Resource Requests Research Articles

Dynamic IoT service placement based on shared parallel architecture in fog-cloud computing

Fog-cloud computing is a promising computing paradigm for processing and storing massive data produced by Internet of Things (IoT) devices. Considering that the fog computing nodes are distributed, heterogeneous and have limited resources, it is not possible to deploy all the services belonging to IoT applications on the fog environment. Therefore, an efficient and autonomous mechanism is needed to map IoT services on the fog environment. The problem becomes more complicated when the various requirements of services including response time and cost are considered. With this motivation, we introduce a mixed-integer linear programming model to address the Fog Service Placement (FSP) problem in fog-cloud computing environments. This model is formulated as a multi-objective model with the aim of minimizing deadline violation, resource loss and service cost, and maximizing resource usage. Meanwhile, we propose an Elitism-based Genetic Algorithm to solve the FSP problem developed with a Shared Parallel Architecture (EGASPE). Our algorithm considers the efficient distribution of resources under certain Quality of Service (QoS) requirements and seeks a balance between exploration and exploitation. Also, EGASPE can save more resources for future requests by considering the deadline of IoT services over time. Extensive experiments have been performed to evaluate the performance of EGASPE on a synthetic fog environment. The obtained results show the effectiveness of EGASPE up to 6.5% on average compared to state-of-the-art algorithms.

A Resource Allocation Algorithm for a History-Aware Frame Graph

We consider the problem of memory consumption by a real-time GPU-accelerated graphical application. A history of a resource is defined for a particular frame to be the final contents of such a resource at the end of the previous frame. When organizing a graphical application using a frame rendering graph approach, it makes sense to implement automatic serving of resource history read requests of nodes. In absence of history resource requests, allocating resources for a fixed frame graph is the classic problem of dynamic storage allocation (DSA). In this paper, we formulate a generalization of DSA that enables memory reuse for resources with history requests and provide a practical approximate algorithm for solving it.

From the lab to the clinic: Lessons learned from a translational working group.

e12576 Background: Comparing an Operation to Monitoring, with or without Endocrine Therapy (COMET), is a phase III multicenter prospective randomized clinical trial comparing the risks/benefits of active monitoring (AM) versus surgery for women with low-risk DCIS. Funded by the Patient-Centered Outcomes Research Institute, accrual opened 06/30/17 and closed 01/13/23 with 995 women enrolled. Biospecimens (blood/tissue) and breast images were collected at specified timepoints (funded by the Breast Cancer Research Foundation) and stored in central tissue/image repositories. To date, more than 90% of requested samples/images have been submitted. Methods: In 2017, the COMET Translational Working Group (TWG) was formed to facilitate the collection, submission, storage, and subsequent analysis of biospecimens/images. The TWG, consisting of clinicians, pathologists, radiologists, researchers, and patient advocates (PAs), discuss topics such as categorization of biospecimens into discovery/validation sets; development of a pathology workflow/sample tracking process; and potential areas of future research that may improve DCIS diagnostics, prognostics, and care management. Results: The TWG consists of multiple partner institutions, and team members have complementary areas of expertise/experience. While competing views may exist, consensus has been achieved in relation to major issues such as authorship, biospecimen ownership, intellectual property and criteria relevant to patient needs, through targeted discussion at monthly meetings. Logistical barriers, including data sharing and the technicalities of biospecimen release, have also been resolved through this process. The TWG has played an integral role in resolving recruitment challenges. In 2019, guidance from the TWG related to standardizing pathology eligibility criteria resulted in evidence-based protocol amendments that subsequently increased accrual. A retrospective review of biospecimens was performed to determine adequacy for ensuing correlative molecular and spatial profiling studies. PAs are integral to the TWG, assisting with logistical issues (resource requests); identifying study topics relevant to patients; providing guidance on existing (commercial) predictive/prognostic tests; promoting effective stewardship of a finite resource (tissue samples); and ensuring that the overall focus of the TWG remains firmly on advancing clinical utility. Conclusions: The COMET TWG serves the important role of overseeing biospecimen/image collection including its use and sharing. This facilitates development of research and technology that can impact the decision for surgery vs AM in women with low-risk DCIS, and ensure the translation of research findings into everyday clinical practice. Subsequent correlative science studies identified by the TWG stand to improve future DCIS pathology diagnostics, prognostics and care management.

