The flash memory has extensive application in database [4], networking [5], biomedical application [6, 7], virtualized storage system [8], cloud computing [9], geographic remote sensing [10], mobile devices [11 13], etc.
Underlying the clouds are compact and virtualized virtual machines (VMs) hosting computation-intensive applications from distributed users.
Using VMs to host multi-tier web map services can effectively address this limitation because virtualized resources, including CPU, memory, and I/O, are decoupled from their physical infrastructures and can be flexibly allocated to different tiers of the web map system [10].
They need to understand the pitfalls of moving too quickly to virtualization or virtualized desktops.
In practice this is rather difficult as the L2 memory in a virtualized environment is never quiet and in most cases there is no physical isolation among the Virtual Machines.
As virtualized environment are loaded into memory, it can also be exploited by resource abuse (privilege escalation and escaping attacks), account hijacking (exploiting authentication protocols or social engineering) and DoS attacks due to large number of users requesting resources use at the same time.
Such an embedding is required to fully harness the potential of virtual FPGAs, in particular to give the virtual circuits access to main memory and operating system services, and to enable a concurrent operation of virtualized and non-virtualized circuitry.
In the virtualized world, the virtual machine, and eventually the unit of work, ignores these separations.
In a virtualized environment, the time measured for a memory write is liable to be inaccurate due to virtualization overheads.
Subsequently, we might want to know how well data is treated amongst disk, CPU, and memory since bottlenecks in such virtualized resources will likely lead to bottlenecks in applications, and will end up being factored in to yet other benchmarks similarly.
