With the growing adoption of network function virtualization, telco core network elements and network functions will increasingly be designed and deployed as cloud-native application instances. To ensure the efficient use of virtualised resources and meet diverse requirements for quality of services a resource scaling algorithm is used to scale the number of application instances up or down depending on variations in offered traffic from customers. Most of the observed performance metrics for a service are a function of the current customer traffic and the current number of application instances providing the service. The ubiquitous use of Kubernetes, the popular open-source framework for deployment and management of cloud-native functions, has resulted in variants of the Kubernetes Horizontal Pod Autoscaling (HPA) algorithm being widely used to change the number of application instances providing network functions as traffic demands vary. This change is done by determining whether a selected performance metric of interest is outside a range set by two input parameters (the desired metric value and the tolerance parameter). In this paper, we investigate the characteristics of the HPA algorithms and prove that there are only a finite number of intervals for its tolerance parameter. Further any choice of the tolerance parameter from each interval leads to similar computational decisions on the recommended number of application instances. As a consequence, the number of parameter setting choices is finite due to the rule that the desired metric value can only be an integer in specific ranges. Additionally, we investigate the use of HPA for scaling application instances that provide session-based services and establish lower and the upper bounds for the performance of the HPA scaling algorithms in this scenario. Our contributions can help operators find appropriate parameter settings efficiently - administrators of Kubernetes clusters only need to select parameters from a limited and finite number of choices (instead of infinite) for scaling cloud-native applications.

Measurement data from wireless sensors deployed in large agricultural areas could be used to help the automation of precision farming activities such as irrigation management, fertilization, etc. The widespread use of sensors with limited battery capacity in precision farming largely depends on data collection methods that reduce the energy consumption of transmitting measurement data and prolong the battery run time. In this article, we investigate joint clustering and multi-UAV-assisted data-gathering schemes to save the energy consumption of sensors. We establish a theoretical lower bound for the energy consumption of sensors to transport data to cluster heads and prove that the energy consumption of sensors approaches the theoretical lower bound if clusters are balanced regarding energy consumption. Therefore, the essential step of proposed heuristic multi-UAV schemes, calledGathering data Assisted by Multi-UAV with a BAlanced Clustering(GAMBAC), is to find balanced or near-balanced clusters concerning energy consumption. For sensor networks with a small number of nodes our heuristic algorithms give results close to the ones obtained by the reference solution. Numerical results show that the GAMBAC schemes extend network lifetime and requires less energy to support a specific number of collection rounds than the best existing approach. Numerical results show that the GAMBAC schemes extend network lifetime and requires less energy to support a specific number of collection rounds than the best existing approach. Therefore, the GAMBAC algorithms could enhance the reliable data collection of sensor networks for precision farming.

Runtime environments for IoT data processing systems based on the actor model often apply a thread pool to serve data streams. In this paper, we propose an approach based on Reinforcement Learning (RL) to find a trade-off between the resource (thread pool in server machines) usage and the quality of service for data streams. We compare our approach and the Thread Pool Executor of Akka, an open-source software toolkit. Simulation results show that our approach outperforms ThreadPoolExecutor with the timeout rule when the thread start times are not negligible. Furthermore, the tuning of our approach is not tedious as the application of the timeout rule requires.

Abstract A quest for geographic routing schemes of wireless sensor networks when sensor nodes are deployed in areas with obstacles has resulted in numerous ingenious proposals and techniques. However, there is a lack of solutions for complicated cases wherein the source or the sink nodes are located close to a specific hole, especially in cavern-like regions of large complex-shaped holes. In this paper, we propose a geographic routing scheme to deal with the existence of complicated-shape holes in an effective manner. Our proposed routing scheme achieves routes around holes with the (1+ $$\epsilon$$ ϵ )-stretch. Experimental results show that our routing scheme yields the highest load balancing and the most extended network lifetime compared to other well-known routing algorithms as well.

Abstract This paper is motivated by the need of deadline-bounded applications in live mobile network environments to obtain the guarantee and the appropriate share of an input and output (I/O) data rate. However, data processing frameworks only support the request of memory and the computing capacity at present. In this paper, we propose a solution that allows the control of disk I/O and network I/O for data processing applications in YARN and Mesos frameworks. Experimental results show that our tool can provision the I/O data rate sharing of competing data processing applications.

In this paper, we construct forecasting models (multivariate long short-term memory recurrent neural networks and multiple linear regression) for the resource usage prediction of four MapReduce applications and applications executed within the Spark framework. We have evaluated the impact of a sample size to prediction accuracy. Also, we propose a phase modelling approach for read/write-intensive applications. Our results show that models based on long short-term memory recurrent neural networks exhibit a higher accuracy than multiple linear regression models and the intensive characteristics of a resource are closely related to the prediction accuracy of forecasting models. We investigated the hyperparameter tuning of such models and showed that a randomly initialised, shallow, well-tuned network may outperform deeper models that use stacked autoencoder initialisation. Furthermore, multivariate long short-term memory recurrent neural network models are more sensitive to sample size than multiple linear regression models. We show that an LSTM model trained in a specific machine may be used to predict the resource usage in another machine.

At present server farms play an important role in the provision of services in IT industry. The energy consumption of server farms significantly contributes to the operational cost. Complementary management actions exploit the capability of the hardware infrastructure and the stochastic nature of computing demands to reduce energy consumption related to the operation of server farms. In this paper, a simple energy-aware policy incorporating allocation schemes of virtual servers is proposed to achieve the aim of green computing. The policy automatically governs physical hosts to a low-energy consuming state when no virtual servers are allocated in a specific physical host and automatically manages a physical host into the operating state of full functionality when virtual servers are assigned. We present an analytical performance model to compare three allocation schemes of dynamic requests for virtual servers in a server farm.

A new queuing model is proposed for the performance evaluation of the High Speed Downlink Packet Access (HSDPA) protocol, with respect to a specified user, in UMTS networks. The model is based on the recently evolved MM ΣΚκ= CPPκGEcLG-queue1, in which the number of servers allocated to a specified user is subjected to vary according to the physical channel allocation policy. This queue is, essentially, an important variant of the so-called Sigma queuing model, and it is able to capture most of the features of HSDPA wireless communications, such as traffic-burstiness, channel fading, channel allocation policy, etc., in an integrated way. Numerical results for the performance of HSDPA with respect to a specified user are obtained, and different HSDPA user equipment categories are compared with respect to their computed model throughputs.

The MM CPP/GE/c/L G-queue has been capable of accommodating large batch sizes of arrivals and services with an efficient solution in the Markovian framework. However it suffers from the restriction on the batch size distribution (i.e. geometric batch size distribution). In order to overcome that restriction, to make the model vastly more flexible and also to accommodate the superposition of multiple arrival streams, a new traffic/queueing model, the Markov modulated MM /spl Sigma//sub k=1//sup K/ CPP/sub k//GE/c/L G-queue is introduced. This is a homogeneous multi-server queue with c servers, GE service times and with the superposition of K independent positive and an independent negative customer arrival streams each of which is a CPP, i.e. a Poisson point process with batch arrivals of geometrically distributed batch size.