Response Time Constraints Research Articles

A Proficient Approach for Load Balancing in Cloud Computing-Join Minimum Loaded Queue

Cloud load balancing is done to persist the services in the cloud environment along with quality of service (QoS) parameters. An efficient load balancing algorithm should be based on better optimization of these QoS parameters which results in efficient scheduling. Most of the load balancing algorithms which exist consider response time or resource utilization constraints but an efficient algorithm must consider both perspectives from the user side and cloud service provider side. This article presents a load balancing strategy that efficiently allocates tasks to virtualized resources to get maximum resource utilization in minimum response time. The proposed approach, join minimum loaded queue (JMLQ), is based on the existing join idle queue (JIQ) model that has been modified by replacing idle servers in the I-queues with servers having one task in execution list. The results of simulation in CloudSim verify that the proposed approach efficiently maximizes resource utilization by reducing the response time in comparison to its other variants.

A Numerical Study on Hydraulic Fracturing Problems via the Proper Generalized Decomposition Method

The hydraulic fracturing is a nonlinear, fluid-solid coupling and transient problem, in most cases it is always time-consuming to simulate this process numerically. In recent years, although many numerical methods were proposed to settle this problem, most of them still require a large amount of computer resources. Thus it is a high demand to develop more effificient numerical approaches to achieve the real-time monitoring of the fracture geometry during the hydraulic fracturing treatment. In this study, a reduced order modeling technique namely Proper Generalized Decomposition (PGD), is applied to accelerate the simulations of the transient, non-linear coupled system of hydraulic fracturing problem, to match this extremely tight response time constraint. The separability of the solution in space and time dimensions is studied for a simplifified model problem. The solid and flfluid equations are coupled explicitly by inverting the solid discrete problem, and a simple iterative procedure to handle the non-linear characteristic of the hydraulic fracturing problem is proposed in this work. Numeral validation illustrates that the results of PGD match well with these of standard fifinite element method in terms of fracture opening and fluid pressure in the hydro-fracture. Moreover, after the off-line calculations, the numerical results can be obtained in real time.

A comparative evaluation of novelty detection algorithms for discrete sequences

The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of candidate methods for the novelty detection problem applied to discrete sequences. The objective of this study is to identify which state-of-the-art methods are efficient and appropriate candidates for a given use case. These recommendations rely on extensive novelty detection experiments based on a variety of public datasets in addition to novel industrial datasets. We also perform thorough scalability and memory usage tests resulting in new supplementary insights of the methods’ performance, key selection criteria to solve problems relying on large volumes of data and to meet the expectations of applications subject to strict response time constraints.

Multiqueue Scheduling of Heterogeneous Tasks With Bounded Response Time in Hybrid Green IaaS Clouds

Cost-effective task scheduling is an important operation in green infrastructure-as-a-service clouds (GICs) as the energy consumed by users’ tasks is drastic. The irregular task arrival forces private GIC to adopt hybrid clouds to outsource some tasks to dynamic and reliable virtual machines (VMs) of public external clouds. However, temporal differences in revenue, electricity prices, wind and solar energy, and VM running prices of public external clouds make it difficult to dispatch all tasks in a cost-effective way while satisfying users’ specified response time constraints. Unlike existing methods, we propose a multiqueue scheduling (MQS) method that investigates such temporal differences in hybrid GICs (HGICs). Specially, this work first gives mathematical relations between rejected tasks and service rates of servers in private GIC. In each iteration of MQS, this paper formulates a profit maximization problem for HGIC and solves it by a novel meta-heuristic optimization method by combing simulated annealing, particle swarm optimization, and genetic algorithm. Trace-driven experiments based on real-life data demonstrate that profit and throughput of MQS are larger than typical task scheduling algorithms while meeting tasks’ response time constraints.

