Job Response Research Articles

Stochastic Analysis of Delayed Mobile Offloading in Heterogeneous Networks

Mobile cloud offloading that migrates heavy computation from mobile devices to powerful cloud servers through communication networks can alleviate the hardware limitations of mobile devices thus providing higher performance and saving energy. Different applications usually give different relative importance to response time and energy consumption. If a delay-tolerant job is deferred up to a given deadline, or until a fast and energy-efficient network becomes available, the transmission time will be extended, which can save energy because a more energy-efficient communication channel and a less energy-restricted computation platform may become available. However, if the reduced service time fails to cover the extra waiting time, this policy may not be competitive. In this paper, we investigate two types of delayed offloading policies, the partial offloading model where jobs can leave from the slow phase of the offloading process and be executed locally on the mobile device, and the full offloading model, where jobs can abandon the WiFi Queue and be offloaded via the Cellular Queue . In both models, we minimize the Energy-Response time Weighted Product (ERWP) metric. Not surprisingly, we find that jobs abandon the queue often when the availability of the WiFi network is low. In general, for delay-sensitive applications the partial offloading model is preferred under a suitable reneging rate, while for delay-tolerant applications the full offloading model shows very good results and outperforms the other offloading model when selecting a large deadline. From the perspective of energy consumption, the full offloading model will always be best, even if the deadline must be extremely long. Only if job response time is of high importance an optimal deadline to abort offloading in the partial offloading model or the WiFi transmission in the full offloading model can be found. For reduction of the energy consumption it will always be better to wait longer rather than compute locally or use the cellular network.

Adaptive Speculation for Efficient Internetware Application Execution in Clouds

Modern Cloud computing systems are massive in scale, featuring environments that can execute highly dynamic Internetware applications with huge numbers of interacting tasks. This has led to a substantial challenge—the straggler problem, whereby a small subset of slow tasks significantly impede parallel job completion. This problem results in longer service responses, degraded system performance, and late timing failures that can easily threaten Quality of Service (QoS) compliance. Speculative execution (or speculation) is the prominent method deployed in Clouds to tolerate stragglers by creating task replicas at runtime. The method detects stragglers by specifying a predefined threshold to calculate the difference between individual tasks and the average task progression within a job. However, such a static threshold debilitates speculation effectiveness as it fails to capture the intrinsic diversity of timing constraints in Internetware applications, as well as dynamic environmental factors, such as resource utilization. By considering such characteristics, different levels of strictness for replica creation can be imposed to adaptively achieve specified levels of QoS for different applications. In this article, we present an algorithm to improve the execution efficiency of Internetware applications by dynamically calculating the straggler threshold, considering key parameters including job QoS timing constraints, task execution progress, and optimal system resource utilization. We implement this dynamic straggler threshold into the YARN architecture to evaluate it’s effectiveness against existing state-of-the-art solutions. Results demonstrate that the proposed approach is capable of reducing parallel job response time by up to 20% compared to the static threshold, as well as a higher speculation success rate, achieving up to 66.67% against 16.67% in comparison to the static method.

DRL-Scheduling: An Intelligent QoS-Aware Job Scheduling Framework for Applications in Clouds

As an increasing number of traditional applications migrated to the cloud, achieving resource management and performance optimization in such a dynamic and uncertain environment becomes a big challenge for cloud-based application providers. In particular, job scheduling is a non-trivial task, which is responsible for allocating massive job requests submitted by users to the most suitable resources and satisfying user QoS requirements as much as possible. Inspired by recent success of using deep reinforcement learning techniques to solve AI control problems, in this paper, we propose an intelligent QoS-aware job scheduling framework for application providers. A deep reinforcement learning-based job scheduler is the key component of the framework. It is able to learn to make appropriate online job-to-VM decisions for continuous job requests directly from its experiences without any prior knowledge. Experimental results using synthetic workloads and real-world NASA workload traces show that compared with other baseline solutions, our proposed job scheduling approach can efficiently reduce average job response time (e.g., reduced by 40.4% compared with the best baseline for NASA traces), guarantee the QoS at a high level (e.g., job success rate is higher than 93% for all simulated changing workload scenarios), and adapt to different workload conditions.

