Coalition Formation and Combinatorial Auctions; Applications to Self-organization and Self-management in Utility Computing

In this paper we propose a two-stage protocol for resource management in a hierarchically organized cloud. The first stage exploits spatial locality for the formation of coalitions of supply agents; the second stage, a combinatorial auction, is based on a modified proxy-based clock algorithm and has two phases, a clock phase and a proxy phase. The clock phase supports price discovery; in the second phase a proxy conducts multiple rounds of a combinatorial auction for the package of services requested by each client. The protocol strikes a balance between low-cost services for cloud clients and a decent profit for the service providers. We also report the results of an empirical investigation of the combinatorial auction stage of the protocol.Comment: 14 page

Elastic neural network method for load prediction in cloud computing grid

Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches

Model-driven Scheduling for Distributed Stream Processing Systems

Distributed Stream Processing frameworks are being commonly used with the evolution of Internet of Things(IoT). These frameworks are designed to adapt to the dynamic input message rate by scaling in/out.Apache Storm, originally developed by Twitter is a widely used stream processing engine while others includes Flink, Spark streaming. For running the streaming applications successfully there is need to know the optimal resource requirement, as over-estimation of resources adds extra cost.So we need some strategy to come up with the optimal resource requirement for a given streaming application. In this article, we propose a model-driven approach for scheduling streaming applications that effectively utilizes a priori knowledge of the applications to provide predictable scheduling behavior. Specifically, we use application performance models to offer reliable estimates of the resource allocation required. Further, this intuition also drives resource mapping, and helps narrow the estimated and actual dataflow performance and resource utilization. Together, this model-driven scheduling approach gives a predictable application performance and resource utilization behavior for executing a given DSPS application at a target input stream rate on distributed resources.Comment: 54 page

Leveraging disaggregated accelerators and non-volatile memories to improve the efficiency of modern datacenters

(English) Traditional data centers consist of computing nodes that possess all the resources physically attached. When there was the need to deal with more significant demands, the solution has been to either add more nodes (scaling out) or increase the capacity of existing ones (scaling-up). Workload requirements are traditionally fulfilled by selecting compute platforms from pools that better satisfy their average or maximum resource requirements depending on the price that the user is willing to pay. The amount of processor, memory, storage, and network bandwidth of a selected platform needs to meet or exceed the platform requirements of the workload. Beyond those explicitly required by the workload, additional resources are considered stranded resources (if not used) or bonus resources (if used). Meanwhile, workloads in all market segments have evolved significantly during the last decades. Today, workloads have a larger variety of requirements in terms of characteristics related to the computing platforms. Those workload new requirements include new technologies such as GPU, FPGA, NVMe, etc. These new technologies are more expensive and thus become more limited. It is no longer feasible to increase the number of resources according to potential peak demands, as this significantly raises the total cost of ownership. Software-Defined-Infrastructures (SDI), a new concept for the data center architecture, is being developed to address those issues. The main SDI proposition is to disaggregate all the resources over the fabric to enable the required flexibility. On SDI, instead of pools of computational nodes, the pools consist of individual units of resources (CPU, memory, FPGA, NVMe, GPU, etc.). When an application needs to be executed, SDI identifies the computational requirements and assembles all the resources required, creating a composite node. Resource disaggregation brings new challenges and opportunities that this thesis will explore. This thesis demonstrates that resource disaggregation brings opportunities to increase the efficiency of modern data centers. This thesis demonstrates that resource disaggregation may increase workloads' performance when sharing a single resource. Thus, needing fewer resources to achieve similar results. On the other hand, this thesis demonstrates how through disaggregation, aggregation of resources can be made, increasing a workload's performance. However, to take maximum advantage of those characteristics and flexibility, orchestrators must be aware of them. This thesis demonstrates how workload-aware techniques applied at the resource management level allow for improved quality of service leveraging resource disaggregation. Enabling resource disaggregation, this thesis demonstrates a reduction of up to 49% missed deadlines compared to a traditional schema. This reduction can rise up to 100% when enabling workload awareness. Moreover, this thesis demonstrates that GPU partitioning and disaggregation further enhances the data center flexibility. This increased flexibility can achieve the same results with half the resources. That is, with a single physical GPU partitioned and disaggregated, the same results can be achieved with 2 GPU disaggregated but not partitioned. Finally, this thesis demonstrates that resource fragmentation becomes key when having a limited set of heterogeneous resources, namely NVMe and GPU. For the case of an heterogeneous set of resources, and specifically when some of those resources are highly demanded but limited in quantity. That is, the situation where the demand for a resource is unexpectedly high, this thesis proposes a technique to minimize fragmentation that reduces deadlines missed compared to a disaggregation-aware policy of up to 86%.(Català) Els datacenters tradicionals consisteixen en un seguit de nodes computacionals que contenen al seu interior tots els recursos necessaris. Quan hi ha una necessitat de gestionar demandes superiors la solució era o afegir més nodes (scale-out) o incrementar la capacitat dels existents (scale-up). Els requisits de les aplicacions tradicionalment són satisfets seleccionant recursos de racks que satisfan millor el seu SLA basats o en la mitjana dels requisits o en el màxim possible, en funció del preu que l'usuari estigui disposat a pagar. La quantitat de processadors, memòria, disc, i banda d'ampla d'un rack necessita satisfer o excedir els requisits de l'aplicació. Els recursos addicionals als requerits per les aplicacions són considerats inactius (si no es fan servir) o addicionals (si es fan servir). Per altra banda, les aplicacions en tots els segments de mercat han evolucionat significativament en les últimes dècades. Avui en dia, les aplicacions tenen una gran varietat de requisits en termes de característiques que ha de tenir la infraestructura. Aquests nous requisits inclouen tecnologies com GPU, FPGA, NVMe, etc. Aquestes tecnologies són més cares i, per tant, més limitades. Ja no és factible incrementar el nombre de recursos segons el potencial pic de demanda, ja que això incrementa significativament el cost total de la infraestructura. Software-Defined Infrastructures és un nou concepte per a l'arquitectura de datacenters que s'està desenvolupant per pal·liar aquests problemes. La proposició principal de SDI és desagregar tots els recursos sobre la xarxa per garantir una major flexibilitat. Sota SDI, en comptes de racks de nodes computacionals, els racks consisteix en unitats individuals de recursos (CPU, memòria, FPGA, NVMe, GPU, etc). Quan una aplicació necessita executar, SDI identifica els requisits computacionals i munta una plataforma amb tots els recursos necessaris, creant un node composat. La desagregació de recursos porta nous reptes i oportunitats que s'exploren en aquesta tesi. Aquesta tesi demostra que la desagregació de recursos ens dona l'oportunitat d'incrementar l'eficiència dels datacenters moderns. Aquesta tesi demostra la desagregació pot incrementar el rendiment de les aplicacions. Però per treure el màxim partit a aquestes característiques i d'aquesta flexibilitat, els orquestradors n'han de ser conscient. Aquesta tesi demostra que aplicant tècniques conscients de l'aplicació aplicades a la gestió de recursos permeten millorar la qualitat del servei a través de la desagregació de recursos. Habilitar la desagregació de recursos porta a una reducció de fins al 49% els deadlines perduts comparat a una política tradicional. Aquesta reducció pot incrementar-se fins al 100% quan s'habilita la consciència de l'aplicació. A més a més, aquesta tesi demostra que el particionat de GPU combinat amb la desagregació millora encara més la flexibilitat. Aquesta millora permet aconseguir els mateixos resultats amb la meitat de recursos. És a dir, amb una sola GPU física particionada i desagregada, els mateixos resultats són obtinguts que utilitzant-ne dues desagregades però no particionades. Finalment, aquesta tesi demostra que la gestió de la fragmentació de recursos és una peça clau quan la quantitat de recursos és limitada en un conjunt heterogeni de recursos. Pel cas d'un conjunt heterogeni de recursos, i especialment quan aquests recursos tenen molta demanda però són limitats en quantitat. És a dir, quan la demanda pels recursos és inesperadament alta, aquesta tesi proposa una tècnica minimitzant la fragmentació que redueix els deadlines perduts comparats a una política de desagregació de fins al 86%.Arquitectura de computador