Reduce the Energy Cost of Elastic Clusters by Queueing Workloads with N1 Queues In Data Centers (DCs), elastic clusters are introduced to cut down the huge energy cost. In elastic clusters, the number of working nodes can be manipulated based on the intensity of workloads. However, affected by the way of distributing workloads to working nodes, the required number of working nodes is different to meet the Service Level Agreement (SLA) of workloads. Workloads consist of several requests which come from clients. In general, workloads are queued and served with NN queues. The first N means that multiple requests can be queued in the service queue maintained by cluster managers. In addition, the second N means that the service queue of each working node can also queue multiple requests. With NN queues, requests are first received to the service queue maintained by cluster managers, and then are distributed to appropriate service queues of working nodes. According to queueing theory, a fact is that the service efficiency of NN queues is lower than that of N1 queues. Here, N1 queues mean that the service queue maintained by cluster managers can queue multiple requests, while no request is allowed to be queued in working nodes. Motivated by this fact, we propose an N1 queueing method to make all service queues work in the form of N1 queues. Thus under same workloads, fewer working nodes are required to meet a same SLA. As a result, without suffering performance degradation, the energy cost of an elastic cluster can be significantly reduced.

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).