At the cutting edge of the chemical and biological research, innovation takes place in a field referred to as Lab on Chip (LoC), a multi-disciplinary area that combines biology, chemistry, electronics, microfabrication, and fluid mechanics. Within this field, droplets have been used as microreactors to produce advanced materials like quantum dots, micro and nanoparticles, active pharmaceutical ingredients, etc. The size of these microreactors offers distinct advantages, which were not possible using batch technologies. For example, they allow for lower reagent waste, minimal energy consumption, increased safety, as well as better process control of reaction conditions like temperature regulation, residence times, and response times among others. One of the biggest drawbacks associated with this technology is its limited production volume that prevents it from reaching industrial applications. The standard production rates for a single droplet microfluidic device is in the range of 1-10mLh-1, whereas industrial applications usually demand production rates several orders of magnitude higher. Although substantial work has been recently undertaken in the development scaled-out solutions, which run in parallel several droplet generators. Complex fluid mechanics and limitations on the manufacturing capacity have constrained these works to explore only in-plane parallelization. This thesis investigates a three-dimensional parallelization by proposing a microfluidic system that is comprised of a stack of droplet generation layers working on the liquid-liquid ow regime. Its realization implied a study of the characteristics of conventional droplet generators and the development of a fabrication process for 3D networks of microchannels. Finally, the combination of these studies resulted in a functional 3D parallelization system with the highest production rate (i.e. 1 Lh-1) at the time of its publication. Additionally, this architecture can reach industrially relevant production rates as more devices can be integrated into the same chip and many chips can compose a manufacturing plant. The thesis also addresses the concerns about system reliability and quality control by proposing capacitive and radio frequency resonator sensors that can measure accurately increments as small as 2.4% in the water-in-oil volume fraction and identify errors during droplet production.

