We consider allocation problems that arise in the context of service allocation in Clouds. More specifically, we assume on the one part that each computing resource is associated to a capacity constraint, that can be chosen using Dynamic Voltage and Frequency Scaling (DVFS) method, and to a probability of failure. On the other hand, we assume that the service runs as a set of independent instances of identical Virtual Machines. Moreover, there exists a Service Level Agreement (SLA) between the Cloud provider and the client that can be expressed as follows: the client comes with a minimal number of service instances which must be alive at the end of the day, and the Cloud provider offers a list of pairs (price,compensation), this compensation being paid by the Cloud provider if it fails to keep alive the required number of services. On the Cloud provider side, each pair corresponds actually to a guaranteed success probability of fulfilling the constraint on the minimal number of instances. In this context, given a minimal number of instances and a probability of success, the question for the Cloud provider is to find the number of necessary resources, their clock frequency and an allocation of the instances (possibly using replication) onto machines. This solution should satisfy all types of constraints during a given time period while minimizing the energy consumption of used resources. We consider two energy consumption models based on DVFS techniques, where the clock frequency of physical resources can be changed. For each allocation problem and each energy model, we prove deterministic approximation ratios on the consumed energy for algorithms that provide guaranteed probability failures, as well as an efficient heuristic, whose energy ratio is not guaranteed.
Memory at Your Service: Fast Memory Allocation for Latency-critical Services
在诸如键值存储和web搜索之类的延迟关键型服务与尽力而为的批处理作业之间的协同定位和存储器共享是提高多租户数据中心系统中的存储器利用率的有吸引力的方法。然而，我们发现，非常不同的目标的工作协同定位和GNU/Linux系统堆栈可能会导致严重的性能下降的延迟关键服务的内存压力下，在多租户系统。我们通过快速内存分配和主动回收来解决延迟关键型服务的内存压力。我们发现，内存分配延迟占主导地位的整体查询延迟，特别是在内存压力。我们分析了GNU/Linux系统栈提供的默认存储管理机制，并找出了它在多租户系统中对延迟关键服务效率低下的原因。我们提出了Hermes，一个快速的内存分配机制，在用户空间，自适应地保留内存的延迟关键的服务。它建议Linux操作系统主动回收批处理作业的内存。我们在GNU C Library中实现了Hermes。实验结果表明，Hermes在微基准测试中，平均内存分配延迟和$99^{th}$百分比内存分配延迟分别降低了54.4%和62.4%.对于两个实际的延迟关键型服务，Hermes将平均和$99^{th}$百分位数尾部查询延迟降低了40.3%。与默认的Glibc、jemalloc和TCMalloc相比，Hermes在内存压力下将服务级别目标违规减少了84.3%。
Service Delay Minimization for Federated Learning over Mobile Devices
University of Houston（休斯顿大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Purple Mountain Laboratories（紫金山实验室）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey
Swinburne University of Technology（斯威本科技大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；Engineering Institute of Technology (EIT)（工程技术学院）；South China University of Technology（华南理工大学）；University of Manitoba（曼尼托巴大学）
Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services
Xiamen University（厦门大学）；University of Hong Kong（香港大学）；Tsinghua University（清华大学）；University at Buffalo–SUNY（纽约州立大学布法罗分校）；Nanyang Technological University（南洋理工大学）；The Hong Kong University of Science and Technology（香港科技大学）
