Over the past few years, data-driven businesses have provided a promising experience for clients from various aspects. For example, Amazon provides personalized product recommendation using clients’ past purchasing records, personal information (e.g., employment status and residence location) as well as the contextual information (e.g., weather). On the other hand, Google’s advertising system is optimized to retrieve the advertisements that the clients are potentially interested in using their browsing cookies, searching history, email contents, and even their friends’ recent purchase records. Given such tremendous data for processing, mining and analyzing, there needs a computing cluster that provides infrastructural supports for those data-intensive applications, at the back-end side of businesses. To enable and optimize the big data analytics, the system usually first decompose the overall computational job into multiple small tasks, then itemizes the usage of systems into large set resources, such as CPU hour, or space of storage/memory, and further allocates computing resources to the tasks.
To optimize the data processing systems, the researchers have put tremendous efforts on job scheduling, resource management, and program design to improve system performance. In this field, there are two widely used schedulers, Fair  and Capacity  schedulers, for managing the resources in the cluster. Fair scheduler is a method of assigning resources to jobs such that all jobs get, on average, an equal share of resources over time. On the other hand, Capacity scheduler is designed to allow sharing a large cluster while giving each organization a minimum capacity guarantee. Although these two schedulers have different strategies for resource management, both of them add jobs to the queues following a first-come-first-serve manner.
However, this manner does not take diverse demands from clients, which include various resource requests and occupancy length, into consideration. Fig 1 illustrates a simplified example of 4 incoming jobs in a cluster with 6 containers. Suppose 4 jobs are submitted in order with a 1 second interval and each of them specifies its resource demand along with expected execution length (as shown on the figure). For example, Job 1 requests 3 (R3) containers and lasts for 10s (L10). If the first-come-first-serve manner is in effect, Job 1 executes first, followed by Job 2, which waits until Job 1 finish and finally, Job 3 & 4 running in parallel. The makespan of four jobs would be 40s. The waiting time for Job 1 to Job 4 is 0s, 9s, 28s, 27s, respectively, and 16s on average. In this schedule, only Job 3 and Job 4 are run in parallel in the system. Towards a more efficient scheduler, it should consider the diverse demands from, not only running jobs, but also pending jobs. In the above intuitive example, if the scheduler can delay the decision making and rearrange the execution order, where Job 1 and 3 run concurrently, and then Job 2 and 4 execute in parallel. Under the new schedule, the makespan reduces to 30s and average waiting time reduces to 5.75s. Although Fig 1 is a simplified example, it shows the benefits that a system can obtain by considering the resource demands from clients.
Motivated by the fact that jobs with large demand will starve the system and delay other jobs, this paper presents DRESS, a Dynamic RESource-reservation Scheme. Compared to the prior work, DRESS collects the resource demands of jobs, distributes them into two categories with separate resource pools. and, rearranges the execution order to increase the degree of parallelism. Specifically, DRESS utilizes a resource reservation ratio, which is calculated based on real-time demands, to allocate system resources to each category. Additionally, DRESS estimates the resource release patterns. In summary, our main contributions are as follows: (1) We propose DRESS for congested data-intensive computing platforms that considers various demands from jobs and reserve resource for different categories. (2) We develop algorithms to estimate the resource release pattern for running jobs in the system. Based on the estimation along with demands from waiting jobs, we dynamically adjust the resource reservation ratio. (3) We present a complete implementation on Hadoop YARN platforms. The experiment-based evaluation shows a significant improvement on compilation time for small jobs, up to 76.1%.
Ii Related Work
Commercial companies and academic researchers utilize the computing systems to perform various jobs from different perspectives [3, 4, 5, 6, 7, 8, 9, 10, 11]. There are many big data computing systems available in the market. Among various of them, the Hadoop YARN and its ecosystems have become major players. Various applications from different users run on top the platforms and share resources in a cluster. Traditionally, Fair  and Capacity  are widely used to ensure each job to get a proper share of the available resources. To improve the performance of the computing systems, many research efforts have been spent on optimizing the system and job scheduling in different directions. Some major related works are introduced as follows.
Resource-aware scheduling focuses on improving the resource utilization of the cluster. In this area, Haste  is a fine-grained resource scheduling which leverages the information of requested resources and resource capacities to improve the resource utilization. In addition, FRESH  and OMO  have developed dynamic resource management schemes according to the various workloads of different jobs. Another direction in system scheduling considers the heterogeneous environment. In this area, Teris  packs tasks to machines based on their multiple resource requirements. LATE , Hopper , and eSplash  aim to prevent unnecessary speculative executions in order to improve the performance in heterogeneous clusters.
