Long-running service workloads (e.g. web search engine) and short-term data analysis workloads (e.g. Hadoop MapReduce jobs) co-locate in today's data centers. Developing realistic benchmarks to reflect such practical scenario of mixed workload is a key problem to produce trustworthy results when evaluating and comparing data center systems. This requires using actual workloads as well as guaranteeing their submissions to follow patterns hidden in real-world traces. However, existing benchmarks either generate actual workloads based on probability models, or replay real-world workload traces using basic I/O operations. To fill this gap, we propose a benchmark tool that is a first step towards generating a mix of actual service and data analysis workloads on the basis of real workload traces. Our tool includes a combiner that enables the replaying of actual workloads according to the workload traces, and a multi-tenant generator that flexibly scales the workloads up and down according to users' requirements. Based on this, our demo illustrates the workload customization and generation process using a visual interface. The proposed tool, called BigDataBench-MT, is a multi-tenant version of our comprehensive benchmark suite BigDataBench and it is publicly available from http://prof.ict.ac.cn/BigDataBench/multi-tenancyversion/.
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite
State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所计算机体系结构国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Baidu（百度）；Alibaba（阿里巴巴）；Huawei（华为）；Tencent（腾讯）；Beijing Academy of Frontier Sciences and Technology（北京前沿科学技术研究院）
HiDVFS: Hierarchical Multi-Agent DVFS for Real-Time OpenMP DAG Workloads
Department of Computer Science Wayne State University（计算机科学系 温尼耶萨大学）；Department of Computer Science University of Texas at Dallas（计算机科学系 德克萨斯大学达拉斯分校）；Department of Computer Science Iowa State University（计算机科学系 俄克拉荷马州立大学）
Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers
School of Electrical, Computer and Biomedical Engineering, Southern Illinois University（南伊利诺伊大学电气、计算机与生物医学工程学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
