Seeing Shapes in Clouds: On the Performance-Cost trade-off for Heterogeneous Infrastructure-as-a-Service

In the near future FPGAs will be available by the hour, however this new Infrastructure as a Service (IaaS) usage mode presents both an opportunity and a challenge: The opportunity is that programmers can potentially trade resources for performance on a much larger scale, for much shorter periods of time than before. The challenge is in finding and traversing the trade-off for heterogeneous IaaS that guarantees increased resources result in the greatest possible increased performance. Such a trade-off is Pareto optimal. The Pareto optimal trade-off for clusters of heterogeneous resources can be found by solving multiple, multi-objective optimisation problems, resulting in an optimal allocation of tasks to the available platforms. Solving these optimisation programs can be done using simple heuristic approaches or formal Mixed Integer Linear Programming (MILP) techniques. When pricing 128 financial options using a Monte Carlo algorithm upon a heterogeneous cluster of Multicore CPU, GPU and FPGA platforms, the MILP approach produces a trade-off that is up to 110% faster than a heuristic approach, and over 50% cheaper. These results suggest that high quality performance-resource trade-offs of heterogeneous IaaS are best realised through a formal optimisation approach.Comment: Presented at Second International Workshop on FPGAs for Software Programmers (FSP 2015) (arXiv:1508.06320

A guide to implementing cloud services

The Australian Government’s policy on cloud computing is that agencies may choose to use cloud computing services where they provide value for money and adequate security, as stated in the April 2011 Australian Government Cloud Computing Strategic Direction Paper1 (the Strategic Direction Paper).   Readers new to cloud computing should read the Strategic Direction Paper which provides an introduction to cloud computing, a definition and an overview of its associated risks and benefits as they apply to Australian Government agencies. The guide supports the Strategic Direction Paper and provides an overarching risk-based approach for agencies to develop an organisational cloud strategy and implement cloud-based services. It is designed as an aid for experienced business strategists, architects, project managers, business analysts and IT staff to realise the benefits of cloud computing technology while managing risks

Internet of Things and Their Coming Perspectives: A Real Options Approach

Internet of things is developing at a dizzying rate, and companies are forced to implement it in order to maintain their operational efficiency. The high flexibility inherent to these technologies makes it necessary to apply an appropriate measure, which properly assesses risks and rewards. Real options methodology is available as a tool which fits the conditions, both economic and strategic, under which investment in internet of things technologies is developed. The contribution of this paper is twofold. On the one hand, it offers an adequate tool to assess the strategic value of investment in internet of things technologies. On the other hand, it tries to raise awareness among managers of internet of things technologies because of their potential to contribute to economic and social progress. The results of the research described in this paper highlight the importance of taking action as quickly as possible if companies want to obtain the best possible performance. In order to enhance the understanding of internet of things technologies investment, this paper provides a methodology to assess the implementation of internet of things technologies by using the real options approach; in particular, the option to expand has been proposed for use in the decision-making process

Scheduling Flexible Demand in Cloud Computing Spot Markets

The rapid standardization and specialization of cloud computing services have led to the development of cloud spot markets on which cloud service providers and customers can trade in near real-time. Frequent changes in demand and supply give rise to spot prices that vary throughout the day. Cloud customers often have temporal flexibility to execute their jobs before a specific deadline. In this paper, the authors apply real options analysis (ROA), which is an established valuation method designed to capture the flexibility of action under uncertainty. They adapt and compare multiple discrete-time approaches that enable cloud customers to quantify and exploit the monetary value of their short-term temporal flexibility. The paper contributes to the field by guaranteeing cloud job execution of variable-time requests in a single cloud spot market, whereas existing multi-market strategies may not fulfill requests when outbid. In a broad simulation of scenarios for the use of Amazon EC2 spot instances, the developed approaches exploit the existing savings potential up to 40 percent – a considerable extent. Moreover, the results demonstrate that ROA, which explicitly considers time-of-day-specific spot price patterns, outperforms traditional option pricing models and expectation optimization

Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services

Cumulative Prospect Theory (CPT) is a modeling tool widely used in behavioral economics and cognitive psychology that captures subjective decision making of individuals under risk or uncertainty. In this paper, we propose a dynamic pricing strategy for Shared Mobility on Demand Services (SMoDSs) using a passenger behavioral model based on CPT. This dynamic pricing strategy together with dynamic routing via a constrained optimization algorithm that we have developed earlier, provide a complete solution customized for SMoDS of multi-passenger transportation. The basic principles of CPT and the derivation of the passenger behavioral model in the SMoDS context are described in detail. The implications of CPT on dynamic pricing of the SMoDS are delineated using computational experiments involving passenger preferences. These implications include interpretation of the classic fourfold pattern of risk attitudes, strong risk aversion over mixed prospects, and behavioral preferences of self reference. Overall, it is argued that the use of the CPT framework corresponds to a crucial building block in designing socio-technical systems by allowing quantification of subjective decision making under risk or uncertainty that is perceived to be otherwise qualitative.Comment: 17 pages, 6 figures, and has been accepted for publication at the 58th Annual Conference on Decision and Control, 201

Cloud Computing in the Quantum Era

Cloud computing has become the prominent technology of this era. Its elasticity, dynamicity, availability, heterogeneity, and pay as you go pricing model has attracted several companies to migrate their businesses' services into the cloud. This gives them more time to focus solely on their businesses and reduces the management and backup overhead leveraging the flexibility of cloud computing. On the other hand, quantum technology is developing very rapidly. Experts are expecting to get an efficient quantum computer within the next decade. This has a significant impact on several sciences including cryptography, medical research, and other fields. This paper analyses the reciprocal impact of quantum technology on cloud computing and vice versa

Strategic Decision Support for Smart-Leasing Infrastructure-as-a-Service

In this work we formulate strategic decision models describing when and how many reserved instances should be bought when outsourcing workload to an IaaS provider. Current IaaS providers offer various pricing options for leasing computing resources. When decision makers are faced with the choice and most importantly with uneven workloads, the decision at which time and with which type of computing resource to work is no longer trivial. We present case studies taken from the online services industry and present solution models to solve the various use case problems and compare them. Following a thorough numerical analysis using both real, as well as augmented workload traces in simulations, we found that it is cost efficient to (1) have a balanced portfolio of resource options and (2) avoiding commitments in the form of upfront payments when faced with uncertainty. Compared to a simple IaaS benchmark, this allows cutting costs by 20%