SPC: An Approach to Guarantee Performance in Cost Oriented Mapping Algorithm for NoC Architectures

The effectiveness of Network on Chip (NoC) is highly dependent on how well the real-time tasks are mapped on NoC communication infrastructure. In this paper an efficient dynamic scheduling algorithm is proposed and implemented on standard Mesh topology as well as on similar recently proposed LECΔ topology. The performance of proposed algorithm is evaluated in terms of Load Imbalance Factor (LIF), makespan, communication cost and system throughput. The results are evaluated with different task distribution profile using Java-based simulator. The behavior of load distribution is estimated and compared with different performance metrices on two considered topologies. The simulation results show an improvement of 32.57% in LIF at lesser execution time. When comparing the performance of two networks in terms of makespan, the results show a reduction of 43.7% in case of 4 x 4 LECΔ topology as compared to 4 x 4 Mesh topology. The comparative study in terms of LIF, makespan and system throughput is carried out by implementing other existing techniques on both the considered NoC architectures which shows that the proposed approach is an optimized solution for task mapping at considerable reduced cost on considered NoC architectures. The analysis of simulation results shows that the proposed approach may be considered a better choice and implemented on similar NoC architectures as well as other than mesh-based topologies.

Network-on-Chip (NoC) has been developed as an interconnect patterns in state-of-the-art multiprocessors. Core mapping and identifying suitable NoC platforms have a substantial effect on NoC system performance and corresponding costs. In this research work, investigated core mapping and optimization of NoC platforms by using an efficient algorithm. Consider two performance metrics (reliability and delay) and two cost metrics (area and power). The proposed mapping algorithm was assessed by applying it to different NoC benchmarks. The simulation results expose that the suggested mapping technique outperformed the other mapping techniques in terms of performance and cost metrics.

Wireless technology is emerging as a key technology for the future networks. Wireless mesh networks (WMNs) have emerged as an indispensable technology for deployment of wireless services for various applications in personal, enterprise, and metropolitan areas. Researchers are working actively in different fields of WMNs for providing better services. Routing protocols play a vital role in WMNs to provide reliable configuration and maintenance of topology of the network. Designing a suitable cost metric for routing protocols to provide quality links for data transmission is the backbone of WMNs. Many cost metrics have been proposed for WMNs and is still an active research topic as new performance metrics need to be discovered due to the dynamics of this field. This paper considers the genetic algorithm-based routing technique for WMN.We have studied the existing cost metrics and proposed a genetic algorithm technique for routing in WMN. To evaluate the genetic algorithm, and to determine the relative performance of the genetic algorithm in the context of routing in WMN, we carry out experiments on two test systems. We have evaluated the quality of the results produced by our algorithm with the traditional hop count metric results. Our results show that routing in WMN using genetic algorithm produces better results as compared to traditional hop count metric results. Finally, we carry out a detailed analysis of results, which help us in gaining an insight into the suitability of genetic algorithm for routing in WMN.

A resource-efficient and scalable wireless mesh routing protocol

This paper presents the results of a detailed quantitative analysis of performance metrics of a sample consisting of 89 transportation projects sponsored by the World Bank. The sample and a subset consisting of 65 projects were evaluated using the performance metrics of project cost, schedule and scope. The effect of project size (dollar value) and project duration on performance metrics was investigated. Also, the achievement of project goals and potential improvement in planning and estimating over time (the learning effect) was studied. It was found that, in general, in transportation projects sponsored by the World Bank, costs are overestimated and schedules are optimistic. The outcome with respect to cost seems counter‐intuitive because previous work by other researchers had shown a systematic underestimation of project costs. There is significant evidence that there are no efficient controls in place to predict or prevent schedule delays. The study also showed that during the past 15 years, no improvement (learning effect) was evident in project cost and duration estimation as the level of accuracy has not changed significantly. Further, it is observed that project duration did not affect the performance with respect to cost and delay.

Edge computingZivkovic, Miodrag Bezdan, Timea Strumberger, Ivana Bacanin, Nebojsa Venkatachalam, K. is a relatively novel technology, which is closely related to the concepts of the Internet of things and cloud computing. The main purpose of edge computing is to bring the resources as close as possible to the clients, to the very edge of the cloud. By doing so, it is possible to achieve smaller response times and lower network bandwidth utilization. Workflow scheduling in such an edge–cloud environment is considered to be an NP-hard problem, which has to be solved by a stochastic approach, especially in the scenario of multiple optimization goals. In the research presented in this paper, a modified Harris hawks optimization algorithm is proposed and adjusted to target cloud–edge workflow scheduling problem. Simulations are carried out with two main objectives—cost and makespan. The proposed experiments have used real workflow models and evaluated the proposed algorithm by comparing it to the other approaches available in the recent literature which were tested in the same simulation environment and experimental conditions. Based on the results from conducted experiments, the proposed improved Harris hawks optimization algorithm outperformed other state-of-the-art approaches by reducing cost and makespan performance metrics.

