Performance models for hierarchical grid architectures

Speeding up the communications on a cluster using MPI by means of Software Defined Networks

In real time communication network capability measuring are crucial tasks because every resource of network is frequently change their state. In the mobile ad hoc network, link capacity utilization computation is very challenging task due to network topology frequently change, that totally depends on their network structure. So that in the ad hoc scenario all the network capacity utilization measure at end of simulation, where include the entire sub graph that will perform communication in any time. In the preceding some author calculates energy utilization, network congestion status and enhances the routing strategies. Through those work here we take every parameter that depends on the communication and find out the utilization, such parameters are network bandwidth, mobile node processing power, buffer, energy and signal strength utilization with respect to communication path between source to receiver. In this paper some of network parameter are initialize constant manner such as node initial energy, channel, buffer size, processing power that prefix and rest of parameter i.e. bandwidth, signal strength dynamically define at the time of communication. After that while communication start than all the parameter utilize that calculate with respect to complete path from source to destination. The utilize parameter apply into the fuzzy classification and calculate class level of particular complete path in `low', `medium', `high' level that helps to identify the network capability utilization. The result section shows identification of network path resource utilization in terms of bandwidth, energy, signal strength, buffer and processing power and impacts of these parameter based on packet delivery fraction, routing load, throughput, no. of data sends, receives and energy consumption etc.

The Web 3.0 and metaverse can empower intelligent application of Connected Autonomous Vehicles (CAVs). The adoption of edge computing can contribute to the low latency interaction between CAVs and the metaverse. Microservices are widely deployed on edge networks and the cloud nowadays. User's requests from CAVs are typically fulfilled through the composition of microservices, which may be hosted by contiguous edge nodes. Requests may differ on their required resources at runtime. Consequently, when requests are continuously injected into edge networks, the usage of heterogenous resources, including CPU, memory, and network bandwidth, may not be the same, or differ significantly, on certain edge nodes. This happens especially when burst requests are injected into the network to be satisfied concurrently. Therefore, the usage of heterogenous resources provided by edge nodes should be co-optimized through re-scheduling microservices. To address this challenge, this paper proposes a Web 3.0-enabled M icroservice R e- S cheduling approach (called MRS ), which is a migration-based mechanism integrating a placement strategy. Specifically, we formulate the microservice re-scheduling task as a multi-objective and multi-constraint optimization problem, which can be solved through a penalty signal-integrated framework and an improved pointer network. Extensive experiments are conducted on two real-world datasets. Evaluation results show that our MRS performs better than the counterparts with improvements of at least 7.7%, 2.4% and 2.2% in terms of network throughput, latency and energy consumption.

Results produced by a parallel application are typically collected and visualized on one display accessible to a single user. Collaboration between several researchers is usually achieved by sharing entire desktops. We have developed a system that shares windows, both from parallel applications and from desktop applications, with other users or to a wall-sized, high resolution display. Parallel applications can create several shared windows for each thread or process, enabling runtime visualization and monitoring. To aid collaboration, we provide multiple cursors for use on a display wall, allowing several researchers to interact simultaneously with windows shared by parallel and desktop applications. We measure the system's performance, and show that using shared windows for runtime visualization of the Mandelbrot computation increases the application's execution time by approximately 1.4%, while performance for sharing desktop application windows is halved as the number of users is doubled.

The cooperative adaptive cruise control (CACC) system is pivotal in improving traffic safety and efficiency by maintaining precise inter-vehicle gaps and regulating speed. However, communication and actuator delays often coexist and significantly affect the stability and operational safety of vehicle platoon systems. Although extensive research has been conducted on both types of delays, most studies have constructed and analyzed each delay independently, lacking a unified mitigation strategy for their combined effects. Therefore, this study introduces a novel distributed hierarchical control architecture that integrates upper-level decision-making with lower-level execution control. The upper-level controller employs a model predictive control (MPC) architecture enhanced by an adaptive Kalman filter (AKF) to mitigate state discrepancies caused by communication delays in car-following scenarios. This AKF-MPC controller further improves the real-time estimation of surrounding vehicle states, resulting in a more accurate desired acceleration. A Proportional-Integral-Derivative (PID) feedback controller is implemented at the lower level to track the desired acceleration from the upper-level controller, simultaneously compensating for actuator delays and enhancing closed-loop vehicle performance. The proposed architecture is validated through comprehensive simulations under diverse traffic conditions, demonstrating its effectiveness in significantly improving CACC system stability and safety under communication and actuator delays. This study can contribute to establishing a unified control architecture for cooperative vehicle systems, thereby providing a theoretical foundation for deploying delay-resilient CACC in intelligent transportation systems with heterogeneous communication constraints.

