Fair Share Policy Research Articles

Can Fair Share Policies Expand Neighborhood Choice? Evidence From Bypassing Exclusionary Zoning Under Massachusetts Chapter 40B

Opening up neighborhoods that offer greater opportunities for social mobility to low- and moderate-income households remains a challenge in the United States. Exclusionary zoning practices act as a barrier to current efforts by restricting the supply of affordable housing. In this paper, we examine whether fair share policies that seek to bypass these restrictive zoning practices offer one potential solution. Focusing on Massachusetts Chapter 40B, we find clear evidence that such policies build affordable housing in neighborhoods with strikingly greater opportunities for social mobility than are otherwise available to low- and moderate-income households. Leveraging novel data on 40B development addresses linked to a wide range of public and administrative records, we find that 40B produces affordable units in neighborhoods with greater economic mobility, higher performing schools, greater social capital, less pollution, better health outcomes, and lower incarceration rates than both the typical neighborhood in Massachusetts and those available to beneficiaries of the state’s Low-Income Housing Tax Credit, Housing Choice Voucher, and public housing programs. Consistent with previous research on policies that have segregated affordable housing and opportunity, we also find that neighborhoods with affordable 40B units are whiter and more affluent than average. An examination of underlying policy mechanisms suggests that 40B’s ability to bypass exclusionary zoning plays a central role in explaining differences in neighborhood characteristics between 40B and other programs. We further find little evidence to support concerns that 40B’s zoning override leads to affordable 40B units being built in the least advantaged areas of municipalities, in polluted zones, or near highways, though a non-trivial share of units are located in industrial zones. These results suggest that policies like 40B may be valuable complements to other major housing programs in the United States.

Optimize Task Scheduling and Resource Allocation Using Nature Inspired Algorithms in Cloud based BDA

Task Scheduling and Resource allocation is a prominent research topic in cloud computing. There are several objectives associated with Optimize Task Scheduling and Resource allocation as cloud computing systems are more complex than the traditional distributed system. There are several challenges like resolving the task mapped to the node on which task to be executed. A simplified but near optimal proposed nature inspired algorithms are focus in this paper. In this paper basic idea about optimization, reliability and complexity is considered while design a solution for modern BDA (Big Data Application). Detailed analysis of experimental results, it is shown that the proposed algorithm has better optimization effect on the fair share policies which are presently available in most of the BDA. In this paper we focused on Dragonfly algorithm and Sea lion algorithms which are nature inspired algorithms. These algorithms are efficient for optimization purpose for solving task scheduling and resource allocation problem. Finally performance of the hybrid DA algorithm and Sea lion is compared with traditional techniques used for modern BDA using Hadoop MapReduce. Simulation results prove the efficacy of the suggested algorithms.

Adjusting the fairshare policy to prevent computing power loss

On a typical WLCG site providing batch access to computing resources according to a fairshare policy, the idle time lapse after a job ends and before a new one begins on a given slot is negligible if compared to the duration of typical jobs. The overall amount of these intervals over a time window increases with the size of the cluster and the inverse of job duration and can be considered equivalent to an average number of unavailable slots over that time window. This value has been investigated for the Tier-1 at CNAF, and observed to occasionally grow and reach up to more than the 10% of the about 20,000 available computing slots. Analysis reveals that this happens when a sustained rate of short jobs is submitted to the cluster and dispatched by the batch system. Because of how the default fairshare policy works, it increases the dynamic priority of those users mostly submitting short jobs, since they are not accumulating runtime, and will dispatch more of their jobs at the next round, thus worsening the situation until the submission flow ends. To address this problem the default behaviour of the fairshare have been altered by adding a correcting term to the default formula for the dynamic priority. The LSF batch system, currently adopted at CNAF, provides a way to define its value by invoking a C function, which returns it for each user in the cluster. The correcting term works by rounding up to a minimum defined runtime the most recently done jobs. Doing so, each short job looks almost like a regular one and the dynamic priority value settles to a proper value. The net effect is a reduction of the dispatching rate of short jobs and, consequently, the average number of available slots greatly improves. Furthermore, a potential starvation problem, actually observed at least once is also prevented. After describing short jobs and reporting about their impact on the cluster, possible workarounds are discussed and the selected solution is motivated. Details on the most critical aspects of the implementation are explained and the observed results are presented.

