Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter.

Memory resource balancing for virtualized computing

Virtualization has become a common abstraction layer in modern data centers. By multiplexing hardware resources into multiple virtual machines (VMs) and thus enabling several operating systems to run on the same physical platform simultaneously, it can effectively reduce power consumption and building size or improve security by isolating VMs. In a virtualized system, memory resource management plays a critical role in achieving high resource utilization and performance. Insufficient memory allocation to a VM will degrade its performance dramatically. On the contrary, over-allocation causes waste of memory resources. Meanwhile, a VM’s memory demand may vary significantly. As a result, effective memory resource management calls for a dynamic memory balancer, which, ideally, can adjust memory allocation in a timely manner for each VM based on their current memory demand and thus achieve the best memory utilization and the optimal overall performance. In order to estimate the memory demand of each VM and to arbitrate possible memory resource contention, a widely proposed approach is to construct an LRU-based miss ratio curve (MRC), which provides not only the current working set size (WSS) but also the correlation between performance and the target memory allocation size. Unfortunately, the cost of constructing an MRC is nontrivial. In this dissertation, we first present a low overhead LRU-based memory demand tracking scheme, which includes three orthogonal optimizations: AVL-based LRU organization, dynamic hot set sizing and intermittent memory tracking. Our evaluation results show that, for the whole SPEC CPU 2006 benchmark suite, after applying the three optimizing techniques, the mean overhead of MRC construction is lowered from 173% to only 2%. Based on current WSS, we then predict its trend in the near future and take different strategies for different prediction results. When there is a sufficient amount of physical memory on the host, it locally balances its memory resource for the VMs. Once the local memory resource is insufficient and the memory pressure is predicted to sustain for a sufficiently long time, a relatively expensive solution, VM live migration, is used to move one or more VMs from the hot host to other host(s). Finally, for transient memory pressure, a remote cache is used to alleviate the temporary performance penalty. Our experimental results show that this design achieves 49% center-wide speedup.

Resource management in location aware cognitive radio networks

Dynamic spectrum access (DSA) aims at utilizing spectral opportunities both in time and frequency domains at any given location, which arise due to variations in spectrum usage. Recently, Cognitive radios (CRs) have been proposed as a means of implementing DSA. In this work we focus on the aspect of resource management in overlaid CRNs. We formulate resource allocation strategies for cognitive radio networks (CRNs) as mathematical optimization problems. Specifically, we focus on two key problems in resource management: Sum Rate Maximization and Maximization of Number of Admitted Users. Since both the above mentioned problems are NP hard due to presence of binary assignment variables, we propose novel graph based algorithms to optimally solve these problems. Further, we analyze the impact of location awareness on network performance of CRNs by considering three cases: Full location Aware, Partial location Aware and Non location Aware. Our results clearly show that location awareness has significant impact on performance of overlaid CRNs and leads to increase in spectrum utilization effciency.

Development of a high performance parallel computing platform and its use in the study of nanostructures : clusters, sheets and tubes .

Small clusters of gallium oxide, technologically important high temperature ceramic, together with interaction of nucleic acid bases with graphene and small-diameter carbon nanotube are focus of first principles calculations in this work. A high performance parallel computing platform is also developed to perform these calculations at Michigan Tech. First principles calculations are based on density functional theory employing either local density or gradient-corrected approximation together with plane wave and gaussian basis sets. The bulk Ga2O3 is known to be a very good candidate for fabricating electronic devices that operate at high temperatures. To explore the properties of Ga2O3 at nonoscale, we have performed a systematic theoretical study on the small polyatomic gallium oxide clusters. The calculated results find that all lowest energy isomers of GamOn clusters are dominated by the Ga-O bonds over the metal-metal or the oxygen-oxygen bonds. Analysis of atomic charges suggest the clusters to be highly ionic similar to the case of bulk Ga2O3. In the study of sequential oxidation of these slusters starting from Ga2O, it is found that the most stable isomers display up to four different backbones of constituent atoms. Furthermore, the predicted configuration of the ground state of Ga2O is recently confirmed by the experimental result of Neumark's group. Guided by the results of calculations the study of gallium oxide clusters, performance related challenge of computational simulations, of producing high performance computers/platforms, has been addressed. Several engineering aspects were thoroughly studied during the design, development and implementation of the high performance parallel computing platform, rama, at Michigan Tech. In an attempt to stay true to the principles of Beowulf revolutioni, the rama cluster was extensively customized to make it easy to understand, and use - for administrators as well as end-users. Following the results of benchmark calculations and to keep up with the complexity of systems under study, rama has been expanded to a total of sixty four processors. Interest in the non-covalent intereaction of DNA with carbon nanotubes has steadily increased during past several years. This hybrid system, at the junction of the biological regime and the nanomaterials world, possesses features which make it very attractive for a wide range of applicatioins. Using the in-house computational power available, we have studied details of the interaction between nucleic acid bases with graphene sheet as well as high-curvature small-diameter carbon nanotube. The calculated trend in the binding energies strongly suggests that the polarizability of the base molecules determines the interaction strength of the nucleic acid bases with graphene. When comparing the results obtained here for physisorption on the small diameter nanotube considered with those from the study on graphene, it is observed that the interaction strength of nucleic acid bases is smaller for the tube. Thus, these results show that the effect of introducing curvature is to reduce the binding energy. The binding energies for the two extreme cases of negligible curvature (i.e. flat graphene sheet) and of very high curvature (i.e. small diameter nanotube) may be considered as upper and lower bounds. This finding represents an important step towards a better understanding of experimentally observed sequence-dependent interaction of DNA with Carbon nanotubes.