An integrated approach of ML-metaheuristics for secure service placement in fog-cloud ecosystem

With the proliferation of the Internet of Things (IoT), a huge amount of data is being produced which requires processing. Usually, Cloud computing provides the resources for processing such requests. However, cloud has limitations in terms of latency and security. To overcome, Fog computing came into existence to complement cloud wherein the consolidation of Fog and Cloud computing substantially improves the Quality of Service (QoS). Due to inherent heterogeneity in the Cloud and Fog nodes, selecting an appropriate node becomes a complex task which requires utmost attention. Fog nodes have minimal and limited resources with a thin processing capacity. To utilize the fog nodes optimally, a secure two-step service placement framework has been proposed in this work. The framework consists of the classification of services to decide on the appropriate tier (Cloud/Fog) followed by the scheduling of the services at the Fog tier. For classification, a machine learning based improved adaptive neuro-fuzzy inference model (ANFIS) is implemented, which predicts the suitable computing tier. Further, a novel metaheuristic-based hybrid algorithm that integrates a chaotic-based grasshopper (CGOA) with genetic algorithm (GA) is applied for scheduling at the Fog tier. Basic GOA suffers from several shortcomings, such as falling into local minima, slow convergence, high time complexity, etc. The concept of chaos theory and opposition-based learning is incorporated into GOA to overcome this. The performance of the proposed model has been analysed on the Google trace data set, and its effectiveness has been studied on makespan, computational cost, and energy dissipation of the network where it yields respectively 9.2%, 4.25% and 2.75% average better results than state of art.

Recognizing Local Champions in Infection Prevention and Control.

Recognizing Local Champions in Infection Prevention and Control.

Dynamic Scaling of Application using Kubernetes Horizontal Pod Auto Scaling

To make sure that the pods have enough resources to run effectively without going overboard, Pod Autoscaling monitors the pods' usage patterns and modifies their resource requests and restrictions accordingly. This keeps programs operating smoothly while maximizing resource use and reducing expenses. In this paper, we shall look into the various types of autoscaling and the uses of using horizontal autoscaling (HPA).

Accurate Prediction of Required Virtual Resources via Deep Reinforcement Learning

Resource provisioning for the ever-increasing applications to host the necessary network functions necessitates the efficient and accurate prediction of required resources. However, the current efforts fail to leverage the inherent features hidden in network traffic, such as temporal stability, service correlation and periodicity, to predict the required resources in an intelligent manner, incurring coarse-grain prediction accuracies. To tackle this problem, in this paper, we propose an Accurate Prediction of Required virtual Resources (APRR) approach via Deep Reinforcement Learning (DRL). We first confirm the resource requests have more similar features and identify the high-dimensional required resources in computing, storage and bandwidth can be effectively consolidated into a single standardized value. Built upon these observations, we then model the required resources as a time-variant network matrix, which includes a number of elements, obtained from the network measurements, and some missing elements needed to be inferred. To obtain accurately predicted results, DRL-based matrix factorization with a set of available rules has been introduced into APRR and alternately executed in agent to minimize the prediction errors. Moreover, the error-prioritized designed for model training with quicker convergence. Simulation experiments on real-world datasets illustrate that APRR can accurately predict the required virtual resources compared with the related approaches.

Edge Resource Pricing and Scheduling for Blockchain: A Stackelberg Game Approach

Blockchain came to prominence as the distributed ledger underneath Bitcoin, which protects the transaction histories in a fully-connected, peer-to-peer network. The blockchain mining process requires high computing power to solve a Proof-of-Work (PoW) puzzle, which is hard to implement on users’ mobile devices. So these miners may leverage the edge/cloud service providers (ESPs/CSP) to calculate the PoW puzzle. The existing edge-assisted blockchain networks assumed that all ESPs have a uniform propagation delay, which is unrealistic. In this article, we consider a more practical scene where ESPs locate in diverse positions of the blockchain network, which causes different propagation delays when supporting the computation of the PoW puzzle. Additionally, these ESPs connect to a remote CSP for resource scheduling when the computing tasks exceed their maximum capacity. The blockchain mining process generally involves complicated competition and games among CSP, ESPs, and miners. Each service provider focuses on how to determine his resource price so that he can maximize his utility. According to the set resource price, each miner concentrates on scheduling his resource requests for each ESP to maximize individual personal utility, which depends on ESPs’ resource price and propagation delays. We first model such a resource pricing and scheduling problem as a three-stage multi-leader multi-follower Stackelberg game and aim at finding the Stackelberg equilibrium. Then, we analyze the subgame optimization problem in each stage and propose an iterative algorithm based on backward induction to achieve the Nash equilibrium of the Stackelberg game. Finally, extensive simulations are conducted to verify the significant performance of the proposed solution.

