Response Time Requirements Research Articles

Enhanced Interpolated Dynamic DFT Synchrophasor Estimator Considering Second Harmonic Interferences †.

In the future, phasor measurement units are expected to be applied in distribution networks (DNs) for their control and monitoring. Because of the widely used power electronic devices in DNs, harmonics are widely present in a voltage/current signal. Particularly, second harmonics have the most significant uncertainty contributions to synchrophasor estimation, which is especially true when a short cycle observation window is used for a fast response. Based on the interpolated dynamic discrete Fourier transform (IpDFT), this paper introduces an enhanced IpDFT (e-IpDFT) synchrophasor estimator that considers second harmonic interferences. First, the adaptive equivalent filters of the IpDFT are given. Based on these, the optimal frequencies where the IpDFT has the least second harmonic interferences are then searched using an enumeration method, and the e-IpDFT synchrophasor estimator is accordingly proposed. Instantaneous frequency responses and several simulation tests show that the e-IpDFT performs much better than the IpDFT in second harmonic suppression, and can meet the P-class response time requirements and most of the M-class accuracy requirements of the IEEE standard C37.118.1 only over a three-cycle window.

The Evaluation of Text String Matching Algorithms as an Aid to Image Search

The main goal of this paper is to analyse intelligent text string matching methods (like fuzzy sets and relations) and evaluate their usefulness for image search. The present study examines the ability of different algorithms to handle multi-word and multi-sentence queries. Eight different similarity measures (N-gram, Levenshtein distance, Jaro coefficient, Dice coefficient, Overlap coefficient, Euclidean distance, Cosine similarity and Jaccard similarity) are employed to analyse the algorithms in terms of time complexity and accuracy of results. The outcomes are used to develop a hierarchy of methods, illustrating their usefulness to image search. The search response time increases significantly in the case of data sets containing several thousand images. The findings indicate that the analysed algorithms do not fulfil the response-time requirements of professional applications. Due to its limitations, the proposed system should be considered only as an illustration of a novel solution with further development perspectives.The use of Polish as the language of experiments affects the accuracy of measures. This limitation seems to be easy to overcome in the case of languages with simpler grammar rules (e.g. English).

Emerging Paradigm of Edge Computing in the Context Of Iot

Centralised Cloud computing is having many challenges with the rapid increasing in IoT( Internet Of Things) applications. The challenges are high latency, low spectral efficiency (SE), and non-adaptive machine type of communication. In order to solve these challenges have moved to the concept of new computing concept that is nothing but edge computing, which calls for moving the data at the edge of the network. Edge computing has the possible to deal with the concerns of battery life constraint, response time requirement, data safety, bandwidth cost saving and privacy. In this paper, we started the explanation with IoT and solution of IOT i.e. edge computing, followed by several case studies, ranging from cloud offloading to smart home and city, as well as collaborative edge to materialize the concept of edge computing.

Fast Functional Safety Verification for Distributed Automotive Applications During Early Design Phase

Both response time and reliability are important functional safety properties that must be simultaneously satisfied learning from the automotive functional safety standard ISO 26262. Safety verification pertains to checking if an application meets a safe set of design specifications and complies with regulations. Introducing verification in the early design phase not only complies with the latest automotive functional safety standard but also avoids unnecessary design effort or reduces the design burden of the late design optimization phase. This study presents a fast functional safety verification (FFSV) method for a distributed automotive application during the early design phase. The first method FFSV1 finds the solution with the minimum response time under the reliability requirement, and the second method FFSV2 finds the solution with the maximum reliability under the response time requirement. We combine FFSV1 and FFSV2 to create union FFSV (UFFSV), which can obtain acceptance ratios higher than those of current methods. Experiments on real-life and synthetic distributed automotive applications show that UFFSV can obtain higher acceptance ratios than their existing counterparts.

