A resource management framework for cloud computing
The cloud computing paradigm is realized through large scale distributed resource management and computation platforms such as MapReduce, Hadoop, Dryad, and Pregel. These platforms enable quick and efficient development of a large range of applications that can be sustained at scale in a fault-tolerant fashion. Two key technologies, namely resource virtualization and feature-rich enterprise storage, are further driving the wide-spread adoption of virtualized cloud environments. Many challenges arise when designing resource management techniques for both native and virtualized data centers. First, parameter tuning of MapReduce jobs for efficient resource utilization is a daunting and time consuming task. Second, while the MapReduce model is designed for and leverages information from native clusters to operate efficiently, the emergence of virtual cluster topology results in overlaying or hiding the actual network information. This leads to two resource selection and placement anomalies: (i) loss of data locality, and (ii) loss of job locality. Consequently, jobs may be placed physically far from their associated data or related jobs, which adversely affect the overall performance. Finally, the extant resource provisioning approach leads to significant wastage as enterprise cloud providers have to consider and provision for peak loads instead of average load (that is many times lower). In this dissertation, we design and develop a resource management framework to address the above challenges. We first design an innovative resource scheduler, CAM, aimed at MapReduce applications running in virtualized cloud environments. CAM reconciles both data and VM resource allocation with a variety of competing constraints, such as storage utilization, changing CPU load and network link capacities based on a flow-network algorithm. Additionally, our platform exposes the typically hidden lower-level topology information to the MapReduce job scheduler, which enables it to make optimal task assignments. Second, we design an online performance tuning system, mrOnline, which monitors the MapReduce job execution, tunes the parameters based on collected statistics and provides fine-grained control over parameter configuration changes to the user. To this end, we employ a gray-box based smart hill-climbing algorithm that leverages MapReduce runtime statistics and effectively converge to a desirable configuration within a single iteration. Finally, we target enterprise applications in virtualized environment where typically a network attached centralized storage system is deployed. We design a new protocol to share primary data de-duplication information available at the storage server with the client. This enables better client-side cache utilization and reduces server-client network traffic, which leads to overall high performance. Based on the protocol, a workload aware VM management strategy is further introduced to decrease the load to the storage server and enhance the I/O efficiency for clients.
- Doctoral Dissertations