Benefits of Pod Dimensioning With Best-Effort Resources in Bare Metal Cloud Native Deployments

Container orchestration platforms automatically adjust resources to evolving traffic conditions. However, these scaling mechanisms are reactive and may lead to service degradation. Traditionally, resource dimensioning has been performed considering guaranteed (or request) resources. Recently, container orchestration platforms included the possibility of allocating idle (or limit) resources for a short time in a best-effort fashion. This letter analyzes the potential of using limit resources as a way to mitigate degradation while reducing the number of allocated request resources. Results show that a 25% CPU reduction can be achieved by relying on limit resources.

Delay and energy aware task scheduling mechanism for fog-enabled IoT applications: A reinforcement learning approach

With the expansion of the internet of things (IoT) devices and their applications, the demand for executing complex and deadline-aware tasks is growing rapidly. Fog-enabled IoT architecture has evolved to accomplish these tasks at the fog layer. However, fog computing devices have limited power supply and computation resources compared to cloud devices. In delay-sensitive applications of fog-enabled IoT architecture, executing tasks with stringent deadlines while reducing the service latency and energy usage of fog resources is a difficult challenge. This paper presents an effective task scheduling strategy to allocate fog computing resources for IoT requests to meet the deadline of the requests and resource availability. Initially, the scheduling problem is formulated as mixed-integer nonlinear programming (MINLP) to reduce the energy consumption of the fog resources and service time of the tasks subject to the deadline and resource availability constraints. To address the high dimensionality issue of the tasks in a dynamic environment, a fuzzy-based reinforcement learning (FRL) mechanism is employed to reduce the service delay of the tasks and energy usage of the fog nodes. Initially, the tasks are prioritized using fuzzy logic. Then the prioritized tasks are scheduled using the on-policy reinforcement learning technique, which enhances the long-term reward compared to the Q-learning approach. Further, the evaluation outcomes reflect that the proposed task scheduling technique outperforms the existing algorithms with an improvement of up to 23% and 18% regarding service latency and energy consumption, respectively.

Leveraging HPC resources with distributed RDataFrame

The declarative approach to data analysis provides high-level abstractions for users to operate on their datasets in a much more ergonomic fashion compared to imperative interfaces. ROOT offers such a tool with RDataFrame, which has been tested in production environments and used in real-world analyses with optimal results. Its programming model acts by creating a computation graph with the operations issued by the user and executing it lazily only when the final results are queried. It has always been oriented towards parallelisation, with native support for multi-thread execution on a single machine. Recently, RDataFrame has been extended with a Python layer that is capable of steering and executing the RDataFrame computation graph over a set of distributed resources. In addition, such a layer requires minimal code changes for an RDataFrame application to run distributedly. The new tool effectively allows running a C++ event loop based on RDataFrame while leveraging common industry tools like Dask to schedule the usage of resources. This work presents results and insights gathered through the distributed RDataFrame tool running a physics analysis connecting multiple nodes with a Dask scheduler that requests resources from a Slurm cluster.

A Data-Driven Approach to Allocating Personal Protective Equipment During the COVID-19 Pandemic in King County, Washington.

From the Field is a semiregular column that provides insight into the experiences of local, county, or state health professionals on the frontlines of health emergencies. National Association of County and City Health Officials members share the challenges faced and the solutions developed as they prepared for and responded to disasters, epidemics, and other major health issues. The aim of sharing these practical experiences is to provide other public health champions with the information and tools they need to help keep their communities safe even in extreme situations. The COVID-19 pandemic created an extraordinarily high demand for personal protective equipment (PPE). Acute need and supply chain disruptions made hospitals, emergency medical services, and other critical care agencies particularly vulnerable to PPE shortages. In March 2020, King County, Washington, developed computational tools, operating procedures, and data visualizations to fulfill its responsibilities to prioritize, allocate, and distribute scarce PPE equitably and efficiently during a public health emergency. King County distributed over 1.6 million gowns, 22 million gloves, 3.9 million surgical masks, and 1.5 million N95 respirators (among other items) during its PPE distribution mission. An algorithm processed resource requests from the community, with respect to available inventory, emergency allocation policies, prioritization constraints, estimated PPE use rates, agency-specific needs, and other parameters. With these inputs and constraints, the requests were translated into instructions for fulfillment and delivery and several tabular and graphical data visualizations were produced for quality assurance and transparency. Access to timely, relevant, and stable data was a constant challenge, and constraints invariably changed as the emergency response unfolded. King County's PPE distribution mission provides a useful case study in how to develop a scalable and data-driven approach to resource allocation and distribution under emergency response conditions.

