Co-designing reliability and performance for datacenter memory

Memory is one of the key components that affects reliability and performance of datacenter servers. Memory in today’s servers is organized and shared in several ways to provide the most performant and efficient access to data. For example, cache hierarchy in multi-core chips to reduce access latency, non-uniform memory access (NUMA) in multi-socket servers to improve scalability, disaggregation to increase memory capacity. In all these organizations, hardware coherence protocols are used to maintain memory consistency of this shared memory and implicitly move data to the requesting cores. This thesis aims to provide fault-tolerance against newer models of failure in the organization of memory in datacenter servers. While designing for improved reliability, this thesis explores solutions that can also enhance performance of applications. The solutions build over modern coherence protocols to achieve these properties. First, we observe that DRAM memory system failure rates have increased, demanding stronger forms of memory reliability. To combat this, the thesis proposes Dvé, a hardware driven replication mechanism where data blocks are replicated across two different memory controllers in a cache-coherent NUMA system. Data blocks are accompanied by a code with strong error detection capabilities so that when an error is detected, correction is performed using the replica. Dvé’s organization offers two independent points of access to data which enables: (a) strong error correction that can recover from a range of faults affecting any of the components in the memory and (b) higher performance by providing another nearer point of memory access. Dvé’s coherent replication keeps the replicas in sync for reliability and also provides coherent access to read replicas during fault-free operation for improved performance. Dvé can flexibly provide these benefits on-demand at runtime. Next, we observe that the coherence protocol itself requires to be hardened against failures. Memory in datacenter servers is being disaggregated from the compute servers into dedicated memory servers, driven by standards like CXL. CXL specifies the coherence protocol semantics for compute servers to access and cache data from a shared region in the disaggregated memory. However, the CXL specification lacks the requisite level of fault-tolerance necessary to operate at an inter-server scale within the datacenter. Compute servers can fail or be unresponsive in the datacenter and therefore, it is important that the coherence protocol remain available in the presence of such failures. The thesis proposes Āpta, a CXL-based, shared disaggregated memory system for keeping the cached data consistent without compromising availability in the face of compute server failures. Āpta architects a high-performance fault-tolerant object-granular memory server that significantly improves performance for stateless function-as-a-service (FaaS) datacenter applications

ON OPTIMIZATIONS OF VIRTUAL MACHINE LIVE STORAGE MIGRATION FOR THE CLOUD

Virtual Machine (VM) live storage migration is widely performed in the data cen- ters of the Cloud, for the purposes of load balance, reliability, availability, hardware maintenance and system upgrade. It entails moving all the state information of the VM being migrated, including memory state, network state and storage state, from one physical server to another within the same data center or across different data centers. To minimize its performance impact, this migration process is required to be transparent to applications running within the migrating VM, meaning that ap- plications will keep running inside the VM as if there were no migration operations at all. In this dissertation, a thorough literature review is conducted to provide a big picture of the VM live storage migration process, its problems and existing solutions. After an in-depth examination, we observe that a severe IO interference between the VM IO threads and migration IO threads exists and causes both types of the IO threads to suffer from performance degradation. This interference stems from the fact that both types of IO threads share the same critical IO path by reading from and writing to the same shared storage system. Owing to IO resource contention and requests interference between the two different types of IO requests, not only will the IO request queue lengthens in the storage system, but the time-consuming disk seek operations will also become more frequent. Based on this fundamental observation, this dissertation research presents three related but orthogonal solutions that tackle the IO interference problem in order to improve the VM live storage migration performance. First, we introduce the Workload-Aware IO Outsourcing scheme, called WAIO, to improve the VM live storage migration efficiency. Second, we address this problem by proposing a novel scheme, called SnapMig, to improve the VM live storage migration efficiency and eliminate its performance impact on user applications at the source server by effectively leveraging the existing VM snapshots in the backup servers. Third, we propose the IOFollow scheme to improve both the VM performance and migration performance simultaneously. Finally, we outline the direction for the future research work. Advisor: Hong Jian

A comprehensive approach to MPSoC security: achieving network-on-chip security : a hierarchical, multi-agent approach

Multiprocessor Systems-on-Chip (MPSoCs) are pervading our lives, acquiring ever increasing relevance in a large number of applications, including even safety-critical ones. MPSoCs, are becoming increasingly complex and heterogeneous; the Networks on Chip (NoC paradigm has been introduced to support scalable on-chip communication, and (in some cases) even with reconfigurability support. The increased complexity as well as the networking approach in turn make security aspects more critical. In this work we propose and implement a hierarchical multi-agent approach providing solutions to secure NoC based MPSoCs at different levels of design. We develop a flexible, scalable and modular structure that integrates protection of different elements in the MPSoC (e.g. memory, processors) from different attack scenarios. Rather than focusing on protection strategies specifically devised for an individual attack or a particular core, this work aims at providing a comprehensive, system-level protection strategy: this constitutes its main methodological contribution. We prove feasibility of the concepts via prototype realization in FPGA technology

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Autonomic Management of Cloud Virtual Infrastructures

The new model of interaction suggested by Cloud Computing has experienced a significant diffusion over the last years thanks to its capability of providing customers with the illusion of an infinite amount of reliable resources. Nevertheless, the challenge of efficiently manage a large collection of virtual computing nodes has just been partially moved from the customer's private datacenter to the larger provider's infrastructure that we generally address as “the cloud”. A lot of effort - in both academic and industrial field - is therefore concentrated on policies for the efficient and autonomous management of virtual infrastructures. The research on this topic is further encouraged by the diffusion of cheap and portable sensors and the availability of almost ubiquitous Internet connectivity that are constantly creating large flows of information about the environment we live in. The need for fast and reliable mechanisms to process these considerable volumes of data has inevitably pushed the evolution from the initial scenario of a single (private or public) cloud towards cloud interoperability, giving birth to several forms of collaboration between clouds. The efficient resource management is further complicated in these heterogeneous environments, making autonomous administration more and more desirable. In this thesis, we initially focus on the challenges of autonomic management in a single-cloud scenario, considering the benefits and shortcomings of centralized and distributed solutions and proposing an original decentralized model. Later in this dissertation, we face the challenge of autonomic management in large interconnected cloud environments, where the movement of virtual resources across the infrastructure nodes is further complicated by the intrinsic heterogeneity of the scenario and difficulties introduced by the higher latency medium between datacenters. According to that, we focus on the cost model for the execution of distributed data-intensive application on multiple clouds and we propose different management policies leveraging cloud interoperability

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

Arkansas Soybean Research Studies 2014

Arkansas is the leading soybean-producing state in the mid-southern United States. Arkansas ranked 10th in soybean production in 2015 when compared to the other soybean-producing states in the U.S. The state represents 4.0% of the total U.S. soybean production and 3.7% of the total acres planted to soybean in 2015. The 2015 state soybean average was 49 bushels per acres, 0.5 bushel per acres less than the state record soybean yield set in 2014 (Table 1). The top five soybean-producing counties in 2015 were Mississippi, Desha, Poinsett, Phillips, and Arkansas Counties. These five counties accounted for 35% of soybean production in Arkansas in 2015

Technologies and Applications for Big Data Value

This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems