A personal networking solution

This paper presents an overview of research being conducted on Personal Networking Solutions within the Mobile VCE Personal Distributed Environment Work Area. In particular it attempts to highlight areas of commonality with the MAGNET initiative. These areas include trust of foreign devices and service providers, dynamic real-time service negotiation to permit context-aware service delivery, an automated controller algorithm for wireless ad hoc networks, and routing protocols for ad hoc networking environments. Where possible references are provided to Mobile VCE publications to enable further reading

Lightweight process migration and memory prefetching in openMosix

We propose a lightweight process migration mechanism and an adaptive memory prefetching scheme called AMPoM (Adaptive Memory Prefetching in openMosix), whose goal is to reduce the migration freeze time in openMosix while ensuring the execution efficiency of migrants. To minimize the freeze time, our system transfers only a few pages to the destination node during process migration. After the migration, AMPoM analyzes the spatial locality of memory access and iteratively prefetches memory pages from remote to hide the latency of inter-node page faults. AMPoM adopts a unique algorithm to decide which and how many pages to prefetch. It tends to prefetch more aggressively when a sequential access pattern is developed, when the paging rate of the process is high or when the network is busy. This advanced strategy makes AMPoM highly adaptive to different application behaviors and system dynamics. The HPC Challenge benchmark results show that AMPoM can avoid 98% of migration freeze time while preventing 85-99% of page fault requests after the migration. Compared to openMosix which does not have remote page fault, AMPoM induces a modest overhead of 0-5% additional runtime. When the working set of a migrant is small, AMPoM outperforms openMosix considerably due to the reduced amount of data transfer. These results indicate that by exploiting memory access locality and prefetching, process migration can be a lightweight operation with little software overhead in remote paging. ©2008 IEEE.published_or_final_versionThe 2008 IEEE International Symposium on Parallel and Distributed Processing (IPDPS 2008), Miami, FL., 14-18 April 2008. In Proceedings of the 22nd IPDPS, 2008, p. 1-1

BAG : Managing GPU as buffer cache in operating systems

This paper presents the design, implementation and evaluation of BAG, a system that manages GPU as the buffer cache in operating systems. Unlike previous uses of GPUs, which have focused on the computational capabilities of GPUs, BAG is designed to explore a new dimension in managing GPUs in heterogeneous systems where the GPU memory is an exploitable but always ignored resource. With the carefully designed data structures and algorithms, such as concurrent hashtable, log-structured data store for the management of GPU memory, and highly-parallel GPU kernels for garbage collection, BAG achieves good performance under various workloads. In addition, leveraging the existing abstraction of the operating system not only makes the implementation of BAG non-intrusive, but also facilitates the system deployment

Building Resilient Cloud Over Unreliable Commodity Infrastructure

Cloud Computing has emerged as a successful computing paradigm for efficiently utilizing managed compute infrastructure such as high speed rack-mounted servers, connected with high speed networking, and reliable storage. Usually such infrastructure is dedicated, physically secured and has reliable power and networking infrastructure. However, much of our idle compute capacity is present in unmanaged infrastructure like idle desktops, lab machines, physically distant server machines, and laptops. We present a scheme to utilize this idle compute capacity on a best-effort basis and provide high availability even in face of failure of individual components or facilities. We run virtual machines on the commodity infrastructure and present a cloud interface to our end users. The primary challenge is to maintain availability in the presence of node failures, network failures, and power failures. We run multiple copies of a Virtual Machine (VM) redundantly on geographically dispersed physical machines to achieve availability. If one of the running copies of a VM fails, we seamlessly switchover to another running copy. We use Virtual Machine Record/Replay capability to implement this redundancy and switchover. In current progress, we have implemented VM Record/Replay for uniprocessor machines over Linux/KVM and are currently working on VM Record/Replay on shared-memory multiprocessor machines. We report initial experimental results based on our implementation.Comment: Oral presentation at IEEE "Cloud Computing for Emerging Markets", Oct. 11-12, 2012, Bangalore, Indi

Holistic Performance Analysis and Optimization of Unified Virtual Memory

The programming difficulty of creating GPU-accelerated high performance computing (HPC) codes has been greatly reduced by the advent of Unified Memory technologies that abstract the management of physical memory away from the developer. However, these systems incur substantial overhead that paradoxically grows for codes where these technologies are most useful. While these technologies are increasingly adopted for use in modern HPC frameworks and applications, the performance cost reduces the efficiency of these systems and turns away some developers from adoption entirely. These systems are naturally difficult to optimize due to the large number of interconnected hardware and software components that must be untangled to perform thorough analysis. In this thesis, we take the first deep dive into a functional implementation of a Unified Memory system, NVIDIA UVM, to evaluate the performance and characteristics of these systems. We show specific hardware and software interactions that cause serialization between host and devices. We further provide a quantitative evaluation of fault handling for various applications under different scenarios, including prefetching and oversubscription. Through lower-level analysis, we find that the driver workload is dependent on the interactions among application access patterns, GPU hardware constraints, and Host OS components. These findings indicate that the cost of host OS components is significant and present across UM implementations. We also provide a proof-of-concept asynchronous approach to memory management in UVM that allows for reduced system overhead and improved application performance. This study provides constructive insight into future implementations and systems, such as Heterogeneous Memory Management