Sla Management in a Collaborative Network Of Federated Clouds: The Cloudland

Cloud services have always promised to be available, flexible, and speedy. However, not a single Cloud provider can deliver such promises to their distinctly demanding customers. Cloud providers have a constrained geographical presence, and are willing to invest in infrastructure only when it is profitable to them. Cloud federation is a concept that collectively combines segregated Cloud services to create an extended pool of resources for Clouds to competently deliver their promised level of services. This dissertation is concerned with studying the governing aspects related to the federation of Clouds through collaborative networking. The main objective of this dissertation is to define a framework for a Cloud network that considers balancing the trade-offs among customers’ various quality of service (QoS) requirements, as well as providers\u27 resources utilization. We propose a network of federated Clouds, CloudLend, that creates a platform for Cloud providers to collaborate, and for customers to expand their service selections. We also define and specify a service level agreement (SLA) management model in order to govern and administer the relationships established between different Cloud services in CloudLend. We define a multi-level SLA specification model to annotate and describe QoS terms, in addition to a game theory-based automated SLA negotiation model that supports both customers and providers in negotiating SLA terms, and guiding them towards signing a contract. We also define an adaptive agent-based SLA monitoring model which identifies the root causes of SLA violations, and impartially distributes any updates and changes in established SLAs to all relevant entities. Formal verification proved that our proposed framework assures customers with maximum optimized guarantees to their QoS requirements, in addition to supporting Cloud providers to make informed resource utilization decisions. Additionally, simulation results demonstrate the effectiveness of our SLA management model. Our proposed Cloud Lend network and its SLA management model paves the way to resource sharing among different Cloud providers, which allows for the providers’ lock-in constraints to be broken, allowing effortless migration of customers’ applications across different providers whenever is needed

Private Data System Enabling Self-Sovereign Storage Managed by Executable Choreographies

With the increased use of Internet, governments and large companies store and share massive amounts of personal data in such a way that leaves no space for transparency. When a user needs to achieve a simple task like applying for college or a driving license, he needs to visit a lot of institutions and organizations, thus leaving a lot of private data in many places. The same happens when using the Internet. These privacy issues raised by the centralized architectures along with the recent developments in the area of serverless applications demand a decentralized private data layer under user control. We introduce the Private Data System (PDS), a distributed approach which enables self-sovereign storage and sharing of private data. The system is composed of nodes spread across the entire Internet managing local key-value databases. The communication between nodes is achieved through executable choreographies, which are capable of preventing information leakage when executing across different organizations with different regulations in place. The user has full control over his private data and is able to share and revoke access to organizations at any time. Even more, the updates are propagated instantly to all the parties which have access to the data thanks to the system design. Specifically, the processing organizations may retrieve and process the shared information, but are not allowed under any circumstances to store it on long term. PDS offers an alternative to systems that aim to ensure self-sovereignty of specific types of data through blockchain inspired techniques but face various problems, such as low performance. Both approaches propose a distributed database, but with different characteristics. While the blockchain-based systems are built to solve consensus problems, PDS's purpose is to solve the self-sovereignty aspects raised by the privacy laws, rules and principles.Comment: DAIS 201

Edge Assignment and Data Valuation in Federated Learning

Federated Learning (FL) is a recent Machine Learning method for training with private data separately stored in local machines without gathering them into one place for central learning. It was born to address the following challenges when applying Machine Learning in practice: (1) Communication cost: Most real-world data that can be useful for training are locally collected; to bring them all to one place for central learning can be expensive, especially in real-time learning applications when time is of the essence, for example, predicting the next word when texting on a smartphone; and (2) Privacy protection: Many applications must protect data privacy, such as those in the healthcare field; the private data can only be seen by its local owner and as such the learning may only use a content-hiding representation of this data, which is much less informative. To fulfill FL’s promise, this dissertation addresses three important problems regarding the need for good training data, system scalability, and uncertainty robustness: 1. The effectiveness of FL depends critically on the quality of the local training data. We should not only incentivize participants who have good training data but also minimize the effect of bad training data on the overall learning procedure. The first problem of my research is to determine a score to value a participant’s contribution. My approach is to compute such a score based on Shapley Value (SV), a concept of cooperative game theory for profit allocation in a coalition game. In this direction, the main challenge is due to the exponential time complexity of the SV computation, which is further complicated by the iterative manner of the FL learning algorithm. I propose a fast and effective valuation method that overcomes this challenge. 2. On scalability, FL depends on a central server for repeated aggregation of local training models, which is prone to become a performance bottleneck. A reasonable approach is to combine FL with Edge Computing: introduce a layer of edge servers to each serve as a regional aggregator to offload the main server. The scalability is thus improved, however at the cost of learning accuracy. The second problem of my research is to optimize this tradeoff. This dissertation shows that this cost can be alleviated with a proper choice of edge server assignment: which edge servers should aggregate the training models from which local machines. Specifically, I propose an assignment solution that is especially useful for the case of non-IID training data which is well-known to hinder today’s FL performance. 3. FL participants may decide on their own what devices they run on, their computing capabilities, and how often they communicate the training model with the aggregation server. The workloads incurred by them are therefore time-varying, and unpredictably. The server capacities are finite and can vary too. The third problem of my research is to compute an edge server assignment that is robust to such dynamics and uncertainties. I propose a stochastic approach to solving this problem

Towards the Development of a Definitive Infrastructure Policy in

The Government of AP , India , needs to rethink its Infrastructure strategy in the light of current state priority. Unless these are in line with state priorities, the strategies will not work and will perpetually be at cross purposes. The current direction taken by the state appears to point towards the development of rural infrastructure,such as irrigation facility and linkages. The paper attempts to assess the need for these linkages and evolve broad policies to be implemented by these strategies.Infrastructure Policy

