Tag Archives: Computer Engineering

Auction-based Resource Management in Computer Architecture


Resource management systems rely on a centralized approach to manage applications running on each resource. The centralized resource management system is not efficient and scalable for large-scale servers as the number of applications running on shared resources is increasing dramatically and the centralized manager may not have enough information about applications’ need.

This work proposes a decentralized auction-based resource management approach to reach an optimal strategy in a resource competition game. The applications learn through repeated interactions to select their optimal action for shared resources. Specifically, we investigate two case studies of cache competition game and main processor and coprocessor congestion game. We enforce costs for each resource and derive bidding strategy. Accurate evaluation of the proposed approach show that our distributed allocation is scalable and outperforms the static and traditional approaches.

Full article > tpds-carma

Big Data Computing: Modeling and Optimization

MapReduce framework is widely used to parallelize batch jobs since it exploits a high degree of multi-tasking to process them. However, it has been observed that when the number of servers increases, the map phase can take much longer than expected. This thesis analytically shows that the stochastic behavior of the servers has a negative effect on the completion time of a MapReduce job, and continuously increasing the number of servers without accurate scheduling can degrade the overall performance. We analytically model the map phase in terms of hardware, system, and application parameters to capture the effects of stragglers on the performance. Mean sojourn time (MST), the time needed to sync the completed tasks at a reducer, is introduced as a performance metric and mathematically formulated. Following that, we stochastically investigate the optimal task scheduling which leads to an equilibrium property in a datacenter with different types of servers. Our experimental results show the performance of the different types of schedulers targeting MapReduce applications. We also show that, in the case of mixed deterministic and stochastic schedulers, there is an optimal scheduler that can always achieve the lowest MST.

• Farshid Farhat, Diman Zad Tootaghaj, Anand Sivasubramaniam, Mahmut Kandemir, and Chita R. Das are with the school of electrical engineering and computer science, the Pennsylvania State University, University Park, PA, 16802, USA. Email: {fuf111,dxz149,anand,kandemir,das}@cse.psu.edu.

• Yuxiong He is with the Cloud Computing Futures group, the Microsoft Research, Redmond, WA 98052 USA. Email: yuxhe@microsoft.com.

• The work was done during my visit from MSR in June 2016 in Redmond WA.