Integrating Amdahl-like Laws and Divisible Load Theory

A simple means of integrating the characteristics of networked processors under divisible loads into Amdahl’s Law is presented. Amdahl’s Law serves as an upper bound to these speedup results. Amdahl’s Law with divisible load processing characteristics included serves as an upper bound to speedup for any model taking into consideration more detailed peculiarities of real systems such as the overhead of task creation, synchronization, resource contention and memory issues.

Adapting Market-Oriented Policies for Scheduling Divisible Loads on Clouds

Cloud computing has become an important alternative for solving big data processing. Nowadays, cloud service providers usually offer users a virtual machine with various combinations of prices. As each user has different circumstances, the problem of choosing the cost-minimized combination under a deadline constraint as well as user's preference is becoming more complex. This article is concerned with the investigation of adapting a user's preference policies for scheduling real-time divisible loads in a cloud computing environment. The workload allocation approach used in this research is using Divisible Load Theory. The proposed algorithm aggregates resources into groups and optimally distributes the fractions of load to the available resources according to user's preference. The proposed algorithm was evaluated by simulation experiments and compared with the baseline approach. The result obtained from the proposed algorithm reveals that a significant reduction in computation cost can be attained when the user's preferences are low priority.