Context-Oriented Programming

Context-dependent behavior is becoming increasingly important for a wide range of application domains, from pervasive computing to common business applications. Unfortunately, mainstream programming languages do not provide mechanisms that enable software entities to adapt their behavior dynamically to the current execution context. This leads developers to adopt convoluted designs to achieve the necessary runtime flexibility. We propose a new programming technique called Context-oriented Programming (COP) which addresses this problem. COP treats context explicitly, and provides mechanisms to dynamically adapt behavior in reaction to changes in context, even after system deployment at runtime. In this paper we lay the foundations of COP, show how dynamic layer activation enables multi-dimensional dispatch, illustrate the application of COP by examples in several language extensions, and demonstrate that COP is largely independent of other commitments to programming style.

Missing Links in the Proposed EU Data Protection Regulation and Cloud Computing Scenarios: A Brief Overview

Applying location-focused data protection law within the context of a location-agnostic cloud computing framework is fraught with difficulties. While the Proposed EU Data Protection Regulation has introduced a lot of changes to the current data protection framework, the complexities of data processing in the cloud involve various layers and intermediaries of actors that have not been properly addressed. This leaves some gaps in the regulation when analyzed in cloud scenarios. This paper gives a brief overview of the relevant provisions of the regulation that will have an impact on cloud transactions and addresses the missing links. It is hoped that these loopholes will be reconsidered before the final version of the law is passed in order to avoid unintended consequences.

Topology and Link quality-aware Geographical opportunistic routing in wireless ad-hoc networks

Opportunistic routing (OR) takes advantage of the broadcast nature and spatial diversity of wireless transmission to improve the performance of wireless ad-hoc networks. Instead of using a predetermined path to send packets, OR postpones the choice of the next-hop to the receiver side, and lets the multiple receivers of a packet to coordinate and decide which one will be the forwarder. Existing OR protocols choose the next-hop forwarder based on a predefined candidate list, which is calculated using single network metrics. In this paper, we propose TLG - Topology and Link quality-aware Geographical opportunistic routing protocol. TLG uses multiple network metrics such as network topology, link quality, and geographic location to implement the coordination mechanism of OR. We compare TLG with well-known existing solutions and simulation results show that TLG outperforms others in terms of both QoS and QoE metrics.

Traffic-Adaptive and Link-Quality-Aware Communication in Wireless Sensor Networks

This paper is a summary of the main contribu- tions of the PhD thesis published in [1]. The main research contributions of the thesis are driven by the research question how to design simple, yet efficient and robust run-time adaptive resource allocation schemes within the commu- nication stack of Wireless Sensor Network (WSN) nodes. The thesis addresses several problem domains with con- tributions on different layers of the WSN communication stack. The main contributions can be summarized as follows: First, a a novel run-time adaptive MAC protocol is intro- duced, which stepwise allocates the power-hungry radio interface in an on-demand manner when the encountered traffic load requires it. Second, the thesis outlines a metho- dology for robust, reliable and accurate software-based energy-estimation, which is calculated at network run- time on the sensor node itself. Third, the thesis evaluates several Forward Error Correction (FEC) strategies to adap- tively allocate the correctional power of Error Correcting Codes (ECCs) to cope with timely and spatially variable bit error rates. Fourth, in the context of TCP-based communi- cations in WSNs, the thesis evaluates distributed caching and local retransmission strategies to overcome the perfor- mance degrading effects of packet corruption and trans- mission failures when transmitting data over multiple hops. The performance of all developed protocols are eval- uated on a self-developed real-world WSN testbed and achieve superior performance over selected existing ap- proaches, especially where traffic load and channel condi- tions are suspect to rapid variations over time.