Despite the abundant research in cloud autoscaling, autoscaling in Kubernetes, arguably the most popular cloud platform today, is largely unexplored. Kubernetes' Cluster Autoscaler can be configured to select nodes either from a single node pool (CA) or from multiple node pools (CA-NAP). We evaluate and compare these configurations using two representative applications and workloads on Google Kubernetes Engine (GKE). We report our results using monetary cost and standard autoscaling performance metrics (under- and over-provisioning accuracy, under- and over-provisioning timeshare, instability of elasticity and deviation from the theoretical optimal autoscaler) endorsed by the SPEC Cloud Group. We show that, overall, CA-NAP outperforms CA and that autoscaling performance depends mainly on the composition of the workload. We compare our results with those of the related work and point out further configuration tuning opportunities to improve performance and cost-saving.

Task mapping is an important issue in MPSoC design. Most recent mapping algorithms perform them at design time, an approach known as static mapping. Nonetheless, applications running in MPSoCs may execute a varying number of simultaneous tasks. In some cases, applications may be defined only after system design, enforcing a scenario that requires the use of dynamic task mapping. Static mappings have as main advantage the global view of the system, while dynamic mappings normally provide a local view, which considers only the neighborhood of the mapping task. This work aims to evaluate the pros and cons of static and dynamic mapping solutions. Due to the global system view, it is expected that static mapping algorithms achieve superior performance (w.r.t. latency, congestion, energy consumption). As dynamic scenarios are a trend in present MPSoC designs, the cost of dynamic mapping algorithms must be known, and directions to improve the quality of such algorithms should be provided without increasing execution time. This quantitative comparison between static and dynamic mapping algorithms is the main contribution of this work.

This study presents a high-level simulator for Network-on-Chip (NoC), designed for many-core architectures, and integrated with the PlatEMO platform. The motivation for developing this tool arose from the need to evaluate the behavior of application mapping algorithms and the routing, both aspects are essential in the implementation and design of NoC architectures. This analysis underscored the importance of having effective NoC simulators as tools that allow for studying and comparing various network technologies while ensuring a controlled simulation environment. During this investigation and evaluation, some simulators, such as Noxim, NoCTweak, and NoCmap, among others, offered configurable parameters for application traffic, options to synthetically define topology and packet traffic patterns. Additionally, they include mapping options that optimize latency and energy consumption, routing algorithms, technological settings such as the CMOS process, and measurement options for evaluating performance metrics such as throughput and power usage. However, while these simulators meet detailed technical demands, they are mostly restricted to analyzing the low-level elements of the architecture, thus hindering quick and easy under- standing for non-specialists. This insight underscored the challenge in developing a tool that balances detailed analysis with a comprehensive learning perspective, considering the specific restrictions of each simulator analyzed. Experiments demonstrated the proposed simulator efficacy in handling algorithms like GA, PSO, and SA variant, and, surprisingly, revealed limitations of the XY algorithm in Mesh topologies, indicating the need for further investigation to confirm these findings. Future work will expand the simulator functionalities, incorporating a broader range of algorithms and performance metrics, to establish it as an indispensable tool for research and development in NoCs.

Offset well analysis is the process of investigating and integrating historical drilling performance from neighboring wells into prospect well design. Traditional offset analysis is a time and resource intensive process that requires a lot of manual input and analysis to make various design decisions. Our proposed workflow automates much of this analysis, while allowing the user to customize designs based on operating metrics resulting in a quicker and more comprehensive offset well analysis that provides the user with intelligent offset recommendations and a base design for the prospect well. Historical data is queried from a structured engineering and operation database comprising of data ranging from subsurface geology, drilling to production and end-of-well. We gather this historical data from nearby wells and learn from those in order to produce a first pass design for the prospect well to begin with. With this data, our proposed workflow implements algorithms to identify representative offset wells with similar geology, trajectory and other characteristics. A similarity analysis based on various geological sequences across the wells is conducted by a deep neural network algorithm, which is trained to analyze sequential patterns within. Trajectory similarity is performed using well surveys (dogleg severity, inclination, and azimuth). A recurrent neural network is employed to learn the well survey patterns and classify wells with similar trajectories. Finally, drilling performance metrics are used to rank the offsets and aid selection of the best offset design to be used as a base template for the prospect well. The proposed workflow significantly reduces analysis time; the software analysis time is less than five minutes. The user can almost instantaneously query the database and obtain wells that are similar in terms of trajectory and geology, and rank those wells on certain pre-defined metrics. The engineer is also able to view key events and hazards history for the offset wells. This provides the engineer with a compilation of hazards and risks by depth and cause, allowing the engineer to focus on mitigating risks and designing better and safer wells. The engineer can accept the top ranked offset and automatically select casing design based on default metrics of cost, time, and NPT, or can implement other metrics and create a composite casing design from different hole-sections from different offsets.

