Scheduling with multi-level data locality: Throughput and heavy-traffic optimality

Xie, Qiaomin; Yekkehkhany, Ali; Lu, Yi

doi:10.1109/infocom.2016.7524416

Cited by 68 publications

(61 citation statements)

References 16 publications

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“…Three types of tasks and a computing cluster of three servers are considered with processing rates depicted in Figure 3, which are not known from the perspective of the load balancing algorithms. Note that this affinity structure does not have the rack structure mentioned in [6] since from the processing rates of task type 2 on the three servers, servers 1 and 2 are in the same rack as server 3, but from the processing rates of task type 3 on the three servers, the second server is in the same rack as the third server, but not the first server. Hence, this affinity setup is more complicated than the one with a rack structure.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In the next subsection, the GB-PANDAS algorithm is proposed when the service rate matrix B µ is known. Balanced-PANDAS for a data center with three levels of data locality is proposed by [6], and here we are proposing the Generalized Balanced-PANDAS algorithm from another perspective which is of its own interest.…”

Section: Queueing Structure For Gb-pandasmentioning

confidence: 99%

“…Although some of these heuristic algorithms are being used in real applications, simple facts about their optimality are not investigated. Recent works including the priority algorithm [22], Join-the-Shortest-Queue-Max-Weight (JSQ-MW) [10] and Weighted-Workload algorithm [6] study the capacity region and throughput optimality for a system with two and three levels of data locality.…”

Section: Related Workmentioning

confidence: 99%

“…Since these two algorithms do not use the task arrival rates and still stabilize the system, they are robust to any change in task arrival rate as long as it is in the capacity region of the system. Both Max-Weight and c-µ-rule algorithms have the same issue as the fluid model planning algorithm on considering one queue per task type which can make the system model complicated as discussed in [6]. Note that Wang et al [10] and Xie et al [6] study the load balancing problem for special cases of two and three levels of data locality, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Both Max-Weight and c-µ-rule algorithms have the same issue as the fluid model planning algorithm on considering one queue per task type which can make the system model complicated as discussed in [6]. Note that Wang et al [10] and Xie et al [6] study the load balancing problem for special cases of two and three levels of data locality, respectively. In the former, delay optimality is analyzed for a special traffic scenario and in the latter delay optimality is analyzed for a general traffic scenario and in both cases there is no issue on the number of queues, but as mentioned, these two algorithms are for special cases of two and three levels of data locality.…”

Section: Introductionmentioning

confidence: 99%

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Blind GB-PANDAS: A Blind Throughput-Optimal Load Balancing Algorithm for Affinity Scheduling

Yekkehkhany

Nagi

2020

IEEE/ACM Trans. Networking

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Dynamic affinity load balancing of multi-type tasks on multi-skilled servers, when the service rate of each task type on each of the servers is known and can possibly be different from each other, is an open problem for over three decades. The goal is to do task assignment on servers in a real time manner so that the system becomes stable, which means that the queue lengths do not diverge to infinity in steady state (throughput optimality), and the mean task completion time is minimized (delay optimality). The fluid model planning, Max-Weight, and c-µ-rule algorithms have theoretical guarantees on optimality in some aspects for the affinity problem, but they consider a complicated queueing structure and either require the task arrival rates, the service rates of tasks on servers, or both. In many cases that are discussed in the introduction section, both task arrival rates and service rates of different task types on different servers are unknown. In this work, the Blind GB-PANDAS algorithm is proposed which is completely blind to task arrival rates and service rates. Blind GB-PANDAS uses an exploration-exploitation approach for load balancing. We prove that Blind GB-PANDAS is throughput optimal under arbitrary and unknown distributions for service times of different task types on different servers and unknown task arrival rates. Blind GB-PANDAS desires to route an incoming task to the server with the minimum weighted-workload, but since the service rates are unknown, such routing of incoming tasks is not guaranteed which makes the throughput optimality analysis more complicated than the case where service rates are known. Our extensive experimental results reveal that Blind GB-PANDAS significantly outperforms existing methods in terms of mean task completion time at high loads.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Queueing Structure For Gb-pandasmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Blind GB-PANDAS: A Blind Throughput-Optimal Load Balancing Algorithm for Affinity Scheduling

Yekkehkhany

Nagi

2020

IEEE/ACM Trans. Networking

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Robustness Comparison of Scheduling Algorithms in MapReduce Framework

Daghighi

Chen

2021

Lecture Notes in Networks and Systems

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Estimating the Rating of the Reviews Based on the Text

Kavousi

Saadatmand

2018

Data Analytics and Learning

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User-generated texts such as reviews and social media are valuable sources of information. Online reviews are important assets for users to buy a product, see a movie, or make a decision. Therefore, rating of a review is one of the reliable factors for all users to read and trust the reviews. This paper analyzes the texts of the reviews to evaluate and predict the ratings. Moreover, we study the effect of lexical features generated from text as well as sentimental words on the accuracy of rating prediction. Our analysis show that words with high information gain score are more efficient compared to words with high TF-IDF value. In addition, we explore the best number of features for predicting the ratings of the reviews.

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Scheduling with multi-level data locality: Throughput and heavy-traffic optimality

Cited by 68 publications

References 16 publications

Blind GB-PANDAS: A Blind Throughput-Optimal Load Balancing Algorithm for Affinity Scheduling

Blind GB-PANDAS: A Blind Throughput-Optimal Load Balancing Algorithm for Affinity Scheduling

Robustness Comparison of Scheduling Algorithms in MapReduce Framework

Estimating the Rating of the Reviews Based on the Text

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