Immersive security personnel training module for active shooter events

There is a need to prepare for emergencies such as active shooter events. Emergency response training drills and exercises are necessary to train for such events as we are unable to predict when emergencies do occur. There has been progress in understanding human behavior, unpredictability, human motion synthesis, crowd dynamics, and their relationships with active shooter events, but challenges remain. This paper presents an immersive security personnel training module for active shooter events in an indoor building. We have created an experimental platform for conducting active shooter drills for training that gives a fully immersive feel of the situation and allow one to perform virtual evacuation drills. The security personnel training module also incorporates four sub-modules namely 1) Situational assessment module, 2) Individual officer intervention module, 3) Team Response Module, and 4) Rescue Task Force module. We have developed an immersive virtual reality training module for active shooter events using an Oculus for course of action, visualization, and situational awareness for active shooter events as shown in Fig.1. The immersive security personnel training module aims to get information about the emergency situation inside the building. The dispatched officer will verify the active shooter situation in the building. The security personnel should find a safe zone in the building and secure the people in that area. The security personnel should also find the number and location of persons in possible jeopardy. Upon completion of the initial assessment, the first security personnel shall advise communications and request resources as deemed necessary. This will allow determining whether to take immediate action alone or with another officer or wait until additional resources are available. After successfully gathering the information, the personnel needs to update the info to their officer through a communication device.

Immersive security personnel training module for active shooter events

There is a need to prepare for emergencies such as active shooter events. Emergency response training drills and exercises are necessary to train for such events as we are unable to predict when emergencies do occur. There has been progress in understanding human behavior, unpredictability, human motion synthesis, crowd dynamics, and their relationships with active shooter events, but challenges remain. This paper presents an immersive security personnel training module for active shooter events in an indoor building. We have created an experimental platform for conducting active shooter drills for training that gives a fully immersive feel of the situation and allow one to perform virtual evacuation drills. The security personnel training module also incorporates four sub-modules namely 1) Situational assessment module, 2) Individual officer intervention module, 3) Team Response Module, and 4) Rescue Task Force module. We have developed an immersive virtual reality training module for active shooter events using an Oculus for course of action, visualization, and situational awareness for active shooter events as shown in Fig.1. The immersive security personnel training module aims to get information about the emergency situation inside the building. The dispatched officer will verify the active shooter situation in the building. The security personnel should find a safe zone in the building and secure the people in that area. The security personnel should also find the number and location of persons in possible jeopardy. Upon completion of the initial assessment, the first security personnel shall advise communications and request resources as deemed necessary. This will allow determining whether to take immediate action alone or with another officer or wait until additional resources are available. After successfully gathering the information, the personnel needs to update the info to their officer through a communication device.

Elastic Adaptive Network Slicing Scheme Based on Multi-Priority Cooperative Prediction in Fi-Wi Access Network

With the development of information technology, optical access network gradually presents the characteristics of multi-domain, multi-layer and multi-vendor. The network slicing technology provides a feasible scheme for flexible management of heterogeneous resources. Fi-Wi access network has attracted much attention because of its unique advantages in integrating with network slicing technology. An elastic adaptive network slicing scheme based on multi-priority cooperative prediction in Fi-Wi access network is proposed in this paper. In this scheme, priority model is used to sort the processing order of requests to reduce the delay of delay-sensitive requests and depth-first search algorithm is used to select links with more idle resources to ensure more uniform resource allocation in access network. At the same time, LSTM model is used to predict the traffic, and the resource boundary is changed in advance to offset the extra delay caused by moving the resource isolation boundary when the in-chip reserved resources are insufficient. Simulation results show that the proposed scheme can not only preferentially process delay-sensitive requests, but also select links that reserve more resources for requests. In addition, compared with static slicing and dynamic slicing based on DES prediction, the proposed scheme has lower bandwidth blocking probablity.

Revenue Management with Heterogeneous Resources: Unit Resource Capacities, Advance Bookings, and Itineraries over Time Intervals

There are a variety of revenue management systems that require making pricing or availability decisions for unique resources. For example, lodging marketplaces, boutique hotels, and bed-and-breakfasts offer unique rooms, apartments, or houses. Matching platforms for freelancers recommend differentiated workers with unique characteristics. When managing unique resources, one has to keep track of the availability of each resource at each time point in the future. Moreover, if the customers substitute between different resources, then the pricing and availability decisions for all resources become interdependent. Thus, it can be challenging to find good policies to make pricing or availability decisions. In “Revenue Management with Heterogeneous Resources: Unit Resource Capacities, Advance Bookings, and Itineraries over Time Intervals,” Rusmevichientong, Sumida, Topaloglu, and Bai consider revenue management problems when unique resources are requested for use over intervals of time under advance reservations. Using the interval structure of resource requests, they give policies with performance guarantees.