Disaster Rescue Task Scheduling: An Evolutionary Multiobjective Optimization Approach

Efficient rescue task scheduling plays a key role in disaster rescue operations. In real-world applications, such an emergency scheduling problem often involves multiple objectives, complex constraints, inherent uncertainty, and limited response time requirement. In this paper, we propose a fuzzy multiobjective optimization problem of rescue task scheduling, the aim of which is to simultaneously maximize the task scheduling efficiency and minimize the operation risk for the rescue team. We then develop an efficient multiobjective biogeography-based optimization (EMOBBO) algorithm for solving the problem. To cope with the uncertainty, we employ three correlated fuzzy ranking criteria, and use the concept of fuzzy dominance for comparing the dominance relation of solutions. In EMOBBO, we define new migration and mutation operators for effectively evolving the permutation-based solutions, use a problem-specific solution rearrangement mechanism for filtering out inefficient solutions, and employ a local neighborhood structure to suppress premature convergence. Computational experiments show that the proposed EMOBBO algorithm outperforms some state-of-the-art evolutionary multiobjective optimization algorithms, and our algorithm has been successfully applied to several real-world disaster rescue operations in recent years.

WSN-EVP: A Novel Special Purpose Protocol for Emergency Vehicle Preemption Systems

Emergency Vehicle Preemption (EVP) systems play a key role in reprioritizing signalized traffic intersections. This role is important for safe and minimum travel delay of emergency vehicles (EV) passing through road intersections. Unfortunately, conventional systems that are currently in use on the roads have deficiencies in terms of cost, reliability, maintainability, and scalability. In this paper, a novel approach is proposed utilizing batteryless wireless sensor networks (WSNs) to overcome the above difficulties and improve the safety and mobility of emergency vehicles. A special purpose preemption protocol is designed based on the collection tree protocol (CTP) as a data collection service. The protocol is called WSN-EVP and is used to handle the EV preemption request in a multi-hop tree-based topology spread across the traffic intersection. The model functions according to predefined rules and optimized parameters to cope with the high topology changes and the fast response time requirement. A prototype implementation using TelosB motes programmed in nesC shows a performance of high preemption accuracy under high level of interference.

The ITER interlock system

ITER involves the integration of numerous sophisticated systems, many of which must operate reliably close to their performance limits in order to achieve the project’s scientific goals. The teams responsible for exploiting the tokamak will require sufficient operational flexibility to explore a wide range of plasma scenarios within an operational framework that ensures that the integrity of the machine and safety of the environment and personnel are not compromised. The instrumentation and control (I&C) systems of ITER are divided into three separate tiers: the conventional I&C, the safety system and the interlock system. This paper focuses on the last of these. The operational experience from existing tokamaks and large superconducting machines, together with many specific aspects of the ITER facility, have been taken into account in the design of the ITER interlock system. This consists of a central element, the Central Interlock System, and several local elements, distributed across the various plant systems of the tokamak and referred to as Plant Interlock Systems. Each Plant Interlock System is connected to dedicated networks and communicates its status and interlock events to the Central Interlock System, which in turn sends the required interlock actions to the Plant Interlock Systems. The Central Interlock System is also responsible for communicating the status of each system to the operators in the main control room. These operators will use the Central Interlock System to perform functionalities such as overrides, resets of central interlock functions and configuration of Plant Interlock Systems. Three different types of architecture have been developed: a slow one, based on PLCs, for functions for which response times longer than 300 ms are adequate, a fast one, based on FPGAs, for functions which require response times beyond the capabilities of the PLC, and a hardwired one to synchronise all the systems involved in a fast discharge of the superconducting coils. The overall design of the Central Interlock System was presented and approved for manufacturing in a Final Design Review in 2016.

Methodology for the deployment of ITER Fast Plant Interlock system. Use case: ITER Poloidal Field and Central Solenoid coil's power converter protection system

Interlocks are the instrumented functions of ITER that protect the machine against failures of the plant system components or incorrect machine operation. Regarding I&C, the Interlock Control System ensures that no failure of the conventional ITER controls can lead to severe damage of the machine integrity or availability.ITER Interlock System incorporates several Plant Systems that require response times below 1 ms to perform the required protection actions. Systems like the Coil Power Supply System (CPSS), Ion Cyclotron Heating and Current Drive (ICH&CD), Neutral Beam Injectors and Current Drive (NBI&CD), Disruption Mitigation System (DMS), and others. ITER developed a methodology to create the fast architecture for the Plant Interlock Systems (PIS) that ensures high integrity on the final solution.The functional safety standard IEC 61508 has been used to define a methodology for the configuration of the RIO (Reconfigurable Input/Output) platform from National Instruments (compactRIO platform).This work describes in detail this methodology, valid for most cases just selecting different input/output signal types to be handled and choosing different numbers of interlock functions to be performed. In addition, this paper describes the development of the interlock system for the ITER Poloidal Field and Central Solenoid Coil’s Power Converter protection, a special case due to its unique configuration, the most complex implemented up to date. The development of this special case “puts to a test” the methodology’s approach. Finally, the discussion of the fitness of the methodology and the performance of the system is presented.

