A 'cool' load balancer for parallel applications

Meeting power requirements of huge exascale machines of the future would be one major challenge. Our focus in this paper is to minimize cooling power and we propose a tech-nique, that uses a combination of DVFS and temperature aware load balancing to constrain core temperatures as well as save cooling energy. Our scheme is specifically designed to suit parallel applications which are typically tightly coupled. The temperature control comes at the cost of execution time and we try to minimize the timing penalty. We experiment with three applications (with different power utilization profiles), run on a 128-core (32-node) cluster with a dedicated air conditioning unit. We calibrate the efficacy of our scheme based on three metrics: ability to control aver-age core temperatures thereby avoiding hot spot occurence, timing penalty minimization, and cooling energy savings. Our results show cooling energy savings of up to 57 % with timing penalty mostly in the range of 2 to 20%. 1

Scaling Virtualized Smartphone Images in the Cloud

Üks selle Bakalaureuse töö eesmärkidest oli Android-x86 nutitelefoni platvormi juurutamine pilvekeskkonda ja välja selgitamine, kas valitud instance on piisav virtualiseeritud nutitelefoni platvormi juurutamiseks ning kui palju koormust see talub. Töös kasutati Amazoni instance'i M1 Small, mis oli piisav, et juurutada Androidi virtualiseeritud platvormi, kuid jäi kesisemaks kui mobiiltelefon, millel teste läbi viidi. M1 Medium instance'i tüüp oli sobivam ja näitas paremaid tulemusi võrreldes telefoniga. Teostati koormusteste selleks vastava tööriistaga Tsung, et näha, kui palju üheaegseid kasutajaid instance talub. Testi läbiviimiseks paigaldasime Dalviku instance'ile Tomcat serveri. Pärast teste ühe eksemplariga, juurutasime külge Elastic Load Balancing ja automaatse skaleerimise Amazon Auto Scaling tööriista. Esimene neist jaotas koormust instance'ide vahel. Automaatse skaleerimise tööriista kasutasime, et rakendada horisontaalset skaleerimist meie Android-x86 instance'le. Kui CPU tõusis üle 60% kauemaks kui üks minut, siis tehti eelmisele identne instance ja koormust saadeti edaspidi sinna. Seda protseduuri vajadusel korrati maksimum kümne instance'ini. Meie teostusel olid tagasilöögid, sest Elastic Load Balancer aegus 60 sekundi pärast ning me ei saanud kõikide välja saadetud päringutele vastuseid. Serverisse saadetud faili kirjutamine ja kompileerimine olid kulukad tegevused ja seega ei lõppenud kõik 60 sekundi jooksul. Me ei saanud koos Load Balancer'iga läbiviidud testidest piisavalt andmeid, et teha järeldusi, kas virtualiseeritud nutitelefoni platvorm Android on hästi või halvasti skaleeruv.In this thesis we deployed a smartphone image in an Amazon EC2 instance and ran stress tests on them to know how much users can one instance bear and how scalable it is. We tested how much time would a method run in a physical Android device and in a cloud instance. We deployed CyanogenMod and Dalvik for a single instance. We used Tsung for stress testing. For those tests we also made a Tomcat server on Dalvik instance that would take the incoming file, the file would be compiled with java and its class file would be wrapped into dex, a Dalvik executable file, that is later executed with Dalvik. Three instances made a Tsung cluster that sent load to a Dalvik Virtual Machine instance. For scaling we used Amazon Auto Scaling tool and Elastic Load Balancer that divided incoming load between the instances

Parallel Programming with Migratable Objects: Charm++ in Practice

The advent of petascale computing has introduced new challenges (e.g. Heterogeneity, system failure) for programming scalable parallel applications. Increased complexity and dynamism in science and engineering applications of today have further exacerbated the situation. Addressing these challenges requires more emphasis on concepts that were previously of secondary importance, including migratability, adaptivity, and runtime system introspection. In this paper, we leverage our experience with these concepts to demonstrate their applicability and efficacy for real world applications. Using the CHARM++ parallel programming framework, we present details on how these concepts can lead to development of applications that scale irrespective of the rough landscape of supercomputing technology. Empirical evaluation presented in this paper spans many miniapplications and real applications executed on modern supercomputers including Blue Gene/Q, Cray XE6, and Stampede

Passive and Active Battery Balancing Methods Implemented on Second Use Lithium-ion Batteries

As the number of electric vehicles (EVs) increases, the number of used battery packs that require disposal increases; however, many of these packs still have useful capacity and can be repurposed. When using repurposed large lithium-ion battery packs, deviations between cells within a pack become problematic. These deviations result in a pack that is unbalanced, affecting performance and proving potentially hazardous when charging. Consequently, a battery management system (BMS) is needed. To provide safety, the BMS in this paper monitors and controls the operation of the battery pack. In addition, it controls the redistribution of charge between the cells within the pack, providing battery balancing and performance benefits. Two designs are prototyped and tested using repurposed battery packs

Dynamic Scaling for Service Oriented Applications: Implications of Virtual Machine Placement on IaaS Clouds

Abstraction of physical hardware using infrastructure-as-a-service (IaaS) clouds leads to the simplistic view that resources are homogeneous and that infinite scaling is possible with linear increases in performance. Support for autonomic scaling of multi-tier service oriented applications requires determination of when, what, and where to scale. \u27When\u27 is addressed by hotspot detection schemes using techniques including performance modeling and time series analysis. \u27What\u27 relates to determining the quantity and size of new resources to provision. \u27Where\u27 involves identification of the best location(s) to provision new resources. In this paper we investigate primarily \u27where\u27 new infrastructure should be provisioned, and secondly \u27what\u27 the infrastructure should be. Dynamic scaling of infrastructure for service oriented applications requires rapid response to changes in demand to meet application quality-of-service requirements. We investigate the performance and resource cost implications of VM placement when dynamically scaling server infrastructure of service oriented applications . We evaluate dynamic scaling in the context of providing modeling-as-a-service for two environmental science models