To achieve end to end (E2E) quality management capability for different services (e.g. voice, video and data), an SOA-based E2E resource orchestration architecture based on hierarchical distributed model and virtualization mechanisms for individual networks is proposed. Possible implementations and operation mechanisms considering possible service scenarios are discussed. Prototype of resource management (RM) system is demonstrated for the first time to manage and control distributed GE-PON-based access network resources, RPR-based metro network resources and GMPLS-based core network resources. Proposed RM system consists of network resource managers (NRMs) virtualizing individual network resources and network resource broker (NRB) leading orchestration of NRMs with a single point of contact to a service control layer. Exposure of E2E network service to a service control layer as well as the exposure of virtualized network services to NRB are represented by a universal Web services interface (WSI). Using the RM system, the heterogeneous resources are orchestrated to provide appropriate bandwidth-managed E2E services following service demand of a network service client, and appropriate resources are applied to assumed voice, video and data flows.

The landing of a fixed-wing UAV on top of a mobile landing platform requires a cooperative control strategy, which is based on relative motion estimates. These estimates typically suffer from communication or processing time delays, which can render an otherwise stable control system unstable. Such effects must therefore be considered during the design process of the cooperative landing controller. In this letter the application of a model-free passivity-based stabilizing controller is proposed, which is based on the monitoring of energy flows in the system, and actively dissipating any given active energy by means of adaptive damping elements. In doing so, overall system passivity and consequently stability is enforced in a straightforward and easy to implement way. The proposed control system is validated in numerical simulations for round trip delays of up to 4 seconds.

In this paper, we present a new linear time algorithm for scheduling UECT (Unit Execution and Communication Time) trees on two identical processors. The chosen criterion is the makespan. The used strategy is based on clustering of tasks. We show that this algorithm builds optimal schedules. Some extensions are discussed for non UECT tasks.

Variation in the abundance of animals has traditionally been explained as the outcome of endogenous forcing from density dependence and exogenous forcing arising from variation in weather and predation. Emerging evidence suggests that the effects of density dependence interact with external influences on population dynamics. In particular, spatial heterogeneity in resources and the presence of capable predators may weaken feedbacks from density dependence to growth of populations. We used the Kalman filter to analyze 23 time series of estimates of abundance of northern ungulate populations arrayed along a latitudinal gradient (latitude range of 40°–70°N) to evaluate the influence of spatial heterogeneity in resources and predation on density dependence. We also used contingency tables to test whether density dependence was independent of the presence of carnivores (our estimate of predation) and multiple regressions to determine the effects of spatial heterogeneity in resources, predation, and latitude on the strength of density dependence. Our results showed that the strength of density dependence of ungulate populations was low in the presence of large carnivores, particularly at northern latitudes with low primary productivity. We found that heterogeneity in elevation, which we assume acted as a surrogate for spatial heterogeneity in plant phenology, also reduced effects of density dependence. Thus, we show that external forces created by heterogeneity in resources and predation interact with internal feedbacks from population density to shape dynamics of populations of northern ungulates.

This paper investigates the latency of system integrity protection schemes (SIPSs) and proposes a bounded model of the communication delay. To be specific, SIPSs can be divided into wide-area protection and substation-area protection. For the former, the data buffering of phasor data concentrators and the automatic protection switching of synchronous optical network/synchronous digital hierarchy are utilized to limit the latency of regional and backbone networks, respectively; then, the communication delay is modeled as bounded, instead of average or stochastic in the literature. For the latter, the network calculus theory is used to restrict the latency in switched Ethernet networks, and the communication delay is modeled as bounded. In practice, SIPSs need to preprogram the time delay of protective relays and expect the communication delay as predictable or predetermined. Hence, the proposed bounded model is more realistic than the average or stochastic model. Further, the bounded model suggests the network dynamics and worst-case performances. It can be a useful tool in the relay setting as well as in the planning, design, and assessment of SIPS networks.