Bargaining Game-Based Scheduling for Performance Guarantees in Cloud Computing

In this article, we focus on request scheduling with performance guarantees of all users in cloud computing. Each cloud user submits requests with average response time requirement, and the cloud provider tries to find a scheduling scheme, i.e., allocating user requests to limited servers, such that the average response times of all cloud users can be guaranteed. We formulate the considered scenario into a cooperative game among multiple users and try to find a Nash bargaining solution (NBS), which can simultaneously satisfy all users’ performance demands. We first prove the existence of NBS and then analyze its computation. Specifically, for the situation when all allocating substreams are strictly positive, we propose a computational algorithm ( CA ), which can find the NBS very efficiently. For the more general case, we propose an iterative algorithm ( IA ), which is based on duality theory. The convergence of our proposed IA algorithm is also analyzed. Finally, we conduct some numerical calculations. The experimental results show that our IA algorithm can find an appropriate scheduling strategy and converges to a stable state very quickly.

Implementation of ITER Fast Plant Interlock System Using FPGAs With CompactRIO

Interlocks are the instrumented functions of ITER that protect the machine against failures of the plant system components or incorrect machine operation. Regarding instrumentation and control, the interlock control system (ICS) ensures that no failure of the conventional ITER controls can lead to a serious damage of the machine integrity or availability. The ICS is in charge of the supervision and control of all the ITER components involved in the instrumented protection of the tokamak and its auxiliary systems. It is constituted by the central interlock system (CIS), different plant interlock systems (PISs), and its networks. The ICS does not include the sensors and actuators of the plant systems, but it is in charge of their control. The ITER interlock system shall be designed, built, and operated according to the highest quality standards. The international standard IEC-61508 has been chosen as the reference. In both CIS and PIS cases, two main architectures are used: 1) a slow architecture, for those functions with response time requirements slower than 100 ms (300 ms for central interlock functions), based on programmable logic controller technologies and 2) a fast architecture, based on field programmable gate array (FPGA) technologies, for the functions with faster requirement times. The proposed design for fast PIS is based on the use of reconfigurable input/output (RIO) technology from National Instruments (NI CompactRIO platform). In order to provide a high integrity solution, failure modes, effects, and diagnostic analysis (FMEDA) has been conducted to analyze the component behavior. According to the output of the FMEDA, a set of diagnostics has been defined and additional redundancy was added to the architecture to improve the integrity figures. The defined configuration has been called the “double-decker solution,” with two chassis running in parallel, communicated between them using a synchronous high-speed serial line, and using redundant modules to implement the input and output measurements/excitations and redundant analog and digital modules to implement the diagnostics of these input/output modules. The integrity figures for the “double-decker” solution are obtained from the classification of the failure rates, obtaining for different configurations a safe failure fraction of 85% and a probability of dangerous failure per hour of less than 1E−07. The FPGA design includes all the hardware to support the data acquisition from the input modules, the implementation of the diagnostic functionalities for analog and digital modules, the voting schema, and the activation/deactivation of digital outputs. The platform includes an external test platform, also based on NI CompactRIO technology, to perform the validation of the system and to register the performance of different interlock functions implemented. The response time obtained for the transistor–transistor logic (TTL) input to TTL output interlock function ranges from 5 to $20~\mu \text{s}$ ; for the analog input to TTL output, the response time is in the range of 41– $90~\mu \text{s}$ , and for interlock functions using 24-V digital input to 24-V digital output, the time can rise up to $643~\mu \text{s}$ .

A dynamic replication management strategy in distributed GIS

Replication strategy is one of effective solutions to meet the requirement of service response time by preparing data in advance to avoid the delay of reading data from disks. This paper presents a brand-new method to create copies considering the selection of replicas set, the number of copies for each replica and the placement strategy of all copies. First, the popularities of all data are computed considering both the historical access records and the timeliness of the records. Then, replica set can be selected based on their recent popularities. Also, an enhanced Q-value scheme is proposed to assign the number of copies for each replica. Finally, a reasonable copies placement strategy is designed to meet the requirement of load balance. In addition, we present several experiments that compare the proposed method with techniques that use other replication management strategies. The results show that the proposed model has better performance than other algorithms in all respects. Moreover, the experiments based on different parameters also demonstrated the effectiveness and adaptability of the proposed algorithm.