Mapreduce performance model for Hadoop 2.x

MapReduce is a popular programming model for distributed processing of large data sets. Apache Hadoop is one of the most common open-source implementations of such paradigm. Performance analysis of concurrent job executions has been recognized as a challenging problem, at the same time, that may provide reasonably accurate job response time estimation at significantly lower cost than experimental evaluation of real setups. In this paper, we tackle the challenge of defining MapReduce performance model for Hadoop 2.x. While there are several efficient approaches for modeling the performance of MapReduce workloads in Hadoop 1.x, they could not be applied to Hadoop 2.x due to fundamental architectural changes and dynamic resource allocation in Hadoop 2.x. Thus, the proposed solution is based on an existing performance model for Hadoop 1.x, but taking into consideration architectural changes and capturing the execution flow of a MapReduce job by using queuing network model. This way, the cost model reflects the intra-job synchronization constraints that occur due the contention at shared resources. The accuracy of our solution is validated via comparison of our model estimates against measurements in a real Hadoop 2.x setup.

An Improved Control and Energy Management Strategy of Three-Level NPC Converter Based DC Distribution Network

To meet the challenge of large-scale renewable energy penetration and take full advantage of existing AC infrastructure, the bipolar DC distribution system is of interest. In this article, the system structure and characteristics of the bipolar DC distribution network are proposed. The three-level Neural Point Clamped Converter (NPC) is used in the proposed system to construct the bipolar DC system. To optimize the DC system performance, an improved cooperative control and energy management strategy is proposed mainly to mitigate DC voltage fluctuation and balance the positive and negative phase voltage. The improved strategy consists of (1) 2-degree of freedom (2DOF) PID controller in traditional voltage control loop; (2) cooperative controller to take full advantage of storage system; (3) voltage equalization controller to balance two-phase voltages; and (4) the energy management system to dispatch the response job to batteries and supercapacitors. Experiments and simulations are performed to validate the effectiveness of the proposed strategy.

Modelling and developing conflict-aware scheduling on large-scale data centres

Large-scale data centres are the growing trend for modern computing systems. Since a large-scale data centre has to manage a large number of machines and jobs, deploying multiple independent schedulers (termed as distributed schedulers in literature) to make scheduling decisions simultaneously has been shown as an effective way to speed up the processing of large quantity of submitted jobs and data. The key drawback of distributed schedulers is that since these schedulers schedule different jobs independently, the scheduling decisions made by different schedulers may conflict with each other due to the possibility that different scheduling decisions refer to the same subset of the resources in the data centre. Conflicting scheduling decisions cause additional scheduling attempts and consequently increase the scheduling cost. More resources each scheduler demands, higher scheduling cost may incur and longer job response times the users may experience. It is useful to investigate the balanced points in terms of resource demands for each of independent schedulers, so that the distributed schedulers can all achieve decent job performance without experiencing undesired resource competition. To address this issue, we model distributed scheduling and resource conflict using the game theory and conduct the quantitative analysis about scheduling cost and job performance. Further, based on the analysis, we develop the conflict-aware scheduling strategies to reduce the scheduling cost and improve job performance. We have conducted the simulation experiments with workload trace and also real experiments on Amazon Web Services(AWS). The experimental results verify the effectiveness of the proposed modelling approach and scheduling strategies.

Light traffic behavior under the power-of-two load balancing strategy: The case of heterogeneous servers

We consider a multi-server queueing system under the power-of-two policy with Poisson job arrivals, heterogeneous servers and a general job requirement distribution; each server operates under the first-come first-serve policy and there are no buffer constraints. We analyze the performance of this system in light traffic by evaluating the first two light traffic derivatives of the average job response time. These expressions point to several interesting structural features associated with server heterogeneity in light traffic: For unequal capacities, the average job response time is seen to decrease for small values of the arrival rate, and the more diverse the server capacities, the greater the gain in performance. These theoretical findings are assessed through limited simulations.