GASP: Genetic Algorithms for Service Placement in Fog Computing Systems

Fog computing is becoming popular as a solution to support applications based on geographically distributed sensors that produce huge volumes of data to be processed and filtered with response time constraints. In this scenario, typical of a smart city environment, the traditional cloud paradigm with few powerful data centers located far away from the sources of data becomes inadequate. The fog computing paradigm, which provides a distributed infrastructure of nodes placed close to the data sources, represents a better solution to perform filtering, aggregation, and preprocessing of incoming data streams reducing the experienced latency and increasing the overall scalability. However, many issues still exist regarding the efficient management of a fog computing architecture, such as the distribution of data streams coming from sensors over the fog nodes to minimize the experienced latency. The contribution of this paper is two-fold. First, we present an optimization model for the problem of mapping data streams over fog nodes, considering not only the current load of the fog nodes, but also the communication latency between sensors and fog nodes. Second, to address the complexity of the problem, we present a scalable heuristic based on genetic algorithms. We carried out a set of experiments based on a realistic smart city scenario: the results show how the performance of the proposed heuristic is comparable with the one achieved through the solution of the optimization problem. Then, we carried out a comparison among different genetic evolution strategies and operators that identify the uniform crossover as the best option. Finally, we perform a wide sensitivity analysis to show the stability of the heuristic performance with respect to its main parameters.

Two-Stage Covering Location Model for Air–Ground Medical Rescue System

This study tackled the multimodal facility location problem in emergency medical rescue. First, an intermodal setting was suggested, i.e., considering cooperation between ground ambulances and helicopters in emergency medical rescues. Specifically, four scheduling modes were structured: air only, ground only, air-ground combined mode if landing and take-off site for helicopters near the wounded is available, and air-ground transshipment if the landing and take-off site for helicopters near the wounded is not available. Second, a two-stage covering location model was proposed. In the first stage, a set-covering model was developed to achieve maximum coverage and minimal total construction cost of emergency rescue facilities. The optimal mixed allocation proportion of helicopters and ground ambulances was then obtained to guarantee cohesion between the hierarchical models and covering characteristics and the economic efficiency of location results. In the second stage, for given emergency locations, an emergency scheduling mode matrix was constructed for meeting response time and total rescue time constraints. The proposed model obtains optimal results in terms of coverage, construction cost, and rescue time. A case study of Beijing, China validated the feasibility and efficiency of the two-stage covering location model for multimodal emergency medical rescue network. The proposed air-ground rescue system and two-stage covering location model can be extended and also used for large-scale disaster rescue management.

Profit-Sensitive Spatial Scheduling of Multi-Application Tasks in Distributed Green Clouds

An increasing number of organizations choose distributed green data centers (DGDCs) and use their infrastructure resources to deploy and manage multiple applications that flexibly provide services to users around the world in a cost-effective way. The dramatic growth of tasks makes it highly challenging to maximize the total profit of a DGDC provider in a market, where the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs all vary with geographical sites. Different from existing studies, this paper designs a profit-sensitive spatial scheduling (PS3) approach to maximize the total profit of a DGDC provider by smartly scheduling all tasks of multiple applications to meet their response time constraints. PS3 can well utilize such spatial diversity of the above factors. In each time slot, the profit maximization for the DGDC provider is formulated as a constrained nonlinear program and solved by the proposed genetic-simulated-annealing-based particle swarm optimization. Real-life trace-driven simulation experiments demonstrate that PS3 realizes higher total profit and throughput than two typical task scheduling methods. Note to Practitioners —This paper investigates the profit maximization problem for a DGDC provider, while the average response time of all arriving tasks of each application is within their corresponding constraint. Existing task scheduling approaches fail to jointly consider the spatial variations in many factors, including the revenue, price of power grid, solar radiation, wind speed, the maximum amount of energy, on-site air density, and the number of servers in DGDCs. Consequently, they cannot schedule all tasks of multiple applications within their response time constraints in a profit-sensitive way. In this paper, a profit-sensitive spatial scheduling (PS3) method that tackles the drawbacks of previous approaches is presented. It is achieved by adopting a proposed genetic-simulated-annealing-based particle swarm optimization algorithm that solves a constrained nonlinear program. Simulation experiments prove that compared with two typical scheduling approaches, it increases the total profit and throughput. It can be readily realized and incorporated into real-life industrial DGDCs. The future work should improve the proposed method by analyzing the indeterminacy in green energy and the uncertainty in tasks.