Local emergency medical communication centres - staffing and populations.

There are several examples of inadequate staffing at local emergency medical communication centres (LEMCs) resulting in limited availability and long waits on the telephone. There are no guidelines for population size or the staffing of a LEMC. In the following, we present models of catchment areas and staffing. Traffic intensity on Saturdays and Sundays was based on data on figures for patient contacts at seven LEMCs in 2014 and 2015. We defined the minimum optimal population base as at least 50 % probability of ≥ 10 contacts in the course of a night duty. The Erlang-C formula was used to estimate service level and hence staffing requirements on the basis of population and response-time requirements. We have surveyed the combined staffing requirements of all the LEMCs in Norway. The minimum optimal population base was 29 134. In 2016, 48 of 103 LEMCs were smaller than this. In order to be able to satisfy the response-time requirements in the Norwegian Emergency Medicine Regulations, 112 LEMC night operators and 158 day operators would be necessary for the whole of Norway. A reduction of the response-time requirement from 120 to ten seconds would require 9.8 % more operators at night and 17 % more operators during the day. The models we have presented provide a basis for planning the population base and staffing of LEMCs. Significantly stricter response-time requirements will result in limited need for more personnel.

In-Datacenter Performance Analysis of a Tensor Processing Unit

Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU) --- deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X -- 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X -- 80X higher. Moreover, using the CPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.

Learning to Speed Up Query Planning in Graph Databases

Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing — Query Planning — is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries. We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including dbpedia, yago, and freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.

Location-Based Parallel Tag Completion for Geo-Tagged Social Image Retrieval

Having benefited from tremendous growth of user-generated content, social annotated tags get higher importance in the organization and retrieval of large-scale image databases on Online Sharing Websites (OSW). To obtain high-quality tags from existing community contributed tags with missing information and noise, tag-based annotation or recommendation methods have been proposed for performance promotion of tag prediction. While images from OSW contain rich social attributes, they have not taken full advantage of rich social attributes and auxiliary information associated with social images to construct global information completion models. In this article, beyond the image-tag relation, we take full advantage of the ubiquitous GPS locations and image-user relationship to enhance the accuracy of tag prediction and improve the computational efficiency. For GPS locations, we define the popular geo-locations where people tend to take more images as Points of Interests (POI), which are discovered by mean shift approach. For image-user relationship, we integrate a localized prior constraint, expecting the completed tag sub-matrix in each POI to maintain consistency with users’ tagging behaviors. Based on these two key issues, we propose a unified tag matrix completion framework, which learns the image-tag relation within each POI. To solve the optimization problem, an efficient proximal sub-gradient descent algorithm is designed. The model optimization can be easily parallelized and distributed to learn the tag sub-matrix for each POI. Extensive experimental results reveal that the learned tag sub-matrix of each POI reflects the major trend of users’ tagging results with respect to different POIs and users, and the parallel learning process provides strong support for processing large-scale online image databases. To fit the response time requirement and storage limitations of Tag-based Image Retrieval (TBIR) on mobile devices, we introduce Asymmetric Locality Sensitive Hashing (ALSH) to reduce the time cost and meanwhile improve the efficiency of retrieval.

Contextual Noise Reduction for Domain Adaptive Near-Duplicate Retrieval on Merchandize Images.

In this paper, we have proposed a novel method which utilizes the contextual relationship among visual words for reducing the Quantization errors in near-duplicate image retrieval (NDR). Instead of following the track of conventional NDR techniques which usually search new solutions by borrowing ideas from the text domain, we propose to model the problem back to image domain, which results in a more natural way of solution search. The idea of the proposed method is to construct a context graph that encapsulates the contextual relationship within an image and treat the graph as a pseudo-image, so that classical image filters can be adopted to reduce the mismapped visual words which are contextually inconsistent with others.With these contextual noises reduced, the method provides purified inputs to the subsequent processes in NDR, and improves the overall accuracy. More importantly, the purification further increases the sparsity of the image feature vectors, which thus speeds up the conventional methods by 1662% times and makes NDR practical to online applications on merchandize images where the requirement of response time is critical. The way of considering contextual noise reduction in image domain also makes the problem open to all sophisticated filters. Our study shows the classic anisotropic diffusion filter can be employed to address the cross-domain issue, resulting in the superiority of the method to conventional ones in both effectiveness and efficiency.

