Efficient Striping Techniques for Variable Bit Rate Continuous Media File Servers

The performance of striped disk arrays is governed by two parameters: the stripe unit size and the degree of striping. In this paper, we describe techniques for determining the stripe unit size and degree of striping for disk arrays storing variable bit rate continuous media data. We present an analytical model that uses the server configuration and the workload characteristics to predict the load on the most heavily loaded disk in redundant and non-redundant arrays. We then use the model to determine the optimal stripe unit size for different workloads. We also use the model to study the effect of various system parameters on the optimal stripe unit size. To determine the degree of striping, we first demonstrate that striping a continuous media stream across all disks in the array causes the number of clients supported to increase sub-linearly with increase in the number of disks. To maximize the number of clients supported in large arrays, we propose a technique that partitions a disk array and stripes each media stream across a single partition. Since load imbalance can occur in such partitioned arrays, we present an analytical model to compute the imbalance across partitions in the array. We then use the model to determine a partition size that minimizes the load imbalance, and hence, maximizes the number of clients supported by the array

On the Promise and Pitfalls of Optimizing Embodied Carbon

To halt further climate change, computing, along with the rest of society, must reduce, and eventually eliminate, its carbon emissions. Recently, many researchers have focused on estimating and optimizing computing's \emph{embodied carbon}, i.e., from manufacturing computing infrastructure, in addition to its \emph{operational carbon}, i.e., from executing computations, primarily because the former is much larger than the latter but has received less research attention. Focusing attention on embodied carbon is important because it can incentivize i) operators to increase their infrastructure's efficiency and lifetime and ii) downstream suppliers to reduce their own operational carbon, which represents upstream companies' embodied carbon. Yet, as we discuss, focusing attention on embodied carbon may also introduce harmful incentives, e.g., by significantly overstating real carbon reductions and complicating the incentives for directly optimizing operational carbon. This position paper's purpose is to mitigate such harmful incentives by highlighting both the promise and potential pitfalls of optimizing embodied carbon.Comment: 2nd Workshop on Sustainable Computer Systems (HotCarbon'23

Quantifying the Benefits of Resource Multiplexing in On-Demand Data Centers

On-demand data centers host multiple applications on server farms by dynamically provisioning resources in response to workload variations. The efficiency of such dynamic provisioning on the required server farm capacity is dependent on several factors — the granularity and frequency of reallocation, the number of applications being hosted, the amount of resource overprovisioning and the accuracy of workload prediction. In this paper, we quantify the effect of these factors on the multiplexing benefits achievable in an on-demand data center. Using traces of real e-commerce workloads, we demonstrate that the ability to allocate fractional server resources at fine time-scales of tens of seconds to a few minutes can increase the multiplexing benefits by 162-188% over coarsegrained reallocation. Our results also show that these benefits increase in the presence of large number of hosted applications as a result of high level of multiplexing. In addition, we demonstrate that such fine-grained multiplexing is achievable even in the presence of real-world (inaccurate) workload predictors and allows overprovisioning slack of nearly 35-70% over coarse-grained multiplexing

Analyzing the Impact of Covid-19 Control Policies on Campus Occupancy and Mobility via Passive WiFi Sensing

Mobile sensing has played a key role in providing digital solutions to aid with COVID-19 containment policies. These solutions include, among other efforts, enforcing social distancing and monitoring crowd movements in indoor spaces. However, such solutions may not be effective without mass adoption. As more and more countries reopen from lockdowns, there remains a pressing need to minimize crowd movements and interactions, particularly in enclosed spaces. This paper conjectures that analyzing user occupancy and mobility via deployed WiFi infrastructure can help institutions monitor and maintain safety compliance according to the public health guidelines. Using smartphones as a proxy for user location, our analysis demonstrates how coarse-grained WiFi data can sufficiently reflect indoor occupancy spectrum when different COVID-19 policies were enacted. Our work analyzes staff and students' mobility data from three different university campuses. Two of these campuses are in Singapore, and the third is in the Northeastern United States. Our results show that online learning, split-team, and other space management policies effectively lower occupancy. However, they do not change the mobility for individuals transitioning between spaces. We demonstrate how this data source can be put to practical application for institutional crowd control and discuss the implications of our findings for policy-making.Comment: 25 pages, 18 figure