Data locality is also considered in the scheduling of big data computing systems. To improve the performance, authors in  propose an optimal task selection algorithm for better data locality and fairness. In addition, job characteristics are taken into consideration in the job-aware scheduling algorithms. In ARIA , a scheduler is proposed to allocate appropriate resources to jobs to meet the predefined deadline. Sparrow  targets on the scheduling problems with a huge amount of small jobs. Piranha  creates an agent layer beyond Hadoop to schedule hybrid types of applications.
Inspired by the preceding works, we develop a dynamic resource allocation scheme, DRESS, to reserve a portion of resources for the applications with small resource requests. With a branch of hybrid jobs assigned in the cluster, based on the characteristics of each job and the estimating resources release of the cluster, DRESS can significantly improve the performance of small jobs with limited impacts on large jobs.
Iii Dress: Dynamic Resource Reservation Scheme
In this section, we present our solution DRESS, which aims to reduce the waiting time of small jobs in a congested cluster and at the same time, maintains a stable makespan among all jobs. The key idea of DRESS is to redirect the jobs into two categories and reserve a certain amount of resources for each category. As the system goes on, various jobs join and leave the categories when arriving and finishing. The challenge lies in dynamically adjusting the reserved resources ratio to each category. If a large portion of resources is reserved for one category, jobs in the other one would keep waiting. Towards a better ratio adjustment, we not only need to know the total available resources, the number of pending jobs in each category, but also the release patterns of running jobs to estimate the future availability of resources. In the rest of this section, we first study the task execution in the parallel system, then, describe our techniques to estimate the overall resource availability in the future. Finally, based on the estimation, we propose an algorithm that dynamically adjusts the reserve ratio. Table I lists the notations that are used in this paper.
|The job of all the jobs () in the cluster|
|The phase of a particular job ()|
|The task of a particular phase ()|
|The start and finish time of|
|The earliest finish time among all the tasks,|
|The starting time of the first / last task in|
|The starting variation of|
|Resource release function. Given , it outputs an estimated number of containers that released by /|
|Available resource function. Given , it outputs an estimated number of available containers in system|
|The number of available containers / total containers in the system|
|The number of containers occupied by /|
|The number of running/completed tasks in phase (represented by the states of containers)|
|/||Reserve ratio / Job indicator|
Iii-a Characteristics of Task Execution
Without prior knowledge about the features of data and algorithms, it’s a challenge to estimate the job execution length. This is due to the fact that different jobs target on diverse data sets in terms of size, type, etc., and various algorithms will be applied to the data. For a better estimation, we experimentally study the task execution in three aspects, starting time variation, heading tasks, and trailing tasks.
Iii-A1 Starting Time Variation
Focusing on a specific job, it consists of multiple tasks that can be grouped into multiple phases. Inside each phase, tasks perform the same operations with the same algorithms on similar data sets in order to process it in parallel. Considering this characteristic, the task execution length in the same phase should be similar with each other. Fig 4 plots a classic MapReduce WordCount job with 20 Map tasks and 4 Reduce tasks. Clearly, the job contains two phases (Map and Reduce) and the tasks can be divided into two groups. As we can see the tasks in the same phase, Map and Reduce as on Fig 4, have a similar execution length. The finishing time of Map tasks is varied due to the different starting time. There are mainly two reasons for the difference in starting time. Firstly, in a congested cluster, the scheduler assigns the containers to jobs through multiple rounds of resource requests. Secondly, the transition delay varies from time to time when a container’s state moves from New to Running, that passes by the other three states, Reserved Allocated, and Acquired
. These reasons result in the starting time variances of jobs in each phase. As shown on Fig4, and for phase 1 and 2.
Iii-A2 Heading Task
Fig 4 illustrates a PageRank example running on YARN with MapReduce. The PageRank job includes two stages and each stage contains one Map and one Reduce phase. Therefore, tasks of a PageRank job can be naturally grouped into four phases. It is clear to find the same trend of starting time variation on the tasks of the PageRank job.
However, there is an abnormal task in Reduce phase if the first stage that consists of 9 Reduce tasks. While the average length of first 8 tasks is 18.25s with the variance of 1.45s, the last task (ID 42) only costs 1.26s that is less than 10% of the others’. This extreme case is caused by the fact that a large data set will be split into small data blocks and each task is responsible for one or more blocks (controlled by the size of map split).