TS-BatPro: Improving Energy Efficiency in Data Centers by Leveraging Temporal–Spatial Batching

With the rapid scaling of data centers, understanding their power characteristics and optimizing data center energy consumption is a critical task. Typically, data centers are provisioned for peak load even when they are mostly operating at low utilization levels. This results in wasteful energy consumption requiring smart energy saving strategies. At the same time, latency critical application workloads have stringent quality of service (QoS) constraints that need to be satisfied. Optimizing data center energy with QoS constraints is challenging since different workloads can have variabilities in job sizes and distinct system utilization levels. Also, server configuration (e.g., the number of cores per server) can be different across data centers. Therefore, a single configuration for energy management that works well across these various scenarios, is not practical. In this paper, we propose TS-BatPro, a novel framework that judiciously integrates spatial and temporal job batching to save energy for multicore data center servers while meeting the QoS constraints for application workloads. TS-BatPro performs effective global job batching and scheduling by modeling job performance and power characteristics of multicore servers without any hardware modifications. TS-BatPro works on commodity server platforms and comprises two components: 1) a temporal batching engine that batches incoming jobs such that the servers can continue to reside in low-power states and 2) a spatial batching engine that prioritizes scheduling job batches to a small subset of servers. We develop a prototype of TS-BatPro on physical testbed with a cluster of servers and evaluate TS-BatPro on a variety of workloads. Our results show that TS-BatPro is able to achieve significant amount of energy savings under various job response time constraints and traffic patterns.

Dataset replica placement strategy under a response time constraint in the cloud

In cloud computing environment, especially data-intensive systems, large amounts of datasets are stored in distributed data centres, and are often retrieved by users in different regions. To reduce the users' response time, replicating the popular datasets to multiple suitable data centres is an advisable choice, as tasks can access the datasets from a nearby site. Nevertheless, the dataset replicas' suitable storage placement selection is still an important issue that should be solved urgently from the response time constraint view, for the reason that too many replicas are infeasible in practice. In this paper, we first propose a comprehensive dataset response time estimation model, then present a replica placement model based on Steiner tree. After that, an approximate replica placement algorithm under a response time constraint in the cloud is given using Kruskal minimum spanning tree. At last, a practical and reasonable performance evaluation is designed and implemented. Both the theoretical analysis and simulations conducted on general (random) datasets show the efficiency and effectiveness of the proposed strategy in the cloud.

Dataset replica placement strategy under a response time constraint in the cloud

Dataset replica placement strategy under a response time constraint in the cloud

Reliability Enhancement Toward Functional Safety Goal Assurance in Energy-Aware Automotive Cyber-Physical Systems

Automotive cyber-physical systems are energy-aware and safety-critical systems where energy consumption should be controlled from a perspective of design constraints and reliability should be enhanced toward functional safety goal assurance. In this paper, we solve the problem of reliability enhancement of an automotive function (i.e., functionality or application) under energy and response-time constraints based on the dynamic voltage and frequency scaling technique. The problem is solved by a two-stage solution, namely, response-time reduction under energy constraint and reliability enhancement under energy and response-time constraints. The first stage is solved by proposing average energy preallocation, and the second stage is solved by proposing a reliability-enhancement technique based on the first stage. Examples and experiments show that the proposed solution can not only assure energy and response-time constraints, but also enhances reliability as much as 16.66% compared with its counterpart.

EEUI: a new measure to monitor and manage energy efficiency in data centers

PurposeData centers (DCs) are similar to traditional factories in many aspects like response time constraints, limited capacity, and utilization levels. Several indicators have been developed to monitor and compare productivity in manufacturing. However, in DCs most used indicators focus on technical aspects of infrastructure, not efficiency of operations. The purpose of this paper is to rely on operations management to define a commensurate and proportionate DC performance indicator: the energy-efficient utilization indicator (EEUI). EEUI makes objective and comparative assessment of efficiency possible independently of the operating environment and its constraints.Design/methodology/approachThe authors followed a design science approach, which follows the practitioner’s initial steps for finding solutions to business relevant problems prior to theory building. Therefore, this approach fits well with this research, as it is primarily motivated by business and management needs. EEUI combines both the amount of energy consumed by different components and their current energy efficiency (EE). It reaches its highest value when all server components are optimally loaded in EE sense. The authors tested EEUI by collecting data from three scientific DCs and performing controlled laboratory tests.FindingsThe results indicate that the optimization of EEUI makes it possible to run computing resources more efficiently. This leads to a higher EE and throughput of the DC while reducing the carbon footprint associated to DC operations. Both energy-related costs and the total cost of ownership are consequently reduced, since the amount of both energy and hardware resources needed decrease, while improving DC sustainability.Practical implicationsIn comparison with current DC operations, the results imply that using the EEUI could help increase the EE of DCs. In order to optimize the proposed EEUIs, DC managers and operators should use resource management policies that increase the resource usage variation of the jobs being processed in the same computing resources (e.g. servers).Originality/valueThe paper provides a novel approach to monitor the EE at which computing resources are used. The proposed indicator not only considers the utilization levels at which server components are used but also takes into account their EE and energy proportionality.