This dissertation focuses on the problem space of heterogeneous-ISA multiprocessors – an architectural design point that is being studied by the academic research community and increasingly available in commodity systems. Since such architectures usually lack globally coherent shared memory, software-based distributed shared memory (DSM) is often used to provide the illusion of such a memory. The DSM abstraction typically provides this illusion using a reader-replicate, writer-invalidate memory consistency protocol that operates at the granularity of memory pages and is usually implemented as a first-class operating system abstraction. This enables symmetric multiprocessing (SMP) programming frameworks, augmented with a heterogeneous-ISA compiler, to use CPU cores of different ISAs for parallel computations as if they are of the same ISA, improving programmability, especially for legacy SMP applications which therefore can run unmodified on such hardware. Past DSMs have been plagued by poor performance, in part due to the high latency and low bandwidth of interconnect network infrastructures. The dissertation revisits DSM in light of modern interconnects that reverse this performance trend. The dissertation presents Xfetch, a bulk page prefetching mechanism designed for the DEX DSM system. Xfetch exploits spatial locality, and aggressively and sequentially prefetches pages before potential read faults, improving DSM performance. Our experimental evaluations reveal that Xfetch achieves up to ≈142% speedup over the baseline DEX DSM that does not prefetch page data. SMP programming models often allow primitives that permit weaker memory consistency semantics, where synchronization updates can be delayed, permitting greater parallelism and thereby higher performance. Inspired by such primitives, the dissertation presents a DSM protocol called MWPF that trades-off memory consistency for higher performance in select SMP code regions, targeting heterogeneous-ISA multiprocessor systems. MWPF also overcomes performance bottlenecks of past DSM systems for heterogeneous-ISA multiprocessors such as due to significant number of invalidation messages, false page sharing, large number of read page faults, and large synchronization overheads by using efficient protocol primitives that delay and batch invalidation messages, aggressively prefetch data pages, and perform cross-domain synchronization with low overhead. Our experimental evaluations reveal that MWPF achieves, on average, 11% speedup over the baseline DSM implementation. The dissertation presents PuzzleHype, a distributed hypervisor that enables a single virtual machine (VM) to use fragmented resources in distributed virtualized settings such as CPU cores, memory, and devices of different physical hosts, and thereby decrease resource fragmentation and increase resource utilization. PuzzleHype leverages DSM implemented in host operating systems to present an unified and consistent view of a continuous pseudo-physical address space to guest operating systems. To transparently utilize CPU and I/O resources, PuzzleHype integrates multiple physical CPUs into a single VM by migrating threads, forwarding interrupts, and by delegating I/O. Our experimental evaluations reveal that PuzzleHype yields speedups in the range of 355%–173% over baseline over-provisioning scenarios which are otherwise necessary due to resource fragmentation. To enable a distributed hypervisor to adapt to resource and workload changes, the dissertation proposes the concept of CPU borrowing that allows a VM's virtual CPU (vCPU) to migrate to an available physical CPU (pCPU) and release it when it is no longer necessary, i.e., CPU returning. CPU borrowing can thus be used when a node is over-committed, and CPU returning can be used when the borrowed CPU resource is no longer necessary. To transparently migrate a vCPU at runtime without incurring a significant downtime, the dissertation presents a suite of techniques including leveraging thread migration, loading/restoring vCPU in KVM states, maintaining a global vCPU location table, and creating a DSM kernel thread for handling on-demand paging. Our experimental evaluations reveal that migrating vCPUs to resource-available nodes achieves a speedup of 1.4x over running the vCPUs on distributed nodes. When a VM spans multiple nodes, it is likelihood for failure increases. To mitigate this, the dissertation presents a distributed checkpoint/restart mechanism that allows a distributed VM to tolerate failures. A user interface is introduced for sending/receiving checkpoint/restart commands to a distributed VM. We implement the checkpoint/restart technique in the native KVM tool, and extend it to a distributed mode by converting Inter-Process Communication (IPC) into message passing between nodes, pausing/resuming distributed vCPU executions, and loading/restoring runtime states on the correct set of nodes. Our experimental evaluations indicate that the overhead of checkpointing a distributed VM is ≈10% or less than that of the native KVM tool with our checkpoint support. Restarting a distributed VM is faster than native KVM with our restart support because no additional page faults occur during restarting. The dissertation's final contribution is PopHype, a system software stack that allows simulation of cache-coherent, shared memory heterogeneous-ISA hardware. PopHype includes a Linux operating system that implements DSM as an OS abstraction for processes, i.e., allows multiple processes running on multiple (ISA-different) machines to share memory. With KVM-enabled, this OS becomes a hypervisor that allows multiple, process-based instances of an architecture emulator such as QEMU to execute in a shared address space, allowing multiple QEMU instances to emulate different ISAs in shared memory, i.e., emulate shared memory heterogeneous-ISA hardware. PopHype also includes a modified QEMU to use process-level DSM and an optimized guest OS kernel for improved performance. Our experimental studies confirm PopHype's effectiveness, and reveal that PopHype achieves an average speedup of 7.32x over a baseline that runs multiple QEMU instances in shared memory atop a single host OS. / Doctor of Philosophy / Computing devices are ubiquitous around us. Each of these devices is powered by specialized chips called processors. These processors take in instructions, process them, and produce output. Such processing is what enables us, humans, to send messages to our loved ones, take photographs, as well as carry out various business functions such as using spreadsheet software. The kinds of instructions these processors execute are classified into so-called Instruction Set Architectures or ISAs. Chip designers build processors adopting different ISAs for various applications ranging from computing on mobile phones to cloud computing data centers used by large technology companies. Within a data center, there are typically hundreds of thousands of computing devices that serve an organization's purpose to serve millions or even billions of users. Programming these computers individually to serve a collective goal is an arduous task requiring hundreds of software engineering experts. To simplify programming these computers on a large scale, this thesis envisions an abstraction where tens of devices appear as one computing unit to the programmer, allowing them to program multiple computers as if they are one. This allows for better resource utilization in the sense that the power of multiple computing devices can be pooled together without the need to acquire newer, larger, and more-expensive computers. Furthermore, such pooling allows the software to leverage multiple different ISAs on different computers instead of a single ISA on one computer. This thesis also envisions a way for software to run on multiple computers with potentially different ISAs without exposing the difficulty of managing them to the software engineers.

