Cloud platforms remain underutilized despite multiple proposals to improve their utilization (e.g., disaggregation, harvesting, and oversubscription). Our characterization of the resource utilization of virtual machines (VMs) in Azure reveals that, while CPU is the main underutilized resource, we need to provide a solution to manage all resources holistically. We also observe that many VMs exhibit complementary temporal patterns, which can be leveraged to improve the oversubscription of underutilized resources.

Cloud platforms have been virtualizing storage devices like flash-based solid-state drives (SSDs) to make effective use of storage resources. They enable either software-isolated instance or hardware-isolated instance for facilitating the storage sharing between multi-tenant applications. However, for decades, they have to combat the fundamental tussle between the performance isolation and resource utilization. They suffer from either long tail latency caused by weak isolation or low storage utilization caused by strong isolation.

Transactional-based distributed storage applications such as key-value stores and databases are widely used in the cloud. Recently, the hardware on which these applications run has been rapidly improving, with faster networks and powerful network interface cards (NICs). A result of these hardware advances is that the inefficiencies of distributed software have become increasingly obvious.To address this problem, we analyze the sources of software overhead in these distributed transactional applications and propose new hardware structures to eliminate them. The proposed hardware includes Bloom filters for a variety of tasks and SmartNICs for efficient remote communication. We then develop HADES, a new distributed transactional protocol that leverages this hardware to support low-overhead distributed transactions. We also propose a hybrid hardware-software implementation of HADES. Our evaluation shows that HADES increases the throughput of distributed transactional workloads by 2.7 × on average over a state-of-the-art distributed transactional system.

Software-defined networking (SDN) and software-defined flash (SDF) have been serving as the backbone of modern data centers. They are managed separately to handle I/O requests. At first glance, this is a reasonable design by following the rack-scale hierarchical design principles. However, it suffers from suboptimal end-to-end performance, due to the lack of coordination between SDN and SDF.

Encryption ransomware has become a notorious malware. It encrypts user data on storage devices like solid-state drives (SSDs) and demands a ransom to restore data for users. To bypass existing defenses, ransomware would keep evolving and performing new attack models. For instance, we identify and validate three new attacks, including (1) garbage-collection (GC) attack that exploits storage capacity and keeps writing data to trigger GC and force SSDs to release the retained data; (2) timing attack that intentionally slows down the pace of encrypting data and hides its I/O patterns to escape existing defense; (3) trimming attack that utilizes the trim command available in SSDs to physically erase data.

To facilitate programming with non-volatile memory (NVM), a set of memory persistency models, such as strict and epoch persistency, have been proposed. Although these models provide high-level guidance for reasoning about the data persistence, implementing them correctly is nontrivial. Our study of the well-developed NVM frameworks and libraries reveals that many of them have deep semantic bugs that are strongly relevant to the model specifications. Furthermore, it is difficult to detect them with existing testing and bug-finding tools.

Distributed applications such as key-value stores and databases avoid frequent writes to secondary storage devices to minimize performance degradation. They provide fault tolerance by replicating variables in the memories of different nodes, and using data consistency protocols to ensure consistency across replicas. Unfortunately, the reduced data durability guarantees provided can cause data loss or slow data recovery. In this environment, non-volatile memory (NVM) offers the ability to attain both high performance and data durability in distributed applications. However, it is unclear how to tie NVM memory persistency models to the existing data consistency frameworks, and what are the durability guarantees that the combination will offer to distributed applications.

Byte-addressable, non-volatile memory (NVM) is emerging as a promising technology. To facilitate its wide adoption, employing NVM in managed runtimes like JVM has proven to be an effective approach (i.e., managed NVM). However, such an approach is runtime specific, it lacks a generic abstraction across different managed languages. Similar to the well-known filesystem primitives that allow diverse programs to access the same file via the block I/O interface, managed NVM deserves the same system-wide property for persistent objects across managed runtimes with low overhead.