Learning to Schedule Multi-Server Jobs With Fluctuated Processing Speeds

Faster Jobs in Distributed Data Processing using Multi-Task Learning

Slow running or straggler tasks in distributed processing frameworks [1, 2] can be 6 to 8 times slower than the median task in a job on a production cluster [3], despite existing mitigation techniques. This leads to extended job completion times, inefficient use of resources, and increased costs. Recently, proactive straggler avoidance techniques [4] have explored the use of predictive models t...

The makespan problem of scheduling multi groups of jobs on multi processors at different speeds

In the paper we mainly study the makespan problem of scheduling n groups of jobs on n special-purpose processors and m general-purpose processors at different speeds. We first propose an improved LPT algorithm and investigate several properties of this algorithm. We then obtain an upper bound for the ratio of the approximate solution T to the optimal solution T ∗ under the improved LPT algorithm.

Consistency aware update schedule in multi-server Distributed Virtual Environments

Multi-server architecture is very popular in Distributed Virtual Environments. Although multi-server architecture has good scalability, the load balance and consistency are still challenging issues. Because of server resource limitations (computation, communication etc), the consistency of the virtual world cannot be guaranteed sometimes. In this paper, we address this problem by developing sch...

Controllable Multi-Server Queue with Balking (TECHNICAL NOTE)

Using the Greedy Approach to Schedule Jobs in a Multi-Cluster System

Clusters, multi-cluster systems and the grid are becoming popular high performance computing infrastructures. How jobs are scheduled on them greatly influence the performance. Since jobs are online in most computer systems, they are scheduled when the entire information about them is unknown. We propose a greedy scheduling algorithm that uses both arrival order and how hard a job is to schedule...