Characterizing and Balancing the Workloads of SemiContainerized Clouds With the development of Cloud computing, many applications are deployed on the Clouds (either public Clouds or private Clouds) for the elastic resource management. A key challenge here is satisfying the QualityofService requirement of online services that requires short response time while maximizing the resource utilization. While applications should be colocated to improve the resource utilization, the colocation results in performance interference between the colocated applications that hurts the QoS of latencysensitive services. To understand this problem in depth, we characterize and analyze the real businesslevel application colocation scenario based on the trace of an actual Cloud product released by Alibaba. Our observation shows that the datacenter suffers from severe load imbalance that often results in QoS violation of the online services on the servers with heavy workload. To balance the workload, we propose a scheduling algorithm based on the resource reservation and utilization of containers. Based on the comparison between the experiment and the existing operation results, the proposed algorithm reduces the standard deviation of the load on servers from 5.27 to 1.43 (CPU) and 9.83 to 1.93 (Memory).

Shared dataaware dynamic resource provisioning and task scheduling for data intensive applications on hybrid clouds using Aneka Abstract   In the recent years, dataintensive applications have been growing at an increasing rate and there is a critical need to solve the highperformance and scalability issues. Hybrid Cloud Computing paradigm provides a promising solution to harness local infrastructure and remote resources and provide high Quality of Service (QoS) for time sensitive and dataintensive applications. Generally, hybrid cloud deployments have a heterogeneous pool of resources and it becomes a challenging task to efficiently utilize resources to provide optimum results. In modern data hungry applications, it is crucial to optimize bandwidth consumption, latency and networking overheads. Moreover, most of them have large extent of file sharing capability. The existing algorithms do not explicitly consider file sharing scenarios that leads large data transmission times and has severe effects on latency. In this direction, this paper focuses on building upon existing dynamic resource provisioning and task scheduling algorithms to provide better QoS in hybrid cloud environments for data intensive applications in a shared file task environment. The efficiency of proposed algorithms is demonstrated by deploying them on Microsoft Azure using Aneka, a platform for developing scalable applications on the Cloud. Experiments using realworld applications and datasets show that proposed algorithms are able to allocate tasks and extend to public cloud resources more efficiently, reducing deadline violations and improving response times to give response time reduction of upto 40.12% for a sample local alignment search application on genome sequences.