Optimizing the resource usage in Cloud based environments: the Synergy approach

Managing resource allocation in a cloud based data centre serving multiple virtual organizations is a challenging issue. In fact, while batch systems are able to allocate resources to different user groups according to specific shares imposed by the data centre administrator, without a static partitioning of such resources, this is not so straightforward in the most common cloud frameworks, e.g. OpenStack. In the current OpenStack implementation, it is only possible to grant fixed quotas to the different user groups and these resources cannot be exceeded by one group even if there are unused resources allocated to other groups. Moreover in the existing OpenStack implementation, when there aren’t resources available, new requests are simply rejected: it is then up to the client to later re-issue the request. The recently started EU-funded INDIGO-DataCloud project is addressing this issue through “Synergy”, a new advanced scheduling service targeted for OpenStack. Synergy adopts a fair-share model for resource provisioning which guarantees that resources are distributed among users following the fair-share policies defined by the administrator, taken also into account the past usage of such resources. We present the architecture of Synergy, the status of its implementation, some preliminary results and the foreseen evolution of the service.

Dynamic provisioning of a HEP computing infrastructure on a shared hybrid HPC system

Experiments in high-energy physics (HEP) rely on elaborate hardware, software and computing systems to sustain the high data rates necessary to study rare physics processes. The Institut fr Experimentelle Kernphysik (EKP) at KIT is a member of the CMS and Belle II experiments, located at the LHC and the Super-KEKB accelerators, respectively. These detectors share the requirement, that enormous amounts of measurement data must be processed and analyzed and a comparable amount of simulated events is required to compare experimental results with theoretical predictions.Classical HEP computing centers are dedicated sites which support multiple experiments and have the required software pre-installed. Nowadays, funding agencies encourage research groups to participate in shared HPC cluster models, where scientist from different domains use the same hardware to increase synergies. This shared usage proves to be challenging for HEP groups, due to their specialized software setup which includes a custom OS (often Scientific Linux), libraries and applications.To overcome this hurdle, the EKP and data center team of the University of Freiburg have developed a system to enable the HEP use case on a shared HPC cluster. To achieve this, an OpenStack-based virtualization layer is installed on top of a bare-metal cluster. While other user groups can run their batch jobs via the Moab workload manager directly on bare-metal, HEP users can request virtual machines with a specialized machine image which contains a dedicated operating system and software stack. In contrast to similar installations, in this hybrid setup, no static partitioning of the cluster into a physical and virtualized segment is required.As a unique feature, the placement of the virtual machine on the cluster nodes is scheduled by Moab and the job lifetime is coupled to the lifetime of the virtual machine. This allows for a seamless integration with the jobs sent by other user groups and honors the fairshare policies of the cluster. The developed thin integration layer between OpenStack and Moab can be adapted to other batch servers and virtualization systems, making the concept also applicable for other cluster operators.This contribution will report on the concept and implementation of an OpenStack-virtualized cluster used for HEP workflows. While the full cluster will be installed in spring 2016, a test-bed setup with 800 cores has been used to study the overall system performance and dedicated HEP jobs were run in a virtualized environment over many weeks. Furthermore, the dynamic integration of the virtualized worker nodes, depending on the workload at the institute's computing system, will be described.

GWpilot: Enabling multi-level scheduling in distributed infrastructures with GridWay and pilot jobs

Current systems based on pilot jobs are not exploiting all the scheduling advantages that the technique offers, or they lack compatibility or adaptability. To overcome the limitations or drawbacks in existing approaches, this study presents a different general-purpose pilot system, GWpilot. This system provides individual users or institutions with a more easy-to-use, easy-to-install, scalable, extendable, flexible and adjustable framework to efficiently run legacy applications. The framework is based on the GridWay meta-scheduler and incorporates the powerful features of this system, such as standard interfaces, fair-share policies, ranking, migration, accounting and compatibility with diverse infrastructures. GWpilot goes beyond establishing simple network overlays to overcome the waiting times in remote queues or to improve the reliability in task production. It properly tackles the characterisation problem in current infrastructures, allowing users to arbitrarily incorporate customised monitoring of resources and their running applications into the system. This functionality allows the new framework to implement innovative scheduling algorithms that accomplish the computational needs of a wide range of calculations faster and more efficiently. The system can also be easily stacked under other software layers, such as self-schedulers. The advanced techniques included by default in the framework result in significant performance improvements even when very short tasks are scheduled.

Decentralized scalable fairshare scheduling

This work addresses Grid fairshare allocation policy enforcement and presents Aequus, a decentralized system for Grid-wide fairshare job prioritization. The main idea of fairshare scheduling is to prioritize users with regard to predefined resource allocation quotas. The presented system builds on three contributions: a flexible tree-based policy model that allows delegation of policy definition, a job prioritization algorithm based on local enforcement of distributed fairshare policies, and a decentralized architecture for non-intrusive integration with existing scheduling systems. The system supports organization of users in virtual organizations and divides usage policies into local and global policy components that are defined by resource owners and virtual organizations. The architecture realization is presented in detail along with an evaluation of the system behavior in an emulated environment. In the evaluation, convergence noise types (mechanisms counteracting policy allocation convergence) are characterized and quantified, and the system is demonstrated to meet scheduling objectives and perform scalably under realistic operating conditions.