Cost and Performance-Based Resource Selection Scheme for Asynchronous Replicated System in Utility-Based Computing Environment

A resource selection problem for asynchronous replicated systems in utility-based computing environment is addressed in this paper. The needs for a special attention on this problem lies on the fact that most of the existing replication scheme in this computing system whether implicitly support synchronous replication and/or only consider read-only job. The problem is undoubtedly complex to be solved as two main issues need to be concerned simultaneously, i.e. 1) the difficulty on predicting the performance of the resources in terms of job response time, and 2) an efficient mechanism must be employed in order to measure the trade-off between the performance and the monetary cost incurred on resources so that minimum cost is preserved while providing low job response time. Therefore, a simple yet efficient algorithm that deals with the complexity of resource selection problem in utility-based computing systems is proposed in this paper. The problem is formulated as a Multi Criteria Decision Making (MCDM) problem. The advantages of the algorithm are two-folds. On one fold, it hides the complexity of resource selection process without neglecting important components that affect job response time. The difficulty on estimating job response time is captured by representing them in terms of different QoS criteria levels at each resource. On the other fold, this representation further relaxed the complexity in measuring the trade-offs between the performance and the monetary cost incurred on resources. The experiments proved that our proposed resource selection scheme achieves an appealing result with good system performance and low monetary cost as compared to existing algorithms.

Availability/Network-aware MapReduce over the Internet

MapReduce offers an ease-of-use programming paradigm for processing large datasets. In our previous work, we have designed a MapReduce framework called BitDew-MapReduce for desktop grid and volunteer computing environment, that allows nonexpert users to run data-intensive MapReduce jobs on top of volunteer resources over the Internet. However, network distance and resource availability have great impact on MapReduce applications running over the Internet. To address this, an availability and network-aware MapReduce framework over the Internet is proposed. Simulation results show that the MapReduce job response time could be decreased by 40.05%, thanks to Weighted Naive Bayes Classifier-based availability prediction and landmark-based network estimation. The effectiveness of the new MapReduce framework is further proved by performance evaluation in a real distributed environment.

중소기업 사내 교육훈련이 직무만족 및 직무성과 향상에 미치는 영향

본 연구는 중소기업 사내 교육훈련이 직무만족 및 직무성과 향상에 미치는 영향을 분석하였다. 연구결과 직무만족도에 영향을 미치는 요인은 교육훈련 활용도와 참여형 실습시간 충분도였으며 직무대응도에 영향을 미치는 요인은 사내 교육훈련의 품질, 교육훈련 활용도, 실습시간 충분도로 나타났다. 향후 지속적 교육훈련 참여 의지에 영향을 미치는 요인은 교육훈련 정보 접근성, 교육장 위치 및 교육 환경, 실습시간 충분도로 나타났으며 여성과 생산직 근로자의 교육훈련 참여 의지가 높았고 20대, 3-5년 근속 근로자의 교육훈련 만족도가 상대적으로 낮아 이를 높일 필요가 있다고 평가된다. 본 연구는 중소기업 사내 교육훈련이 직무만족 및 직무성과 향상에 미치는 영향을 분석함으로써 사내 교육훈련의 성과를 높이기 위해 효과적인 요인을 탐색하고 그 영향력을 검증한 데 연구의 의의가 있다. This study analyzed the effect of education and training of small businessess on improving job satisfaction and job performance. The study results showed availability of training and sufficientness of practice hours affected job satisfaction, quality & availability of training and sufficientness of practice hours affected job responsiveness, information accessability of training, training place & facility and sufficientness of practice hours affected willingness to participate in training. Women and procuction worker had higher willingness to learn, yet who are in their 20s and continuously worked for 3-5 years had lower training satisfaction so it is recommended to improve it. This study provided implications for exploring effective factors to enhance company training performance and verified the effect.

Tempo

Multi-tenant database systems have a component called the Resource Manager, or RM that is responsible for allocating resources to tenants. RMs today do not provide direct support for performance objectives such as: "Average job response time of tenant A must be less than two minutes", or "No more than 5% of tenant B's jobs can miss the deadline of 1 hour." Thus, DBAs have to tinker with the RM's low-level configuration settings to meet such objectives. We propose a framework called Tempo that brings simplicity , self-tuning , and robustness to existing RMs. Tempo provides a simple interface for DBAs to specify performance objectives declaratively, and optimizes the RM configuration settings to meet these objectives. Tempo has a solid theoretical foundation which gives key robustness guarantees. We report experiments done on Tempo using production traces of data-processing workloads from companies such as Facebook and Cloudera. These experiments demonstrate significant improvements in meeting desired performance objectives over RM configuration settings specified by human experts.

