Name
Abstract | ||
|---|---|---|
Allocating memory dynamically for virtual machines (VMs) according to their demands provides significant benefits as well as great challenges. Efficient memory resource management requires knowledge of the memory demands of applications or systems at runtime. A widely proposed approach is to construct a miss ratio curve (MRC) for a VM, which not only summarizes the current working set size (WSS) of the VM but also models the relationship between its performance and the target memory allocation size. Unfortunately, the cost of monitoring and maintaining the MRC structures is nontrivial. This article first introduces a low-cost WSS tracking system with effective optimizations on data structures, as well as an efficient mechanism to decrease the frequency of monitoring. We also propose a Memory Balancer (MEB), which dynamically reallocates guest memory based on the predicted WSS. Our experimental results show that our prediction schemes yield a high accuracy of 95.2p and low overhead of 2p. Furthermore, the overall system throughput can be significantly improved with MEB, which brings a speedup up to 7.4 for two to four VMs and 4.54 for an overcommitted system with 16 VMs. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2851501 | TACO |
Keywords | Field | DocType |
Algorithms,Performance,Virtualization,data center,memory,performance,working set size | Virtualization,Dynamic random-access memory,Virtual machine,Computer science,Virtual memory,Parallel computing,Real-time computing,Memory management,Flat memory model,Working set size,Speedup,Embedded system | Journal |
Volume | Issue | ISSN |
13 | 1 | 1544-3566 |
Citations | PageRank | References |
3 | 0.38 | 23 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhigang Wang | 1 | 3 | 0.38 |
| Xiao-lin Wang | 2 | 76 | 4.32 |
| Fang Hou | 3 | 3 | 0.38 |
| Yingwei Luo | 4 | 315 | 41.30 |
| Zhenlin Wang | 5 | 91 | 6.68 |