  It is significant to apply load-balancing strategy to improve the performance
and reliability of resource in data centers. One of the challenging scheduling
problems in Cloud data centers is to take the allocation and migration of
reconfigurable virtual machines (VMs) as well as the integrated features of
hosting physical machines (PMs) into consideration. In the reservation model,
the workload of data centers has fixed process interval characteristics. In
general, load-balance scheduling is NP-hard problem as proved in many open
literatures. Traditionally, for offline load balance without migration, one of
the best approaches is LPT (Longest Process Time first), which is well known to
have approximation ratio 4/3. With virtualization, reactive (post) migration of
VMs after allocation is one popular way for load balance and traffic
consolidation. However, reactive migration has difficulty to reach predefined
load balance objectives, and may cause interruption and instability of service
and other associated costs. In view of this, we propose a new paradigm, called
Prepartition, it proactively sets process-time bound for each request on each
PM and prepares in advance to migrate VMs to achieve the predefined balance
goal. Prepartition can reduce process time by preparing VM migration in advance
and therefore reduce instability and achieve better load balance as desired. We
also apply the Prepartition to online (PrepartitionOn) load balance and compare
it with existing online scheduling algorithms. Both theoretical and
experimental results are provided.

Distributed, Parallel, and Cluster Computing