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Virtual machine (VM) consolidation strategies are widely used in cloud data centers (CDC) to optimize resource utilization and reduce total energy consumption. Although existing strategies consider current and future resource utilization, the impact of sudden bursts in historical resource utilization on the hosts has been underestimated in uncertain future periods. Insufficient analysis of historical resource utilization may increase the risk of host overloading and Service Level Agreement Violation (SLAV). By defining historical and future trends based on resource utilization, we propose a novel combined trend VM consolidation (CTVMC) strategy which can effectively reduce energy consumption and SLAV. The VMs with the largest combined trend are selected for migration to prevent host overloading. Based on the temporal locality and prediction technique, CTVMC then employs the past, present, and future resource utilization to filter candidate hosts, and identifies the most complementary host to place VM using combined trends. We conduct extensive simulation experiments with PlanetLab Trace and Google Cluster Trace in the CloudSim simulator. Compared with the well-known strategies, CTVMC strategy using the PlanetLab Trace can reduce the number of migrations by over 72.39%, SLAV by over 75.85%, and ESV (a combined metric that judges the trade-off between energy consumption and SLAV) by over 81.54%. According to the Google Cluster Trace, our strategy can reduce the number of migrations by over 61.51%, SLAV by over 37.37%, and ESV by over 35.30%.

With high scalability and flexibility, serverless computing is becoming the most promising computing model. Existing serverless computing platforms initiate a container for each function invocation, which leads to a huge waste of computing resources. Our examinations reveal that (i) executing invocations concurrently within a single container can provide comparable performance to that provided by multiple containers (i.e., traditional approaches); (ii) redundant resources generated within a container result in memory resource waste, which prolongs the execution time of function invocations. Motivated by these insightful observations, we propose FaaSBatch - a serverless framework that reduces invocation latency and saves scarce computing resources. In particular, FaaSBatch first classifies concurrent function requests into different function groups according to the invocation information. Next, FaaSBatch batches the invocations of each group, aiming to minimize resource utilization. Then, FaaSBatch utilizes an inline parallel policy to map each group of batched invocations into a single container. Finally, FaaSBatch expands and executes invocations of containers in parallel. To further reduce invocation latency and resource utilization, within each container, FaaSBatch reuses redundant resources created during function execution. We conduct extensive experiments based on Azure traces to evaluate the effectiveness and performance of FaaSBatch. We compare FaaSBatch with three state-of-the-art schedulers Vanilla, SFS, and Kraken. Our experimental results show that FaaSBatch effectively and remarkably slashes invocation latency and resource overhead. For instance, when executing I/O functions, FaaSBatch cuts back the invocation latency of Vanilla, SFS, and Kraken by up to 72.58%, 74.10%, and 72.62%, respectively; FaaSBatch also slashes the resource overhead of Vanilla, SFS, and Kraken by 70.2% to 98.40%, 67.74% to 98.12%, and 43.01% to 78.90%, respectively.