Meta Analysis of Variables related to Emotional Labor of Hospital Nurses

Purpose: The purpose of this study was to use meta-analysis to evaluate factors and variables related to the emotional labor of hospital nurses. Method: The study included variables obtained from 66 recently published studies, doctoral dissertations, and master’s theses. Results: The related variables were categorized into five factor groups: internal character factor, nursing care related factor, environmental factor, emotional response, and job response. For the defensive variable, job related response (effect size [ES]=-.17), environment (ES=-.14), internal character (ES=-.08), nursing care (ES=-.06), and emotional response (ES=-.06) showed significant effect size among the five categorized variable groups. For the risk variable, internal character (ES=.44), emotional response (ES=.46), environment (ES=.27), job related response (ES=.27), and nursing care (ES=.19) showed significant effect size among the five categorized variable groups. Conclusions: The results of this study identified defensive and risk variables related to emotional labor of hospital nurses. Self-efficacy, social support, leaders’ care, and communication are important factors for managing emotional labor of hospital nurses. 주요어: ì •ì‹ ì  ìŠ¤íŠ¸ë ˆìŠ¤, 병원, 메타분석, 간호사 Key words: Emotional stress, Hospitals, Meta-Analysis, Nurses

Scheduling jobs strategies for grid computing: A review

Distributed computing environments have been very much in spectacle for the last one and a half decade. But while widely used high performance computing platforms for scheduling of jobs becomes crucial due to the job starvation and resource fragmentations problems which exist in existing scheduling strategies. In the Meanwhile, these existing strategies are rarely balance fairness and performance aiming at these issues; we reviewed lot of literature and closely observed and tried to analyze the best and bad in of various methods available. A lot work has been done towards to promote job response time and throughput but they are not able to handle the “starvation” problem and lead to unfairness. However, fairness and performance in the cluster depiction is rarely taken into consideration simultaneously.

A Survey Report on Distributed System Using Load Balancing Approach

A number of load balancing algorithm were developed in order to improve the execution of a distributed application in any kind of distributed architecture. Load balancing involves assigning tasks to each processor and minimizing the execution time of the program. In practice, it would be possible even to execute the applications on any machine of worldwide distributed system. This results in a significant performance improvement for the users. This paper describes the necessary, newly developed, principal concepts for several load balancing techniques in a distributed computing environment. This paper also includes various types of load balancing strategies, their merits, demerits and comparison depending on certain parameters. Load balancing is the process of distributed the load among various nodes of a distributed system to improve both job response time and resource utilization while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or lightly loaded.

Performance evaluation of task migration in contiguous allocation for mesh interconnection topology

In this paper, several efficient migration algorithms have been proposed to improve existing non-preemptive sub-mesh allocation strategies in multiprocessors which are two-row boundary migration algorithm (TRBMA) and different types of combined migration mechanisms. These methods are presented to solve the external fragmentation problem in contiguous allocation. In this process, the mentioned methods contain the algorithms, called the minimum threshold and prime number (MT&MPN), which determine the best sub-mesh size for the incoming job. These algorithms are used to increase continuity in multiprocessor systems. Using extensive simulations, we evaluated the proposed methods and compared them with previous strategies. Simulation outcomes clearly showed the TRBMA and the MT&MPN procedure produced the best average job execution time and average system utilization. Also, hybrid migration methods produced the best average job waiting time and average number of waiting jobs in comparison with the previous algorithms. Meanwhile, most of the traditional methods had better average job response time.

Optimized Load Balancing Strategy In Cloud Computing : A Review

Now a days every organization is migrating towards cloud computing as cloud computing is considered being more flexible and scalable as compared to other technologies. The technology simply means to provide the computing resources and services through a network. This paper discusses the existing approaches for scheduling algorithms that can maintain the load balancing and provides better improved strategies through efficient job scheduling and modified resource allocation techniques. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time.

Exploring the perceptive effect of technology allocation on work outcomes among IT professionals

Considering intensive interaction with technology, the effect of technology allocation on IT professional’s work outcomes should not be ignored. Our study thus tries to explore the interplay of technology characteristics and individual factors which in turn will influence the job outcomes. Theoretical model proposes that certain technology level factors like market dominance, intellectual challenge and work hour requirements interacts with individual level factors such as growth need, competence and need of life style integration to affect job responses like commitment, burnout and motivation. Using qualitative approach this study attempt to explicate the persontechnology fit in an IT organization context.