Massive data centers housing thousands of computing nodes have become commonplace in enterprise computing, and the power consumption of such data centers is growing at an unprecedented rate. Exacerbating such costs, data centers are often over-provisioned to avoid costly outages associated with the potential overloading of electrical circuitry. However, such over provisioning is often unnecessary since a data center rarely operates at its maximum capacity. It is imperative that we realize effective strategies to control the power consumption of the server and improve the energy efficiency of data centers. Adding to the problem is the inability of the servers to exhibit energy proportionality which diminishes the overall energy efficiency of the data center. Therefore in this dissertation, we investigate whether it is possible to achieve energy proportionality at the server- and cluster-level by efficient power and resource provisioning. Towards this end, we provide a thorough analysis of energy proportionality at the server and cluster-level and provide insight into the power saving opportunity and mechanisms to improve energy proportionality. Specifically, we make the following contribution at the server-level using enterprise-class workloads. We analyze the average power consumption of the full system as well as the subsystems and describe the energy proportionality of these components, characterize the instantaneous power profile of enterprise-class workloads using the on-chip energy meters, design a runtime system based on a load prediction model and an optimization framework to set the appropriate power constraints to meet specific performance targets and then present the effects of our runtime system on energy proportionality, average power, performance and instantaneous power consumption of enterprise applications. We then make the following contributions at the cluster-level. Using data serving, web searching and data caching as our representative workloads, we first analyze the component-level power distribution on a cluster. Second, we characterize how these workloads utilize the cluster. Third, we analyze the potential of power provisioning techniques (i.e., active low-power, turbo and idle low-power modes) to improve the energy proportionality. We then describe the ability of active low-power modes to provide trade-offs in power and latency. Finally, we compare and contrast power provisioning and resource provisioning techniques. This thesis sheds light on mechanisms to tune the power provisioned for a system under strict performance targets and opportunities to improve energy proportionality and instantaneous power consumption via efficient power and resource provisioning at the server- and cluster-level. / Ph. D.

Cloud computing allows users to tap into a massive pool of shared computing resources such as servers, storage, and network. These resources are provided as a service to the users allowing them to “plug into the cloud” similar to a utility grid. The promise of the cloud is to free users from the tedious and often complex task of managing and provisioning computing resources to run applications. At the same time, the cloud brings several additional benefits including: a pay-as-you-go cost model, easier deployment of applications, elastic scalability, high availability, and a more robust and secure infrastructure. One important class of applications that users are increasingly deploying in the cloud is database management systems. Database management systems differ from other types of applications in that they manage large amounts of state that is frequently updated, and that must be kept consistent at all scales and in the presence of failure. This makes it difficult to provide scalability and high availability for database systems in the cloud. In this thesis, we show how we can exploit cloud technologies and relational database systems to provide a highly available and scalable database service in the cloud. The first part of the thesis presents RemusDB, a reliable, cost-effective high availability solution that is implemented as a service provided by the virtualization platform. RemusDB can make any database system highly available with little or no code modifications by exploiting the capabilities of virtualization. In the second part of the thesis, we present two systems that aim to provide elastic scalability for database systems in the cloud using two very different approaches. The three systems presented in this thesis bring us closer to the goal of building a scalable and reliable transactional database service in the cloud.

Cloud computing allows users to tap into a massive pool of shared computing resources such as servers, storage, and network. These resources are provided as a service to the users allowing them to “plug into the cloud” similar to a utility grid. The promise of the cloud is to free users from the tedious and often complex task of managing and provisioning computing resources to run applications. At the same time, the cloud brings several additional benefits including: a pay-as-you-go cost model, easier deployment of applications, elastic scalability, high availability, and a more robust and secure infrastructure. One important class of applications that users are increasingly deploying in the cloud is database management systems. Database management systems differ from other types of applications in that they manage large amounts of state that is frequently updated, and that must be kept consistent at all scales and in the presence of failure. This makes it difficult to provide scalability and high availability for database systems in the cloud. In this thesis, we show how we can exploit cloud technologies and relational database systems to provide a highly available and scalable database service in the cloud. The first part of the thesis presents RemusDB, a reliable, cost-effective high availability solution that is implemented as a service provided by the virtualization platform. RemusDB can make any database system highly available with little or no code modifications by exploiting the capabilities of virtualization. In the second part of the thesis, we present two systems that aim to provide elastic scalability for database systems in the cloud using two very different approaches. The three systems presented in this thesis bring us closer to the goal of building a scalable and reliable transactional database service in the cloud.

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