Although the data blocks have the same limit on size, for tasks at the end, they may result in processing less data than previous tasks. Fig. 5 shows an example of a job that targets on a data set of two chunks of 1,664MB (Data A) and 1,280MB (Data B). The block size and map split are set to 512MB. Thus, Data A and B will be stored in four and three data blocks, respectively. The last blocks of Data A and B are underloaded with only 128MB and 256MB, which lead to heading tasks.
Iii-A3 Trailing Tasks
plots a PageRank job running with Spark-on-YARN, which is a two-layer scheduling system, and we only collect data from Hadoop YARN. Unlike the previous heading task example, there is no distinct Map and Reduce phases on Spark. In a Spark-on-YARN system, each task handles a partition of a large data set. However, due to the Data Skew Problem[23, 24, 25], some partitions may much larger than others that lead to a longer execution time of those tasks, which we named trailing tasks. We can easily locate one trailing task on Fig 4 that costs 17.6s which consumes 38% more than the second longest one. Comparing with normal tasks, the trailing tasks occupy resources in a different pattern and significantly longer than others.
Iii-B Estimation Function
The proposed solution, DRESS, relies on an important parameter, which is the estimated resource availability in the system. It is a critical factor that directly affects the estimated accuracy. Utilizing characteristics in the previous subsection, we present our estimation function. In our problem setting, we consider a system running multiple jobs simultaneously. Each running job holds a certain amount of resources that are represented by containers. A job is divided into multiple tasks and each of them runs in a container. Our objective is to estimate the overall resource availability of the system in the near future.
Suppose there are jobs, in the system. For each one, , we define a function to represent ’s estimated resource release frequency at time unit . Let denotes the total number of available containers. Therefore, we have,
Where is the number of currently available containers in the system that the scheduler can observe by monitoring the available resources on slave nodes, and are the estimated releasing resources for two categories.
For a specific , before running, it does not occupy any containers. Thus, , if is not started yet. When finished all tasks, since all occupied resources have been released. Let starts at time unit and finishes at , where and can be easily measured through heartbeat messages from slave nodes. The main activities, which including starting / executing tasks, occupying / releasing containers, and etc, happen inside the interval . As described before, throughout the job execution, the tasks can be grouped into multiple phases that tasks in each particular phase have the same operations, and similar input/output data for parallel processing. In an ideal setting, tasks in each phase will start and finish at the same time. However, in a real system, due to the limited resources and characteristics of task execution, the resource occupation and task completion time varied in phases. Assume there are phases in the task execution of and let be a function of resource release pattern for the phase in , then we have,
Specifically, phase , where , will not release any container until one of its task finishes. When the first task finishes its execution, containers that occupied by this task will be returned to the system. Other tasks in phase , which has the same operations and similar data sets, should about to complete. Depending on the starting time, the completion time of tasks in phase varies in a short period. We assume that the task completion time is equally distributed in the short period of , where can be measured from starting time variation. As a result, we have,
where are the start and finish time of (); is the total number of containers that occupied by ; is the earliest finishing time of the tasks in ; is a percentage that represents the release progress in this phase.
Iv Parameter Analysis and Algorithms
From the analysis of the previous section, we can use Equation 1, 2, 3 to predict the resource release. However, both and are based on several unknown parameters. In this section, we analyze the parameters that are required by the equations and present the algorithms.
Iv-a Calculation of starting variation for each phase
Calculating , we need to identify the phases for and determine the value of and for each . The key idea to estimate container release patterns of jobs is to group the task into different phases. Tasks of a phase run in parallel to achieve the same goal (e.g. producing intermediate output). Therefore, as the first step, we have to identify each phase of a job. As discussed in the previous section, ideally, tasks in the same phase would start simultaneously. However, in reality, there is a starting time variation between them. For each in , we need to identify the start time of the first task in that denotes by and start time of last task in that presents by , which denoted by . We use a window-based algorithm to identify each phase.