We propose and study a mapping algorithm optimized for shared-cache multicore processors. Performance requirement of modern applications is constantly growing. Processing huge amount of data in real-time is a trend even for mobile devices. It is common to find a octa-core processor in mobile phones or tablets. We will be able to see embedded devices with tens of cores in the next few years, if the trend continues. Conventional mapping algorithms are not well designed for shared-cache multicore processors. We discuss the importance of application mapping in terms of inter-application communication and shared-cache access delay. An algorithm is proposed with optimizations of the two aspects. We introduce a method with low computation complexity. First the mapping region is calculated with the congregate degree of nodes, then the region is expanded with a strategy in which the nearest nodes with lowest average cache latency are selected. The comparison with other mapping algorithms shows up to 13.9% improvement in average inter-application communication distance, with near optimal values considering the average cache latency. The results from real applications show that, the execution time and power consumption of the proposed algorithm has improved for 8% and 16.7% respectively, compared with an incremental mapping algorithm.

The cost‐performance tradeoff is a fundamental issue in a data center for cloud computing, which is closely related to two key metrics that both cloud consumers and service providers care the most, that is, quality of service and cost of service. While there are different definitions of quality of service, the average response time is a common choice of performance metric. While there are various considerations in cost of service, the average power consumption is a common choice of cost metric. Hence, the cost‐performance tradeoff becomes the power‐performance tradeoff. In this article, we deal with the power‐performance tradeoff at the data center level. We study cost‐performance ratio optimization by using the techniques of workload management and server speed setting. In particular, we make the following tangible contributions. We solve three optimization problems, that is, (1) the workload management problem—to find a workload distribution, such that the cost‐performance ratio is minimized; (2) the server speed setting problem—to find a server speed setting, such that the cost‐performance ratio is minimized; (3) the workload management and server speed setting problem—to find a workload distribution and a server speed setting, such that the cost‐performance ratio is minimized. All the three optimization problems are analytically defined as multivariable optimization problems based on M/M/m queueing systems for multiple heterogeneous multiserver systems, together with two power consumption models, that is, the idle‐speed model and the constant‐speed model. Our approach makes it possible to quantitatively evaluate and optimize the cost‐performance ratio of a data center within a rigorously developed framework. Each multivariable optimization problem is transformed to a nonlinear system of equations. Due to the sophistication of these equations, they are solved algorithmically by a numerical procedure. Furthermore, we provide approximate, accurate, and analytical solutions to the first two problems. Performance data are demonstrated for each problem, and the accuracy of our approximate solutions are also discussed. To the best of the author's knowledge, this is the first paper which analytically and algorithmically minimizes the cost‐performance ratio of a data center with multiple heterogeneous multiserver systems using the techniques of workload management and server speed setting.

Due to shrinking technology sizes, more and more processing elements and memory blocks are being integrated on a single die. However, traditional communication infrastructures (e.g., bus or point-to-point) cannot handle the synchronization problems of these large systems. Using network-on-chip (NoC) is a step towards solving this communication problem. Energy- and communication-efficient application mapping is a previously studied problem for mesh-based NoC architectures; however, there is still need for intelligent mapping algorithms since current algorithms either take too much running time or do not determine accurate results. To fill this need, in this study, we propose two mapping algorithms (one based on simulated annealing and one based on genetic algorithm) for energy- and communication-aware mapping problems of mesh-based NoC architectures. We compare these two algorithms with an integer linear programming-based method and a heuristic method using several multimedia and synthetic benchmarks.

Steam systems consume approximately one third of energy applied at U.S. industrial facilities. To reduce energy consumption, steam system energy assessments have been conducted on a wide range of industry types over the course of five years through the Energy Savings Assessment (ESA) program administered by the U.S. Department of Energy (U.S. DOE). ESA energy assessments result in energy efficiency measure recommendations that are given potential energy and energy cost savings and potential implementation cost values. Saving and cost metrics that measure the impact recommended measures will have at facilities, described as percentages of facility baseline energy and energy cost, are developed from ESA data and used in analyses. Developed savings and cost metrics are examined along with implementation and rejection rates of recommended steam system energy efficiency measures. Based on analyses, implementation of steam system energy efficiency measures is driven primarily by cost metrics: payback period and measure implementation cost as a percentage of facility baseline energy cost (implementation cost percentage). Stated reasons for rejecting recommended measures are primarily based upon economic concerns. Additionally, implementation rates of measures are not only functions of savings and cost metrics, but time as well.

The mobile cloud computing (MCC) that takes wireless access network as transmission medium and uses mobile devices as client becomes the newest evolution trends of cloud computing. When offloading the complicated multi-task application to the MCC environment, each task executes individually in terms of its own computation, storage and bandwidth requirement. Due to user's mobility, the provided resources contain different performance metrics that may affect the destination choice. Nevertheless, these heterogeneous MCC resources lack integrated management and can hardly cooperate with each other. Thus, how to choose the appropriate offload destination and orchestrate the resources for multi-task is a challenging problem. This paper decouples resource control of mobile cloud from user plane, where a centralized controller is responsible for resource orchestration, offload and migration. The resource orchestration is formulated as multi-objective optimal problem that contains the metrics of energy consumption, cost and availability. Finally, a particle swarm algorithm is used to obtain the approximate optimal solutions. Simulation results show that the solutions can hit Pareto optimum of resource orchestration in acceptable time.