LsPS: A Job Size-Based Scheduler for Efficient Task Assignments in Hadoop

The MapReduce paradigm and its open source implementation Hadoop are emerging as an important standard for large-scale data-intensive processing in both industry and academia. A MapReduce cluster is typically shared among multiple users with different types of workloads. When a flock of jobs are concurrently submitted to a MapReduce cluster, they compete for the shared resources and the overall system performance in terms of job response times, might be seriously degraded. Therefore, one challenging issue is the ability of efficient scheduling in such a shared MapReduce environment. However, we find that conventional scheduling algorithms supported by Hadoop cannot always guarantee good average response times under different workloads. To address this issue, we propose a new Hadoop scheduler, which leverages the knowledge of workload patterns to reduce average job response times by dynamically tuning the resource shares among users and the scheduling algorithms for each user. Both simulation and real experimental results from Amazon EC2 cluster show that our scheduler reduces the average MapReduce job response time under a variety of system workloads compared to the existing FIFO and Fair schedulers.

Beyond the mean in fork-join queues: Efficient approximation for response-time tails

Fork-join queues are natural models for various computer and communications systems that involve parallel multitasking and the splitting and resynchronizing of data, such as parallel computing, query processing in distributed databases, and parallel disk access. Job response time in a fork-join queue is a critical performance indicator but its exact analysis is challenging. We introduce a stochastic model for K-node homogeneous fork-join queues (K≥2) that focuses on the difference in length between any node-queue and the shortest one, truncating the state space such that the maximum difference is at most a constant C. Whilst most previous methods focus on the mean response time, our model is also able to evaluate the response time distribution, as well as accommodating phase-type processing times and Markovian arrival processes. In order to tackle scenarios with high loads, which require a large value of C to provide sufficient accuracy, we develop an efficient algorithm using matrix-analytic methods. Tests against simulation show that the proposed model yields accurate results for 2-node fork-join queues. As the model becomes numerically intractable for large values of K, we further propose an approximate approach, based on properties of order statistics and extreme values. The approximation gives a high degree of accuracy on response time tails, and has the advantage of being efficient and scalable, requiring only the analytical results for a single-node and 2-node fork-join queues, which we obtain with the aforementioned matrix-analytic model. Comparison with simulation results shows that our approximation yields good fits for the tails, even in very large cases with general processing and inter-arrival times.

Implement of Dynamic Time Quantum Shortest Load First Scheduling for Efficient Load Balancing

Cloud Computing is a modern eminent technology in this era. Cloud service providers requires handling vast request for scaling up the size of cloud, but Cloud Computing faces critical challenges on its glorious future. Those challenges create problem for exploring for its perfect realization. Load Balancing is one of the hindrances in that case. Load Balancing is a process of distributing workload among different others nodes of distributed system to improve the perfect utilization of resources and job response time. Load Balancing should be done properly, as failure in any one of the node can lead of unavailability of data. There are already different research works has been done on Load Balancing using various scheduling techniques such as Round Robin (RR), First-ComeFirst-Served (FCFS), Shortest Load First (SLF) etc for reduce the execution time, reduce average waiting time for giving better quality of services in Cloud Computing. In this paper new scheduling has been approached ,that is Dynamic Shortest Load First Algorithm (DSLF).Shortest Load first is one of the sophisticated scheduling algorithm, for reduce the average waiting time compare to other existing algorithm and give better load balancing but this has some drawbacks. The burst time of processes have to predict before CPU start the execution. But it is impossible to predict the burst time of the process before the process complete the execution. If the process did not end their execution in definite predicted burst time then which algorithm will execute, then this question will come. In case of FCFS, starvation will occur, though the average waiting time will increase in that case. In my approach the main concept is, if the process remain in ready queue after ending of their predicted Burst Time, a new quantum will create dynamically and each unfinished process will get the amount of time quantum to execute in CPU if any process does not completed after execution of that amount of time (Time Quantum) it will go to tail of the ready queue and waiting for its turn [1].