Designing systems for emerging memory technologies

Emerging memory technologies open new challenges in system software: diversity and large capacity. Non-volatile memory (NVM) technologies will have excellent performance, byte- addressability, and large capacity, blurring the line between traditional volatile DRAM and non-volatile storage. NVM diverges from DRAM in significant ways, like limited write bandwidth. It is likely that future storage market will be diversified, having DRAM, NVM, SSD, and hard disk. Unfortunately, current file systems, built on top of old design ideas, cannot provide an efficient way to take advantage of the different storage media. Strata is a cross-media file system, fundamentally redesigning file systems to leverage different strengths of storage technologies while compensating their weaknesses. Modern applications such as large-scale machine learning and graph analytics want to load huge datasets into memory for fast computation. For these workloads, merely adding more RAM to a machine reaches a point of diminishing returns for performance because their poor spatial locality causes them to suffer high virtual to physical memory translation costs. NVM will make this problem worse because it provides cheaper cost-per-capacity than DRAM. Ingens, a efficient memory management system, addresses the shortcomings in modern operating systems and hypervisors that underlies these excessive address translation overheads and redesign huge page memory systems to make huge page widely used in practice.Computer Science

Data as a Service (DaaS) for sharing and processing of large data collections in the cloud

Data as a Service (DaaS) is among the latest kind of services being investigated in the Cloud computing community. The main aim of DaaS is to overcome limitations of state-of-the-art approaches in data technologies, according to which data is stored and accessed from repositories whose location is known and is relevant for sharing and processing. Besides limitations for the data sharing, current approaches also do not achieve to fully separate/decouple software services from data and thus impose limitations in inter-operability. In this paper we propose a DaaS approach for intelligent sharing and processing of large data collections with the aim of abstracting the data location (by making it relevant to the needs of sharing and accessing) and to fully decouple the data and its processing. The aim of our approach is to build a Cloud computing platform, offering DaaS to support large communities of users that need to share, access, and process the data for collectively building knowledge from data. We exemplify the approach from large data collections from health and biology domains.Peer ReviewedPostprint (author's final draft

Elevating commodity storage with the SALSA host translation layer

To satisfy increasing storage demands in both capacity and performance, industry has turned to multiple storage technologies, including Flash SSDs and SMR disks. These devices employ a translation layer that conceals the idiosyncrasies of their mediums and enables random access. Device translation layers are, however, inherently constrained: resources on the drive are scarce, they cannot be adapted to application requirements, and lack visibility across multiple devices. As a result, performance and durability of many storage devices is severely degraded. In this paper, we present SALSA: a translation layer that executes on the host and allows unmodified applications to better utilize commodity storage. SALSA supports a wide range of single- and multi-device optimizations and, because is implemented in software, can adapt to specific workloads. We describe SALSA's design, and demonstrate its significant benefits using microbenchmarks and case studies based on three applications: MySQL, the Swift object store, and a video server.Comment: Presented at 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS

Rethinking Digital Forensics

© IAER 2019In the modern socially-driven, knowledge-based virtual computing environment in which organisations are operating, the current digital forensics tools and practices can no longer meet the need for scientific rigour. There has been an exponential increase in the complexity of the networks with the rise of the Internet of Things, cloud technologies and fog computing altering business operations and models. Adding to the problem are the increased capacity of storage devices and the increased diversity of devices that are attached to networks, operating autonomously. We argue that the laws and standards that have been written, the processes, procedures and tools that are in common use are increasingly not capable of ensuring the requirement for scientific integrity. This paper looks at a number of issues with current practice and discusses measures that can be taken to improve the potential of achieving scientific rigour for digital forensics in the current and developing landscapePeer reviewe