Multi-capacity bin packing with dependent items and its application to the packing of brokered workloads in virtualized environments

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP) problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem , and we evaluate its efficiency using simulations on various application workloads, and network models.This work was done while author was at Boston University. It was partially supported by NSF CISE awards #1430145, #1414119, #1239021 and #1012798. (1430145 - NSF CISE; 1414119 - NSF CISE; 1239021 - NSF CISE; 1012798 - NSF CISE

Rational coordination of crowdsourced resources for geo-temporal request satisfaction

Existing mobile devices roaming around the mobility field should be considered as useful resources in geo-temporal request satisfaction. We refer to the capability of an application to access a physical device at particular geographical locations and times as GeoPresence, and we pre- sume that mobile agents participating in GeoPresence-capable applica- tions should be rational, competitive, and willing to deviate from their routes if given the right incentive. In this paper, we define the Hitch- hiking problem, which is that of finding the optimal assignment of re- quests with specific spatio-temporal characteristics to competitive mobile agents subject to spatio-temporal constraints. We design a mechanism that takes into consideration the rationality of the agents for request sat- isfaction, with an objective to maximize the total profit of the system. We analytically prove the mechanism to be convergent with a profit com- parable to that of a 1/2-approximation greedy algorithm, and evaluate its consideration of rationality experimentally.Supported in part by NSF Grants; #1430145, #1414119, #1347522, #1239021, and #1012798

Mechanism design for spatio-temporal request satisfaction in mobile networks

Mobile agents participating in geo-presence-capable crowdsourcing applications should be presumed rational, competitive, and willing to deviate from their routes if given the right incentive. In this paper, we design a mechanism that takes into consideration this rationality for request satisfaction in such applications. We propose the Geo-temporal Request Satisfaction (GRS) problem to be that of finding the optimal assignment of requests with specific spatio-temporal characteristics to competitive mobile agents subject to spatio-temporal constraints. The objective of the GRS problem is to maximize the total profit of the system subject to our rationality assumptions. We define the problem formally, prove that it is NP-Complete, and present a practical solution mechanism, which we prove to be convergent, and which we evaluate experimentally.National Science Foundation (1012798, 0952145, 0820138, 0720604, 0735974

Incentive compatible route coordination of crowdsourced resources and its application to GeoPresence-as-a-Service

With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen- ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent's exibility is exploited to maximize the coverage of a mo- bility field, with an objective to maximize the revenue collected from sat- isfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1 2 -approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent's truthfulness about its exibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798

Scheduling of data-intensive workloads in a brokered virtualized environment

Providing performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource management solutions that consider the brokered nature of these workloads, as well as the special demands of their intra-dependent components. In this paper, we present an offline mechanism for scheduling batches of brokered data-intensive workloads, which can be extended to an online setting. The objective of the mechanism is to decide on a packing of the workloads in a batch that minimizes the broker's incurred costs, Moreover, considering the brokered nature of such workloads, we define a payment model that provides incentives to these workloads to be scheduled as part of a batch, which we analyze theoretically. Finally, we evaluate the proposed scheduling algorithm, and exemplify the fairness of the payment model in practical settings via trace-based experiments

Network-constrained packing of brokered workloads in virtualized environments

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources.With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP)problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem, and we evaluate its efficiency using simulations on various application workloads, and network models.This work is supported by NSF CISE CNS Award #1347522, # 1239021, # 1012798

What’s in it for me? Incentive-compatible route coordination of crowdsourced resources

With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresence-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1/2-approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798

An integrated review of "unplanned" dialysis initiation: reframing the terminology to "suboptimal" initiation

