Programming Persistent Memory

Beginning and experienced programmers will use this comprehensive guide to persistent memory programming. You will understand how persistent memory brings together several new software/hardware requirements, and offers great promise for better performance and faster application startup times—a huge leap forward in byte-addressable capacity compared with current DRAM offerings. This revolutionary new technology gives applications significant performance and capacity improvements over existing technologies. It requires a new way of thinking and developing, which makes this highly disruptive to the IT/computing industry. The full spectrum of industry sectors that will benefit from this technology include, but are not limited to, in-memory and traditional databases, AI, analytics, HPC, virtualization, and big data. Programming Persistent Memory describes the technology and why it is exciting the industry. It covers the operating system and hardware requirements as well as how to create development environments using emulated or real persistent memory hardware. The book explains fundamental concepts; provides an introduction to persistent memory programming APIs for C, C++, JavaScript, and other languages; discusses RMDA with persistent memory; reviews security features; and presents many examples. Source code and examples that you can run on your own systems are included. What You’ll Learn Understand what persistent memory is, what it does, and the value it brings to the industry Become familiar with the operating system and hardware requirements to use persistent memory Know the fundamentals of persistent memory programming: why it is different from current programming methods, and what developers need to keep in mind when programming for persistence Look at persistent memory application development by example using the Persistent Memory Development Kit (PMDK) Design and optimize data structures for persistent memory Study how real-world applications are modified to leverage persistent memory Utilize the tools available for persistent memory programming, application performance profiling, and debugging Who This Book Is For C, C++, Java, and Python developers, but will also be useful to software, cloud, and hardware architects across a broad spectrum of sectors, including cloud service providers, independent software vendors, high performance compute, artificial intelligence, data analytics, big data, etc

Notes on Cloud computing principles

This letter provides a review of fundamental distributed systems and economic Cloud computing principles. These principles are frequently deployed in their respective fields, but their inter-dependencies are often neglected. Given that Cloud Computing first and foremost is a new business model, a new model to sell computational resources, the understanding of these concepts is facilitated by treating them in unison. Here, we review some of the most important concepts and how they relate to each other

Investigating the tension between cloud-related actors and individual privacy rights

Historically, little more than lip service has been paid to the rights of individuals to act to preserve their own privacy. Personal information is frequently exploited for commercial gain, often without the person’s knowledge or permission. New legislation, such as the EU General Data Protection Regulation Act, has acknowledged the need for legislative protection. This Act places the onus on service providers to preserve the confidentiality of their users’ and customers’ personal information, on pain of punitive fines for lapses. It accords special privileges to users, such as the right to be forgotten. This regulation has global jurisdiction covering the rights of any EU resident, worldwide. Assuring this legislated privacy protection presents a serious challenge, which is exacerbated in the cloud environment. A considerable number of actors are stakeholders in cloud ecosystems. Each has their own agenda and these are not necessarily well aligned. Cloud service providers, especially those offering social media services, are interested in growing their businesses and maximising revenue. There is a strong incentive for them to capitalise on their users’ personal information and usage information. Privacy is often the first victim. Here, we examine the tensions between the various cloud actors and propose a framework that could be used to ensure that privacy is preserved and respected in cloud systems

Could the Outsourcing of Incident Response Management provide a Blueprint for Managing Other Cloud Security Requirements?

Postprin

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Academic Cloud Computing Research: Five Pitfalls and Five Opportunities

This discussion paper argues that there are five fundamental pitfalls, which can restrict academics from conducting cloud computing research at the infrastructure level, which is currently where the vast majority of academic research lies. Instead academics should be conducting higher risk research, in order to gain understanding and open up entirely new areas. We call for a renewed mindset and argue that academic research should focus less upon physical infrastructure and embrace the abstractions provided by clouds through five opportunities: user driven research, new programming models, PaaS environments, and improved tools to support elasticity and large-scale debugging. The objective of this paper is to foster discussion, and to define a roadmap forward, which will allow academia to make longer-term impacts to the cloud computing community.Comment: Accepted and presented at the 6th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud'14

A Case Study for Business Integration as a Service

This paper presents Business Integration as a Service (BIaaS) to allow two services to work together in the Cloud to achieve a streamline process. We illustrate this integration using two services; Return on Investment (ROI) Measurement as a Service (RMaaS) and Risk Analysis as a Service (RAaaS) in the case study at the University of Southampton. The case study demonstrates the cost-savings and the risk analysis achieved, so two services can work as a single service. Advanced techniques are used to demonstrate statistical services and 3D Visualisation services under the remit of RMaaS and Monte Carlo Simulation as a Service behind the design of RAaaS. Computational results are presented with their implications discussed. Different types of risks associated with Cloud adoption can be calculated easily, rapidly and accurately with the use of BIaaS. This case study confirms the benefits of BIaaS adoption, including cost reduction and improvements in efficiency and risk analysis. Implementation of BIaaS in other organisations is also discussed. Important data arising from the integration of RMaaS and RAaaS are useful for management and stakeholders of University of Southampton