Immigrant students’ psychological adjustment in the school context: A meta-synthesis of qualitative research on acculturation

This contribution focuses on the characteristics of the school context and their impact on immigrant students’ acculturation and adjustment at school. Research suggests that the ways immigrants acculturate is related to their well being (e.g. Phinney, et al., 2001; Ward & Rana-Deuba, 1999), although findings have been contradictory across methods and studies (e.g. Rogler, 1991; Escobar & Vega, 2001). Debates in acculturation research currently center on issues of acculturation measurement (e.g. Berry, 2009; Rudmin, 2009), as most research is conducted in the quantitative tradition. In addition, some have suggested (Birman, 2011) that research on acculturation in the tradition of cross-cultural psychology adopts an overly individualistic perspective, and lacks attention to the specific contexts of acculturation. Alternatively, the contextual approach proposes that the relationship between acculturation and adjustment is shaped by the surrounding context (Birman & Simon, 2013). For immigrant children, schools are the setting where the process of acculturation unfolds, and an important context in which to study their adjustment and well being (Birman, et al., 2007; Makarova & Herzog, 2011). Though rarely used in this tradition of acculturation research (Chirkov, 2009), qualitative methods are uniquely suited to gain insight to facilitate theory development, as well as appreciate the contextual nature of the acculturation process. Yet we are not aware of efforts to synthesize the empirical qualitative literature on this topic. Applying the methodology of meta-synthesis for qualitatieve research (Walsh & Downe, 2005) our contribution attempts to integrate results from qualitative studies on impact of acculturation on immigrant students’ psychological adjustment in the school context. For this purpose 84 articles which matched the inclusion criteria were selected. Overall, the results of our study show that within the school context a number of structural as well as process characteristics can be identified as crucial for immigrant youth psychological adjustment. Moreover, our findings indicate that immigrant youths’ psychological adjustment is related to other individual outcomes of acculturation in the school context such as behavioral adjustment, peer-relationships, academic achievement and identity development of immigrant youth.

Synergistic User <-> Context Analytics

Various avours of a new research field on (socio-)physical or personal analytics have emerged, with the goal of deriving semantically-rich insights from people's low-level physical sensing combined with their (online) social interactions. In this paper, we argue for more comprehensive data sources, including environmental (e.g. weather, infrastructure) and application-specific data, to better capture the interactions between users and their context, in addition to those among users. To illustrate our proposed concept of synergistic user <-> context analytics, we first provide some example use cases. Then, we present our ongoing work towards a synergistic analytics platform: a testbed, based on mobile crowdsensing and the Internet of Things (IoT), a data model for representing the different sources of data and their connections, and a prediction engine for analyzing the data and producing insights.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.

Towards data-aware cost-driven adaptation for service orchestrations.

Several activities in service oriented computing, such as automatic composition, monitoring, and adaptation, can benefit from knowing properties of a given service composition before executing them. Among these properties we will focus on those related to execution cost and resource usage, in a wide sense, as they can be linked to QoS characteristics. In order to attain more accuracy, we formulate execution costs / resource usage as functions on input data (or appropriate abstractions thereof) and show how these functions can be used to make better, more informed decisions when performing composition, adaptation, and proactive monitoring. We present an approach to, on one hand, synthesizing these functions in an automatic fashion from the definition of the different orchestrations taking part in a system and, on the other hand, to effectively using them to reduce the overall costs of non-trivial service-based systems featuring sensitivity to data and possibility of failure. We validate our approach by means of simulations of scenarios needing runtime selection of services and adaptation due to service failure. A number of rebinding strategies, including the use of cost functions, are compared.

Understanding HCI policy in Spain in the context of acces

Spain pioneered policies related to disability and accessibility, especially in the physical environment. The first major piece of legislation was Act 13/1982 on the Social Integration of People with Disabilities. The law was published just a few years after the reinstitution of democracy in Spain in 1977 and the approval of the Spanish Constitution in 1978. Act 13/1982 is a general law that applies accessibility to social services and education, as well as to workplace and physical accessibility. This law targeted first and foremost the social integration of people with disabilities, and it was a significant success, especially in the field of employment, as it made it mandatory for private companies and public administrations to employ a certain percentage of persons with disabilities. This greatly increased the employability of people with disabilities, as highlighted in a document celebrating 30 years of Act 13/1982. Over the past 20 years, policy makers have also focused on accessibility to information and communication technologies (ICT), developing first the national computing accessibility standards and then specialized legislation. Initially, Spanish activities were mostly national but have now gained an international dimension. All these initiatives are strongly related to the discipline of human-computer interaction, as the key component of an accessible system is its user interface.