirage application synthesis framework, in the form of library implementations of a modular controller and an extensible OpenFlow-enabled switch, able to expose the underlying network infrastructure to cloud applications. By linking into the application, this provides a safe yet highly extensible framework for programming network control that, although unoptimised, still provides reasonable performance when compared with existing controllers.