Algorithm 1 describes how we calculate for . First of all, we initialize the parameters, where is a phase index, start time of that represents by , is a boolean indicator that is used to determine whether has started, and are initialized to 0. is a set of running tasks for that grouped by each phases, and is a function that returns the number of running tasks in at system time (line 1-3). The algorithm keeps tracking containers that have been assigned to but not in the “Running” state (may in Reserved, Allocated, and Acquired states). If transits to “Running” state, which means the task is executing, it updates corresponding parameters (line 4-8). In addition, we update the job starting time, , if is the first running task for (line 9-10). Then, within a given window, the algorithm monitors the number of running tasks. If the difference is larger than a threshold(), it decides that the first task in has started at earliest starting time of in (line 11-13). On the other hand, if stays the same in the window, the algorithm determines that the last task in has started at the latest starting time of in , calculates , and update the total number of running tasks in as well as the phase index (line 14-17).
Iv-B Calculation of starting release time of each phase
Besides the , another key parameter for the estimation is , which is represented by the earliest finish time among tasks in . According to equation 3, the phase will start releasing container in a period of after .
Algorithm 2 presents a procedure to calculate the start releasing time, , of in . As the first step, it initializes the parameters, which are for , the starting release time for , ending indicator(), running task set for , completed task of that is represented by , and completed task function that returns the number of completed tasks for at time (line 1-3). The algorithm keeps tracking the states of running tasks in , which are grouped by each phase. When a container transits to “Completed” state, it indicates that the corresponding task, is finishing. The algorithm adds it into complete task’ set, , and updates as well as (line 4-7). In a given period (), if there are a certain number () of tasks move to “Completed” state, it decides that tasks in have started finishing and records (line 8-10). The threshold, , is designed to filter out heading tasks. If has started finishing (), but remain the same for a period (), additionally, for is not empty, it indicates that there are trailing tasks in . In this case, we count trailing tasks into next phase (line 11-12). Finally, if for becomes 0, all the tasks have finished and containers have been releases (line 13-14).
Iv-C Dynamic configuration for reserved resource ratio
In DRESS, we estimate the available resources that guide the scheduler to dynamically adjust the reserved resource ratio for each category. Besides available resources which can be predicted through Equation 1, two more factors should be taken into consideration, (1) the number of pending jobs in each category, (2) and the resource demands from them. Our objective of the dynamic configuration is to reduce the average waiting and completion length, at the same time, maintain a stable overall system performance.
While there are many approaches to split the jobs into different categories, such as job execution lengths, Map or Reduce intensive, and size of data sets, most of them require additional user-specified information. Requesting clients to input jobs’ features need them fully understand both their jobs and targeted platforms, which is not practical or feasible. In DRESS, we use the resource demands of jobs as the indicator, which can be directly obtained from the resource requests. We denote as a preset indicator factor such that if the resource request is larger than , the job will be classified to “large demand”(), otherwise, it will join “small demand”(). In our problem setting, there are two categories in the cluster and we set as the indicator. It’s easy to classify incoming jobs into more categories by applying a similar strategy.
Each category maintains its own pool of jobs that consists of pending and running jobs. For pending jobs, the scheduler records the total demands of resources, in terms of containers. For running jobs, the scheduler records the total occupied containers that will be returned to each category when the task completed. The number of currently available containers, , which can be observed from system heartbeats and can be further divided for each of the category, and , and . Depending on jobs who release the containers, we can estimate available resource for each category with Equation 1, where and are values for category 1 and 2.
With the parameters, DRESS use Algorithm 3 to dynamically adjust the reserved resource ratio, , which means containers are assigned to “small demand” jobs, , and are for “large demand” jobs, , where is the total number of containers in the system. Firstly, we initialize the parameters (line 1-2). Then, the algorithm calculates total resource demands, and from all pending jobs in each category(line 3-6). If, for , the estimated available resources are more than at time , it assigns redundant resources to by reducing (line 7-8). On the other hand, if cannot be satisfied at time in , and, has redundant resources, it enlarges (line 9-11). If both and cannot be met by estimated resources, we sort the jobs in each category by their resource demands , and start from the job with smallest resource demand, try to assign as many jobs as possible to utilize resources (line 12-20). After the assignments, each of the categories may have some leftover ( and may larger than 0). This is caused by the diversity in demands. For example, if the smallest demand is 5 containers but the available resources are 4, these 4 containers are leftover. In this scenario, the algorithm tries to move the leftover from to since the jobs in require fewer resources. Starting from the request of , we check whether is less than the combined leftovers from and . It makes the maximum usage by checking iteratively until the next job request is larger than . The will be enlarged accordingly (line 21-24). Finally, the system will return the value of (line 25).