Planning virtual infrastructures for time critical applications with multiple deadline constraints

Executing time critical applications within cloud environments while satisfying execution deadlines and response time requirements is challenging due to the difficulty of securing guaranteed performance from the underlying virtual infrastructure. Cost-effective solutions for hosting such applications in the Cloud require careful selection of cloud resources and efficient scheduling of individual tasks. Existing solutions for provisioning infrastructures for time constrained applications are typically based on a single global deadline. Many time critical applications however have multiple internal time constraints when responding to new input. In this paper we propose a cloud infrastructure planning algorithm that accounts for multiple overlapping internal deadlines on sets of tasks within an application workflow. In order to better compare with existing work, we adapted the IC-PCP algorithm and then compared it with our own algorithm using a large set of workflows generated at different scales with different execution profiles and deadlines. Our results show that the proposed algorithm can satisfy all overlapping deadline constraints where possible given the resources available, and do so with consistently lower host cost in comparison with IC-PCP.

CARB: A C-State Power Management Arbiter for Latency-Critical Workloads

Latency-critical workloads in datacenters have tight response time requirements to meet service-level agreements (SLAs). Sleep states (c-states) enable servers to reduce their power consumption during idle times; however entering and exiting c-states is not instantaneous, leading to increased transaction latency. In this paper we propose a c-state arbitration technique, CARB, that minimizes response time, while simultaneously realizing the power savings that could be achieved from enabling c-states. CARB adapts to incoming request rates and processing times and activates the smallest number of cores for processing the current load. CARB reshapes the distribution of c-states and minimizes the latency cost of sleep by avoiding going into deep sleeps too often. We quantify the improvements from CARB with memcached running on an 8-core Haswell-based server.

System design of the internet of things for residential smart grid

Internet-of-Things (IoTs) envisions to integrate, coordinate, communicate, and collaborate real-world objects in order to perform daily tasks in a more intelligent and efficient manner. To comprehend this vision, this paper studies the design of a large scale IoT system for smart grid application, which constitutes a large number of home users and has the requirement of fast response time. In particular, we focus on the messaging protocol of a universal IoT home gateway, where our cloud enabled system consists of a backend server, unified home gateway (UHG) at the end users, and user interface for mobile devices. We discuss the features of such IoT system to support a large scale deployment with a UHG and real-time residential smart grid applications. Based on the requirements, we design an IoT system using the XMPP protocol, and implemented in a testbed for energy management applications. To show the effectiveness of the designed testbed, we present some results using the proposed IoT architecture.

Efficient Algorithms for Capacitated Cloudlet Placements

Mobile cloud computing is emerging as a main ubiquitous computing platform to provide rich cloud resources for various applications of mobile devices. Although most existing studies in mobile cloud computing focus on energy savings of mobile devices by offloading computing-intensive jobs from mobile devices to remote clouds, the access delays between mobile users and remote clouds usually are long and sometimes unbearable. Cloudlet as a new technology is capable to bridge this gap, and can enhance the performance of mobile devices significantly while meeting the crisp response time requirements of mobile users. In this paper, we study the cloudlet placement problem in a large-scale Wireless Metropolitan Area Network (WMAN) consisting of many wireless Access Points (APs). We first formulate the problem as a novel capacitated cloudlet placement problem that places $K$ cloudlets to some strategic locations in the WMAN with the objective to minimize the average access delay between mobile users and the cloudlets serving the users. We then propose an exact solution to the problem by formulating it as an Integer Linear Programming (ILP). Due to the poor scalability of the ILP, we instead propose an efficient heuristic for the problem. For a special case of the problem where all cloudlets have identical computing capacities, we devise novel approximation algorithms with guaranteed approximation ratios. We also devise an online algorithm for dynamically allocating user requests to different cloudlets, if the $K$ cloudlets have already been placed. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising and scalable.