MOD

In recent years there has been interest in achieving application-level consistency criteria without the latency and availability costs of strongly consistent storage infrastructure. A standard technique is to adopt a vocabulary of commutative operations;...

A Symmetric MultiProcessing (SMP) virtual machine (VM) enables users to take advantage of a multiprocessor infrastructure in supporting scalable job throughput and request responsiveness. It is known that hypervisor scheduling activities can heavily ...

As the study of large graphs over hundreds of gigabytes becomes increasingly popular for various data-intensive applications in cloud computing, developing large graph processing systems has become a hot and fruitful research area. Many of those ...

In this paper, we present Sailfish, a new Map-Reduce framework for large scale data processing. The Sailfish design is centered around aggregating intermediate data, specifically data produced by map tasks and consumed later by reduce tasks, to improve ...

Precise fingerprinting of an operating system (OS) is critical to many security and virtual machine (VM) management applications in the cloud, such as VM introspection, penetration testing, guest OS administration (e.g., kernel update), kernel dump ...

Current cloud datastores usually trade consistency for performance and availability. However, it is often not clear how an application is affected when it runs under a low level of consistency. In fact, current application designers have basically no ...

To better understand the challenges in developing effective cloud-based resource schedulers, we analyze the first publicly available trace data from a sizable multi-purpose cluster. The most notable workload characteristic is heterogeneity: in resource ...

The performance of non-volatile memories (NVM) has grown by a factor of 100 during the last several years: Flash devices today are capable of over 1 million I/Os per second. Unfortunately, this incredible growth has put strain on software storage ...

In data center applications, predictability in service time and controlled latency, especially tail latency, are essential for building performant applications. This is especially true for applications or services built by accessing data across ...

The disconnect between the resource-centric interface exposed by today's cloud providers and tenant goals hurts both entities. Tenants are encumbered by having to translate their performance and cost goals into the corresponding resource requirements, ...

Modern Internet-scale distributed networks have hundreds of thousands of servers deployed in hundreds of locations and networks around the world. Canonical examples of such networks are content delivery networks (called CDNs) that we study in this ...

This paper presents a scheduling model for a class of interactive services in which requests are time bounded and lower result quality can be traded for shorter execution time. These applications include web search engines, finance servers, and other ...

"Big Data" computing increasingly utilizes the MapReduce programming model for scalable processing of large data collections. Many MapReduce jobs are I/O-bound, and so minimizing the number of I/O operations is critical to improving their performance. ...

Cake is a coordinated, multi-resource scheduler for shared distributed storage environments with the goal of achieving both high throughput and bounded latency. Cake uses a two-level scheduling scheme to enforce high-level service-level objectives (SLOs)...

Resource allocation is an integral, evolving part of many data center management problems such as virtual machine placement in data centers, network virtualization, and multi-path network routing. Since the problems are inherently NP-Hard, most existing ...

The elapsed time of a parallel job depends on the completion time of its longest running constituent. We present a static load balancing algorithm that distributes work evenly across the reducers in a MapReduce job resulting in significant elapsed time ...

The need to backup huge quantities of data has led to the development of a number of distributed deduplication techniques that aim to reproduce the operation of centralized, single-node backup systems in a cluster-based environment. At one extreme, ...

In Internet architectures, data systems are typically categorized into source-of-truth systems that serve as primary stores for the user-generated writes, and derived data stores or indexes which serve reads and other complex queries. The data in these ...

Distributed data systems systems are used in a variety of settings like online serving, offline analytics, data transport, and search, among other use cases. They let organizations scale out their workloads using cost-effective commodity hardware, while ...

Infrastructure-as-a-system compute clouds such as Amazon's EC2 allow users to pay a flat hourly rate to run their virtual machine (VM) on a server providing some combination of CPU access, storage, and network. But not all VM instances are created equal:...

Workload consolidation is a key technique in reducing costs in virtualized datacenters. When considering storage consolidation, a key problem is the unpredictable performance behavior of consolidated workloads on a given storage system. In practice, ...

Causal consistency is the strongest consistency model that is available in the presence of partitions and provides useful semantics for human-facing distributed services. Here, we expose its serious and inherent scalability limitations due to write ...

With the growth of Infrastructure as a Service (IaaS) cloud providers, many have begun to seriously consider cloud services as a substrate for HPC applications. While the cloud promises many benefits for the HPC community, it currently does not come ...

Data-intensive computing (DISC) frameworks scale by partitioning a job across a set of fault-tolerant tasks, then diffusing those tasks across large clusters. Multi-tenanted clusters must accommodate service-level objectives (SLO) in their resource ...

As cloud resources and applications grow more heterogeneous, allocating the right resources to different tenants' activities increasingly depends upon understanding tradeoffs regarding their individual behaviors. One may require a specific amount of RAM,...

As MapReduce/Hadoop grows in importance, we find more exotic applications being written this way. Not every program written for this platform performs as well as we might wish. There are several reasons why a MapReduce program can underperform ...

In recent years, distributed programming has become a topic of widespread interest among developers. However, writing reliable distributed programs remains stubbornly difficult. In addition to the inherent challenges of distribution---asynchrony, ...

Open source cloud technologies such as OpenStack, CloudStack, OpenNebula, Eucalyptus, OpenShift, and Cloud Foundry have gained significant momentum in the last few years. For a researcher and practitioner, they present a unique opportunity to analyze, ...