V-a Implementation, Testbed and Workloads
V-A1 Implementation, Testbed and Parameters
We implement our solution DRESS on Hadoop YARN 2.7.4. An enriched heartbeat message is used to transfer the required information, such as starting delays, between the master and slave nodes. All the experiments are conducted on NSF Cloudlab  data center at University of Wisconsin. We use the c220g2 server that has two Intel E5-2660 v3 10-core CPUs at 2.6 GHz (Haswell EP), 160 GB ECC Memory and three disks (1 480 GB SATA SSD and 2 1.2 TB HDDs ).
We launch a cluster with 5 nodes to evaluate DRESS. As used by estimation functions, we set to 5s, phase window () to 10s, initial to 10% and the job indicator , such that large jobs request more than 10%. Particularly, we choose a 5-node cluster, instead of a very large cluster, to simulate a congested working environment for DRESS. Moreover, due to the page limit, we omit the analysis of thresholds and phase window.
To evaluate our system, we utilize a widely accepted benchmark suite named HiBench . In our settings, DRESS
can serve various types of jobs. There are 10 different benchmarks of 5 types, including micro benchmarks, machine learning, database, websearch benchmarks, and graph benchmarks. Specifically, we have tested the following benchmarks: (1)WordCount: count the occurrence of each word in the input data, which are generated using RandomTextWriter . (2) Sort: sort its text input data, which is generated using RandomTextWriter. (3) TeraSort: sort (key,value) tuples on the key with the synthetic data as input. (4) K-means clustering: a well-known clustering algorithm for knowledge discovery and data mining and the input data set is generated by GenKMeansDataset . (5) Logistic Regression
: the Logistic Regression is implemented and the input data set is generated by LabeledPointDataGenerator. (6) Bayesian Classification: test the Naive Bayesian trainer with automatically generated documents whose words follow the zipfian distribution. (7-8) Scan/Join: SQL (Hive) queries. (9) PageRank: a search engine ranking benchmark. (10) NWeight: an iterative graph-parallel algorithm that computes associations between two vertices that are n-hop away.
Noted that streaming benchmarks are not included in the evaluation since they are long-running jobs and do not have resource release patterns. In addition, we conduct the experiments on two type of platforms, Hadoop YARN (benchmarks 1-10) and Spark-on-YARN (benchmarks 4-6 and 9-10). When running the experiments, we consider 3 different combinations of jobs. (1) MapReduce jobs: we randomly pick up jobs for the Hadoop YARN platform and generate various sizes of datasets for each job. (2) Spark jobs: we randomly pick up the Spark jobs and execute them on Spark-on-YARN, which is a two-layer scheduling system (Spark has its own scheduler) and DRESS only run on the YARN layer. (3) Mixed job setting : we randomly pick up jobs. After selecting the jobs, they are submitted to the system one by one with a 5 seconds interval.
V-A3 Evaluation Metrics
In the experiments, we mainly consider two performance metrics. From the system view, we compute the makespan that is the total execution time for all jobs. From the view of each individual job, we measure the waiting time and job completion time, e.g. from , the waiting time is the length from the submission of to the start of its first task, and the completion time is the length from the submission of to the completion of its last task. The makespan reflects an overall system performance across all the jobs, and the waiting time along with the completion time indicate how the system impact on each individual job.
V-B Experiment Results
Fig 7 plots the waiting times of 20 jobs running on Spark-on-YARN, which contains a two-layer scheduling system, and DRESS only runs on the YARN layer. Overall, for the first 6 jobs running in the system, as the system is idle and the resources are enough to run the jobs in parallel, the waiting times of Jobs 1 to 6 are much shorter than others. Starting from Job 7 in Capacity scheduler, the waiting time for each job becomes higher than previous jobs. This is because after running Job 1-6, the remaining resources in the system is not enough for Job 7 and it has to wait until one of them finish and release the resource. Among tested jobs, ID 4, 5, 7, 9, 10, and 12 are jobs with small demands (less than 10 containers). As illustrated in this figure, DRESScan significantly reduce the waiting times of these small jobs compared to the Capacity scheduler. Especially, for DRESS, the waiting time of Job 7 is more than 10x less than the one in Capacity scheduler (28.903s vs 304.705s). Since Job 7 is a small job, it can use the reserved resources and after Job 4-5, the DRESS increased the reserved ratio to accept more small jobs. The same trend is found at Job 9, 10, and 12. We also notice that the waiting time of Job 3 with DRESS is much longer than Capacity scheduler (98.863s vs 35.519s). After tracing back to the execution of jobs, we found that job 3 is delayed since a part of the resources are reserved for jobs with fewer demands. Fig 7 illustrates the completion times of all 20 jobs. In DRESS, for small jobs, the average reduction rate of the completion time is 27.6%, with a maximum 51.2% reduced completion time for Job 7. We observe an increase on Job 3, 13, 14 of 32.0%, 10.2%, 6.1%, and on average 16.1%. In this case, the strategy of reserve resources does affect some jobs, however, it achieves a significant performance improvement on small jobs. Table II compares the makespan, average waiting time, average completion time as well as their median values of DRESSand Capacity scheduler. As illustrated in this table, the overall system performance, in terms of makespan, remains stable.
