A hybrid neuro--wavelet predictor for QoS control and stability

For distributed systems to properly react to peaks of requests, their adaptation activities would benefit from the estimation of the amount of requests. This paper proposes a solution to produce a short-term forecast based on data characterising user behaviour of online services. We use \emph{wavelet analysis}, providing compression and denoising on the observed time series of the amount of past user requests; and a \emph{recurrent neural network} trained with observed data and designed so as to provide well-timed estimations of future requests. The said ensemble has the ability to predict the amount of future user requests with a root mean squared error below 0.06\%. Thanks to prediction, advance resource provision can be performed for the duration of a request peak and for just the right amount of resources, hence avoiding over-provisioning and associated costs. Moreover, reliable provision lets users enjoy a level of availability of services unaffected by load variations

Beyond the Win: Pathways for Policy Implementation

When it comes to policy, a lot of attention is given to "the win." Whether it is something new and big like the Affordable Care Act, a piece of legislation in a large federal omnibus bill, or inclusion of critical language in a state policy, seeing the fruits of advocacy efforts put into law makes advocates and champions feel that their hard work, often many years in the making, has paid off.However, in reality, "the win" is just the beginning -- a necessary first step in a much longer and equally as fraught process of policy implementation. Once a policy is created, there are numerous factors that shape and determine how that policy is implemented -- and ultimately, the impact it will have -- regardless of how well the policy is formulated. Some of these factors include rulemaking, funding, capacity of local implementing agencies, and fights to repeal or modify wins, among many others.And, just as in the case of "the win," advocacy plays an important role in shaping implementation whether in advocating across these factors or participating in ongoing monitoring over time. Interestingly, while the role of advocacy in agenda setting, policy formulation, and policy adoption has been widely explored in theory and practice, the role of advocacy in the policy implementation process has received less attention in the literature.To learn more about the role of advocacy at the policy implementation stage, ORS Impact spoke with organizations that engage in, or provide funding for, advocacy efforts at the state and/or federal level. We focused on the following questions:When had advocates played a positive role in policy implementation?When had implementation not gone as well as expected, and what did advocates take away from that?Our conversations yielded important learnings about the unique characteristics of, and range of approaches to, advocacy efforts during the implementation phase. The two following scenarios illustrate some of the different types and levels of advocacy intervention, as well as the results they produce, to demonstrate the ways advocacy can play out when shifting from policymaking to implementation

What Should we Expect from Innovation? A Model-Based Assessment of the Environmental and Mitigation Cost Implications of Climate-Related R&D

This paper addresses two basic issues related to technological innovation and climate stabilisation objectives: i) Can innovation policies be effective in stabilising greenhouse gas concentrations? ii) To what extent can innovation policies complement carbon pricing (taxes or permit trading) and improve the economic efficiency of a mitigation policy package? To answer these questions, we use an integrated assessment model with multiple externalities and an endogenous representation of technical progress in the energy sector. We evaluate a range of innovation policies, both as a stand-alone instrument and in combination with other mitigation policies. Even under fairly optimistic assumptions about the funding available for, and the returns to R&D, our analysis indicates that innovation policies alone are unlikely to stabilise global concentration and temperature. The efficiency gains of combining innovation and carbon pricing policies are found to reach about 10% for a stabilisation target of 535 ppm CO2eq. However, such gains are reduced when more plausible (sub-optimal) global innovation policy arrangements are considered.climate change, environmental policy, energy R&D fund, stabilisation costs