Dynamic Memory Resource Management in Virtual Machines with Different Applications

2016 ◽  
Vol 22 (10) ◽  
pp. 2745-2749
Author(s):  
Masaki Sakamoto ◽  
Saneyasu Yamaguchi
Keyword(s):  
Resource Management ◽  
Virtual Machines ◽  
Dynamic Memory ◽  
Memory Resource

Performance of Memory Virtualization Using Global Memory Resource Balancing

2019 ◽  
Vol 9 (1) ◽  
pp. 16-32 ◽  
Author(s):  
Pvss Gangadhar ◽  
Ashok Kumar Hota ◽  
Mandapati Venkateswara Rao ◽  
Vedula Venkateswara Rao
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machines ◽  
Global Memory ◽  
Memory Allocation ◽  
Dynamic Memory ◽  
High Resource ◽  
Memory Resource ◽  
Performance Penalty ◽  
Working Set

Virtualization has become a universal generalization layer in contemporary data centers. By multiplexing hardware resources into multiple virtual machines and facilitating several operating systems to run on the same physical platform at the same time, it can effectively decrease power consumption and building size or improve security by isolating virtual machines. In a virtualized system, memory resource supervision acts as a decisive task in achieving high resource employment and performance. Insufficient memory allocation to a virtual machine will degrade its performance drastically. On the contrasting, over allocation reasons ravage of memory resources. In the meantime, a virtual machine's memory stipulates may differ drastically. As a consequence, effective memory resource management calls for a dynamic memory balancer, which, preferably, can alter memory allocation in a timely mode for each virtual machine-based on their present memory stipulate and therefore realize the preeminent memory utilization and the best possible overall performance. Migrating operating system instances across discrete physical hosts is a helpful tool for administrators of data centers and clusters: It permits a clean separation among hardware and software, and make easy fault management. In order to approximate the memory, the stipulate of each virtual machine and to adjudicate probable memory resource disagreement, an extensively planned approach is to build an Least Recently Used based miss ratio curve which provides not only the current working set size but also the correlation between performance and the target memory allocation size. In this paper, the authors initially present a low overhead LRU-based memory demand tracking scheme, which includes three orthogonal optimizations: AVL based Least Recently Used association, dynamic hot set sizing. This assessment outcome confirms that, for the complete SPEC CPU 2006 benchmark set, subsequent to pertaining the 3 optimizing techniques, the mean overhead of MRC construction are lowered from 173% to only 2%. Based on current WSS, the authors then predict its trend in the near future and take different tactics for different forecast results. When there is an adequate amount of physical memory on the host, it locally balances its memory resource for the VMs. Once the local memory resource is insufficient and the memory pressure is predicted to sustain for a sufficiently long time, VM live migration, is used to move one or more VMs from the hot host to other host(s). Finally, for transient memory pressure, a remote cache is used to alleviate the temporary performance penalty. These experimental results show that this design achieves 49% center-wide speedup.

scholarly journals Symbiotic Dynamic Memory Balancing for Virtual Machines in Smart TV Systems

ETRI Journal ◽  
2014 ◽  
Vol 36 (5) ◽  
pp. 741-751
Author(s):  
Junghoon Kim ◽  
Taehun Kim ◽  
Changwoo Min ◽  
Hyung Kook Jun ◽  
Soo Hyung Lee ◽  
...  
Keyword(s):  
Virtual Machines ◽  
Dynamic Memory ◽  
Smart Tv

scholarly journals A dynamic programming approach to manage virtual machines allocation in cloud computing

2018 ◽  
Vol 7 (4.6) ◽  
pp. 128
Author(s):  
Abdellah Ouammou ◽  
Mohamed Hanini ◽  
Abdelghani Ben Tahar ◽  
Said El Kafhali
Keyword(s):  
Cloud Computing ◽  
Dynamic Programming ◽  
Resource Management ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Stochastic Modelling ◽  
Programming Approach ◽  
Conflicting Objectives ◽  
Machine Allocation ◽  
Virtual Machine Allocation

As a result of the dynamic nature of Virtual Machine allocation in cloud computing, it is not easy to manage system resources or choose the best configuration based solely on human experience.  In this work, we used stochastic modelling instead of comprehensive experiments to evaluate the best resource management of the system. In such complex systems, choosing the best decision is a challenge, for this reason we have designed a heuristic algorithm, specifically, dynamic programming as a resource management and programming tool that finds a way that attempts to satisfy the conflicting objectives of high performance and low power consumption. As a scenario for using this algorithm, we addressed the problem of virtual machine allocation, a subset of physical machines is designated as "reserve", and the reserves are actives when the number of jobs in the system is sufficiently high. The question is how to decide when to activate the reserves. The simulation results demonstrated the benefit of using our framework to identify the policy for consolidation or for a low energy consumption and in order to have a good quality of service in the system

scholarly journals A Thread-Oriented Memory Resource Management Framework for Mobile Edge Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 45881-45890 ◽  
Author(s):  
Zongwei Zhu ◽  
Fan Wu ◽  
Jing Cao ◽  
Xi Li ◽  
Gangyong Jia
Keyword(s):  
Resource Management ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Management Framework ◽  
Memory Resource
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