Multi-criteria decision-making for collaborative COVID-19 surge management and inter-hospital patients’ transfer optimisation

The rapid spread of the COVID-19 virus and the massive influx of patients exceeded the intensive care units capacity limit. Hospitals have engaged community partners to request resources or transfer patients to facilities with available resources. This setting fosters the need for rationing decisions to allocate scarce resources consistently, rather than basing decisions on clinicians’ intuition in the heat of the moment. In this paper, we identify the inter-hospital COVID-19 patients’ transfer criteria, and we apply a multi-criteria decision-making methodology to classify and rank these criteria in order to determine the transfer priority scores between hospitals. We then develop an optimisation model for patient systematic referral from saturated to recipient hospitals with contingency capacity expansion and a ventilator donation scheme. To deal with the uncertainty related to the number of ill patients requesting beds in hospitals, a two-stage stochastic model is presented and solved using the sample average approximation. A case with real data related to the COVID-19 French context is presented, which allows us to assess the resilience of the hospital network under the proposed solutions. The results show how local and regional coalitions can avert the saturation of the hospital system and reduce the number of rejected patients.

Prioritized Energy Efficient Resource Scheduling in Cloud Computing

Resource scheduling in cloud computing is one of the impactful area of research. Cloud service providers maintain its efficacy through proper resource management schemes. Users experience seamless cloud services when cloud service provider manages its resources efficiently. Another aspect in resource management is energy efficient schemes. Energy efficiency largely depends on employment of minimum number of resource servers. This article discusses energy efficient resource scheduling mechanism on multi-layer prioritized resource requests. Resource requests are segregated into three categories and those are scheduled on resource servers based on their fast availability. The work focuses on scheduling resource requests on previously active servers as much as possible before activating any idle servers. Proposed scheduling scheme has been tested on a standard benchmark datasets and performance of the proposed method establishes its efficacy.*

Optimized Resource Allocation and Queue Management for Traffic Control in MANET

A set of mobile devices that employs wireless transmission for communication is termed Mobile Ad hoc Networks (MANETs). Offering better communication services among the users in a centralized organization is the primary objective of the MANET. Due to the features of MANET, this can directly End-to-End Delay (EED) the Quality of Service (QoS). Hence, the implementation of resource management becomes an essential issue in MANETs. This paper focuses on the efficient Resource Allocation (RA) for many types of Traffic Flows (TF) in MANET. In Mobile Ad hoc Networks environments, the main objective of Resource Allocation (RA) is to process consistently available resources among terminals required to address the service requirements of the users. These three categories improve performance metrics by varying transmission rates and simulation time. For solving that problem, the proposed work is divided into Queue Management (QM), Admission Control (AC) and RA. For effective QM, this paper develops a QM model for elastic (EL) and inelastic (IEL) Traffic Flows. This research paper presents an AC mechanism for multiple TF for effective AC. This work presents a Resource Allocation Using Tokens (RAUT) for various priority TF for effective RA. Here, nodes have three cycles which are: Non-Critical Section (NCS), Entry Section (ES) and Critical Section (CS). When a node requires any resources, it sends Resource Request Message (RRM) to the ES. Elastic and inelastic TF priority is determined using Fuzzy Logic (FL). The token holder selects the node from the inelastic queue with high priority for allocating the resources. Using Network Simulator-2 (NS-2), simulations demonstrate that the proposed design increases Packet Delivery Ratio (PDR), decrease Packet Loss Ratio (PLR), minimise the Fairness and reduce the EED.

An efficient load balancing framework for deploying resource schedulingin cloud based communication in healthcare

In distributed environments, cloud computing is widely used to manage user requests for resources and services. Resource scheduling is used to handle user requests for resources based on priorities within a given time frame. In today's environment, healthcare systems and management rely on smart devices connected to the internet. These devices deal with the massive amounts of data processed and detected by smart medical sensors without sacrificing performance factors like throughput and latency. This has prompted the requirement for load balancing among the smart operational devices to prevent any insensitivity. Load balancing is used to manage large amounts of data in both a centralised and distributed manner. In this paper, a load balancing framework for deploying resource scheduling for cloud-based healthcare environments is presented. We use reinforcement learning algorithms such as GA, SARSA, and Q-learning for resource scheduling. These algorithms are used to predict the optimal solution to manage load in cloud-based healthcare environments. The proposed mechanism is energy efficient, has a low make span, and consumes less latency time. The proposed mechanism is implemented using MATLAB. The performance of the proposed mechanism is analysed using latency, make-span, and throughput performance metrics. In the proposed mechanism, throughput is high, whereas make span is less than in the existing technique.