Scheduling DAGs with communication times is the theoretical basis for achieving efficient parallelism on distributed memory systems. We generalize Graham’s task-level in a manner to incorporate the effects of computation, data size, and network latency. A new scheduling that uses the proposed task-level to make early reservation of resources for critical computation and communication is proposed. We also propose an optimization called Iterative Refinement Scheduling (IRS) that alternatively schedules the computation graph and its associated reverse. The task-level used in some scheduling iteration is the task’s starting time that is achieved in the very previous iteration. IRS enables searching and optimizing solutions as the result of using more refined task-level in each scheduling iteration. Evaluation and analysis of the results are carried out for different instances of problem granularities, parallelism, and network latency such as the fully connected, hypercube, and ring. The finish time obtained from two-iteration scheduling outperforms those generated by other recently proposed scheduling as well as the clustering approaches. IRS allows exploring a space of solutions whose size grows with the amount of parallelism and communication granularity. Solutions generated by IRS largely outperform all known approaches specially for fine-grain problems where other approaches fail. IRS enables optimizing the solution specially for critical instances such as fine-grain DAGs and large parallelism. The time complexity of one IRS iteration is O(pn).

SummaryMany scientific applications consist of large and computationally intensive loops. Dynamic loop self‐scheduling (DLS) techniques are used to parallelize and to balance the load of such applications during execution. Load imbalance arises from variations in the loop iteration (or tasks) execution times, caused by problem, algorithmic, or systemic characteristics. Variations in systemic characteristics are referred to as perturbations. Our hypothesis is that no single DLS technique can achieve the absolute best performance under various perturbations on heterogeneous high‐performance computing (HPC) systems. Therefore, the selection of the most efficient DLS technique is critical to achieve the best application performance. The goal of this work is to solve the algorithm selection problem for the scheduling of computationally intensive applications under perturbations. Existing work only considers perturbations caused by variations in the delivered computational speed of the HPC systems. However, perturbations in available network bandwidth or latency are inevitable on production HPC systems. A simulation‐assisted scheduling algorithm selection (SimAS) approach is introduced herein as a novel control‐theoretic‐inspired approach to select DLS techniques dynamically that improve the performance of applications executing on heterogeneous HPC systems under perturbations. The present work examines the performance of seven applications on a heterogeneous HPC system under all the above system perturbations. SimAS is evaluated using native and simulative experiments. The performance results confirm the original hypothesis that motivates this work. The experimental evaluation shows that the SimAS‐based DLS selection identifies the most efficient technique and improves application performance in most cases.

We study the scheduling of computational tasks on one local processor and one remote processor with communication delay. This problem has important application in cloud computing. Although the communication time to transmit a task can be inferred from the known data size of the task and the transmission bandwidth, the processing time of the task is generally unknown until it is processed to completion. Given a set of independent tasks with unknown processing times, we propose a Semi-online Partitioning and Communication (SPaC) algorithm, to jointly select the subset of tasks to be offloaded to the remote processor and their order of transmission, with an aim to minimize the overall makespan to process all tasks. Even though the offline version of this problem, with a priori known processing times, is NP-hard, we show that the proposed semi-online algorithm achieves a small competitive ratio when the communication times of tasks are smaller than their processing times at the remote processor. For general communication times, we use simulation to demonstrate that SPaC outperforms online list scheduling and performs comparably well with known offline heuristics.

There is a growing interest in development of in-network dispersed computing paradigms that leverage the computing capabilities of heterogeneous resources dispersed across the network for processing massive amount of data is collected at the edge of the network. We consider the problem of task scheduling for such networks, in a dynamic setting in which arriving computation jobs are modeled as chains, with nodes representing tasks, and edges representing precedence constraints among tasks. In our proposed model, motivated by significant communication costs in dispersed computing environments, the communication times are taken into account. More specifically, we consider a network where servers are capable of serving all task types, and sending the results of processed tasks from one server to another server results in some communication delay that makes the design of optimal scheduling policy significantly more challenging than classical queueing networks. As the main contributions of the paper, we first characterize the capacity region of the network, then propose a novel virtual queueing network encoding the state of the network. Finally, we propose a Max-Weight type scheduling policy, and considering the virtual queueing network in the fluid limit, we use a Lyapunov argument to show that the policy is throughput-optimal.

Communication delay in hypercubes in the presence of bit-reversal traffic