Risk-based Monitoring Network Design for Geologic Carbon Storage Sites

A comprehensive risk-based monitoring assessment methodology is developed to (i) incorporate background data for thresholds and monitoring design, (ii) use stochastic leakage simulations and monitoring modeling for risk scenarios given uncertainty, (iii) apply statistical methods to combine multiple monitoring techniques and provide decision support for evaluating proposed monitoring plans, and (iv) facilitate implementation in the National Risk Assessment Partnership (NRAP) integrated assessment model (IAM) framework. This methodology is illustrated using a case representative of the High Plains aquifer, considering the stochastic leakage events simulated using reactive transport simulations. The resulting groundwater quality changes were reflected in three groundwater monitoring parameters (pH, TDS and benzene concentrations), which were used to calculate the corresponding detection probability for each simulation, based on the background distributions and the selected thresholds. The consequent detection probability was then used to evaluate the monitoring well density and the proposed network designs. Beyond the detectability, the earliest response time and spatial coverage constraints of a monitoring network design are considered, and the role of adaptive monitoring strategies for compliance monitoring and incorporation of expert judgment on likely leakage locations is considered.

LAMCS: A leakage aware DVFS based mixed task set scheduler for multi-core processors

This paper proposes Leakage Aware Multi-Core Scheduler (LAMCS), an energy efficient mixed task set dynamic voltage and frequency scaling scheduler for multi-core processors. LAMCS schedules mixed task set where hard and soft real-time tasks co-exist. It takes care of all the deadline constraints of hard real-time tasks and response time constraints of soft real-time tasks. LAMCS achieves energy efficiency by considering static and dynamic energy consumptions. The static energy saving is achieved by shutting down the core(s) for a duration greater than a threshold whenever it is feasible. The dynamic energy saving is achieved by applying DVFS whenever possible. DVFS lengthens the execution intervals by decreasing the voltage and frequency of the core which may reduce the chances of shutdown. This increases energy consumption because of other factors such as preemptions, cache flushes etc. LAMCS minimizes these factors using procrastination. The energy efficiency is achieved while satisfying all the scheduling constraints of mixed task set. It tries to achieve minimum response time for all the soft real-time tasks and meets all deadlines of hard real-time tasks. The experimental results with synthetically generated benchmark program suites show that the proposed energy efficient solution shows appreciable energy savings as compared to the existing solutions.

A new high-performance open CNC system and its energy-aware scheduling algorithm

Computer numerical control (CNC) systems are shifting to a direction of open architecture which has better flexibility, adaptability, versatility, and expansibility. Existing CNC systems tend to have a high level energy consumption. This paper introduces a new open CNC system based on the low-power embedded platform, named open and high-performance CNC (OHP-CNC). OHP-CNC is able to achieve high precision, high efficiency, and low power consumption by making use of international standards, open components such as hardware and software, and an energy-aware real-time scheduling algorithm. The proposed algorithm for mixed tasks, including periodic and aperiodic tasks, is divided into two phases. Firstly, the slack time and utilization are calculated on each processor and tasks are assigned to the processor according to the load. Secondly, because there is a trade-off between the energy-saving and the response times of the aperiodic task, the scheduling server is used to schedule aperiodic tasks in order to meet the response time constraints of aperiodic tasks. Meanwhile, periodic tasks recycle the slack time with dynamic voltage scaling technology to achieve low power consumption. Experiment results show that the energy-aware real-time algorithm yields high-performance and effective machining processes.

