Optimizing simultaneous autoscaling for serverless cloud computing

This paper explores resource allocation in serverless cloud computing platforms and proposes an optimization approach for autoscaling systems. Serverless computing relieves users from resource management tasks, enabling focus on application functions. However, dynamic resource allocation and function replication based on changing loads remain crucial. Typically, autoscalers in these platforms utilize threshold-based mechanisms to adjust function replicas independently. We model applications as interconnected graphs of functions, where requests probabilistically traverse the graph, triggering associated function execution. Our objective is to develop a control policy that optimally allocates resources on servers, minimizing failed requests and response time in reaction to load changes. Using a fluid approximation model and Separated Continuous Linear Programming (SCLP), we derive an optimal control policy that determines the number of resources per replica and the required number of replicas over time. We evaluate our approach using a simulation framework built with Python and simpy. Comparing against threshold-based autoscaling, our approach demonstrates significant improvements in average response times and failed requests, ranging from 15% to over 300% in most cases. We also explore the impact of system and workload parameters on performance, providing insights into the behavior of our optimization approach under different conditions. Overall, our study contributes to advancing resource allocation strategies, enhancing efficiency and reliability in serverless cloud computing platforms

Ionospheric Non-linear Effects Observed During Very-Long-Distance HF Propagation

A new super-long-range wave propagation technique was implemented at different High Frequency (HF) heating facilities. The HF waves radiated by a powerful heater were scattered into the ionospheric waveguide by the stimulated field aligned striations. This waveguide was formed in a valley region between the E- and F- layers of the ionosphere. The wave trapping and channeling provide super-long-range propagation of HF heater signals detected at the Ukrainian Antarctic Academik Vernadsky Station (UAS) which is many thousand kilometers away from the corresponding HF heating facility. This paper aims to study the excitation of the ionospheric waveguide due to the scattering of the HF heating wave by artificial field aligned irregularities. In addition, the probing of stimulated ionospheric irregularities can be obtained from analyses of the signals received at far distance from the HF heater. The paper uses a novel method of scattering of the HF radiation by the heating facility for diagnostics of non-linear effects at the super-long radio paths. Experiments were conducted at three different powerful HF facilities: EISCAT (Norway), HAARP (Alaska), and Arecibo (Puerto Rico) and by using different far spaced receiving sites. The key problems for super-long-range propagation regime is the feeding of ionospheric waveguide. Then the energy needs to exit from the waveguide at a specific location to be detected by the surface-based receiver. During our studies the waveguide feeding was provided by the scattering of HF waves by the artificial ionospheric turbulence (AIT) above the HF heater. An interesting opportunity for the channeling of the HF signals occurs due to the aspect scattering of radio waves by field aligned irregularities (FAI), when the scattering vector is parallel to the Earth surface. Such FAIs geometry takes place over the Arecibo facility. Here FAI are oriented along the geomagnetic field line inclined by 43 degrees. Since the Arecibo HF beam is vertical, the aspect scattered waves will be oriented almost horizontally toward the South. Such geometry provides unique opportunity to channel the radio wave energy into the ionospheric waveguide and excites the whispering gallery modes

Learning the Parameters of Bayesian Networks from Uncertain Data

The creation of Bayesian networks often requires the specification of a large number of parameters, making it highly desirable to be able to learn these parameters from historical data. In many cases, such data has uncertainty associated with it, including cases in which this data comes from unstructured analysis or from sensors. When creating diagnosis networks, for example, unstructured analysis algorithms can be run on the historical text descriptions or images of previous cases so as to extract data for learning Bayesian network parameters, but such derived data has inherent uncertainty associated with it due to the nature of such algorithms. Because of the inability of current Bayesian network parameter learning algorithms to incorporate such uncertainty, common approaches either ignore this uncertainty, thus reducing the resulting accuracy, or completely disregard such data. We present an approach for learning Bayesian network parameters that explicitly incorporates such uncertainty, and which is a natural extension of the Bayesian network formalism. We present a generalization of the Expectation Maximization parameter learning algorithm that enables it to handle any historical data with likelihood-evidence-based uncertainty, as well as an empirical validation demonstrating the improved accuracy and convergence enabled by our approach. We also prove that our extended algorithm maintains the convergence and correctness properties of the original EM algorithm, while explicitly incorporating data uncertainty in the learning process

HF-Induced Modifications of the Electron Density Profile in the Earth’s Ionosphere Using the Pump Frequencies near the Fourth Electron Gyroharmonic

We discuss results on plasma density profile modifications in the F-region ionosphere that are caused by HF heating with the frequency f0 in the range [(−150 kHz)–(+75 kHz)] around the fourth electron gyroharmonic 4fc. The experiments were conducted at the HAARP facility in June 2014. A multi-frequency Doppler sounder (MDS), which measures the phase and amplitude of reflected sounding radio waves, complemented by the observations of the stimulated electromagnetic emission (SEE) were used for the diagnostics of the plasma perturbations. We detected noticeable plasma expulsion from the reflection region of the pumping wave and from the upper hybrid region, where the expulsion from the latter was strongly suppressed for f0 ≈ 4fc. The plasma expulsion from the upper hybrid region was accompanied by the sounding wave’s anomalous absorption (AA) slower development for f0 ≈ 4fc. Furthermore, slower development and weaker expulsion were detected for the height region between the pump wave reflection and upper hybrid altitudes. The combined MDS and SEE allowed for establishing an interconnection between different manifestations of the HF-induced ionospheric turbulence and determining the altitude of the most effective pump wave energy input to ionospheric plasma by using the dependence on the offset between f0 and 4fc

Leveraging domain expertise in architectural exploration

Domain experience is a key driver behind design quality, especially during the early design phases of a product or service. Currently, the only practical way to bring such experience into a project is to directly engage subject matter experts, which means there is the potential for a resource availability bottleneck because the experts are not available when required. Whilst many domain specific tools have attempted to capture expert knowledge in embedded analytics thus allowing less experienced engineers to perform complex tasks, this is certainly not the case for highly complex systems of systems where their architectures can go far beyond what a single human being can comprehend. This paper proposes a new approach to leveraging design expertise in a manner that facilitates architectural exploration and architecture optimization by using pre-defined architecture patterns. In addition, we propose a means to streamline such a process by delineating the knowledge creation process and architectural exploration analytics with the means to facilitate information flow from the former to the latter through a carefuly designed integration framework