|Makespan||Avg. W.||Median||Avg. C.||Median|
V-B2 Hadoop YARN
Fig 9 and Fig 9 illustrate the results from experiments of 20 MapReduce jobs running on Hadoop YARN. Comparing with the previous experiments, a similar trend can be discovered from Fig 9. In 20 tested jobs, Job 4, 5, 6, 8, 10, 11 are jobs with small resource requests. As we can see from the figure, the waiting times for Jobs 1 to 9 are significantly shorter than others. Unlike Job 3 in the Spark-on-YARN experiments, in Hadoop YARN tests, Job 7 has been delayed for the later small jobs (ID 8, 10, and 11). In DRESS, waiting time for Job 9 is much less than the same job running with Capacity (19.981s vs 189.246s). Although Job 9 is not a small job, it also gets benefit from the delayed running of Job 7 since unused resources not only distributes to the queue of small jobs, but also to the queue that targets on regular jobs.
Fig 9 presents the completion times for those 20 MapReduce jobs. As the figure shows, DRESS reduces 25.7%, on average, of completion times for small jobs. In addition, it also benefits the large jobs of 9, 12, and 13. Their completion times decrease 23.2%, 17.5%, and 10.0%, respectively. DRESS sacrifices Job 7, whose completion time increased 29.3%, and affects Job 14 and 15, which increased 12.2% and 13.8%. Overall, in DRESS, the completion times of 12 jobs are decreased by 18.5% on average and the ones of other 8 jobs are increased by 8.2% on average.
V-B3 Mixed Job Setting
Next, we present the results from a mixed job setting, where a cluster accepts both MapReduce and Spark jobs. In addition, the number of jobs with small resource demands is another important for the system since it is directly related to the dynamic configuration of reserved reservation ratio, which is controlled by Algorithm 3.
Fig 11,11,13,13 plot the experiments of mixed settings with 10%, 20%, 30%, and 40% of small jobs. They plot the waiting time and the execution time for each job and the sum of them is the job completion time. Two bars are illustrated for each job ID. The left bar shows the evaluation results of DRESS and the right bar shows the ones of Capacity scheduler. Through analyzing the data in the figures, we can derive a similar trend as we found from previous experiments. Overall, the completion time of small jobs is significantly reduced in DRESS compared to Capacity scheduler. For instance, the completion times of Job 6 and 8, have been reduced from 484.5s, 540.3s to 111.3s, 134.4s, which is decreased by 76.1% on average. The reductions of the completion time for small jobs in the other three job settings are 36.2%, 21.9%, and 23.7% on average.
This paper investigates the resource management in congested clusters. Our goal is to reduce the waiting time and improve the completion time for jobs with fewer resource requests. To achieve our objectives, we present DRESS, a dynamic resource reservation scheme. Specifically, depending on incoming jobs’ demands, it categories them into two categories. DRESS reserves a portion of resources for jobs with small resource requests. The reserve ratio can be dynamically adjusted according to the number of jobs in each queue. We implement DRESS in the Hadoop YARN and evaluate it with both MapReduce and Spark jobs. The experiment result shows a significant improvement in small jobs, up to 76.1% reduction on the average completion time, in the meanwhile, achieves a stable overall system performance.
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-  Cloudlab. https://cloudlab.us/.
-  Hibench. https://github.com/intel-hadoop/HiBench.
-  Randomtextwriter. https://hadoop.apache.org/docs/r1.2.1/api/org/apache/hadoop/examples/RandomTextWriter.html.
-  Genkmeansdataset. https://github.com/intel-hadoop/HiBench/blob/master/autogen/src/main/java/org/apache/mahout/clustering/kmeans/GenKMeansDataset.java.
-  Labeledpointdatagenerator. https://www.programcreek.com/scala/org.apache.spark.mllib.regression.LabeledPoint.