<p>Abstract</p> <p>Background</p> <p>Ideally, care prior to the initiation of dialysis should increase the likelihood that patients start electively outside of the hospital setting with a mature arteriovenous fistula (AVF) or peritoneal dialysis (PD) catheter. However, unplanned dialysis continues to occur in patients both known and unknown to nephrology services, and in both late and early referrals. The objective of this article is to review the clinical and socioeconomic outcomes of unplanned dialysis initiation. The secondary objective is to explore the potential cost implications of reducing the rate of unplanned first dialysis in Canada.</p> <p>Methods</p> <p>MEDLINE and EMBASE from inception to 2008 were used to identify studies examining the clinical, economic or quality of life (QoL) outcomes in patients with an unplanned versus planned first dialysis. Data were described in a qualitative manner.</p> <p>Results</p> <p>Eight European studies (5,805 patients) were reviewed. Duration of hospitalization and mortality was higher for the unplanned versus planned population. Patients undergoing a first unplanned dialysis had significantly worse laboratory parameters and QoL. Rates of unplanned dialysis ranged from 2449%. The total annual burden to the Canadian healthcare system of unplanned dialysis in 2005 was estimated at $33 million in direct hospital costs alone. Reducing the rate of unplanned dialysis by one-half yielded savings ranging from$13.3 to $16.1 million.</p> <p>Conclusion</p> <p>The clinical and socioeconomic impact of unplanned dialysis is significant. To more consistently characterize the unplanned population, the term <it>suboptimal initiation </it>is proposed to include dialysis initiation in hospital and/or with a central venous catheter and/or with a patient not starting on their chronic modality of choice. Further research and implementation of initiatives to reduce the rate of <it>suboptimal initiation </it>of dialysis in Canada are needed.</p

Implementation for New Dyeing Technique

This research project introduces a new dyeing technique using a Continuous Dyeing Machine for polyester fabric, one of the most widely used textiles. The machine design incorporates specific requirements for dyeing synthetic materials, such as high temperatures up to 135 degrees Celsius, crucial for effective dye absorption. The three-stage process involves immersing the fabric in a chemical sink for optimal dye penetration, removing excess chemicals through a padder under 2 bar pressure, and subjecting the fabric to a high-temperature treatment in the shock and furnace section. The objectives focus on reducing the current six-hour dyeing time and consolidating four processes into a single machine for improved space utilization and reducing time. The control system adheres to classic control theory principles, with component selection based on its applicability to fabric movement through the Continuous Dyeing Machine. This research represents a significant advancement in polyester fabric dyeing, offering insights into process efficiency and space optimization in the textile industry

Conséquences de la survenue du cancer sur les parcours professionnels : une analyse sur données médico-administratives

Ce rapport étudie les effets à court et moyen termes de la survenue d’un cancer sur l’emploi et l’activité. Il utilise la base de données Hygie, produite par l’Irdes à partir des données administra-tives de la Caisse nationale de l’Assurance maladie et de la Caisse nationale d’assurance vieillesse. Hygie permet de reconstituer la carrière de l’individu ainsi que les événements de santé. Une première partie exploite la dimension rétrospective liée à la carrière et confirme l’effet pénali-sant du cancer sur l’activité et l’augmentation des arrêts maladie. Les résultats d’un modèle de double différence avec appariement exact montrent la persistance des effets de la maladie sur l’éloignement du marché du travail, la probabilité d’être employé au moins un trimestre dans l’année diminuant jusqu’à un horizon de cinq ans. Nous mesurons également l’effet des douze cancers les plus prévalents dans la base Hygie et de maladies chroniques ayant un impact potentiel sur le marché du travail. Les effets les plus marqués sont relatifs au cancer du poumon et des bronches, à la schizophrénie et au VIH. Les maladies chroniques autres que le cancer ont des effets nettement plus atténués probablement parce que leurs traitements au long cours améliorent la qualité de vie. Une seconde partie exploite la dimension panel de l’échantillon pour étudier les transitions profes-sionnelles consécutives au diagnostic de cancer. Elle montre que la population touchée rencontre de grandes difficultés pour rester dans l’emploi, ou y retourner. Ces difficultés sont accentuées pour les salariés débutants ou ayant connu une carrière peu stable, ou marquée par des arrêts maladie signifi-catifs. De plus, les séquelles des soins induisent un passage plus fréquent vers le chômage et l’inactivité au détriment de l’emploi. Développer les modes d’accompagnement professionnel pour des personnes dont les carrières ont été plus heurtées, et dont le statut socioprofessionnel est moins protecteur, serait donc une des pistes d’amélioration de leurs conditions de vie