Location-aware interest-related micro-cloud topology construction and bacteria foraging-based offloading strategy

Micro-cloud, as one kind of mobile cloud computing modes, can reduce network delay and alleviate resource constraint of mobile devices. In this mode, mobile devices use their idle resources to provide services for others. Due to the mobility, irregularity of mobile devices, the stability problem of micro-cloud environment still remains challenging. In order to increase the stability of micro-cloud and decrease network delay, the interest-related and location-aware micro-cloud topology is proposed in this paper. The interest-related mobile device nodes are classified into the same micro-cloud which can raise the probability of resource reuse. For reducing failed requests and enhancing the scalability of micro-cloud topology, both node location and node service capacity are considered. Moreover, the bacteria foraging-based offloading algorithm for interest-related micro-cloud topology is proposed, which is an energy-efficient and response time constraint offloading approach. The experiments are conducted and the experiment results show that the location-aware interest-related micro-cloud topology has better stability and scalability, while the bacteria foraging-based offloading algorithm performs better in terms of reducing response time and energy.

Stochastic programming for individualized test assembly with mixture response time models

Early research on response time modeling assumed that a test taker would show consistent response time behavior, often referred to as working speed, over the course of a test. Such models may be unrealistic for various reasons — a warm-up effect may cause a test taker to respond more slowly than expected to the early items, fatigue may cause a test taker to respond more slowly than expected toward the end of a test, or as time runs out the test taker may quickly guess the answers to the last items on a test. To take these variations in working speed into account, mixture response time models have recently been investigated. Until now, mixture response time models have only been applied for post hoc analyses. This research expands the use of these models by exploring their application in the context of the assembly of individualized computer-based assessments (CBAs). Response time constraints are probabilistic in nature. Stochastic programming was compared to three existing strategies for dealing with probabilistic constraints. Stochastic programming proved to be a very suitable strategy for solving test assembly problems with mixture response time models. Using stochastic programming, computer-based tests could be assembled in such a way that response time information could be used to the fullest extent.

Traffic-Aware Patching for Cyber Security in Mobile IoT

The various types of communication technologies and mobility features in Internet of Things (IoT) on the one hand enable fruitful and attractive applications, but on the other hand facilitates malware propagation, thereby raising new challenges on handling IoT-empowered malware for cyber security. Comparing with the malware propagation control scheme in traditional wireless networks where nodes can be directly repaired and secured, in IoT, compromised end devices are difficult to be patched. Alternatively, blocking malware via patching intermediate nodes turns out to be a more feasible and practical solution. Specifically, patching intermediate nodes can effectively prevent the proliferation of malware propagation by securing infrastructure links and limiting malware propagation to local device-to-device dissemination. This article proposes a novel traffic-aware patching scheme to select important intermediate nodes to patch, which applies to the IoT system with limited patching resources and response time constraint. Experiments on real-world trace datasets in IoT networks are conducted to demonstrate the advantage of the proposed traffic-aware patching scheme in alleviating malware propagation.

Response patterns for the identification of fakers: Detecting drifting dissimulators

In this research, we provide a simple, novel operationalization of a method for identifying fakers on a self-report measure of personality. This operationalization is applied to six distinct samples of experimentally instructed fakers (total N=1360) who completed the NEO-FFI under varying instructions, modes of test administration and answering, and response time constraints. Based on quantifying individual item response patterns that indicate changes in response positivity over items, the new index of faking demonstrated medium to large effect sizes for identifying faking. Further, this index generally demonstrated added value relative to a standard validity scale for accounting for variability in faking.

Heuristics for multi-item two-echelon spare parts inventory control subject to aggregate and individual service measures

We consider a multi-item two-echelon spare parts inventory system in which the central warehouse operates under a (Q, R) policy and local warehouses implement (S−1,S) policy. The objective is to find the policy parameters minimizing expected system-wide inventory holding and fixed ordering subject to aggregate and individual response time constraints. Using an exact evaluation we provide a very efficient and effective heuristic, and also a tight lower bound for real-world, large-scale two-echelon spare parts inventory problems. An extensive numerical study reveals that as the number of parts increases – which is usually the case in practice – the relative gap between the cost of the heuristic solution and the lower bound approaches zero. In line with our findings, we show that the heuristic and the lower bound are asymptotically optimal and asymptotically tight, respectively, in the number of parts. In practice, this means we can solve real-life problems with large numbers of items optimally. We propose an alternative approach between system and item approaches, which are based on setting individual and aggregate service level constraints, respectively. Using our alternative approach, we show that it is possible to keep the cost benefit of using aggregate service levels while avoiding long individual response times. We also show that the well-known sequential determination of policy parameters, i.e., determining the batch sizes first, and then finding the other policy parameters using those batch sizes, which is known for its high performance in single-item models, performs relatively poor for multi-item systems.