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1. Scheduling Challenges for Variable Capacity Resources

   Zhang, Chaojie; Chien, Andrew A. (May 2021, Workshop on Job Scheduling for Parallel Processing (JSSPP))

   Cirne, Walfredo; Rodrigo, Gonzalo P.; Klusáček, Dalibor (Ed.)

   Datacenter scheduling research often assumes resources as a constant quantity, but increasingly external factors shape capacity dynamically, and beyond the control of an operator. Based on emerging examples, we define a new, open research challenge: the variable capacity resource scheduling problem. The objective here is effective resource utilization despite sudden, perhaps large, changes in the available resources. We define the problem, key dimensions of resource capacity variation, and give specific examples that arise from the natural world (carboncontent, power price, datacenter cooling, and more). Key dimensions of the resource capacity variation include dynamic range, frequency, and structure. With these dimensions, an empirical trace can be characterized, abstracting it from the many possible important real-world generators of variation. Resource capacity variation can arise from many causes including weather, market prices, renewable energy, carbon emission targets, and internal dynamic power management constraints. We give examples of three different sources of variable capacity. Finally, we show variable resource capacity presents new scheduling challenges. We show how variation can cause significant performance degradation in existing schedulers, with up to 60% goodput reduction. Further, initial results also show intelligent scheduling techniques can be helpful. These insights show the promise and opportunity for future scheduling studies on resource volatility. 

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2. Beyond PUE: Flexible Datacenters Empowering the Cloud to Decarbonize

   Andrew A. Chien; Chaojie Zhang, Liuzixuan Lin (July 2022, USENIX Hot Carbon)

   Traditional datacenter design and optimization for TCO and PUE is based on static views of power grids as well as computational loads. Power grids exhibit increasingly variable price and carbon-emissions, becoming more so as government initiatives drive further decarbonization. The resulting opportunities require dynamic, temporal metrics (eg. not simple averages), flexible systems and intelligent adaptive control. Two research areas represent new opportunities to reduce both carbon and cost in this world of variable power, carbon, and price. First, the design and optimization of flexible datacenters. Second, cloud resource, power, and application management for variable-capacity datacenters. For each, we describe the challenges and potential benefits. 

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3. Demand-adaptive VNF placement and scheduling with low latency in optical datacenter networks

   https://doi.org/10.1117/12.2518327  

   Gao, Tao; Li, Xin; Wu, Yu; Tornatore, Massimo; Mukherjee, Biswanath; Zou, Weixia; Huang, Shanguo; Li, Zhaohui (October 2018, Proc. SPIE 11048, 17th International Conference on Optical Communications and Networks (ICOCN2018))

   We investigate virtual-network-function placement and scheduling problem in optical datacenter networks, considering the installation/de-installation latency of VNF and the rapid variation of low-latency-demands. The proposed scheme achieves low blocking probability, latency, and spectrum resource consumption. 

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4. Adapting Datacenter Capacity for Greener Datacenters and Grid

   https://doi.org/10.1145/3575813.3595197  

   Lin, Liuzixuan; Chien, Andrew A (June 2023, ACM Symposium on Future Energy Systems (E-Energy 2023))

   Cloud providers are adapting datacenter (DC) capacity to reduce carbon emissions. With hyperscale datacenters exceeding 100 MW individually, and in some grids exceeding 15% of power load, DC adaptation is large enough to harm power grid dynamics, increasing carbon emissions, power prices, or reduce grid reliability. To avoid harm, we explore coordination of DC capacity change varying scope in space and time. In space, coordination scope spans a single datacenter, a group of datacenters, and datacenters with the grid. In time, scope ranges from online to day-ahead. We also consider what DC and grid information is used (e.g. real-time and day-ahead average carbon, power price, and compute backlog). For example, in our proposed PlanShare scheme, each datacenter uses day-ahead information to create a capacity plan and shares it, allowing global grid optimization (over all loads, over entire day). We evaluate DC carbon emissions reduction. Results show that local coordination scope fails to reduce carbon emissions significantly (3.2%–5.4% reduction). Expanding coordination scope to a set of datacenters improves slightly (4.9%–7.3%). PlanShare, with grid-wide coordination and full-day capacity planning, performs the best. PlanShare reduces DC emissions by 11.6%–12.6%, 1.56x–1.26x better than the best local, online approach’s results. PlanShare also achieves lower cost. We expect these advantages to increase as renewable generation in power grids increases. Further, a known full-day DC capacity plan provides a stable target for DC resource management. 

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5. Multi-mode on Multi-core: Making the best of both worlds with Omni

   https://doi.org/10.1109/RTSS55097.2022.00020  

   Gifford, Robert; Phan, Linh Thi (December 2022, 2022 IEEE Real-Time Systems Symposium (RTSS))

   When scheduling multi-mode real-time systems on multi-core platforms, a key question is how to dynamically adjust shared resources, such as cache and memory bandwidth, when resource demands change, without jeopardizing schedulability during mode changes. This paper presents Omni, a first end-to-end solution to this problem. Omni consists of a novel multi-mode resource allocation algorithm and a resource-aware schedulability test that supports general mode-change semantics as well as dynamic cache and bandwidth resource allocation. Omni's resource allocation leverages the platform's concurrency and the diversity of the tasks' demands to minimize overload during mode transitions; it does so by intelligently co-distributing tasks and resources across cores. Omni's schedulability test ensures predictable mode transitions, and it takes into account mode-change effects on the resource demands on different cores, so as to best match their dynamic needs using the available resources. We have implemented a prototype of Omni, and we have evaluated it using randomly generated multi-mode systems with several real-world benchmarks as the workload. Our results show that Omni has low overhead, and that it is substantially more effective in improving schedulability than the state of the art 

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