As datacenters grow in scale, increasing energy costs and carbon emissions have led data centers to seek renewable energy, such as wind and solar energy. However, tackling the challenges associated with the intermittency and variability of renewable energy is difficult. This paper proposes a scheme called GreenMatch, which deploys an SSD cache to match green energy supplies with a time-shifting workload schedule while maintaining low latency for online data-intensive services. With the SSD cache, the process for a latency-sensitive request to access a disk is divided into two stages: a low-energy/low-latency online stage and a high-energy/high-latency off-line stage. As the process in the latter stage is off-line, it offers opportunities for time-shifting workload scheduling in response to variations of green energy supplies. We also allocate an HDD cache to guarantee data availability when renewable energy is inadequate. Furthermore, we design a novel replacement policy called Inactive P-disk First for the HDD cache to avoid inactive disk accesses. The experimental results show that GreenMatch can make full use of renewable energy while minimizing the negative impacts of intermittency and variability on performance and availability.

To reduce energy consumption and carbon emission, many data centers have deployed (or anticipate to build) their own renewable-energy power plants. However, the renewable energy (such as wind, tide, and solar energy) has the serious issues of intermittency and variability that prevent the green energy from being utilized effectively in practice. To cope with the issues, new power-supply management policies and workload scheduling algorithms have been designed. However, most existing work focuses on power optimization on computation only. In this paper, we introduce a novel scheme called OptiMatch to optimize the match between the power supply and the user-workload demand for massive storage systems that are mostly powered by renewable energy sources. OptiMatch has a hierarchical architecture, which consists of a number of heterogeneous storage devices. OptiMatch systematically utilizes the performance disparities between heterogeneous storage devices (i.e., performance per watt, IOPS/watt) to split the process for every write request into two stages: an on-line stage and a deferred off-line stage. The deferred off-line requests are used to match the green energy supplies. To maximize green energy utilization and minimize power budget without sacrificing quality of service, the fundamental methodology is to make the aggregate power supplies be proportional to the I/O workload demand at any time. To this end, our OptiMatch employs novel co-design optimizations. (1) We propose a dual-drive power control approach that makes the number of active nodes proportional to the workload demand when the green power supply is insufficient, meanwhile be proportional to the green power supply when green power is sufficient. (2) During periods of insufficient green supplies, we exploit virtualization consolidation schemes which enable a fine-grained power control to minimize the grid budgets. (3) During the periods of sufficient green supplies, we design an intelligent workload scheduling scheme which enables a near-optimal off-line requests assignment to maximize the green utilization. The experimental results demonstrate that the new OptiMatch framework can achieve high green utilization (up to 94.9%) with a minor performance degradation (less than 9.8%).

Distributed data store is a fundamental building block for various Internet services. For large-scale distributed data store, the scalability and consistency of metadata services are prone to be the bottleneck. Various schemes are proposed to tackle the challenge of scalability and consistency within metadata services. While centralized single-node metadata services with low scalability provide low- overhead consistency maintenance, distributed metadata servers with high scalability often suffer complicated management and high-overhead consistency maintenance. As some key-value object-based storage systems locate and access an object by hashing function (e.g., consistent hashing table), there are no dedicated physical servers for metadata services. For key-value store without dedicated metadata servers, we exploited a scheme called virtual metadata servers (virtual MDS), which can create an opportunity to provide high performance and multi- level consistency. While conventional key-value data store distributes metadata across data nodes, our scheme uses proxy nodes, where virtual disks created, as virtual MDS to hold the metadata of virtual disks. Meanwhile, we also combine the characteristic of virtual disks and metadata services to implement a multi-level consistency strategy for the key-value object-based store without dedicated physical metadata servers. With virtual MDS, we use version information to update data asynchronously and check the version consistency periodically, then correct the stale entries properly. In this way, our virtual MDS can provide multi-level of consistency to cope with different read performance demand from users. The experiment results demonstrate that our scheme with relaxed consistency can enhance random write performance by 50% and improve random read performance by 16% compared with the standard storage system with strict consistency.