Graph processing is crucial for unstructured-data-driven applications in various domains.In recent years, there has been a growing need to perform real-time analytics on largescale evolving graphs, which involves evaluating a graph query on a sequence of snapshots within a given time window.Some prior studies have explored utilizing persistent memory (PM) technologies, such as non-volatile memory, for efficient evolving graph analytics.However, the latest incremental processing designs fail to fully exploit the PM potential, suffering from severe read and write amplification during update ingestion and query evaluation.In this paper, we develop PIE, a PM-based incremental processing framework for fast and scalable evolving graph analytics.We first observe that leveraging CommonGraph, a recently proposed DRAM-based incremental approach that transforms costly deletions into additions, can significantly improve efficiency for evolving graph analytics in PM, although the direct adaptation introduces significant PM access inefficiencies.To enable PM-friendly incremental processing, PIE introduces a logical graph view abstraction that is detached from the physical storage to avoid extra PM writes, and a

Faster app launching is crucial for the user experience on mobile devices. Apps launched from a background cached state, called hot-launching, have much better performance than apps launched from scratch. To increase the number of hot-launches, leading mobile vendors now cache more apps in the background by enabling swap. Recent work also proposed reducing the Java heap to increase the number of cached apps. However, this paper found that existing methods deteriorate app hot-launch performance while increasing the number of cached apps. To simultaneously improve the number of cached apps and hot-launch performance, this paper proposes Fleet, a foreground/background-aware GC-swap co-design framework. To enhance app-caching capacity, Fleet limits the tracing range of GC to background objects only, avoiding touching long-lifetime foreground objects. To improve hot-launch performance, Fleet identifies objects that will be accessed during the next hot-launch and uses runtime information to guide the swap scheme in the OS. In addition, Fleet aggregates small objects with similar access patterns into the same pages to improve swap efficiency. We implemented Fleet in AOSP and evaluated its performance with different types of apps. Experimental results show that Fleet achieves a 1.59× faster hot-launch time and caches 1.21× more apps than Android.

The Raft algorithm maintains strong consistency across data replicas in Cloud. This algorithm places nodes, i.e., leader and follower, to serve read/write requests spanning geo-diverse sites. As the workload increases, Raft shall provide proportional scale-out performance. However, traditional scale-out techniques are bottlenecked in Raft with an exponentially increased performance penalty when provisioned sites exhaust local resources. To provide scalability in Raft, this paper presents a cost-effective mechanism that enables elastic auto scaling in Raft, called BW-Raft. BW-Raft extends the original Raft with the following abstractions: (1)secretarynodes that take over expensive log synchronization operations from the leader, relaxing the performance constraint on locks. (2)observernodes that handle reads only, improving throughput for typical data intensive services. These abstractions are stateless, allowing elastic scale-out on unreliable yet cheap spot instances. In theory, we prove that BW-Raft can preserve the strong consistency guarantee from Raft at scale-out, handling 50X more nodes, compared to the original Raft. We have prototyped the BW-Raft on key-value services and evaluated it with many state-of-the-arts on Amazon EC2 and Alibaba Cloud. Our results show that within the same budget, BW-Raft incurs 5-7X less resource footprint increment than Multi-Raft. Using spot instances, BW-Raft can reduces costs by 84.5%, compared to Multi-Raft. In the real world experiments, BW-Raft improves goodput of the 95th-percentile SLO by 9X, thus, serves an alternative for distributed service scaling out with strong consistency.

Emerging non-volatile memory (NVM) has been considered as a promising candidate for the next generation memory architecture because of its excellent characteristics. However, the endurance of NVM is much lower than DRAM. Without additional wear management technology, its lifetime can be very short, which extremely limits the use of NVM. This paper observes that the tail wear with a very small percentage of extreme deviation significantly hurts the lifetime of NVM, which the existing methods do not effectively solve. We present Lamina to address the tail wear issue, in order to improve the lifetime of NVM. Lamina consists of two parts: bounded tail wear leveling (BTWL) and lightweight wear enhancement (LWE). BTWL is used to make the wear degree of all pages close to the average value and control the upper limit of tail wear. LWE improves the accuracy of BTWL by exploiting the locality to interpolate low-frequency sampling schemes in virtual memory space. Our experiments show that compared with the state-of-the-art methods, Lamina can significantly improve the lifetime of NVM with low overhead.

Raft is a protocol to maintain strong consistency across data replicas in cloud. It is widely used, especially by workloads that span geographically distributed sites. As these workloads grow, Raft's costs should grow, as least proportionally. However, auto scaling approaches for Raft inflate costs by provisioning at all sites when one site exhausts its local resources. This paper presents Geo-Raft, a scale-out mechanism that enables precise auto scaling for Raft. Geo-Raft extends Raft with the following abstractions: (1) secretaries which takes log processing for the leader and (2) observers which process read requests for followers. These abstractions are stateless, allowing for elastic auto scaling, even on unreliable spot instances. Geo-Raft provably preserves strong consistency guarantees provided by Raft. We implemented and evaluated Geo-Raft with multiple auto scaling techniques on Amazon EC2. Geo-Raft scales in resource footprint increments 5-7X smaller than Multi-Raft, the state of the art. Using spot instances, Geo-Raft reduces costs by 84.5% compared to Multi-Raft. Geo-Raft improves goodput of 95th-percentile SLO by 9X.Geo-Raft operates key-value services for 6 months without losing data or crash.