Web3: The Next Internet Revolution

Since the first appearance of the World Wide Web, people more rely on the Web for their cyber social activities. The second phase of World Wide Web, named Web 2.0, has been extensively attracting worldwide people that participate in building and enjoying the virtual world. Nowadays, the next internet revolution: Web3 is going to open new opportunities for traditional social models. The decentralization property of Web3 is capable of breaking the monopoly of the internet companies. Moreover, Web3 will lead a paradigm shift from the Web as a publishing medium to a medium of interaction and participation. This change will deeply transform the relations among users and platforms, forces and relations of production, and the global economy. Therefore, it is necessary that we technically, practically, and more broadly take an overview of Web3. In this paper, we present a comprehensive survey of Web3, with a focus on current technologies, challenges, opportunities, and outlook. This article first introduces several major technologies of Web3. Then, we illustrate the type of Web3 applications in detail. Blockchain and smart contracts ensure that decentralized organizations will be less trusted and more truthful than that centralized organizations. Decentralized finance will be global, and open with financial inclusiveness for unbanked people. This paper also discusses the relationship between the Metaverse and Web3, as well as the differences and similarities between Web 3.0 and Web3. Inspired by the Maslow's hierarchy of needs theory, we further conduct a novel hierarchy of needs theory within Web3. Finally, several worthwhile future research directions of Web3 are discussed.Comment: Preprint. 5 figures, 2 table

Modern computing: vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Modern computing: Vision and challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

Health care financing challenges in the Pacific: priority setting and resource allocation

As explained in the Introduction, Pacific Islands Countries (PICs) have some of the highest rates of Non-Communicable Diseases (NCDs) in the world, and also face severe fiscal constraints. The original research contained in this thesis by compilation aims to address knowledge gaps, and help PIC governments respond to the emerging NCD crisis. Chapter 2 investigates the health financing options available to governments in five PICs. The share of government expenditure going to health in these countries is already some of the highest in the world. Most options available to middle-income countries globally to increase the fiscal space for health are unavailable, or inappropriate, to PICs. Improving allocative and technical efficiency in existing government health expenditure is the most feasible option left for PICs. Chapters 3 and 4 use two case studies to better understand the budgetary pressures that, given the above fiscal constraints, NCDs will increasingly impose in the Pacific and to raise fundamental questions about the cost-effectiveness and sustainability of health financing in the Pacific. Chapter 3, as well as re-considering the fiscal options of Chapter 2 in the context of Samoa, finds that dialysis treatment per patient in Samoa is twelve times the GDP per capita, user fees cover just 1.6% of the program, and two-thirds of patients die within two years. Chapter 4 investigates the cost to government of purchasing drugs to prevent and treat diabetes and hypertension in Vanuatu. Government pharmaceutical costs rise in large, step-wise, patterns as diabetes or hypertension progressively becomes more severe. About 20% of the population in Vanuatu has three or more risk factors for acquiring diabetes, but only 1.3% of the total population could be treated with insulin before the total Government drug budget for the country was fully spent. Chapter 5 finds that, contrary to general perceptions, the population of the PICs is “ageing” (i.e. the share of the population aged 60 years + is increasing). Current health systems are poorly designed to respond to their health needs. Ageing, combined with the high birth rates in many PICs, is likely to worsen the “dependency ratio” in countries, putting further strain on government budgets. This is exacerbated for those PICs with high levels of out-migration. With limited financing options (Chapter 2), prohibitively expensive treatment protocols (Chapters 3 and 4), and an ageing population (Chapter 5), a strong case for any budgetary interventions is needed. Chapter 6 identifies, based on a literature survey and stakeholder views, how Ministries of Health can improve their capacity to negotiate better health financing from Ministries of Finance, and development partners. It identifies ten attributes of effective budget requests. The academic contribution of the thesis is to help fill the research gap relating to the effectiveness and sustainability of NCD and other health care costs in the Pacific. The policy contribution is to provide the analysis underpinning the Pacific’s response to the NCD crisis. The “NCD Road Map”, which I drafted and which Pacific governments have now approved, is summarized in the Conclusion

Fog function virtualization: A flexible solution for IoT applications

The Internet of Things applications must carefully assess certain crucial factors such as the real-time and largely distributed nature of the “things”. Fog Computing provides an architecture to satisfy those requirements through nodes located from near the “things” till the edge. The problem comes with the integration of the Fog nodes into current infrastructures. This process requires the development of complex software solutions and prevents Fog growth. In this paper we propose three innovations to enhance Fog: (i) a new orchestration policy, (ii) the creation of constellations of nodes, and (iii) Fog Function Virtualization (FFV). All together will complement Fog to reach its true potential as a generic scalable platform, running multiple IoT applications simultaneously. Deploying a new service is reduced to the development of the application code, fact that brings the democratization of the Fog Computing paradigm through ease of deployment and cost reduction.The authors thanks Rodolfo Milito for his insightful comments and revisions. Damian Roca work was supported by a Doctoral Scholarship provided by Fundación La Caixa. Josue V. Quiroga work was supported by a Doctoral Scholarship provided by the Mexican National Council of Science and Technology (CONACyT). This work has been supported by the Spanish Government (Severo Ochoa grants SEV2015-0493) and by the Spanish Ministry of Science and Innovation (contracts TIN2015-65316-P).Peer ReviewedPostprint (author's final draft