Milk Marketing Policy Options for the Dairy Industry in New England

New England dairy farmers are under intense price pressure resulting from important growth in milk production from lower cost of production Southwest states as well as by retailers’ market power. Agricultural officials and legislative bodies in New England and in other Northeast US states are aware of these pressures and have been reacting with emergency dairy farm aid, following a very low 2006 milk price, and with state legislations in an attempt to address perceived excess retailing margins for fluid milk. In this paper, we suggest that a sigmoid demand relationship exists for fluid milk. This demand relationship would explain fluid milk asymmetric price transmission, high-low pricing, and the creation of a large retailing margin (chain surplus) often observed for fluid milk. It is also argued that a sigmoid demand relationship offers an opportunity for state legislators to help Northeast dairy farmers capturing a larger share of the dollar of the consumers through various policy options. Therefore, 5 milk market channel regulatory mechanisms (status quo, price gouging, supply control, fair share policy, and chain surplus return) are discussed and compared. The supply control mechanism was found the most effective at redistributing the chain surplus, associated with the sigmoid demand relationship for fluid milk, to dairy farmers. However, this option is unlikely to be politically acceptable in the United States. Second-best options for increasing dairy farmers’ share of the consumers’ dollar are the fair price policy and the chain surplus return. The former mechanism would distribute the chain surplus between retailers, processors, and farmers, whereas the latter would distribute it between consumers, retailers, and farmers. Remaining mechanisms would either transfer the chain surplus to retailers (status quo) or to consumers (price gouging).

Pricing and Policy Problems in the Northeast Fluid Milk Industry

This article documents the need for reform of milk pricing in the Northeast. The New York price gouging law can be recast as a fair share law. This new milk policy “kills two birds with one stone.” It corrects regional inequities in raw milk pricing by reforming the pricing of milk at retail by limiting and redistributing excessive retail margins to farmers and consumers. The fair share policy relieves allocative price inefficiency, improves the performance of the federal milk market order pool, and improves the general performance of the Northeast dairy farming and fluid milk industries.

Packet loss probability for DiffServ over IP and MPLS reliable homogeneous multicast networks

Multicasting has become increasingly important with the emergence of Internet-based applications such as IP telephony, audio/video conferencing, distributed databases and software upgrading. IP multicasting is an efficient way to distribute information from a single source to multiple destinations at different locations. In practice IP is considered as a layer 3 protocol. Multiprotocol Label Switching (MPLS) replaces the IP forwarding by a simple label lookup. MPLS combines the flexibility of layer 3 routing and layer 2 switching. In order to provide QoS in group communications for real time applications such as video conferencing, reliable multicasting is used. Miscellaneous efforts have been undertaken to provide reliability on top of IP multicast. Two error control strategies have been popular in practice. These are the FEC (Forward Error Correction) strategy, which uses error correction alone, and the ARQ (Automatic Repeat Request) strategy, which uses error detection, combined with retransmission of data. In this paper, we present a new fair share policy (FSP) that utilizes Differentiated Services to solve the problems of QoS and congestion control when reliable ARQ multicast is used. The results should provide insight into the comparisons of the residual packet loss probability between IP multicast in MPLS networks using FSP and plain IP multicasting using the same policy when DiffServ are adopted and when reliable ARQ multicast is considered.

Table-based QoS control for embedded real-time systems

This paper proposes a new QoS control scheme that is suited for embedded real-time systems. Our scheme focused on real-time systems where both device control and multimedia processing are required. Such systems needs to keep timing constraints of control tasks while providing the highest possible quality of service(QoS) to multimedia processing tasks. Although many QoS control schemes are proposed and used in distributed multimedia systems, they are not suited for such real-time systems. Their QoS control policies cannot exactly keep the timing constraints of control tasks.To overcome this problem we chose a scheme which uses a table describing resource requirements of all tasks. Resource allocations for tasks and total resource utilization in a system can be calculated from the table. Using our scheme, any QoS control policies, such as a fair-share policy or a priority based policy, can be implemented. In other words, it has become possible for the first time to allow the intention of system designers to be directly reflected on QoS control.We have implemented a CPU time QoS control mechanism based on our proposed scheme and evaluated it on a μ-ITRON Ver. 3.0 based real-time OS. The evaluation results demonstrate that the QoS control scheme can keep deadline misses low and CPU utilization high under an overloaded state. The results also show that overhead of the QoS control mechanism is small enough to support both multimedia and control applications.