In this paper, we intend to answer one key question to the success of cloud
computing: in cloud, do many task computing (MTC) or high throughput computing
(HTC) service providers, which offer the corresponding computing service to end
users, benefit from the economies of scale? Our research contributions are
three-fold: first, we propose an innovative usage model, called dynamic service
provision (DSP) model, for MTC or HTC service providers.

To save cost, recently more and more users choose to provision virtual
machine resources in cluster systems, especially in data centres. Maintaining a
consistent member view is the foundation of reliable cluster managements, and
it also raises several challenge issues for large scale cluster systems
deployed with virtual machines (which we call virtualized clusters). In this
paper, we introduce our experiences in design and implementation of scalable
member view management on large-scale virtual clusters.

Previous work shows request tracing systems help understand and debug the
performance problems of multi-tier services. However, for large-scale data
centers, more than hundreds of thousands of service instances provide online
service at the same time. Previous work such as white-box or black box tracing
systems will produce large amount of log data, which would be correlated into
large quantities of causal paths for performance debugging. In this paper, we
propose an innovative algorithm to eliminate valueless logs of multitiers
services.

For a large organization, different departments often maintain dedicated
cluster systems for different workloads, for example parallel batch jobs or Web
services. In this paper, we design and implement an innovative cloud computing
system software, Phoenix Cloud, to consolidate heterogeneous workloads of the
same organization on cloud computing platforms. For Phoenix Cloud, we propose
cooperative resource provision and management polices for the affiliated
departments of a large organization to share cluster systems.